diaspara database creation script

The main structure of the database has been proposed during the online diaspara meeting : database structure. This stucture would allow to store the data necessary to run the international stock models for the different working groups working on diadromous fishes. The structure of the proposed database and the structure of the wgeel database are very similar. This documents creates the diaspara database, first creating referential table for the different types proposed, and then populates the database with the contents of the wgeel and wgnas database. All chunks are run sequentially to run the database, but some are later marked with eval = FALSE to speed up the process of running the quarto document.

Hierarchical structure of the database

The database could start with the simplest structure, with a basic table corresponding to all data. Then three tables could be created, one per species. This is to allow querying the different tables independently. From these species tables, three different tables could be produced, one for data, one for parameters outputs, one for parameters priors. Since SQL server does not handle inheritance, once the table built, some of those will have to be replaced with views or partitioned views.

From these three table on could envisage the creation of specific table for working groups. Meaning one table for eel (wgeel), one table for salmon (wgnas), one table for salmon (wgbast), and one table for trutta (wgtrutta).

Referential tables

[TO BE EDITED LATER WHEN WE KNOW EXACTLY WHAT WE HAVE DONE]

Similarly, referential tables could be created with a mother table from which specific (or wg specific) tables would inherit. All mother table will be held in a schema called ref. Having working group specific tables make the setting up of consistent foreign key more easy. For instance wgbast could reference different age class than wgnas, and the stage would be completely different between wgeel and wgnas reference daughter tables. Some of these referential table would be common between species (e.g. source from ICES vocab, which corresponds to working group or accession events (datacalls)).

Unicity constraints

Another important point to add (at least to the salmoglob database) is unicity constraint. As some values would be null, creating unicity constraints with indexes would be necessary. These allow to have different levels of constraints for instance the unicity would be defined for : (year, age, area, parameter) (year, age, parameter) (year, area, parameter) (year, parameter)

One of the table will have to contain twice the area it will have to be treated separately. (year, area, area, parameter)

Creating the diaspara database

All along this document, the database will be named diaspara. The database is created with postgres.

Some rules

• By default values will be capitalised for the first letter e.g. Public for the code in dataaccess.

• Code ELE, ANG, TRT are capitalized, as are the working group names WGEEL, WGNAS, or country codes.

• Units are lowercase e.g. g, mm …

• All integer used for primary keys are called id all text used for primary keys are called code, the text is always called description.

• All tables end with a 3 letter summary which allows to identify the source of one column so the table dataaccess will be called tr_dataaccess_dta. And the column for code will be named

• Referential tables or dictionaries are called tr_(sometable), tables build in conjuction from other tables and not in the dictionaries are called t_(sometable).

• Foreign key are used instead of check constrainst as check constraint might not ensure the integrity of data after their integration (only when new rows are created or modified).

• Foreign key are name fk_columnname where the column name is the name in the table

• Primary keys are names tablename_pkey (with the constraint possibly referering to more than one column).

• Other constraints are check constraints ck_columnname and unique contraints uk_columnname

• All tables and column have a definition, we will ask the working groups to check those.

• Column and table name are ALWAYS LOWERCASE, the underscore is only used to separate type of table and table shortcode t_table_abc. In column is separates table code abc_def_code (table abc will reference the column def_code in table def).

This is run in localhost, check the wp3_habitat repository for code to set up access to the database. In the future we will grant diaspara_admin and diaspara_read to specific users for example with a used named trout GRANT diaspara_admin TO trout ;

DIASPARA Note to self

need to edit the pb_hba.conf on the server if not in localhost to allow access to diaspara.

dbExecute(con_diaspara_admin, "DROP schema if exists ref CASCADE;");
dbExecute(con_diaspara_admin, "CREATE schema ref;")
dbExecute(con_diaspara_admin, "GRANT ALL PRIVILEGES ON SCHEMA ref TO diaspara_admin ;")
dbExecute(con_diaspara_admin, "GRANT ALL PRIVILEGES ON SCHEMA public TO diaspara_read ;")
dbExecute(con_diaspara_admin, paste0("GRANT CONNECT ON DATABASE ",cred$dbnamediaspara," TO diaspara_read;"))
dbExecute(con_diaspara_admin, paste0("ALTER DATABASE ",cred$dbnamediaspara," OWNER TO diaspara_admin;"))
dbExecute(con_diaspara_admin, "DROP schema if exists refeel CASCADE;");
dbExecute(con_diaspara_admin, "CREATE SCHEMA refeel;")
dbExecute(con_diaspara_admin, "ALTER SCHEMA refeel OWNER TO diaspara_admin;")
dbExecute(con_diaspara_admin, "DROP schema if exists refnas CASCADE;");
dbExecute(con_diaspara_admin, "CREATE SCHEMA refnas;")
dbExecute(con_diaspara_admin, "ALTER SCHEMA refnas OWNER TO diaspara_admin;")
dbExecute(con_diaspara_admin, "DROP schema if exists refbast CASCADE;");
dbExecute(con_diaspara_admin, "CREATE SCHEMA refbast;")
dbExecute(con_diaspara_admin, "ALTER SCHEMA refbast OWNER TO diaspara_admin;")
dbExecute(con_diaspara_admin, "DROP schema if exists reftrutta CASCADE;");
dbExecute(con_diaspara_admin, "CREATE SCHEMA reftrutta;")
dbExecute(con_diaspara_admin, "ALTER SCHEMA reftrutta OWNER TO diaspara_admin;")

# Create foreign data wrapper to wgeel database

dbExecute(con_diaspara_admin, "CREATE EXTENSION IF NOT EXISTS postgres_fdw;")

dbExecute(con_diaspara_admin,"
CREATE SERVER wgeel_data_wrapper
  FOREIGN DATA WRAPPER postgres_fdw
  OPTIONS (host 'localhost', port '5432', dbname 'wgeel');")
dbExecute(con_diaspara_admin,"
CREATE SERVER wgnas_data_wrapper
  FOREIGN DATA WRAPPER postgres_fdw
  OPTIONS (host 'localhost', port '5432', dbname 'salmoglob');")
dbExecute(con_diaspara_admin,"
CREATE USER MAPPING FOR USER
  SERVER wgeel_data_wrapper
  OPTIONS (user 'postgres', password 'postgres');")
dbExecute(con_diaspara_admin,"  
CREATE SCHEMA refwgeel;")
dbExecute(con_diaspara_admin,"IMPORT FOREIGN SCHEMA ref    
    FROM SERVER wgeel_data_wrapper
    INTO refwgeel;")

dbExecute(con_diaspara_admin, paste0("COMMENT ON DATABASE ",cred$dbnamediaspara," IS 'This database is named Frankenstein :-)'"))    
dbExecute(con_diaspara_admin,
"GRANT ALL PRIVILEGES ON SCHEMA refwgeel TO diaspara_admin;")

SQL code to additional data schema

Listing 1: Building additional schema

--| echo: TRUE
--| eval: FALSE


-- this one is straight into sql ... 

DROP SCHEMA IF EXISTS dat CASCADE;
CREATE SCHEMA dat;
ALTER SCHEMA dat OWNER TO diaspara_admin;
COMMENT ON SCHEMA dat IS 'SCHEMA common to all migratory fish, filled by inheritance';

DROP SCHEMA IF EXISTS datang CASCADE;
CREATE SCHEMA datang;
ALTER SCHEMA datang OWNER TO diaspara_admin;
COMMENT ON SCHEMA datang IS 'SCHEMA for WGEEL';

DROP SCHEMA IF EXISTS datnas CASCADE;
CREATE SCHEMA datnas;
ALTER SCHEMA datnas OWNER TO diaspara_admin;
COMMENT ON SCHEMA datnas IS 'SCHEMA for WGNAS';

DROP SCHEMA IF EXISTS datbast CASCADE;
CREATE SCHEMA datbast;
ALTER SCHEMA datbast OWNER TO diaspara_admin;
COMMENT ON SCHEMA datbast IS 'SCHEMA for WGBAST';


DROP SCHEMA IF EXISTS dattrutta CASCADE;
CREATE SCHEMA dattrutta;
ALTER SCHEMA dattrutta OWNER TO diaspara_admin;
COMMENT ON SCHEMA dattrutta IS 'SCHEMA for WKTRUTTA';

Now the database has been created with different schema (Figure 1). The main schema for dictionaries is ref, and a schema is created per working group for specific referential tables. The Schema refwgeel is a schema created with a foreign data wrapper to get the data from wgeel, the same schema exists for wgnas. We’ll see later for wgbast and wgtrutta.

Figure 1: Structure of the schema in diaspara. Dashed arrow indicate an import of data from existing

Now the database has been created with different schema (Figure 1). The main schema for dictionaries is ref, and a schema is created per working group for specific referential tables. The Schema refwgeel has been filled in with a foreign data wrapper to get the data from wgeel, the same schema exists for wgnas. We’ll see later for wgbast and wgtrutta. The schema dat is the common schema for all data. For each working group, schema datbast, dateel, datnas are created. The tables will be created in dat and later similar or a bit more complex tables (with some more columns) will be created using the INHERIT FROM syntax, which will allow to have a hierarchical structure in the db, and maintain the structure in a table common to all fishes. Note thatdat should not containt any data, but will hold all the views and inherited tables comming from the different schema.

Creating referentials

Species (tr_species_spe)

The first thing is to create a referential table for species. Anyways, we searched a bit for other species, even if we don’t plan to start storing data on Alosa and Lamprey it’s good to prepare the database. There are no code in ICES vocal for Alosa alosa, Alosa fallax, Petromyzon marinus, Lampetra fluviatilis.

Note Cédric : Thinking later about this, I chose to get rid of ELE and select ANG. This column with species will be everywhere. We want to avoid generations of researchers to avoid what ELE is (apart from elephants). For the others I’ve taken as close as possible to the latin name.

QUESTION ICES: species code

ANG, ALA, ALF, PET, LAM are these internal code OK ? Should we use SpecWoRMS or is Aphia OK ?

ANSWER ICES : Maria

For species, we would recommend that you use AphiaIDs (a copy of which is SpecWoRMs). You can also use the FAO ASFIS list, or both, but we would recommend having the AphiaIDs for sure.

Creating a referential table for species - code and queries to ICES

# No code for Lampetra, Alosa, petromyzon
sp <- getCodeList("IC_Species")
grep("Lampetra", sp$description)

integer(0)

grep("Petromyzon", sp$description)

integer(0)

grep("Alosa",  sp$description)

integer(0)

bind_rows(
  ele <- getCodeDetail("IC_Species","ELE")$detail,
  sal <- getCodeDetail("IC_Species","SAL")$detail,
  trs <- getCodeDetail("IC_Species","TRS")$detail) %>%
knitr::kable(caption = "Codes for migratory species in ICES, no code found for other species (Lamprey, Alosa ...)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
if (file.exists("data/tr_species_spe_temp.Rdata")) {
  load("data/tr_species_spe_temp.Rdata") } else {
  species_list <- tibble(
    spe_code = c("SAL", "ANG", "TRT", "ALA", "ALF", "PET", "LAM"),
    spe_icspecieskey = c("SAL", "ELE", "TRT", NA,NA,NA,NA),
    spe_commonname = c("Atlantic salmon", "European eel", "Sea trout", "Twait shad", "Allis shad", "Sea lamprey", "European river lamprey"),
    spe_scientificname = c("Salmo salar", "Anguilla anguilla", "Salmo trutta", "Alosa alosa", "Alosa fallax", "Petromyzon marinus", "Lampetra fluviatilis")
  )
  tr_species_spe_temp <- species_list %>%
    rowwise() %>%
    mutate(
      spe_codeaphia = findAphia(spe_scientificname, latin = TRUE)
    ) %>%
    ungroup()
  save(tr_species_spe_temp, file = "data/tr_species_spe_temp.Rdata")
  }
  knitr::kable(tr_species_spe_temp) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed"))

Table 1: Species in ICES

(a) Code found in IC_Species

Key	Description	LongDescription	Modified	Deprecated
ELE	Anguilla anguilla	NA	2019-04-15	FALSE
SAL	Salmo salar	NA	2019-04-15	FALSE
TRS	Salmo trutta	NA	2019-04-15	FALSE

(b) Three letter code for species. Should we use ang instead of ele ?

spe_code	spe_commonname	spe_scientificname	spe_codeaphia
SAL	Atlantic salmon	Salmo salar	127186
ELE	European eel	Anguilla anguilla	126281
TRT	Sea trout	Salmo trutta	127187
ALA	Twait shad	Alosa alosa	126413
ALF	Allis shad	Alosa fallax	126415
SLP	Sea lamprey	Petromyzon marinus	101174
RLP	European river lamprey	Lampetra fluviatilis	101172

dbExecute(con_diaspara_admin, "DROP TABLE IF EXISTS ref.tr_species_spe;")
dbExecute(con_diaspara_admin,"CREATE TABLE ref.tr_species_spe (
     spe_code CHARACTER VARYING(3) PRIMARY KEY,
     spe_commonnname TEXT,
     spe_scientificname TEXT,
     spe_codeaphia numeric NOT NULL,
     spe_description TEXT)");
dbExecute(con_diaspara_admin, "GRANT ALL ON TABLE ref.tr_species_spe to diaspara_admin")
dbExecute(con_diaspara_admin, "GRANT SELECT ON TABLE ref.tr_species_spe to diaspara_read")
dbWriteTable(conn=con_diaspara, name = "tr_species_spe_temp", value = tr_species_spe_temp, overwrite = TRUE)
dbExecute(con_diaspara,"INSERT INTO ref.tr_species_spe SELECT * FROM tr_species_spe_temp")#7
dbExecute(con_diaspara,"DROP TABLE tr_species_spe_temp")
dbExecute(con_diaspara_admin, "COMMENT ON TABLE  ref.tr_species_spe IS 
'Table of fish species, spe_code SAL, ANG, TRT, ALA, ALF, PET, LAM, with 
reference to ICES vocabularies.'")

Table 2: Working groups table in the diaspara DB

spe_code	spe_commonnname	spe_scientificname	spe_codeaphia	spe_description
SAL	Atlantic salmon	Salmo salar	127186	NA
TRT	Sea trout	Salmo trutta	127187	NA
ALA	Twait shad	Alosa alosa	126413	NA
ALF	Allis shad	Alosa fallax	126415	NA
SLP	Sea lamprey	Petromyzon marinus	101174	NA
RLP	European river lamprey	Lampetra fluviatilis	101172	NA
ANG	European eel	Anguilla anguilla	126281	NA

Working group (tr_icworkinggroup_wkg)

Species is not enough. Data will be split by working groups. Several working groups working on the same species. There is a table for working group. Proposed table is Table 3 but need confirmation for WKTRUTTA2.

QUESTION ICES/ WGTRUTTA/ DIASPARA

what is the latest working group for WGTRUTTA, I found a WKTRUTTA2 but that is quite old. Do we want to refer to other groups on diadromous fishes there ? The name of the WGEEL is wrong in the referential, needs to reference GFCM… JOINT EIFAAC/ICES/GFCM WORKING GROUP ON EEL.

dbExecute(con_diaspara_admin,
"DROP TABLE IF EXISTS ref.tr_icworkinggroup_wkg CASCADE;")
dbExecute(con_diaspara_admin,
"CREATE TABLE ref.tr_icworkinggroup_wkg (
wkg_code TEXT PRIMARY KEY,
wkg_description TEXT,
wkg_icesguid uuid,
wkg_stockkeylabel TEXT
);")

dbExecute(con_diaspara_admin, "COMMENT ON TABLE ref.tr_icworkinggroup_wkg 
IS 'Table corresponding to the IC_WorkingGroup referential;';")
# Using the jsonlite to download the guid also
tbl <- jsonlite::fromJSON(
  "https://vocab.ices.dk/services/api/Code/3f6fb38a-a3c5-4f5c-bf31-2045e12536ee")



temp_tr_icworkinggroup_wkg <- tbl %>%
  select(key,description,guid) %>%
  rename(wkg_code = key,
         wkg_description = description,
         wkg_icesguid = guid)%>%
  filter(wkg_code %in% c("WGEEL", "WGNAS", "WGBAST"))
temp_tr_icworkinggroup_wkg <- bind_rows(temp_tr_icworkinggroup_wkg,
                                        data.frame(wkg_code="WKTRUTTA"))
temp_tr_icworkinggroup_wkg$wkg_stockkeylabel <-
  c("sal.27.22–31","ele.2737.nea","sal.neac.all",NA)
dbWriteTable(con_diaspara_admin, 
             "temp_tr_icworkinggroup_wkg", 
             temp_tr_icworkinggroup_wkg,
             overwrite = TRUE)

dbExecute(con_diaspara_admin, "INSERT INTO ref.tr_icworkinggroup_wkg 
          SELECT 
          wkg_code,
          wkg_description,
          wkg_icesguid::uuid,
          WKG_stockkeylabel
         FROM temp_tr_icworkinggroup_wkg;") #4
dbExecute(con_diaspara_admin, "DROP TABLE temp_tr_icworkinggroup_wkg;")

dbExecute(con_diaspara_admin, "COMMENT ON COLUMN 
ref.tr_icworkinggroup_wkg.wkg_code IS 
'Working group code uppercase, WGEEL, WGNAS, WGBAST, WGTRUTTA';")


dbExecute(con_diaspara_admin, "GRANT ALL ON ref.tr_icworkinggroup_wkg 
          TO diaspara_admin;")
dbExecute(con_diaspara_admin, "GRANT SELECT ON ref.tr_icworkinggroup_wkg 
          TO diaspara_read;")

Table 3: Working groups table in the diaspara DB

wkg_code	wkg_description	wkg_icesguid	wkg_stockkeylabel
WGBAST	Baltic Salmon and Trout Assessment Working Group	2cac261d-c837-459a-961b-e63e36cc19ec	sal.27.2231
WGEEL	Joint EIFAAC/ICES Working Group on Eels	7c13a79e-7855-4d0b-b567-21300bcaaf9a	ele.2737.nea
WGNAS	Working Group in North Atlantic Salmon	b5fd158e-b153-4e2e-a6da-c4b0536d684e	sal.neac.all
WKTRUTTA	NA	NA	NA

Country (tr_country_cou)

Countries are mostly OK in the wgeel database but we need to add american countries. The shapefiles have been downloaded from https://gisco-services.ec.europa.eu/distribution/v2/countries/download/#countries source EuroGeographics and UN-FAO. Countries (Table 4) are ordered from North to South starting from the Baltic and ending in the Mediterranean, with American number being the highest in order.

Code to create table from wgeel and NUTS

dbExecute(con_diaspara_admin, "DROP TABLE IF EXISTS ref.tr_country_cou;")
dbExecute(con_diaspara_admin, "CREATE TABLE ref.tr_country_cou (
    cou_code character varying(2) NOT NULL,
    cou_country text NOT NULL,
    cou_order integer NOT NULL,
    geom public.geometry,
    cou_iso3code character varying(3)
);")


dbExecute(con_diaspara_admin,
          "INSERT INTO ref.tr_country_cou 
          SELECT * FROM refwgeel.tr_country_cou;") #40
# Add some constraints
dbExecute(con_diaspara_admin, "ALTER TABLE ref.tr_country_cou 
          ADD CONSTRAINT t_country_cou_pkey PRIMARY KEY (cou_code);")
dbExecute(con_diaspara_admin, "ALTER TABLE ref.tr_country_cou 
          ADD CONSTRAINT uk_cou_iso3code UNIQUE (cou_iso3code);")

# missing values from America downloaded from https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/ref-nuts-2024-01m.gdb.zip
# uploaded to postgres

# the tables

# ref-countries-2024-01m — CNTR_RG_01M_2024_4326
# have been copied to folder area ref-countries was renamed
# ALTER TABLE area."ref-countries-2024-01m — CNTR_RG_01M_2024_4326" 
# RENAME TO "ref-countries-2024-01m-4326";


dbExecute(con_diaspara_admin,
"INSERT INTO ref.tr_country_cou ( cou_code,
    cou_country,    
    cou_iso3code,
    geom, 
    cou_order)
SELECT \"CNTR_ID\" AS cou_code, \"NAME_ENGL\" AS cou_country,  \"ISO3_CODE\" 
AS cou_isocode, geom,
CASE WHEN \"CNTR_ID\" = 'GL' THEN 47
     WHEN \"CNTR_ID\" = 'CA' THEN 48
     ELSE 49 END AS cou_order
FROM  area.\"ref-countries-2024-01m-4326\"
WHERE \"CNTR_ID\" IN ('GL', 'CA', 'US');") #3

# Svalbard et Jan Mayen SJM NO Territory    
dbExecute(con_diaspara_admin,
"INSERT INTO ref.tr_country_cou ( cou_code,
    cou_country,    
    cou_iso3code,
    geom, 
    cou_order)
SELECT \"CNTR_ID\" AS cou_code, \"NAME_ENGL\" AS cou_country,  \"ISO3_CODE\" 
AS cou_isocode, geom,
CASE WHEN \"CNTR_ID\" = 'GL' THEN 47
     WHEN \"CNTR_ID\" = 'CA' THEN 48
     ELSE 49 END AS cou_order
FROM  area.\"ref-countries-2024-01m-4326\"
WHERE \"CNTR_ID\" IN ('SJ');") #3

dbExecute(con_diaspara_admin,
"UPDATE ref.tr_country_cou 
SET geom = nuts.geom  
FROM  area.\"ref-countries-2024-01m-4326\" nuts 
WHERE nuts.\"CNTR_ID\" = tr_country_cou.cou_code;") # 40

dbExecute(con_diaspara_admin,"COMMENT ON TABLE ref.tr_country_cou IS
          'Table of country codes source EuroGeographics and UN-FAO.';")
dbExecute(con_diaspara_admin,"ALTER TABLE ref.tr_country_cou 
          OWNER TO diaspara_admin;")
dbExecute(con_diaspara_admin,
          "GRANT SELECT ON TABLE ref.tr_country_cou TO diaspara_read;")

tr_country_cou <- dbGetQuery(con_diaspara, "SELECT cou_code,cou_country,cou_order, cou_iso3code FROM ref.tr_country_cou order by cou_order")
knitr::kable(tr_country_cou) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed"))

Table 4: Country table in the diaspara DB

cou_code	cou_country	cou_order	cou_iso3code
IS	Iceland	0	ISL
NO	Norway	1	NOR
SE	Sweden	2	SWE
AX	Åland	3	ALA
FI	Finland	4	FIN
EE	Estonia	5	EST
LV	Latvia	6	LVA
LT	Lithuania	7	LTU
RU	Russia	8	RUS
PL	Poland	9	POL
CZ	Czech republic	10	CZE
DE	Germany	11	DEU
DK	Denmark	12	DNK
NL	Netherlands	13	NLD
BE	Belgium	14	BEL
LU	Luxembourg	15	LUX
IE	Ireland	16	IRL
GB	Great Britain	17	GBR
FR	France	18	FRA
ES	Spain	19	ESP
PT	Portugal	20	PRT
IT	Italy	21	ITA
MT	Malta	22	MLT
SI	Slovenia	23	SVN
HR	Croatia	24	HRV
BA	Bosnia-Herzegovina	25	BIH
ME	Montenegro	26	MNE
AL	Albania	27	ALB
GR	Greece	28	GRC
TR	Turkey	34	TUR
CY	Cyprus	35	CYP
SY	Syria	36	SYR
LB	Lebanon	37	LBN
IL	Israel	38	ISR
EG	Egypt	39	EGY
LY	Libya	40	LBY
TN	Tunisia	41	TUN
DZ	Algeria	42	DZA
MA	Morocco	43	MAR
VA	Vattican	46	VAT
GL	Greenland	47	GRL
CA	Canada	48	CAN
US	United States	49	USA
SJ	Svalbard and Jan Mayen	49	SJM

if (file.exists("data/country_sf.Rdata")) load("data/country_sf.Rdata") else {
country_sf <- sf::st_read(con_diaspara,
                          query = "SELECT cou_code, ST_MakeValid(geom) 
                          from ref.tr_country_cou") %>%
  sf::st_transform(4326) 
save(country_sf, file="data/country_sf.Rdata")
}
#see here : https://stackoverflow.com/questions/70756215/
#plot-geodata-on-the-globe-perspective-in-r
# Note there is a problem of geometry for some of the polygons, and this require 
# ST_Makevalid before intersection

# projection string used for the polygons & ocean background
crs_string <- "+proj=ortho +lon_0=-30 +lat_0=30"

# background for the globe - center buffered by earth radius
ocean <- sf::st_point(x = c(0,0)) %>%
  sf::st_buffer(dist = 6371000) %>%
  sf::st_sfc(crs = crs_string)
country_sf2 <-  country_sf %>% 
  sf::st_intersection(ocean %>% sf::st_transform(4326)) %>% 
  # select visible area only
  sf::st_transform(crs = crs_string) # reproject to ortho
# now the action!
g <- ggplot(data = country_sf2) +
  geom_sf(data = ocean, fill = "aliceblue", color = NA) + # background first
  geom_sf(aes(fill = cou_code), lwd = .1) + # now land over the oceans
  scale_fill_discrete(guide = "none") +
  theme_void()

# this part is used to avoid long computations
png(filename="images/fig-country.png", bg="transparent")
print(g)
dev.off()

Figure 2: Map of countries in the diaspara DB © EuroGeographics

Unit (tr_units_uni)

Creating the unit from wgeel and checking ICES code

First we import from wgeel

Then we standarize using ICES codes, it takes a while to scroll through the vocab. Sometimes several vocab are available for the same thing. We used the p06 as the most common source. Hopefully that was the right choices ?

dbExecute(con_diaspara_admin, "DROP TABLE IF EXISTS ref.tr_units_uni CASCADE;")

dbExecute(con_diaspara_admin,
"CREATE TABLE ref.tr_units_uni (
    uni_code varchar(20) NOT NULL,
    uni_description text NOT NULL,
  uni_icesvalue character varying(4),  
  uni_icesguid uuid,
  uni_icestablesource text,
    CONSTRAINT t_units_uni_pkey PRIMARY KEY (uni_code),
    CONSTRAINT uk_uni_description UNIQUE (uni_description),
  CONSTRAINT uk_uni_icesguid UNIQUE (uni_icesguid),
  CONSTRAINT uk_uni_icesvalue UNIQUE (uni_icesvalue)
);")

dbExecute(con_diaspara_admin,"INSERT INTO ref.tr_units_uni (
uni_code, uni_description)
SELECT * FROM refwgeel.tr_units_uni;")#25

dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='KGXX' 
          where uni_code = 'kg';") 
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='MTON'
          where uni_code = 't';") 
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='UCNT' 
          where uni_code = 'nr';") 
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='UGRM' 
          where uni_code = 'g';")
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='UPMS'
          where uni_code = 'nr/m2';")
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='UPMM' 
          where uni_code = 'nr/m3';")
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='UYRS' 
          where uni_code = 'nr year';")
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='UXMM' 
          where uni_code = 'mm';")
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='NGPG' 
          where uni_code = 'ng/g';")
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='HCTR' 
          where uni_code = 'ha';")
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='UTAA' 
          where uni_code = 'nr day';")
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='NOPH'
          where uni_code = 'nr/h';")
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='NGPG'
          where uni_code = 'ng/g';")
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='UPCT'
          where uni_code = 'percent';")
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set 
          (uni_icesvalue, uni_description)=
          ('XXXX', 'Not applicable (without unit)')
          where uni_code = 'wo';")          

dbExecute(con_diaspara_admin, "INSERT INTO ref.tr_units_uni 
          VALUES ('year-1', 'Per year', 'XXPY');")          
dbExecute(con_diaspara_admin, "INSERT INTO ref.tr_units_uni 
          VALUES ('s', 'Seconds', 'UTBB');")



p06 <- icesVocab::getCodeList('p06')
SamplingUnit <- icesVocab::getCodeList('SamplingUnit')
uni <- dbGetQuery(con_diaspara_admin, "SELECT * FROM ref.tr_units_uni;")
tempuni <- inner_join(uni, p06, by=join_by(uni_icesvalue==Key))
dbWriteTable(con_diaspara_admin, "tempuni", tempuni, overwrite=TRUE)
dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni 
set uni_icesguid = \"GUID\"::uuid
          FROM tempuni 
          where tempuni.uni_icesvalue=tr_units_uni.uni_icesvalue;") #16
dbExecute(con_diaspara_admin, "DROP TABLE tempuni;")

dbExecute(con_diaspara_admin,
"UPDATE ref.tr_units_uni set uni_icestablesource = 'p06' where uni_icesvalue 
IS NOT NULL AND
uni_icestablesource IS NULL;") # 16

query <- sprintf("INSERT INTO ref.tr_units_uni (uni_code,uni_description, uni_icesvalue, uni_icestablesource,uni_icesguid) VALUES ('%s','%s','%s','%s','%s'::uuid);", 
                 "gd", 
                 "Gear days for fyke/trap nets",
                 "gd", 
                 "MUNIT",
                 "bf0570b7-45f2-41c7-9a46-de912a2b9ad4")              
dbExecute(con_diaspara_admin,  query)


dbExecute(con_diaspara_admin, "UPDATE ref.tr_units_uni set uni_icesvalue='idx', 
          uni_icestablesource = 'MUNIT',
          uni_icesguid ='87a9cf7f-fff4-4712-b693-76eec1403254'::uuid
          where uni_code = 'index';")

# p06[grep('Ton',p06$Description),c("Description","Key")] 
# p06[grep('Without',tolower(p06$Description)),c("Description","Key")] 
# p06[grep('nanogram',tolower(p06$Description)),c("Description","Key")]
# p06[grep('index',tolower(p06$Description)),c("Description","Key")]
# p06[grep('hour',tolower(p06$Description)),c("Description","Key")]
# p06[grep('kilogram',tolower(p06$Description)),c("Description","Key")]
# p06[grep('nanogram',tolower(p06$Description)),c("Description","Key")]
# p06[grep('haul',tolower(p06$Description)),c("Description","Key")]

dbExecute(con_diaspara_admin, "COMMENT ON TABLE ref.tr_units_uni IS 
'Table of units, values from tables MUNIT and p06 have corresponding ICES code.'")
dbExecute(con_diaspara_admin, "COMMENT ON COLUMN ref.tr_units_uni.uni_code IS 
'Unit code, lowercase, nr number, otherwise standard units.'")
dbExecute(con_diaspara_admin, "COMMENT ON COLUMN ref.tr_units_uni.uni_description
 IS 'Unit code, lowercase, nr number, otherwise standard units.'")
dbExecute(con_diaspara_admin, "COMMENT ON COLUMN ref.tr_units_uni.uni_icesvalue IS 
'ICES code standard from the British Oceanographic Data Centre (p06) or MUNIT 
table.';") 
dbExecute(con_diaspara_admin, 
"COMMENT ON COLUMN ref.tr_units_uni.uni_icestablesource IS 
'Table source in ICES.';") 
dbExecute(con_diaspara_admin, 
"COMMENT ON COLUMN ref.tr_units_uni.uni_icesguid IS 
'GUID, type https://vocab.ices.dk/?codetypeguid=<guidcode> to get access to the 
vocab in ICES.';") 
dbExecute(con_diaspara_admin, "GRANT ALL ON TABLE ref.tr_units_uni 
          to diaspara_admin;")
dbExecute(con_diaspara_admin, "GRANT SELECT ON TABLE ref.tr_units_uni 
          to diaspara_read;")

Table 5: tr_units_uni table, check missing values currently not found in ICES Vocab

uni_code	uni_description	uni_icesvalue	uni_icesguid	uni_icestablesource
kg/d	kilogramme per day	NA	NA	NA
kg/boat/d	kilogramme per boat per day	NA	NA	NA
nr haul	number of haul	NA	NA	NA
nr electrofishing	number of electrofishing campain in the year to collect the recruitment index	NA	NA	NA
nr/haul	number per haul	NA	NA	NA
kg/ha	weight in kilogrammes per surface in hectare	NA	NA	NA
nr net.night	number of net and night	NA	NA	NA
nr fyke.day	number of fyke and day	NA	NA	NA
nr site	number of site	NA	NA	NA
nr/net/day	number per net and day	NA	NA	NA
kg	weight in kilogrammes	KGXX	c1361a50-86e2-412f-b41b-5f1a006fccb1	p06
t	weight in tonnes	MTON	4fc26258-9a01-48f9-8b17-3e20f7a38faa	p06
nr	number	UCNT	24eb2d51-8ee3-4e2e-a054-54d18db2d161	p06
g	gram	UGRM	f0f3c481-168b-4426-b63f-92614d325782	p06
nr/m2	number per square meter	UPMS	d482b028-598d-4266-a9df-81f565297734	p06
nr/m3	number per cubic meter	UPMM	a3314231-4d85-47c8-9615-20f7283f0389	p06
nr year	number of years	UYRS	b5da453a-51f3-46bb-bff2-0ae5a7a0db42	p06
mm	milimeter	UXMM	85d197e0-324c-4343-8314-2c934eca5315	p06
ha	Surface	HCTR	4d1a5474-67a8-46fe-98f9-82c405e025de	p06
nr day	number of days	UTAA	5571062f-f40f-49d9-8b13-8cdd8f9ed6d1	p06
nr/h	number per hour	NOPH	f9095fc1-bfee-40df-b289-e2850000dd26	p06
ng/g	nanogram per gram	NGPG	5ffd54a7-bc8d-4d00-9e60-e1554d57bd89	p06
percent	percentage	UPCT	355bd386-001b-40e8-bb7e-8515b7b737f4	p06
wo	Not applicable (without unit)	XXXX	896e5f8e-f467-49b5-97f3-f6df093a5293	p06
year-1	Per year	XXPY	a69c5e6b-789d-411c-86b3-70c3b89e8884	p06
s	Seconds	UTBB	9bc370c3-ff97-4dde-9e5c-3eba6074d224	p06
gd	Gear days for fyke/trap nets	gd	bf0570b7-45f2-41c7-9a46-de912a2b9ad4	MUNIT
index	calculated value following a specified protocol	idx	87a9cf7f-fff4-4712-b693-76eec1403254	MUNIT

Some of the values are missing from ICES vocab (Table Table 5).

QUESTION ICES: missing values for units, what do we do ?

What do we do with units without correspondance? These come from FAO (if I remember well). Do we try to search for those in the wgeel database, and then remove if not existing or try to change existing values ?

• Kg/day there is a kg/hour do we need to change to that type and convert existing series ?

• Nr haul There is a definition of haul in the ICES vocab but it seems very related to sampling box, basket. And it’s not the number of haul.

• Before working any further I would like your opinion there.

Parameters

Parameters are a simple way to reduce the complexity of data. It will correspond to all nimble variables, reduced to their lower level (e.g. 3 dimensional arrays with dimensions [area, year, stage] will be translated as many lines with the corresponding values in columns area, year, and stage), and the identifyer of the variable will be used for all the lines necessary to store this dataset. In practise, parameters also correspond to input data, and output data in the model. The parameters will be described by their metadata as illustrated in Figure 3

Figure 3: Mind map of the metadata structure

We can have a look at the metadata in the analysis done on the WGNAS database WGNAS description.

There is a problem in the “order or the dimensions which need to be aligned. For instance a column can hold year, or age. This is not good. The description could be used within a type array. SQL server does not work with array so it’s not a good idea to use those.

Checking stock codes using icesASD and icesSD packages

# install.packages('icesASD', repos = c('https://ices-tools-prod.r-universe.dev', 'https://cloud.r-project.org'))
#install.packages("icesSD", repos = c("https://ices-tools-prod.r-universe.dev", "https://cloud.r-project.org"))

library('icesASD')
library('icesSD')
# this does not give the 
advice <- getAdviceViewRecord()
advice[grepl('ele',advice$stockCode),
       c('adviceDOI', 'stockCode','assessmentYear')] %>% kable
sd <- mapply(getSD, year= 2020:2024, SIMPLIFY=FALSE)
sd <- do.call(rbind,sd)
ww <- grepl('ele',sd$StockKeyLabel) | grepl('Salmo',sd$SpeciesScientificName)
sd[ww,] %>% kable()

Table 6: Access to the advice using icesASD

	adviceDOI	stockCode	assessmentYear
16	https://doi.org/10.17895/ices.advice.4825	ele.2737.nea	2019
799	https://doi.org/10.17895/ices.advice.19772374.v1	ele.2737.nea	2022
866	https://doi.org/10.17895/ices.pub.3440	ele.2737.nea	2017
975	https://doi.org/10.17895/ices.advice.7752	ele.2737.nea	2021
1014	https://doi.org/10.17895/ices.advice.5898	ele.2737.nea	2020
1048	https://doi.org/10.17895/ices.pub.4601	ele.2737.nea	2018
1311	https://doi.org/10.17895/ices.advice.21907860.v2	ele.2737.nea	2023
1402	https://doi.org/10.17895/ices.advice.27100516	ele.2737.nea	2024

	StockDatabaseID	StockKey	StockKeyLabel	StockKeyDescription	PreviousStockKey	PreviousStockKeyLabel	ActiveYear	SpeciesScientificName	SpeciesCommonName	EcoRegion	ExpertGroup	ExpertGroupDescription	AdviceDraftingGroup	AdviceDraftingGroupDescription	DataCategory	YearOfLastAssessment	AssessmentFrequency	YearOfNextAssessment	AssessmentType	AdviceReleaseDate	AdviceCategory	AdviceType	UseOfDiscardsInAdvice	PABufferApplied	TrophicGuild	FisheriesGuild	SizeGuild	Published	GeneratedOn	SectionNumber	AssessmentKey	ModifiedDate	ModelType	ModelName
149	2600	169241	sal.27.22-31	Salmon (Salmo salar) in subdivisions 22-31 (Baltic Sea, excluding the Gulf of Finland)	136678	sal-2431	2020	Salmo salar	Salmon	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2019	1	2020	Analytical model, Bayesian state-space model for a majority of rivers in AUs 1–4	NA	MSY	Catches	Assessment	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:41.203+01:00	8.3.15	13726	2019-05-29T11:25:06.9+01:00	O	Unknown
186	2590	169095	ele.2737.nea	European eel (Anguilla anguilla) throughout its natural range	136527	ele-nea	2020	Anguilla anguilla	Eel	Arctic Ocean, Azores, Bay of Biscay and the Iberian Coast, Barents Sea, Baltic Sea, Celtic Seas, Faroes, Greenland Sea, Icelandic Waters, Greater North Sea, Norwegian Sea, Oceanic Northeast Atlantic	WGEEL	Joint EIFAAC/ICES/GFCM Working Group on Eels	ADGEEL	Eel Advice Drafting GRoup	3.14	2020	1	2021	Trends-based assessment	NA	PA	No directed fisheries	Not used	No	demersal benthivore	Demersal	medium demersal	TRUE	2025-03-28T16:29:41.203+01:00	9.3.13	14025	2020-11-12T15:09:35.93+01:00	NA	NA
187	2601	169242	sal.27.32	Salmon (Salmo salar) in Subdivision 32 (Gulf of Finland)	136679	sal-32	2020	Salmo salar	Salmon	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	RG/ADGSalmon	Salmon Review and Advice Drafting Group	3	2020	1	2021	Qualitative assessment based on monitored parr densities and expert judgement.	NA	PA	Catches	Topped up	No	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:41.203+01:00	8.3.16	13453	2020-11-12T12:12:48.513+01:00	O	Unknown
236	2512	169291	trs.27.22-32	Sea trout (Salmo trutta) in subdivisions 22-32 (Baltic Sea)	136729	trt-bal	2020	Salmo trutta	Sea trout	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	NA	NA	4	2019	2	2021	Evaluation of the relative status of stocks by comparing the observed parr densities to model-predicted maximal parr densities.	NA	PA	Reduction	Not used	No	demersal piscivore	Pelagic	large pelagic	TRUE	2025-03-28T16:29:41.203+01:00	8.3.18	12866	2019-05-29T11:34:00.447+01:00	NA	NA
259	2652	199793	sal.neac.all	Salmon (Salmo salar) in Northeast Atlantic and Arctic Ocean	NA	NA	2020	Salmo salar	Salmon	Arctic Ocean, Azores, Bay of Biscay and the Iberian Coast, Barents Sea, Celtic Seas, Faroes, Greenland Sea, Icelandic Waters, Greater North Sea, Norwegian Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2018	3	2021	Analytical model, run-reconstruction models and Bayesian forecasts, taking into account uncertainties in data and process error (results presented in a risk analysis framework)	NA	MSY/PA	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:41.203+01:00	10.3.3	NA	2020-11-12T13:18:52.573+01:00	O	Unknown
260	2650	199794	sal.nac.all	Salmon (Salmo salar) from North America	NA	NA	2020	Salmo salar	Salmon	Greenland Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2019	2	2021	Analytical model, run-reconstruction models and Bayesian forecasts, taking into account uncertainties in the data	NA	MSY/PA	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:41.203+01:00	10.3.1	NA	2021-01-11T10:35:02.487+01:00	O	Unknown
261	2651	199795	sal.wgc.all	Salmon (Salmo salar) in Subarea 14 and NAFO division 1 (east and west of Greenland)	NA	NA	2020	Salmo salar	Salmon	Greenland Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2019	2	2021	Analytical model, run reconstruction models and Bayesian forecasts, taking into account uncertainties in the data	NA	MSY/PA	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:41.203+01:00	10.3.2	NA	2020-12-09T11:49:52.74+01:00	O	Unknown
412	2819	169241	sal.27.22-31	Salmon (Salmo salar) in subdivisions 22-31 (Baltic Sea, excluding the Gulf of Finland)	136678	sal-2431	2021	Salmo salar	Salmon	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2021	1	2022	Analytical model, Bayesian state-space model for a majority of rivers in AUs 1–4	NA	MSY	Catches	Assessment	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:42.173+01:00	8.3.15	13726	2021-10-29T11:41:04.66+01:00	O	Unknown
448	2856	169095	ele.2737.nea	European eel (Anguilla anguilla) throughout its natural range	136527	ele-nea	2021	Anguilla anguilla	Eel	Arctic Ocean, Azores, Bay of Biscay and the Iberian Coast, Barents Sea, Baltic Sea, Celtic Seas, Faroes, Greenland Sea, Icelandic Waters, Greater North Sea, Norwegian Sea, Oceanic Northeast Atlantic	WGEEL	Joint EIFAAC/ICES/GFCM Working Group on Eels	ADGEEL	Eel Advice Drafting GRoup	3.14	2021	1	2022	Trends-based assessment	NA	PA	No directed fisheries	Not used	No	demersal benthivore	Demersal	medium demersal	TRUE	2025-03-28T16:29:42.173+01:00	9.3.13	16876	2021-11-11T11:41:40.003+01:00	NA	NA
449	2857	169242	sal.27.32	Salmon (Salmo salar) in Subdivision 32 (Gulf of Finland)	136679	sal-32	2021	Salmo salar	Salmon	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	RG/ADGSalmon	Salmon Review and Advice Drafting Group	3	2021	1	2022	Qualitative assessment based on monitored parr densities and expert judgement.	NA	PA	Catches	Topped up	No	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:42.173+01:00	8.3.16	13453	2021-12-10T11:13:59.86+01:00	O	Unknown
498	2906	169291	trs.27.22-32	Sea trout (Salmo trutta) in subdivisions 22-32 (Baltic Sea)	136729	trt-bal	2021	Salmo trutta	Sea trout	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	NA	NA	4	2021	2	2023	Evaluation of the relative status of stocks by comparing the observed parr densities to model-predicted maximal parr densities.	NA	PA	Reduction	Not used	No	demersal piscivore	Pelagic	large pelagic	TRUE	2025-03-28T16:29:42.173+01:00	8.3.18	12866	2021-12-10T11:12:39.54+01:00	NA	NA
520	2929	199793	sal.neac.all	Salmon (Salmo salar) in Northeast Atlantic and Arctic Ocean	NA	NA	2021	Salmo salar	Salmon	Arctic Ocean, Azores, Bay of Biscay and the Iberian Coast, Barents Sea, Celtic Seas, Faroes, Greenland Sea, Icelandic Waters, Greater North Sea, Norwegian Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2021	1	2022	Analytical model, run-reconstruction models and Bayesian forecasts, taking into account uncertainties in data and process error (results presented in a risk analysis framework)	NA	MSY	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:42.173+01:00	10.3.3	NA	2021-10-29T11:46:05.567+01:00	O	Unknown
521	2930	199794	sal.nac.all	Salmon (Salmo salar) from North America	NA	NA	2021	Salmo salar	Salmon	Greenland Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2021	1	2022	Analytical model, run-reconstruction models and Bayesian forecasts, taking into account uncertainties in the data	NA	MSY/PA	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:42.173+01:00	10.3.1	NA	2021-10-29T11:44:11.297+01:00	O	Unknown
522	2931	199795	sal.wgc.all	Salmon (Salmo salar) in Subarea 14 and NAFO division 1 (east and west of Greenland)	NA	NA	2021	Salmo salar	Salmon	Greenland Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2021	3	2024	Analytical model, run reconstruction models and Bayesian forecasts, taking into account uncertainties in the data	NA	MSY	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:42.173+01:00	10.3.2	NA	2021-10-29T11:47:18.467+01:00	O	Unknown
677	3058	169241	sal.27.22-31	Salmon (Salmo salar) in subdivisions 22-31 (Baltic Sea, excluding the Gulf of Finland)	136678	sal-2431	2022	Salmo salar	Salmon	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2021	1	2022	Analytical model, Bayesian state-space model for a majority of rivers in AUs 1–4	NA	MSY	Catches	Assessment	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:43.117+01:00	8.3.15	13726	2021-10-29T11:41:04.66+01:00	O	Unknown
713	3100	169095	ele.2737.nea	European eel (Anguilla anguilla) throughout its natural range	136527	ele-nea	2022	Anguilla anguilla	Eel	Arctic Ocean, Azores, Bay of Biscay and the Iberian Coast, Barents Sea, Baltic Sea, Celtic Seas, Faroes, Greenland Sea, Icelandic Waters, Greater North Sea, Norwegian Sea, Oceanic Northeast Atlantic	WGEEL	Joint EIFAAC/ICES/GFCM Working Group on Eels	ADGEEL	Eel Advice Drafting GRoup	3.14	2022	1	2023	Trends-based assessment	NA	PA	No directed fisheries	Not used	No	demersal benthivore	Demersal	medium demersal	TRUE	2025-03-28T16:29:43.117+01:00	9.3.13	17615	2023-01-03T10:52:52.937+01:00	NA	NA
714	3101	169242	sal.27.32	Salmon (Salmo salar) in Subdivision 32 (Gulf of Finland)	136679	sal-32	2022	Salmo salar	Salmon	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	RG/ADGSalmon	Salmon Review and Advice Drafting Group	3	2021	1	2022	Qualitative assessment based on monitored parr densities and expert judgement.	NA	PA	Catches	Topped up	No	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:43.117+01:00	8.3.16	13453	2021-12-10T11:13:59.86+01:00	O	Unknown
763	3144	169291	trs.27.22-32	Sea trout (Salmo trutta) in subdivisions 22-32 (Baltic Sea)	136729	trt-bal	2022	Salmo trutta	Sea trout	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	NA	NA	4	2021	2	2023	Evaluation of the relative status of stocks by comparing the observed parr densities to model-predicted maximal parr densities.	NA	PA	Reduction	Not used	No	demersal piscivore	Pelagic	large pelagic	TRUE	2025-03-28T16:29:43.117+01:00	8.3.18	12866	2023-01-05T10:54:51.217+01:00	NA	NA
785	3167	199793	sal.neac.all	Salmon (Salmo salar) in Northeast Atlantic and Arctic Ocean	NA	NA	2022	Salmo salar	Salmon	Arctic Ocean, Azores, Bay of Biscay and the Iberian Coast, Barents Sea, Celtic Seas, Faroes, Greenland Sea, Icelandic Waters, Greater North Sea, Norwegian Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2021	1	2022	Analytical model, run-reconstruction models and Bayesian forecasts, taking into account uncertainties in data and process error (results presented in a risk analysis framework)	NA	MSY	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:43.117+01:00	10.3.3	NA	2021-10-29T11:46:05.567+01:00	O	Unknown
786	3168	199794	sal.nac.all	Salmon (Salmo salar) from North America	NA	NA	2022	Salmo salar	Salmon	Greenland Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2021	1	2022	Analytical model, run-reconstruction models and Bayesian forecasts, taking into account uncertainties in the data	NA	MSY/PA	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:43.117+01:00	10.3.1	NA	2021-10-29T11:44:11.297+01:00	O	Unknown
787	3169	199795	sal.wgc.all	Salmon (Salmo salar) in Subarea 14 and NAFO division 1 (east and west of Greenland)	NA	NA	2022	Salmo salar	Salmon	Greenland Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2021	3	2024	Analytical model, run reconstruction models and Bayesian forecasts, taking into account uncertainties in the data	NA	MSY	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:43.117+01:00	10.3.2	NA	2021-10-29T11:47:18.467+01:00	O	Unknown
943	3279	169241	sal.27.22-31	Salmon (Salmo salar) in subdivisions 22-31 (Baltic Sea, excluding the Gulf of Finland)	136678	sal-2431	2023	Salmo salar	Salmon	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2023	1	2024	Analytical model, Bayesian state-space model for a majority of rivers in AUs 1–4	NA	MSY	Catches	Assessment	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:44.07+01:00	8.3.15	13726	2023-05-31T13:58:57.367+01:00	B	Unknown
979	3320	169095	ele.2737.nea	European eel (Anguilla anguilla) throughout its natural range	136527	ele-nea	2023	Anguilla anguilla	Eel	Arctic Ocean, Bay of Biscay and the Iberian Coast, Celtic Seas, Greater North Sea, Oceanic Northeast Atlantic	WGEEL	Joint EIFAAC/ICES/GFCM Working Group on Eels	ADGEEL	Eel Advice Drafting GRoup	3.14	2022	1	2023	Trends-based assessment	NA	PA	No directed fisheries	Not used	No	demersal benthivore	Demersal	medium demersal	TRUE	2025-03-28T16:29:44.07+01:00	9.3.13	18459	2023-11-14T09:05:55.627+01:00	NA	NA
980	3321	169242	sal.27.32	Salmon (Salmo salar) in Subdivision 32 (Gulf of Finland)	136679	sal-32	2023	Salmo salar	Salmon	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	RG/ADGSalmon	Salmon Review and Advice Drafting Group	3	2023	1	2024	Qualitative assessment based on monitored parr densities and expert judgement.	NA	PA	Catches	Topped up	No	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:44.07+01:00	8.3.16	13453	2023-11-14T09:59:06.097+01:00	O	Unknown
1028	3364	169291	trs.27.22-32	Sea trout (Salmo trutta) in subdivisions 22-32 (Baltic Sea)	136729	trt-bal	2023	Salmo trutta	Sea trout	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	NA	NA	4	2023	2	2025	Evaluation of the relative status of stocks by comparing the observed parr densities to model-predicted maximal parr densities.	NA	PA	Reduction	Not used	No	demersal piscivore	Pelagic	large pelagic	TRUE	2025-03-28T16:29:44.07+01:00	8.3.18	12866	2023-05-31T14:05:23.573+01:00	NA	NA
1050	3431	199793	sal.neac.all	Salmon (Salmo salar) in Northeast Atlantic and Arctic Ocean	NA	NA	2023	Salmo salar	Salmon	Arctic Ocean, Azores, Bay of Biscay and the Iberian Coast, Barents Sea, Celtic Seas, Faroes, Greenland Sea, Icelandic Waters, Greater North Sea, Norwegian Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2021	3	2024	Analytical model, run-reconstruction models and Bayesian forecasts, taking into account uncertainties in data and process error (results presented in a risk analysis framework)	NA	MSY	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:44.07+01:00	10.3.3	NA	2023-10-10T09:13:51.963+01:00	O	Unknown
1051	3432	199794	sal.nac.all	Salmon (Salmo salar) from North America	NA	NA	2023	Salmo salar	Salmon	Greenland Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2021	3	2024	Analytical model, run-reconstruction models and Bayesian forecasts, taking into account uncertainties in the data	NA	MSY	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:44.07+01:00	10.3.1	NA	2023-10-10T09:09:42.107+01:00	O	Unknown
1052	3433	199795	sal.wgc.all	Salmon (Salmo salar) in Subarea 14 and NAFO division 1 (east and west of Greenland)	NA	NA	2023	Salmo salar	Salmon	Greenland Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2021	3	2024	Analytical model, run reconstruction models and Bayesian forecasts, taking into account uncertainties in the data	NA	MSY	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:44.07+01:00	10.3.2	NA	2021-10-29T11:47:18.467+01:00	O	Unknown
1208	3561	169241	sal.27.22-31	Salmon (Salmo salar) in subdivisions 22–31 (Baltic Sea, excluding the Gulf of Finland)	136678	sal-2431	2024	Salmo salar	Salmon	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2023	1	2024	Analytical model, Bayesian state-space model for a majority of rivers in AUs 1–4	NA	MSY	Catches	Assessment	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:44.98+01:00	8.3.15	13726	2024-01-11T09:30:20.893+01:00	B	Unknown
1244	3601	169095	ele.2737.nea	European eel (Anguilla anguilla) throughout its natural range	136527	ele-nea	2024	Anguilla anguilla	Eel	Arctic Ocean, Bay of Biscay and the Iberian Coast, Celtic Seas, Greater North Sea, Oceanic Northeast Atlantic	WGEEL	Joint EIFAAC/ICES/GFCM Working Group on Eels	ADGEEL	Eel Advice Drafting GRoup	3.14	2022	1	2023	Trends-based assessment	NA	PA	No directed fisheries	Not used	No	demersal benthivore	Demersal	medium demersal	TRUE	2025-03-28T16:29:44.98+01:00	9.3.13	19270	2024-09-25T14:21:32.487+01:00	NA	NA
1245	3602	169242	sal.27.32	Salmon (Salmo salar) in Subdivision 32 (Gulf of Finland)	136679	sal-32	2024	Salmo salar	Salmon	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	RG/ADGSalmon	Salmon Review and Advice Drafting Group	3	2023	1	2024	Qualitative assessment based on monitored parr densities and expert judgement.	NA	PA	Catches	Topped up	No	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:44.98+01:00	8.3.16	19019	2024-01-11T10:26:17.923+01:00	O	Unknown
1293	3645	169291	trs.27.22-32	Sea trout (Salmo trutta) in subdivisions 22–32 (Baltic Sea)	136729	trt-bal	2024	Salmo trutta	Sea trout	Baltic Sea	WGBAST	Assessment Working Group on Baltic Salmon and Trout	NA	NA	4	2023	2	2025	Evaluation of the relative status of stocks by comparing the observed parr densities to model-predicted maximal parr densities.	NA	PA	Reduction	Not used	No	demersal piscivore	Pelagic	large pelagic	TRUE	2025-03-28T16:29:44.98+01:00	8.3.18	12866	2024-01-11T09:08:41.113+01:00	NA	NA
1316	3709	199793	sal.neac.all	Salmon (Salmo salar) in Northeast Atlantic and Arctic Ocean	NA	NA	2024	Salmo salar	Salmon	Arctic Ocean, Azores, Bay of Biscay and the Iberian Coast, Barents Sea, Celtic Seas, Faroes, Greenland Sea, Icelandic Waters, Greater North Sea, Norwegian Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2021	3	2024	Analytical model, run-reconstruction models and Bayesian forecasts, taking into account uncertainties in data and process error (results presented in a risk analysis framework)	NA	MSY	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:44.98+01:00	10.3.3	NA	2023-10-10T09:13:51.963+01:00	O	Unknown
1317	3710	199794	sal.nac.all	Salmon (Salmo salar) from North America	NA	NA	2024	Salmo salar	Salmon	Greenland Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2021	3	2024	Analytical model, run-reconstruction models and Bayesian forecasts, taking into account uncertainties in the data	NA	MSY	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:44.98+01:00	10.3.1	NA	2023-10-10T09:09:42.107+01:00	O	Unknown
1318	3711	199795	sal.wgc.all	Salmon (Salmo salar) in Subarea 14 and NAFO division 1 (east and west of Greenland)	NA	NA	2024	Salmo salar	Salmon	Greenland Sea, Oceanic Northeast Atlantic	WGNAS	Working Group on North Atlantic Salmon	RG/ADGSalmon	Salmon Review and Advice Drafting Group	1	2021	3	2024	Analytical model, run reconstruction models and Bayesian forecasts, taking into account uncertainties in the data	NA	MSY	Catches	Not used	NA	pelagic piscivore	Pelagic	large benthopelagic	TRUE	2025-03-28T16:29:44.98+01:00	10.3.2	NA	2021-10-29T11:47:18.467+01:00	O	Unknown

The code for creating metadata is listed below

SQL code to create tables

-- the code is here : SQL/tr_metadata_met.sql
-- we first create all referential tables then the metadata itself

DROP TABLE IF EXISTS ref.tr_objecttype_oty CASCADE;
CREATE TABLE ref.tr_objecttype_oty (
oty_code TEXT PRIMARY KEY,
oty_description TEXT
);

INSERT INTO ref.tr_objecttype_oty VALUES ('Single_value', 'Single value');
INSERT INTO ref.tr_objecttype_oty VALUES ('Vector', 'One dimension vector');
INSERT INTO ref.tr_objecttype_oty VALUES ('Matrix', 'Two dimensions matrix');
INSERT INTO ref.tr_objecttype_oty VALUES ('Array', 'Three dimensions array');

COMMENT ON TABLE ref.tr_objecttype_oty IS 
'Table indicating the dimensions of the object stored in the model, 
single value, vector, matrix, array';

COMMENT ON COLUMN ref.tr_objecttype_oty.oty_code IS 
'code of the object type, single_value, vector, ...';

COMMENT ON COLUMN ref.tr_objecttype_oty.oty_code IS 'description of the object type';
GRANT ALL ON ref.tr_objecttype_oty TO diaspara_admin;
GRANT SELECT ON ref.tr_objecttype_oty TO diaspara_read;
--nimble

DROP TABLE IF EXISTS ref.tr_nimble_nim CASCADE;
CREATE TABLE ref.tr_nimble_nim (
nim_code TEXT PRIMARY KEY,
nim_description TEXT
);

COMMENT ON TABLE ref.tr_nimble_nim IS 
'Indicate the type of data, parameter constant, parameter estimate, output, other ...';
-- Note this is a mix of nimble and status, which mean the same....

INSERT INTO ref.tr_nimble_nim VALUES ('Data', 'Data entry to the model');
INSERT INTO ref.tr_nimble_nim 
VALUES ('Parameter constant', 'Parameter input to the model');
INSERT INTO ref.tr_nimble_nim 
VALUES ('Parameter estimate', 'Parameter input to the model');
INSERT INTO ref.tr_nimble_nim 
VALUES ('Output', 'Output from the model, derived quantity');
-- Do we want another type here ?
--INSERT INTO ref.tr_nimble_nim VALUES ('observation', 'Observation not used in the model');
INSERT INTO ref.tr_nimble_nim 
VALUES ('Other', 'Applies currently to conservation limits');
GRANT ALL ON ref.tr_nimble_nim TO diaspara_admin;
GRANT SELECT ON ref.tr_nimble_nim TO diaspara_read;

-- It seems to me that metadata should contain information about historical 
-- variables, so I'm moving this from the main table and adding to metadata
-- some variables might get deprecated in time. 
-- Unless they get a new version this might not change
-- I have removed reference to stockkey as there are several stock keys
-- for the work of WGNAS
DROP TABLE IF EXISTS ref.tr_version_ver CASCADE;
CREATE TABLE ref.tr_version_ver(
ver_code TEXT PRIMARY KEY,
ver_year INTEGER NOT NULL,
ver_spe_code CHARACTER VARYING(3),
--ver_stockkey INTEGER NOT NULL, 
ver_stockkeylabel TEXT,
---ver_stockadvicedoi TEXT NOT NULL,
ver_datacalldoi TEXT NULL,
ver_version INTEGER NOT NULL,
ver_description TEXT,
CONSTRAINT fk_ver_spe_code FOREIGN KEY (ver_spe_code) 
REFERENCES ref.tr_species_spe(spe_code)
ON UPDATE CASCADE ON DELETE CASCADE
);
COMMENT ON TABLE ref.tr_version_ver
IS 'Table of data or variable version, essentially one datacall or advice.';
COMMENT ON COLUMN ref.tr_version_ver.ver_version 
IS 'Version code, stockkey-year-version.';
COMMENT ON COLUMN ref.tr_version_ver.ver_year 
IS 'Year of assessement.';
COMMENT ON COLUMN ref.tr_version_ver.ver_spe_code 
IS 'Species code e.g. ''ele'' references tr_species_spe.';
--COMMENT ON COLUMN ref.tr_version_ver.ver_stockkey 
--IS 'Stockkey (integer) from the stock database.';
COMMENT ON COLUMN ref.tr_version_ver.ver_stockkeylabel 
IS 'Ver_stockkeylabel e.g. ele.2737.nea.';
--COMMENT ON COLUMN ref.tr_version_ver.ver_stockadvicedoi 
--IS 'Advice DOI corresponding to column adviceDOI 
--when using icesASD::getAdviceViewRecord().';
COMMENT ON COLUMN ref.tr_version_ver.ver_datacalldoi 
IS 'Data call DOI, find a way to retreive that information 
and update this comment';
COMMENT ON COLUMN ref.tr_version_ver.ver_version 
IS 'Version code in original database, eg 2,4 for wgnas, dc_2020 for wgeel.';
COMMENT ON COLUMN ref.tr_version_ver.ver_description 
IS 'Description of the data call / version.';
GRANT ALL ON ref.tr_version_ver TO diaspara_admin;
GRANT SELECT ON ref.tr_version_ver TO diaspara_read;
-- metric 

DROP TABLE IF EXISTS  ref.tr_metric_mtr CASCADE;
CREATE TABLE ref.tr_metric_mtr(
mtr_code TEXT PRIMARY KEY,
mtr_description TEXT
);


INSERT INTO ref.tr_metric_mtr VALUES
('Estimate' , 'Estimate');
INSERT INTO ref.tr_metric_mtr VALUES
('Index', 'Index');
INSERT INTO ref.tr_metric_mtr VALUES
('Bound', 'Either min or max');
INSERT INTO ref.tr_metric_mtr VALUES
('Hyperparameter', 'Hyperparameter (prior)');
INSERT INTO ref.tr_metric_mtr VALUES
('SD', 'Standard deviation');
INSERT INTO ref.tr_metric_mtr VALUES
('CV', 'Coefficient of variation');
INSERT INTO ref.tr_metric_mtr VALUES
('Precision', 'Inverse of variance');
INSERT INTO ref.tr_metric_mtr VALUES
('Mean', 'Mean');
INSERT INTO ref.tr_metric_mtr VALUES 
('Min','Minimum');
INSERT INTO ref.tr_metric_mtr VALUES 
('Max','Maximum');

GRANT ALL ON ref.tr_metric_mtr TO diaspara_admin;
GRANT SELECT ON ref.tr_metric_mtr TO diaspara_read;
COMMENT ON TABLE ref.tr_metric_mtr IS 
'Table metric describe the type of parm used, Index, Bound ...';

-- tr_category_cat

DROP TABLE IF EXISTS ref.tr_category_cat CASCADE;
CREATE TABLE ref.tr_category_cat (
cat_code TEXT PRIMARY KEY,
cat_description TEXT
);

INSERT INTO ref.tr_category_cat VALUES 
('Catch', 'Catch, including recreational and commercial catch.');
INSERT INTO ref.tr_category_cat VALUES (
'Effort', 'Parameter measuring fishing effort.');
INSERT INTO ref.tr_category_cat VALUES (
'Biomass', 'Biomass of fish either in number or weight.');
INSERT INTO ref.tr_category_cat VALUES (
'Mortality', 'Mortality either expressed in year-1 (instantaneous rate) 
as F in exp(-FY) but can also be harvest rate.');
INSERT INTO ref.tr_category_cat VALUES (
'Release', 'Release or restocking.');
INSERT INTO ref.tr_category_cat VALUES (
'Density', 'Fish density.');
INSERT INTO ref.tr_category_cat VALUES (
'Count', 'Count or abundance or number of fish.');
INSERT INTO ref.tr_category_cat VALUES (
'Conservation limit', 'Limit of conservation in Number or Number of eggs.');
INSERT INTO ref.tr_category_cat VALUES (
'Life trait', 'Life trait parameterized in model, e.g. growth parameter, 
fecundity rate ...');
INSERT INTO ref.tr_category_cat VALUES (
'Other', 'Other variable/ parameter used in the model other than the previous categories, 
origin distribution of catches, proportions, parameters setting the beginning and ending dates....');
COMMENT ON TABLE ref.tr_category_cat IS 
'Broad category of data or parameter, catch, effort, biomass, mortality, count ...,
 more details in the table ref.tr_parameter_parm e.g. commercial catch,
recreational catch are found in the parameter value and definition and unit, 
this list is intended to be short.';

GRANT ALL ON ref.tr_category_cat TO diaspara_admin;
GRANT SELECT ON ref.tr_category_cat TO diaspara_read;

DROP TABLE IF EXISTS ref.tr_destination_des CASCADE;
CREATE TABLE ref.tr_destination_des (
des_code TEXT PRIMARY KEY,
des_description TEXT
);

COMMENT ON TABLE ref.tr_destination_des IS 
'Table of fish destination. When dealing with fish, e.g. in landings,what is the future of the fish, e.g. Released (alive), Seal damage, 
Removed (from the environment)'; 
INSERT INTO ref.tr_destination_des VALUES 
('Removed', 'Removed from the environment, e.g. caught and kept');
INSERT INTO ref.tr_destination_des VALUES (
'Seal damaged', 'Seal damage');
INSERT INTO ref.tr_destination_des VALUES (
'Discarded', 'Discards');
INSERT INTO ref.tr_destination_des VALUES (
'Released', 'Released alive');


GRANT ALL ON ref.tr_destination_des TO diaspara_admin;
GRANT SELECT ON ref.tr_destination_des TO diaspara_read;


SELECT * FROM refsalmoglob."database" JOIN
refnas.tr_metadata_met AS tmm ON tmm.met_var = var_mod
WHERE "year" IS NULL


DROP TABLE IF EXISTS ref.tr_metadata_met CASCADE;
CREATE TABLE ref.tr_metadata_met (
  met_var TEXT NOT NULL,
  met_spe_code character varying(3) NOT  NULL,
  met_wkg_code TEXT NOT NULL,
  met_ver_code TEXT NULL,
  met_oty_code TEXT NOT NULL,
  met_nim_code TEXT NOT NULL,
  met_dim integer ARRAY,
  met_dimname TEXT ARRAY,
  met_modelstage TEXT NULL, 
  met_type TEXT NULL,
  met_location TEXT NULL,
  met_fishery TEXT NULL,
  met_mtr_code TEXT NULL,
  met_des_code TEXT NULL,
  met_uni_code TEXT NULL,
  met_cat_code TEXT NULL,
  met_definition TEXT NULL, 
  met_deprecated BOOLEAN DEFAULT FALSE,
  CONSTRAINT t_metadata_met_pkey PRIMARY KEY(met_var, met_spe_code),
  CONSTRAINT fk_met_spe_code FOREIGN KEY (met_spe_code)
  REFERENCES ref.tr_species_spe(spe_code) 
  ON DELETE CASCADE
  ON UPDATE CASCADE,
    CONSTRAINT fk_met_wkg_code FOREIGN KEY (met_wkg_code)
  REFERENCES ref.tr_icworkinggroup_wkg(wkg_code) 
  ON DELETE CASCADE
  ON UPDATE CASCADE,
  CONSTRAINT fk_met_ver_code FOREIGN KEY (met_ver_code)
  REFERENCES ref.tr_version_ver(ver_code) 
  ON DELETE CASCADE
  ON UPDATE CASCADE,
  CONSTRAINT fk_met_oty_code FOREIGN KEY (met_oty_code) 
  REFERENCES ref.tr_objecttype_oty (oty_code) ON DELETE CASCADE
  ON UPDATE CASCADE,
  CONSTRAINT fk_met_nim_code FOREIGN KEY (met_nim_code) 
  REFERENCES ref.tr_nimble_nim (nim_code) ON DELETE CASCADE
  ON UPDATE CASCADE,  
  CONSTRAINT fk_met_mtr_code FOREIGN KEY (met_mtr_code)
  REFERENCES ref.tr_metric_mtr(mtr_code)
  ON DELETE CASCADE
  ON UPDATE CASCADE,
  CONSTRAINT fk_met_uni_code FOREIGN KEY (met_uni_code)
  REFERENCES ref.tr_units_uni(uni_code)
  ON DELETE CASCADE
  ON UPDATE CASCADE,
  CONSTRAINT fk_met_cat_code FOREIGN KEY (met_cat_code)
  REFERENCES ref.tr_category_cat(cat_code)
  ON DELETE CASCADE
  ON UPDATE CASCADE,
  CONSTRAINT fk_met_des_code FOREIGN KEY (met_des_code)
  REFERENCES ref.tr_destination_des(des_code)
  ON DELETE CASCADE
  ON UPDATE CASCADE
);
COMMENT ON TABLE ref.tr_metadata_met IS 
'Table (metadata) of each variable (parameter) in the database.';
COMMENT ON COLUMN ref.tr_metadata_met.met_var 
IS 'Variable code, primary key on both met_spe_code and met_var.';
COMMENT ON COLUMN ref.tr_metadata_met.met_spe_code 
IS 'Species, ANG, SAL, TRT ... primary key on both met_spe_code and met_var.';
COMMENT ON COLUMN ref.tr_metadata_met.met_ver_code 
IS 'Code on the version of the model, see table tr_version_ver.';
COMMENT ON COLUMN ref.tr_metadata_met.met_oty_code 
IS 'Object type, single_value, vector, matrix see table tr_objecttype_oty.';
COMMENT ON COLUMN ref.tr_metadata_met.met_nim_code 
IS 'Nimble type, one of data, constant, output, other.';
COMMENT ON COLUMN ref.tr_metadata_met.met_dim 
IS 'Dimension of the Nimble variable, use {10, 100, 100} 
to insert the description of an array(10,100,100).';
COMMENT ON COLUMN ref.tr_metadata_met.met_dimname 
IS 'Dimension of the variable in Nimble, use {''year'', ''stage'', ''area''}.';
COMMENT ON COLUMN ref.tr_metadata_met.met_modelstage 
IS 'Currently one of fit, other, First year.';
COMMENT ON COLUMN ref.tr_metadata_met.met_type 
IS 'Type of data in the variable, homewatercatches, InitialISation first year,
abundance ....';
COMMENT ON COLUMN ref.tr_metadata_met.met_location 
IS 'Describe process at sea, e.g. Btw. FAR - GLD fisheries, or Aft. Gld fISheries.';
COMMENT ON COLUMN ref.tr_metadata_met.met_fishery 
IS 'Description of the fishery.';
COMMENT ON COLUMN ref.tr_metadata_met.met_des_code 
IS 'Outcome of the fish, e.g. Released (alive), Seal damage,
Removed (from the environment), references table tr_destination_des.';
COMMENT ON COLUMN ref.tr_metadata_met.met_uni_code 
IS 'Unit, references table tr_unit_uni.';
COMMENT ON COLUMN ref.tr_metadata_met.met_cat_code 
IS 'Broad category of data or parameter, 
catch, effort, biomass, mortality, count ...references table tr_category_cat.';
COMMENT ON COLUMN ref.tr_metadata_met.met_mtr_code 
IS 'Code of the metric, references tr_metric_mtr, Estimate, Bound, SD, CV ....';
COMMENT ON COLUMN ref.tr_metadata_met.met_definition 
IS 'Definition of the metric.';
COMMENT ON COLUMN ref.tr_metadata_met.met_deprecated
IS'Is the variable still used ?';

GRANT ALL ON ref.tr_metadata_met TO diaspara_admin;
GRANT SELECT ON ref.tr_metadata_met TO diaspara_read;

DIASPARA

Not yet completely sure about this …. Environment (sea, transition …), age, life stage and complex are in the metadata. But they are also in the main table. To start with, we remove them to reduce complexity and avoid errors. The complex will be derived from the spatial structure still in construction

DIASPARA

As in the diagram, added a category (data type in figure Figure 3). The idea is to be able to get quickly all parameters related to a type, e.g. catch, mortality, biomass. Please check and also check definitions.

DIASPARA

WGBAST, WGNAS, WGEEL, WGTRUTTA will have to check definitions in tr_destination_dest. Note this is just a column in tr_metadata_met not in the main table. Check if it could not simply be removed if the definition of the parameter is clear ?

Object type (tr_objectype_oty)

This table (Table 7) is used in metadata

Table 7: Object type

oty_code	oty_description
Single_value	Single value
Vector	One dimension vector
Matrix	Two dimensions matrix
Array	Three dimensions array

Type of parm / data (tr_nimble_nim)

In the salmoglob db, this table (Table 8) corresponded to both tables status and nimble which most often contained the same information.

Table 8: Nimble

nim_code	nim_description
Data	Data entry to the model
Parameter constant	Parameter input to the model
Parameter estimate	Parameter input to the model
Output	Output from the model, derived quantity
Other	Applies currently to conservation limits

Version (tr_version_ver)

Currently in the salmoglob metadata there is no information about version. The version number is in the table itself. It seems to correspond to different versions of the same parameter. While currently this information is stored in the database and database_archive it seems to that metadata should also contain information about historical variables. For instance we create a new variable, so we know when it was introduced. So in addition with the data in the main table I’m adding a version number to metadata, Some variables might get deprecated over time. The year will be the year when the variable was introduced. All variables in this version of the DB in metadata will start with SAL-2024-1, the tr_version_ver should be able to store a version number but currently I need to understand to what it corresponds.

Note that the version (Table 9) contains both reference to the datacall (when data are loaded) and to the advice. The advice might still be null at the time the values will be entered into the database. The version will be handled in inherited tables for WGNAS, WGEEL and WGBAST. Here I’m currently entering the WGNAS data.

Question to WNGAS / Etienne

Check that this is correct, original values in the table metadata were “Const_nimble” “Data_nimble” “Output” “other”

#sd <-do.call(rbind,mapply(icesSD::getSD, year= 2020:2024, SIMPLIFY=FALSE))
#sd[grepl('Working Group on North Atlantic Salmon',sd$ExpertGroupDescription),]
 


tr_version_ver <- data.frame(
ver_code = paste0("SAL-",2020:2024,"-1"),
ver_year = 2020:2024,
ver_spe_code = "SAL",
ver_datacalldoi=c(NA,NA,NA,NA,"https://doi.org/10.17895/ices.pub.25071005.v3"), 
ver_stockkeylabel =c("sal.neac.all"), # sugested by Hilaire. 
# TODO FIND other DOI (mail sent to ICES)
ver_version=c(1,1,1,1,1), # TODO WGNAS check that there is just one version per year
ver_description=c(NA,NA,NA,NA,NA)) # TODO WGNAS provide model description

DBI::dbWriteTable(con_diaspara_admin, "temp_tr_version_ver", tr_version_ver, 
overwrite = TRUE)
dbExecute(con_diaspara_admin, "INSERT INTO refnas.tr_version_ver SELECT * FROM temp_tr_version_ver;") # 5
 DBI::dbExecute(con_diaspara_admin, "DROP TABLE temp_tr_version_ver;")


# TODO eel and wgbast
#"ele.2737.nea","sal.27.22–31",

Question to WNGAS / ICES

• Check there is just one version per year
• Provide descriptions of the version of the model
• Questions sent to ICES to provide access to historical DOI
• Exchanges currently on this (17/03/2025) with Etienne / Pierre Yves Hernvann

Table 9: Version

ver_code	ver_year	ver_spe_code	ver_stockkeylabel	ver_datacalldoi	ver_version	ver_description
SAL-2020-1	2020	SAL	NA	sal.neac.all	1	NA
SAL-2021-1	2021	SAL	NA	sal.neac.all	1	NA
SAL-2022-1	2022	SAL	NA	sal.neac.all	1	NA
SAL-2023-1	2023	SAL	NA	sal.neac.all	1	NA
SAL-2024-1	2024	SAL	https://doi.org/10.17895/ices.pub.25071005.v3	sal.neac.all	1	NA

QUESTION ICES: What is the vocabulary for datacalls

I would like to access to this table : datacall (see link in ICES webpage). Currently we see the current year, this is nice, how do we access to historical data, is there a way to get it using a query ? We’ve found a link for advice or stocks but not data calls.

Metric (tr_metric_mtr)

Table 10: Metric, type of parm used in the model

mtr_code	mtr_description
Estimate	Estimate
Index	Index
Bound	Either min or max
Hyperparameter	Hyperparameter (prior)
SD	Standard deviation
CV	Coefficient of variation
Precision	Inverse of variance
Mean	Mean
Min	Minimum
Max	Maximum

NOTE

This list is probably incomplete. But the metric can be NULL in case of a number of fish released, not of the above (Table 10) will apply.

Category (tr_category_cat)

categories Table 11 were in the salmoglob metadata, we have simplified to reduce the number of categories and be able to get for instance all parameters dealing with catch.

Table 11: category of parameters

cat_code	cat_description
Catch	Catch, including recreational and commercial catch.
Effort	Parameter measuring fishing effort.
Biomass	Biomass of fish either in number or weight.
Mortality	Mortality either expressed in year-1 (instantaneous rate) as F in exp(-FY) but can also be harvest rate.
Release	Release or restocking.
Density	Fish density.
Count	Count or abundance or number of fish.
Conservation limit	Limit of conservation in Number or Number of eggs.
Life trait	Life trait parameterized in model, e.g. growth parameter, fecundity rate ...
Other	Other variable/ parameter used in the model other than the previous categories, origin distribution of catches, proportions, parameters setting the beginning and ending dates...

Destination (tr_destination_dest)

A bit cumbersome this table, the idea is “what becomes of this fish”. Different types of landings. But it’s just in metatdata and we need need in WGBAST

Table 12: category of parameters

des_code	des_description
Removed	Removed from the environment, e.g. caught and kept
Seal damaged	Seal damage
Discarded	Discards
Released	Released alive

NOTE DIASPARA

Here Hilaire say that naming the table “outcome” wasn’t ideal so I’ve followed his suggestion

Metadata (tr_metadata_met)

Table 13: metadata

met_var	met_spe_code	met_wkg_code	met_ver_code	met_oty_code	met_nim_code	met_dim	met_dimname	met_modelstage	met_type	met_location	met_fishery	met_mtr_code	met_des_code	met_uni_code	met_cat_code	met_definition	met_deprecated
log_C5_NAC_1_lbnf_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	neNF fishery	SD	NA	NA	Catch	Standard-deviation of observed catches (log scale) of NAC 1 SW by early Newfoundland fishery	NA
log_C5_NAC_2_lbnf_lab_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	LB fishery	SD	NA	NA	Catch	Standard-deviation of observed catches (log scale) of NAC Labrador-origin 1 SW by late Newfoundland, Labrador and Saint Pierre et Miquelon fisheries	NA
log_C5_NAC_2_lbnf_oth_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	LB/SPM/swNF fishery	SD	NA	NA	Catch	Standard-deviation of observed catches (log scale) of NAC Labrador-excluded 1 SW by late Newfoundland, Labrador and Saint Pierre et Miquelon fisheries	NA
log_C5_NEC_1_far_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	FAR fishery	SD	NA	NA	Catch	Standard-deviation of observed total catches (log scale) of 1SW adults from the NEC complex in Faroe islands	NA
log_C8_2_gld_tot_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Gld fisheries	GLD fishery	SD	NA	NA	Catch	Standard-deviation of observed total catches (log scale) of 2SW adults (mixed complexes) in Greenland	NA
log_C8_NAC_1_lbnf_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	neNF fishery	SD	NA	NA	Catch	Standard-deviation of observed catches (log scale) of NAC 1 SW by Labrador/Newfoundland fishery	NA
log_C8_NAC_3_lbnf_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	neNF fishery	SD	NA	NA	Catch	Standard-deviation of observed catches (log scale) of NAC 2 SW by early Newfoundland fishery	NA
log_C8_NAC_4_lbnf_lab_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	LB fishery	SD	NA	NA	Catch	Standard-deviation of observed catches (log scale) of NAC Labrador-origin 2 SW by late Newfoundland, Labrador and Saint Pierre et Miquelon fisheries	NA
log_C8_NAC_4_lbnf_oth_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{52,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	LB/SPM/swNF fishery	SD	NA	NA	Catch	Standard-deviation of observed catches (log scale) of NAC Labrador-excluded 2 SW by late Newfoundland, Labrador and Saint Pierre et Miquelon fisheries	NA
log_C8_NEC_1_far_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{50,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	FAR fishery	SD	NA	NA	Catch	Standard-deviation of observed total catches (log scale) of 2SW individuals from the NEC complex before maturation in Faroe islands	NA
log_C8_NEC_3_far_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{50,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	FAR fishery	SD	NA	NA	Catch	Standard-deviation of observed total catches (log scale) of 2SW individuals from the NEC complex after maturation in Faroe islands	NA
log_C5_NAC_1_lbnf_mu	SAL	WGNAS	SAL-2024-1	Vector	Data	{52,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	neNF fishery	Mean	NA	NA	Catch	Observed catches (log scale) of NAC 1 SW by early Newfoundland fishery	NA
log_C5_NAC_2_lbnf_lab_mu	SAL	WGNAS	SAL-2024-1	Vector	Data	{50,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	LB fishery	Mean	NA	NA	Catch	Observed catches (log scale) of Labrador-origin 1 SW by late Newfoundland, Labrador and Saint Pierre et Miquelon fisheries	NA
log_C5_NAC_2_lbnf_oth_mu	SAL	WGNAS	SAL-2024-1	Vector	Data	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	LB/SPM/swNF fishery	Mean	NA	NA	Catch	Observed catches (log scale) of NAC Labrador-excluded 1 SW by late Newfoundland, Labrador and Saint Pierre et Miquelon fisheries	NA
log_C5_NEC_1_far_mu	SAL	WGNAS	SAL-2024-1	Vector	Data	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	FAR fishery	Mean	NA	NA	Catch	Observed total catches (log scale) of 1SW adults from the NEC complex in Faroe islands	NA
log_C8_2_gld_tot_mu	SAL	WGNAS	SAL-2024-1	Vector	Data	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Gld fisheries	GLD fishery	Mean	NA	NA	Catch	Observed total catches (log scale) of 2SW adults (mixed complexes) in Greenland	NA
log_C8_NAC_1_lbnf_mu	SAL	WGNAS	SAL-2024-1	Vector	Data	{52,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	neNF fishery	Mean	NA	NA	Catch	Observed catches (log scale) of NAC 1 SW by Labrador/Newfoundland fishery	NA
log_C8_NAC_3_lbnf_mu	SAL	WGNAS	SAL-2024-1	Vector	Data	{53,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	neNF fishery	Mean	NA	NA	Catch	Observed catches (log scale) of NAC 2 SW by early Newfoundland fishery	NA
log_C8_NAC_4_lbnf_lab_mu	SAL	WGNAS	SAL-2024-1	Vector	Data	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	LB fishery	Mean	NA	NA	Catch	Observed catches (log scale) of Labrador-origin 2 SW by late Newfoundland, Labrador and Saint Pierre et Miquelon fisheries	NA
log_C8_NAC_4_lbnf_oth_mu	SAL	WGNAS	SAL-2024-1	Vector	Data	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	LB/SPM/swNF fishery	Mean	NA	NA	Catch	Observed catches (log scale) of NAC Labrador-excluded 2 SW by late Newfoundland, Labrador and Saint Pierre et Miquelon fisheries	NA
log_C8_NEC_1_far_mu	SAL	WGNAS	SAL-2024-1	Vector	Data	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	FAR fishery	Mean	NA	NA	Catch	Observed total catches (log scale) of 2SW individuals from the NEC complex before maturation in Faroe islands	NA
log_C8_NEC_3_far_mu	SAL	WGNAS	SAL-2024-1	Vector	Data	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	FAR fishery	Mean	NA	NA	Catch	Observed total catches (log scale) of 2SW individuals from the NEC complex after maturation in Faroe islands	NA
C5_NAC_1	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,6,0}	{'Year',"'Stock unit NAC'",NULL}	Fit	Sea catches	Bef. Gld fisheries	neNF fishery	Estimate	NA	NA	Catch	Catches of 1SW adults from the NAC complex in early Newfoundland fisheries	NA
C5_NAC_1_tot	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	neNF fishery	Estimate	NA	NA	Catch	Total catch of 1 SW adults by Newfoundland and Labrador fisheries over all NAC SUs	NA
C5_NAC_2	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,6,0}	{'Year',"'Stock unit NAC'",NULL}	Fit	Sea catches	Bef. Gld fisheries	LB-LB/SPM/swNF fishery	Estimate	NA	NA	Catch	Catches of 1SW adults from the NAC complex in late Newfoundland, Labrador and Saint Pierre et Miquelon fisheries	NA
C5_NAC_2_lab	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	LB fishery	Estimate	NA	NA	Catch	Catch of 1SW adults from the Labrador SU by late regional fisheries in Labrador	NA
C5_NAC_2_other	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	LB/SPM/swNF fishery	Estimate	NA	NA	Catch	Catch of 1SW adults from NAC SUs excluding Labrador by late regional fisheries in Labrador and Saint Pierre et Miquelon	NA
C5_NEC_1	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,19,0}	{'Year',"'Stock unit NEC'",NULL}	Fit	Sea catches	Bef. Gld fisheries	FAR fishery	Estimate	NA	NA	Catch	Catches of 1SW adults from the NEC complex in Faroe islands	NA
C5_NEC_1_tot	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	FAR fishery	Estimate	NA	NA	Catch	Total catch of 1 SW adults by Faroes fisheries over all NEC SUs	NA
C8_2	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Sea catches	Gld fisheries	GLD fishery	Estimate	NA	NA	Catch	Catches of 2SW individuals by common fisheries in Greenland waters	NA
C8_2_comp	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,2,0}	{'Year','Complex',NULL}	Fit	Sea catches	Gld fisheries	GLD fishery	Estimate	NA	NA	Catch	Catches of 2SW individuals by common fisheries in Greenland waters for each complex	NA
C8_2_tot	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Gld fisheries	GLD fishery	Estimate	NA	NA	Catch	Total catch of 2SW individuals by common Greenland fisheries over all Sus	NA
C8_NAC_1	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,6,0}	{'Year',"'Stock unit NAC'",NULL}	Fit	Sea catches	Bef. Gld fisheries	neNF fishery	Estimate	NA	NA	Catch	Catches of 2SW from the NAC complex by Newfoundland and Labrador before Greenland fisheries	NA
C8_NAC_1_tot	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	neNF fishery	Estimate	NA	NA	Catch	Total catch of 1 SW non mature by Newfoundland and Labrador fisheries over all NAC SUs	NA
C8_NAC_3	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	neNF fishery	Estimate	NA	NA	Catch	Catch of 2SW adults from the NAC complex by late regional fisheries in Newfoundland	NA
C8_NAC_3_tot	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	neNF fishery	Estimate	NA	NA	Catch	Total catch of NAC 2SW adults by late regional fisheries in Newfoundland	NA
C8_NAC_4	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,6,0}	{'Year',"'Stock unit NAC'",NULL}	Fit	Sea catches	Aft. Gld fisheries	LB-LB/SPM/swNF fishery	Estimate	NA	NA	Catch	Catch of 2SW adults from the NAC complex by late regional fisheries in Labrador and Saint Pierre et Miquelon	NA
C8_NAC_4_lab	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	LB fishery	Estimate	NA	NA	Catch	Catch of 2SW adults from the Labrador SU by late regional fisheries in Labrador	NA
C8_NAC_4_other	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	LB/SPM/swNF fishery	Estimate	NA	NA	Catch	Catch of 2SW adults from NAC SUs excluding Labrador by late regional fisheries in Labrador and Saint Pierre et Miquelon	NA
C8_NEC_1	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,19,0}	{'Year',"'Stock unit NEC'",NULL}	Fit	Sea catches	Bef. Gld fisheries	FAR fishery	Estimate	NA	NA	Catch	Catches of 2SW from the NEC complex before their maturation by Faroes fisheries	NA
C8_NEC_1_tot	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	FAR fishery	Estimate	NA	NA	Catch	Total catch of 2SW individuals over all NEC SUs by Faroes fisheries	NA
C8_NEC_3	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,19,0}	{'Year',"'Stock unit NEC'",NULL}	Fit	Sea catches	Aft. Gld fisheries	FAR fishery	Estimate	NA	NA	Catch	Catch of 2SW adults from the NEC complex by late regional fisheries in Faroes	NA
C8_NEC_3_tot	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	FAR fishery	Estimate	NA	NA	Catch	Total catch of NEC 2SW adults by late regional fisheries in Faroes	NA
p_C5_NEC_1_far_mu	SAL	WGNAS	SAL-2024-1	Matrix	Data	{47,19,0}	{'Year',"'Stock unit NEC'",NULL}	Fit	Origin distribution in sea catches	FAR - by SU	FAR fishery	Mean	NA	NA	Other	Observed proportion of each NEC SU in the total sea catches of 1SW mature individuals of the NEC complex	NA
p_C8_2_NAC_gld_mu	SAL	WGNAS	SAL-2024-1	Matrix	Data	{53,6,0}	{'Year',"'Stock unit NAC'",NULL}	Fit	Origin distribution in sea catches	GLD - by NAC SU	GLD fishery	Mean	NA	NA	Other	Observed proportion of NAC salmon caught in Greenland fisheries	NA
p_C8_2_NEC_gld_mu	SAL	WGNAS	SAL-2024-1	Matrix	Data	{47,19,0}	{'Year',"'Stock unit NEC'",NULL}	Fit	Origin distribution in sea catches	GLD - by NEC SU	GLD fishery	Mean	NA	NA	Other	Observed proportion of NEC salmon caught in Greenland fisheries	NA
p_C8_2_NECNAC_gld_mu	SAL	WGNAS	SAL-2024-1	Matrix	Data	{53,2,0}	{'Year','Complex',NULL}	Fit	Origin distribution in sea catches	GLD - by cplx	GLD fishery	Mean	NA	NA	Other	Observed proportion of each complex in the total Greenland catches	NA
p_C8_NEC_1_far_mu	SAL	WGNAS	SAL-2024-1	Matrix	Data	{50,19,0}	{'Year',"'Stock unit NEC'",NULL}	Fit	Origin distribution in sea catches	FAR - by SU	FAR fishery	Mean	NA	NA	Other	Observed proportion of each NEC SU in the total sea catches of 1SW non mature (future 2SW) the NEC complex	NA
p_C8_NEC_3_far_mu	SAL	WGNAS	SAL-2024-1	Matrix	Data	{52,19,0}	{'Year',"'Stock unit NEC'",NULL}	Fit	Origin distribution in sea catches	FAR - by SU	FAR fishery	Mean	NA	NA	Other	Observed proportion of individuals of each SU in the total catch of 2SW at Faroes	NA
p_C5_NEC_1	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,19,0}	{'Year',"'Stock unit NEC'",NULL}	Fit	Origin distribution in sea catches	FAR fisheries	NA	Estimate	NA	NA	Other	Proportion of individuals of each SU in the total catch of NEC 1SW at Faroes	NA
p_C8_2_NAC	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,6,0}	{'Year',"'Stock unit NAC'",NULL}	Fit	Origin distribution in sea catches	GLD fisheries	NA	Estimate	NA	NA	Other	Proportion of NAC 2SW individuals in the total Greenland catches for each NAC SU	NA
p_C8_2_NEC	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,19,0}	{'Year',"'Stock unit NEC'",NULL}	Fit	Origin distribution in sea catches	GLD fisheries	NA	Estimate	NA	NA	Other	Proportion of NEC 2SW individuals in the total Greenland catches for each NEC SU	NA
p_C8_2_NECNAC	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,2,0}	{'Year','Complex',NULL}	Fit	Origin distribution in sea catches	GLD fisheries	NA	Estimate	NA	NA	Other	Proportion of 2SW individuals in the total Greenland catches for each complex	NA
p_C8_NEC_1	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,19,0}	{'Year',"'Stock unit NEC'",NULL}	Fit	Origin distribution in sea catches	FAR fisheries	NA	Estimate	NA	NA	Other	Proportion of individuals of each SU in the total catch of NEC 2SW at Faroes	NA
p_C8_NEC_3	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,19,0}	{'Year',"'Stock unit NEC'",NULL}	Fit	Origin distribution in sea catches	FAR fisheries	NA	Estimate	NA	NA	Other	Proportion of 2SW individuals of each SU in the total Faroes catches of the NEC complex	NA
CV_hw	SAL	WGNAS	SAL-2024-1	Single_value	Parameter constant	{1,0,0}	{'CV_hw',NULL,NULL}	Fit	Homewater catches	_	NA	CV	NA	NA	Catch	Standard-deviation for catches in home waters	NA
log_C6_delSp_sd	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{50,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Homewater catches	_	_	SD	NA	NA	Catch	Standard deviation of homewaters catches of 1SW delayed spawners	NA
log_C6_sd	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Homewater catches	_	_	SD	NA	NA	Catch	Standard deviation of homewater catches of 1SW returns	NA
log_C9_delSp_sd	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Homewater catches	_	_	SD	NA	NA	Catch	Standard deviation of homewaters catches of 2SW delayed spawners	NA
log_C9_sd	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Homewater catches	_	_	SD	NA	NA	Catch	Standard deviation of homewater catches of 2SW returns	NA
log_C6_delSp_mu	SAL	WGNAS	SAL-2024-1	Matrix	Data	{52,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Homewater catches	_	delayed spawners	Mean	NA	NA	Catch	Observed catches (log scale) of 1SW delayed spawners in home waters	NA
log_C6_mu	SAL	WGNAS	SAL-2024-1	Matrix	Data	{52,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Homewater catches	_	main	Mean	NA	NA	Catch	Observed catches (log scale) of returning 1 SW adults in home waters	NA
log_C9_delSp_mu	SAL	WGNAS	SAL-2024-1	Matrix	Data	{50,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Homewater catches	_	delayed spawners	Mean	NA	NA	Catch	Observed catches (log scale) of 2SW delayed spawners in home waters	NA
log_C9_mu	SAL	WGNAS	SAL-2024-1	Matrix	Data	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Homewater catches	_	main	Mean	NA	NA	Catch	Observed catches (log scale) of returning 2 SW adults in home waters	NA
C6_hw	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Homewater catches	_	main	Estimate	NA	NA	Catch	Homewater catches of returning 1SW spawners	NA
C6_hw_delSp	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Homewater catches	_	delayed spawners	Estimate	NA	NA	Catch	Homewater catches of 1SW delayed spawners	NA
C9_hw	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Homewater catches	_	main	Estimate	NA	NA	Catch	Homewater catches of returning 2SW spawners	NA
C9_hw_delSp	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Homewater catches	_	delayed spawners	Estimate	NA	NA	Catch	Homewater catches of 2SW delayed spawners	NA
log_N6_sd	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Returns	_	NA	SD	NA	NA	Count	Standard deviation of observed returns (log scale) of 1 SW adults in log scale	NA
log_N9_sd	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{52,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Returns	_	NA	SD	NA	NA	Count	Standard deviation of observed returns (log scale) of 2 SW adults in log scale	NA
log_N6_mu	SAL	WGNAS	SAL-2024-1	Matrix	Data	{52,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Returns	_	NA	Mean	NA	NA	Count	Observed returns (log scale) (log scale) of 1 SW adults in log scale	NA
log_N9_mu	SAL	WGNAS	SAL-2024-1	Matrix	Data	{50,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Returns	_	NA	Mean	NA	NA	Count	Observed returns (log scale) of 2 SW adults in log scale	NA
eggs	SAL	WGNAS	SAL-2024-1	Array	Parameter constant	{2,25,47}	{"'Age sea winter'","'Stock unit'",'Year'}	Fit	Fecundity rate	_	NA	Mean	NA	NA	Life trait	Fecundity rates of 1SW and 2SW spawners	NA
prop_female	SAL	WGNAS	SAL-2024-1	Array	Parameter constant	{2,25,50}	{"'Age sea winter'","'Stock unit'",'Year'}	Fit	Sex ratio	_	_	Mean	NA	NA	Life trait	Proportion of females for all years in all SUs	NA
theta6_delSp	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Proportion of delayed individuals	_	NA	Mean	NA	NA	Other	Proportion of delayed individuals among 1SW spawners	NA
N2_pr	SAL	WGNAS	SAL-2024-1	Matrix	Output	{7,25,0}	{'Year',"'Stock unit'",NULL}	First year	Abundance	_	NA	Mean	NA	NA	Count	First year: Pre-smolt abundance	NA
theta9_delSp	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Proportion of delayed individuals	_	NA	Mean	NA	NA	Other	Proportion of delayed individuals among 2SW spawners	NA
p_smolt	SAL	WGNAS	SAL-2024-1	Array	Parameter constant	{52,6,25}	{'Year',"'Age fresh water'","'Stock unit'"}	Fit	Smolt age structure	_	NA	Mean	NA	NA	Life trait	Prior on the proportions of each smolt age in the total number of smolts	NA
p_smolt_gamma	SAL	WGNAS	SAL-2024-1	Array	Output	{47,6,25}	{'Year',"'Age fresh water'","'Stock unit'"}	Fit	Smolt age structure	_	NA	Estimate	NA	NA	Life trait	Gamma distribution hyperparameters used to build the Dirichlet distribution for smolt ages proportions	NA
p_smolt_stoch	SAL	WGNAS	SAL-2024-1	Array	Output	{47,6,25}	{'Year',"'Age fresh water'","'Stock unit'"}	Fit	Smolt age structure	_	NA	Estimate	NA	NA	Life trait	Proportions of each smolt age in the total number of smolts	NA
Stocking_2SW	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{52,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Stocking	_	NA	Mean	NA	NA	Count	Stocking of 2SW from aquaculture	NA
B	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{25,0,0}	{"'Stock unit'",NULL,NULL}	First year	Survival rate	_	NA	Mean	NA	NA	Mortality	First year: density dependence parameter B	NA
CV_theta1	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{25,0,0}	{"'Stock unit'",NULL,NULL}	Fit	Survival rate	_	NA	CV	NA	NA	Mortality	Standard-deviation for eggs-smolt survival rate	NA
E_theta1	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{50,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Survival rate	_	NA	Mean	NA	NA	Mortality	Mean eggs-smolts survival rate	NA
theta6_surv	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Survival rate	_	NA	Mean	NA	NA	Mortality	Survival rate to additional rate for 1SW spawners in home waters	NA
theta9_surv	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{52,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Survival rate	_	NA	Mean	NA	NA	Mortality	Survival rate to additional rate for 2SW spawners in home waters	NA
deltat5_1	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{25,0,0}	{"'Stock unit'",NULL,NULL}	Fit	Time spent at sea	Bef. Fisheries	NA	Mean	NA	NA	Life trait	Time spent (months) by 1SW adults in the environment before Newfoundland fisheries	NA
deltat5_2	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{25,0,0}	{"'Stock unit'",NULL,NULL}	Fit	Time spent at sea	Return aft. First fishery	NA	Mean	NA	NA	Life trait	Time spent (months) by 1SW adults in the environment after Newfoundland fisheries and before Labrador fisheries	NA
deltat8_1	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{25,0,0}	{"'Stock unit'",NULL,NULL}	Fit	Time spent at sea	Bef. Fisheries	NA	Mean	NA	NA	Life trait	Time spent (months) by 2SW individuals in the environment before early catches by Newfoundland and Labrador fisheries	NA
deltat8_2	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{25,0,0}	{"'Stock unit'",NULL,NULL}	Fit	Time spent at sea	Aft. First fisheries	NA	Mean	NA	NA	Life trait	Time spent (months) by 2SW individuals in the environment after early catches by Newfoundland and Labrador fisheries and before reaching Greenland	NA
deltat8_2_1	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{25,0,0}	{"'Stock unit'",NULL,NULL}	Fit	Time spent at sea	Aft. Second fisheries	NA	Mean	NA	NA	Life trait	Time spent (months) by 2SW individuals between Greenland and late Newfoundland, Labrador and St Pierre et Miquelon fisheries	NA
deltat8_2_2	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{25,0,0}	{"'Stock unit'",NULL,NULL}	Fit	Time spent at sea	Return aft. Second fishery	NA	Mean	NA	NA	Life trait	Time spent (months) by 2SW individuals after late Newfoundland, Labrador and St Pierre et Miquelon fisheries	NA
cons_lim	SAL	WGNAS	SAL-2024-1	Vector	Other	{17,0,0}	{'Countries',NULL,NULL}	other	Conservation limits	_	_	Mean	NA	NA	Conservation limit	Conservation limits by country	NA
log_N1_pr	SAL	WGNAS	SAL-2024-1	Matrix	Output	{7,25,0}	{'Year',"'Stock unit'",NULL}	First year	Abundance	_	NA	Mean	NA	NA	Count	First year: Number (log scale) of eggs deposited in rivers by spawners	NA
log_N9_pr	SAL	WGNAS	SAL-2024-1	Vector	Output	{25,0,0}	{"'Stock unit'",NULL,NULL}	First year	Abundance	_	NA	Estimate	NA	NA	Count	First year: Abundance (log scale) of 2SW adults returning to homewaters	NA
logN2	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Abundance	_	NA	Estimate	NA	NA	Count	Observed number of smolts in log scale	NA
logN2_pr	SAL	WGNAS	SAL-2024-1	Matrix	Output	{7,25,0}	{'Year',"'Stock unit'",NULL}	First year	Abundance	_	NA	Mean	NA	NA	Count	First year: Pre-smolt abundance (log scale)	NA
logN4	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Abundance	_	NA	Estimate	NA	NA	Count	Pre-fishery salmons abundance (log scale)	NA
logN5	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Abundance	_	NA	Estimate	NA	NA	Count	Abundance (log scale) of mature salmons after 1SW	NA
logN8	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Abundance	_	NA	Estimate	NA	NA	Count	Abundance (log scale) of mature salmons after 2SW	NA
mean_log_N9_pr	SAL	WGNAS	SAL-2024-1	Vector	Output	{25,0,0}	{"'Stock unit'",NULL,NULL}	First year	Abundance	_	NA	Mean	NA	NA	Count	First year: mean for distribution for abundance (log scale) of 2SW adults returning to homewaters	NA
N1	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Abundance	_	NA	Estimate	NA	NA	Count	Number of eggs deposited in rivers by spawners	NA
N1_pr	SAL	WGNAS	SAL-2024-1	Matrix	Output	{7,25,0}	{'Year',"'Stock unit'",NULL}	First year	Abundance	_	NA	Estimate	NA	NA	Count	First year: Number of eggs deposited in rivers by spawners	NA
N10	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Abundance	_	NA	Estimate	NA	NA	Count	Abundance of 2SW spawners in homewaters	NA
N3	SAL	WGNAS	SAL-2024-1	Array	Output	{47,6,25}	{'Year',"'Age fresh water'","'Stock unit'"}	Fit	Abundance	_	NA	Estimate	NA	NA	Count	Abundance of smolts of each age starting migration	NA
N3_pr	SAL	WGNAS	SAL-2024-1	Array	Output	{13,6,25}	{'Year',"'Age fresh water'","'Stock unit'"}	First year	Abundance	_	NA	Estimate	NA	NA	Count	First year: Abundance of smolts of each age starting migration	NA
N3_tot	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Abundance	_	NA	Estimate	NA	NA	Count	Total abundance of smolts of each age starting migration over all Sus	NA
N4	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Abundance	_	NA	Estimate	NA	NA	Count	Pre-fishery salmons abundance	NA
N5	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Abundance	_	NA	Estimate	NA	NA	Count	Abundance of mature salmons after 1SW	NA
N7	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Abundance	_	NA	Estimate	NA	NA	Count	Abundance of 1SW spawners in homewaters	NA
N8_1	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Abundance	_	NA	Estimate	NA	NA	Count	Abundance of 2SW individuals from the NAC complex after early catches by Newfoundland and Labrador catches	NA
N8_2	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Abundance	_	NA	Estimate	NA	NA	Count	Abundance of 2SW individuals (mixed complexes) arriving at Greenland	NA
CV_dummy	SAL	WGNAS	SAL-2024-1	Single_value	Parameter constant	{1,0,0}	{'CV_dummy',NULL,NULL}	Fit	Demographic transitions	_	NA	CV	NA	NA	Other	Coefficient of variation for demographic transitions	NA
mu_psm_pr	SAL	WGNAS	SAL-2024-1	Array	Output	{7,6,25}	{'Year',"'Age fresh water'","'Stock unit'"}	First year	First year	_	NA	Hyperparameter	NA	NA	Other	First year: Prior on the proportions of each smolt age in the total number of smolts	NA
h5_NAC_1	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	High seas harvest rates	NF/LB fisheries	neNF	Estimate	NA	NA	Mortality	Harvest rate of 1SW adults from the NAC complex due to early Newfoundland fisheries	NA
h5_NAC_2	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,6,0}	{'Year',"'Stock unit NAC'",NULL}	Fit	High seas harvest rates	NF/LB/SPM fisheries	LB-LB/SPM/swNF fishery	Estimate	NA	NA	Mortality	Harvest rate of 1SW adults from the NAC complex due to late Newfoundland, Labrador and Saint Pierre et Miquelon fisheries	NA
h5_NEC_1	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,19,0}	{'Year',"'Stock unit NEC'",NULL}	Fit	High seas harvest rates	FAR fisheries	FAR fishery	Estimate	NA	NA	Mortality	Harvest rate of 1SW adults from the NEC complex due to exploitation in Faroe islands	NA
h8_2	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	High seas harvest rates	GLD fisheries	GLD fishery	Estimate	NA	NA	Mortality	Harvest rate of 2SW individuals from all complexes due to Greenland fisheries	NA
h8_NAC_1	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	High seas harvest rates	NF/LB fisheries	neNF	Estimate	NA	NA	Mortality	Harvest rate of 2SW individuals from the NAC complex due to Newfoundland and Labrador fisheries before reaching Greenland	NA
h8_NAC_3	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	High seas harvest rates	NF fisheries	neNF	Estimate	NA	NA	Mortality	Harvest rate of NAC 2SW adults due to late regional fisheries in Newfoundland	NA
h8_NAC_4	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,6,0}	{'Year',"'Stock unit NAC'",NULL}	Fit	High seas harvest rates	LB/SPM fisheries tot	LB-LB/SPM/swNF fishery	Estimate	NA	NA	Mortality	Harvest rate of NAC 2SW adults due to late regional fisheries in Labrador and Saint Pierre et Miquelon	NA
h8_NAC_4_lab	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	High seas harvest rates	LB/SPM fisheries in Lb	LB fishery	Estimate	NA	NA	Mortality	Harvest rate of NAC 2SW adults from the Labrador SU due to late regional fisheries in Labrador	NA
h8_NAC_4_other	SAL	WGNAS	SAL-2024-1	Vector	Output	{47,0,0}	{'Year',NULL,NULL}	Fit	High seas harvest rates	LB/SPM fisheries out. Lb	LB/SPM/swNF fishery	Estimate	NA	NA	Mortality	Harvest rate of 2SW adults from NAC SUs excluding Labrador due to late regional fisheries in Labrador and Saint Pierre et Miquelon	NA
tau_theta3	SAL	WGNAS	SAL-2024-1	Matrix	Output	{25,25,0}	{"'Stock unit'","'Stock unit'",NULL}	Fit	Survival rate	_	NA	Precision	NA	NA	Mortality	Standard deviation between smolt and pre-fishery stages	NA
h8_NEC_1	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,19,0}	{'Year',"'Stock unit NEC'",NULL}	Fit	High seas harvest rates	FAR fisheries	FAR fishery	Estimate	NA	NA	Mortality	Harvest rate of 2SW adults from the NEC complex due to exploitation in Faroe islands	NA
h8_NEC_3	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,19,0}	{'Year',"'Stock unit NEC'",NULL}	Fit	High seas harvest rates	FAR fisheries	FAR fishery	Estimate	NA	NA	Mortality	Harvest rate of NEC 2SW adults due to late regional fisheries in Faroes	NA
CV_N1_pr	SAL	WGNAS	SAL-2024-1	Single_value	Parameter constant	{1,0,0}	{'CV_N1_pr',NULL,NULL}	First year	Initialization first year	_	NA	CV	NA	NA	Other	First year: CV for Number of eggs deposited in rivers by spawners	NA
E_theta1_pr	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{7,25,0}	{'Year',"'Stock unit'",NULL}	First year	Initialization first year	_	NA	Mean	NA	NA	Other	First year: Mean eggs-smolts survival rate	NA
max_log_N9	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{25,0,0}	{"'Stock unit'",NULL,NULL}	First year	Initialization first year	_	NA	Bound	NA	NA	Other	First year: upper bound for Abundance of 2SW adults returning to homewaters	NA
min_log_N9	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{25,0,0}	{"'Stock unit'",NULL,NULL}	First year	Initialization first year	_	NA	Bound	NA	NA	Other	First year: lower bound for Abundance of 2SW adults returning to homewaters	NA
theta1	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Survival rate	_	NA	Estimate	NA	NA	Mortality	Eggs-smolts survival rate	NA
mu_N1_pr	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{25,0,0}	{"'Stock unit'",NULL,NULL}	First year	Initialization first year	_	NA	Mean	NA	NA	Other	First year: mean for distribution Number of eggs deposited in rivers by spawners	NA
p_smolt_pr	SAL	WGNAS	SAL-2024-1	Array	Parameter constant	{8,6,25}	{'Year',"'Age fresh water'","'Stock unit'"}	First year	Initialization first year	_	NA	Mean	NA	NA	Other	First year: Hyperparameters of the Dirichlet distribution used for representing smolt age proportions	NA
logit_theta4	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Maturation rate	_	NA	Estimate	NA	NA	Life trait	Maturation rate (logit) between delimiting the transition between the pre-fishery stage and 1SW mature salmons	NA
tau_theta4	SAL	WGNAS	SAL-2024-1	Matrix	Output	{25,25,0}	{"'Stock unit'","'Stock unit'",NULL}	Fit	Maturation rate	_	NA	Precision	NA	NA	Life trait	Precision matrix used to model variance-covariance structure of the maturation rate PFA-1SW between Sus	NA
theta4	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Maturation rate	_	NA	Estimate	NA	NA	Life trait	Maturation rate between delimiting the transition between the pre-fishery stage and 1SW mature salmons	NA
CV_M	SAL	WGNAS	SAL-2024-1	Single_value	Parameter constant	{1,0,0}	{"'Stock unit'",NULL,NULL}	Fit	Natural mortality rate	_	NA	CV	NA	NA	NA	Coefficient of variation of the monthly natural mortality for post-smolt stages at sea	NA
E_log_M	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Natural mortality rate	_	NA	Mean	NA	NA	NA	Mean (logscale) of the monthly natural mortality for post-smolt stages at sea when using lognormal distribution	NA
E_M	SAL	WGNAS	SAL-2024-1	Single_value	Parameter constant	{1,0,0}	{'E_M',NULL,NULL}	Fit	Natural mortality rate	_	NA	Mean	NA	NA	NA	Mean single value used to set mortality rate values	NA
nSm	SAL	WGNAS	SAL-2024-1	Single_value	Parameter constant	{1,0,0}	{'nSm',NULL,NULL}	Fit	Number of smolt ages	_	NA	Index	NA	NA	NA	Number of smolt ages considered	NA
N	SAL	WGNAS	SAL-2024-1	Single_value	Parameter constant	{1,0,0}	{'N',NULL,NULL}	Fit	Number of SU	_	NA	Index	NA	NA	Other	Number of Stock units considered	NA
N_NAC	SAL	WGNAS	SAL-2024-1	Single_value	Parameter constant	{1,0,0}	{'N_NAC',NULL,NULL}	Fit	Number of SU	_	NA	Index	NA	NA	Other	Number of SU in the NAC complex	NA
N_NEC	SAL	WGNAS	SAL-2024-1	Single_value	Parameter constant	{1,0,0}	{'N_NEC',NULL,NULL}	Fit	Number of SU	_	NA	Index	NA	NA	Other	Number of SU in the NEC complex	NA
date_begin	SAL	WGNAS	SAL-2024-1	Single_value	Parameter constant	{1,0,0}	{'date_begin',NULL,NULL}	Fit	Number of years	_	NA	Index	NA	NA	Other	First time-step of the model	NA
date_end	SAL	WGNAS	SAL-2024-1	Single_value	Parameter constant	{1,0,0}	{'date_end',NULL,NULL}	Fit	Number of years	_	NA	Index	NA	NA	Other	Length of the up-to-date variables	NA
date_end_hindcast	SAL	WGNAS	SAL-2024-1	Single_value	Parameter constant	{1,0,0}	{'date_end_hindcast',NULL,NULL}	Fit	Number of years	_	NA	Index	NA	NA	Other	Final time-step of the hindcast period	NA
N_Sample	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{50,0,0}	{'Year',NULL,NULL}	Fit	Prior hyperparameter	_	NA	Hyperparameter	NA	NA	Other	Equivalent sampling size in the Dirichlet distribution used for fish distribution between Sus	NA
N_Sample_sm	SAL	WGNAS	SAL-2024-1	Single_value	Parameter constant	{1,0,0}	{'N_sample_sm',NULL,NULL}	Fit	Prior hyperparameter	_	NA	Hyperparameter	NA	NA	Other	Equivalent sampling size in the Dirichlet distribution used for representing smolt ages proportions	NA
omega	SAL	WGNAS	SAL-2024-1	Matrix	Parameter constant	{25,25,0}	{"'Stock unit'","'Stock unit'",NULL}	Fit	Prior hyperparameter	_	NA	Hyperparameter	NA	NA	Other	Identify matrix for the formulationof Wishart priors	NA
p_smolt_gamma_pr	SAL	WGNAS	SAL-2024-1	Array	Output	{7,6,25}	{'Year',"'Age fresh water'","'Stock unit'"}	First year	Prior hyperparameter	_	NA	Hyperparameter	NA	NA	Other	First year: Gamma distribution hyperparameters used to build the Dirichlet distribution for smolt ages proportions	NA
p_smolt_stoch_pr	SAL	WGNAS	SAL-2024-1	Array	Output	{7,6,25}	{'Year',"'Age fresh water'","'Stock unit'"}	First year	Prior hyperparameter	_	NA	Hyperparameter	NA	NA	Other	First year: Proportions of each smolt age in the total number of smolts	NA
N6	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Returns	_	NA	Estimate	NA	NA	Count	Abundance of 1SW adults returning to homewaters	NA
N9	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Returns	_	NA	Estimate	NA	NA	Count	Abundance of 2SW adults returning to homewaters	NA
h6_hw	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	River harvest rates	_	main	Estimate	NA	NA	Mortality	Harvest rate for 1SW spawners in home waters	NA
h6_hw_delSp	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	River harvest rates	_	delayed spawners	Estimate	NA	NA	Mortality	Harvest rate for delayed 1SW spawners in home waters	NA
h9_hw	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	River harvest rates	_	main	Estimate	NA	NA	Mortality	Harvest rate for 2SW spawners in home waters	NA
h9_hw_delSp	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	River harvest rates	_	delayed spawners	Estimate	NA	NA	Mortality	Harvest rate for delayed 2SW spawners in home waters	NA
logit_theta3	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Survival rate	_	NA	Estimate	NA	NA	Mortality	Survival (logit) between smolt and pre-fishery stages	NA
logit_theta3_pr	SAL	WGNAS	SAL-2024-1	Vector	Output	{25,0,0}	{"'Stock unit'",NULL,NULL}	First year	Survival rate	_	NA	Estimate	NA	NA	Mortality	First year: Survival (logit) between smolt and pre-fishery stages	NA
theta1_ddp	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Survival rate	_	NA	Estimate	NA	NA	Mortality	Eggs-smolts survival rate when representing density-dependence	NA
theta1_ddp_pr	SAL	WGNAS	SAL-2024-1	Matrix	Output	{7,25,0}	{'Year',"'Stock unit'",NULL}	First year	Survival rate	_	NA	Mean	NA	NA	Mortality	First year: Eggs-smolts survival rate when representing density-dependence	NA
theta3	SAL	WGNAS	SAL-2024-1	Matrix	Output	{47,25,0}	{'Year',"'Stock unit'",NULL}	Fit	Survival rate	_	NA	Estimate	NA	NA	Mortality	Survival between smolt and pre-fishery stages	NA
theta3_pr	SAL	WGNAS	SAL-2024-1	Vector	Output	{25,0,0}	{"'Stock unit'",NULL,NULL}	First year	Survival rate	_	NA	Estimate	NA	NA	Mortality	First year: Survival between smolt and pre-fishery stages	NA
theta5_1_NAC	SAL	WGNAS	SAL-2024-1	Vector	Output	{6,0,0}	{"'Stock unit NAC'",NULL,NULL}	Fit	Survival rate	Bef. NF/LB fisheries	NA	Mean	NA	NA	Mortality	Survival rate of 1SW adults from the NAC complex before Newfoundland fisheries	NA
theta5_1_NEC	SAL	WGNAS	SAL-2024-1	Vector	Output	{19,0,0}	{"'Stock unit NEC'",NULL,NULL}	Fit	Survival rate	Bef. FAR fisheries	NA	Mean	NA	NA	Mortality	Survival rate of 1SW adults from the NEC complex before Faroes fisheries	NA
theta5_2_NAC	SAL	WGNAS	SAL-2024-1	Vector	Output	{6,0,0}	{"'Stock unit NAC'",NULL,NULL}	Fit	Survival rate	Aft. NF/LB fisheries	NA	Mean	NA	NA	Mortality	Survival rate of 1SW adults from the NAC complex after Newfoundland fisheries and before Labrador fisheries	NA
theta5_2_NEC	SAL	WGNAS	SAL-2024-1	Vector	Output	{19,0,0}	{"'Stock unit NEC'",NULL,NULL}	Fit	Survival rate	Aft. FAR fisheries	NA	Mean	NA	NA	Mortality	Survival rate of 1SW adults from the NEC complex after Faroes fisheries	NA
theta8_1_NAC	SAL	WGNAS	SAL-2024-1	Vector	Output	{6,0,0}	{"'Stock unit NAC'",NULL,NULL}	Fit	Survival rate	Bef. NF/LB fisheries	NA	Mean	NA	NA	Mortality	Survival rate of 2SW individuals from the NAC complex before Newfoundland and Labrador fisheries	NA
theta8_1_NEC	SAL	WGNAS	SAL-2024-1	Vector	Output	{19,0,0}	{"'Stock unit NEC'",NULL,NULL}	Fit	Survival rate	Bef. FAR fisheries	NA	Mean	NA	NA	Mortality	Survival rate of 2SW individuals from the NEC complex before Faroes fisheries	NA
theta8_2	SAL	WGNAS	SAL-2024-1	Vector	Output	{25,0,0}	{"'Stock unit'",NULL,NULL}	Fit	Survival rate	_	NA	Estimate	NA	NA	Mortality	Survival rate of NEC and NAC 2SW individuals after early fisheries and before reaching Greenland	NA
theta8_2_1_NAC	SAL	WGNAS	SAL-2024-1	Vector	Output	{6,0,0}	{"'Stock unit NAC'",NULL,NULL}	Fit	Survival rate	Btw. NF/LB - GLD fisheries	NA	Mean	NA	NA	Mortality	Survival rate of NAC 2SW individuals in the environment after Greenland catches and before late Newfoundland, Labrador and St Pierre et Miquelon fisheries	NA
theta8_2_1_NEC	SAL	WGNAS	SAL-2024-1	Vector	Output	{19,0,0}	{"'Stock unit NEC'",NULL,NULL}	Fit	Survival rate	Btw. FAR - GLD fisheries	NA	Mean	NA	NA	Mortality	Survival rate of NEC 2SW individuals in the environment after Greenland catches and before Faroes fisheries	NA
theta8_2_2_NAC	SAL	WGNAS	SAL-2024-1	Vector	Output	{6,0,0}	{"'Stock unit NAC'",NULL,NULL}	Fit	Survival rate	Btw. GLD - NF/LB fisheries	NA	Mean	NA	NA	Mortality	Survival rate of NAC 2SW individuals in the environment after late Newfoundland, Labrador and St Pierre et Miquelon fisheries	NA
theta8_2_2_NEC	SAL	WGNAS	SAL-2024-1	Vector	Output	{19,0,0}	{"'Stock unit NEC'",NULL,NULL}	Fit	Survival rate	Btw. GLD - FAR fisheries	NA	Mean	NA	NA	Mortality	Survival rate of NEC 2SW individuals in the environment after late Faroes fisheries	NA

Area (tr_area_are)

Here I’m trying to build something consistent. I guess only the final result will tell but I have to start somewhere … A good idea to simploify the structure of the db is to get together marine and continental areas.

Continental areas

dbExecute(con_diaspara_admin,
"DROP TABLE IF EXISTS ref.tr_level_lev CASCADE;")

dbExecute(con_diaspara_admin,
"CREATE TABLE ref.tr_level_lev(
   lev_code TEXT PRIMARY KEY,
   lev_description TEXT  
);")

dbExecute(con_diaspara_admin,
  "INSERT INTO ref.tr_level_lev VALUES( 
  'Panpopulation',
  'This is the highest geographic level for assessement.'  
  );")

dbExecute(con_diaspara_admin,
  "INSERT INTO ref.tr_level_lev VALUES( 
  'Complex',
  'Corresponds to large sublevels at which the Panpopulation is assessed, e.g.
  NAC NEC for WGNAST, Gulf of Bothnia for WGBAST, Mediterranean for WGEEL.'  
  );")

  dbExecute(con_diaspara_admin,
  "INSERT INTO ref.tr_level_lev VALUES( 
  'Stock',
  'Correspond to stock units for which advices are provided in ICES, this can be the level of the panpopulation,
  or another level e.g. .'  
  );")

dbExecute(con_diaspara_admin,
  "INSERT INTO ref.tr_level_lev VALUES( 
  'Country',
  'Corresponds to one or more units, but in almost all stocks
  this level is relevant to split data.'
  );")

dbExecute(con_diaspara_admin,
  "INSERT INTO ref.tr_level_lev VALUES( 
  'Assessment_unit',
  'Corresponds to an assessment unit in the Baltic sea, and area for  
  WGNAS, and EMU for WGEEL.'
  );")

dbExecute(con_diaspara_admin,
  "INSERT INTO ref.tr_level_lev VALUES( 
  'Regional',
  'Corresponds to subunits of stock assessment units or 
  basins grouping several river. Although it is not used yet for
  some models, regional genetic difference or difference in stock
  dynamic support collecting a regional level.'
  );")

dbExecute(con_diaspara_admin,
  "INSERT INTO ref.tr_level_lev VALUES( 
  'River',
  'One river is a unit corresponding practically almost always to a watershed.'
  );")

dbExecute(con_diaspara_admin,
  "INSERT INTO ref.tr_level_lev VALUES( 
  'river_section',
  'Section of river, only a part of a basin, for instance to separate between
  wild and mixed river category in the Baltic.'
  );")

dbExecute(con_diaspara_admin,
  "INSERT INTO ref.tr_level_lev VALUES( 
  'Major',
  'Major fishing areas from ICES.'
  );")

dbExecute(con_diaspara_admin,
  "INSERT INTO ref.tr_level_lev VALUES( 
  'Subarea',
  'Subarea from ICES, FAO and NAFO'
  );")

dbExecute(con_diaspara_admin,
  "INSERT INTO ref.tr_level_lev VALUES( 
  'Division',
  'Division from ICES, GFCM and NAFO'
  );")

dbExecute(con_diaspara_admin,
  "INSERT INTO ref.tr_level_lev VALUES( 
  'Subdivision',
  'Subdivision level from ICES, GFCM and NAFO'
  );")

dbExecute(con_diaspara_admin, "COMMENT ON TABLE ref.tr_level_lev 
IS 'Table of geographic levels stock, complex, country, region, basin, river,
the specific order depend according to working groups.';")

dbExecute(con_diaspara_admin, "GRANT ALL ON ref.tr_level_lev 
          TO diaspara_admin;")
dbExecute(con_diaspara_admin, "GRANT SELECT ON ref.tr_level_lev 
          TO diaspara_read;")


dbExecute(con_diaspara_admin,
"DROP TABLE IF EXISTS ref.tr_area_are CASCADE;")
dbExecute(con_diaspara_admin,
"CREATE TABLE ref.tr_area_are (
   are_id INTEGER PRIMARY KEY,
   are_are_id INTEGER,
   are_code  TEXT,
   are_lev_code TEXT,
   are_wkg_code TEXT,
   are_ismarine BOOLEAN,
   geom geometry(MULTIPOLYGON, 4326),
  CONSTRAINT fk_are_are_id FOREIGN KEY (are_are_id) 
  REFERENCES ref.tr_area_are (are_id) ON DELETE CASCADE
  ON UPDATE CASCADE,
  CONSTRAINT uk_are_code UNIQUE (are_code),
  CONSTRAINT fk_area_lev_code FOREIGN KEY (are_lev_code) REFERENCES
  ref.tr_level_lev(lev_code) ON UPDATE CASCADE ON DELETE CASCADE,
  CONSTRAINT fk_area_wkg_code FOREIGN KEY (are_wkg_code) REFERENCES
  ref.tr_icworkinggroup_wkg(wkg_code) ON UPDATE CASCADE ON DELETE CASCADE
);")

dbExecute(con_diaspara_admin, "GRANT ALL ON ref.tr_area_are 
          TO diaspara_admin;")
dbExecute(con_diaspara_admin, "GRANT SELECT ON ref.tr_area_are 
          TO diaspara_read;")

dbExecute(con_diaspara_admin, "COMMENT ON TABLE ref.tr_area_are 
IS 'Table corresponding to different geographic levels, from stock 
to river section.';")

Table 14: Geographical level tr_level_lev

lev_code	lev_description
Stock	This is the highest geographic level for assessement, stock level.
Complex	Corresponds to large sublevels at which the stock is assessed, e.g. NAC NEC for WGNAST, Gulf of Bothnia for WGBAST, Mediterranean for WGEEL.
Country	Corresponds to one or more units, but in almost all stocks this level is relevant to split data.
Assessment_unit	Corresponds to an assessment unit in the Baltic sea, and area for WGNAS, and EMU for WGEEL.
Regional	Corresponds to subunits of stock assessment units or basins grouping several river. Although it is not used yet for some models, regional genetic difference or difference in stock dynamic support collecting a regional leve
River	One river is a unit corresponding practically almost always to a watershed.
river_section	Section of river, only a part of a basin, for instance to separate between wild and mixed river category in the Baltic.
Major	Major fishing areas from ICES.
Subarea	Subarea from ICES, FAO and NAFO
Division	Division from ICES, GFCM and NAFO
Subdivision	Subdivision level from ICES, GFCM and NAFO

Note from Hilaire

The working groups might change over time, referencing a working group there is probably not the best. Cédric : Added stockkeylabel, this table is necessary to aggregate data (species is not enough).

Table 15: Geographic areas

are_id	are_are_id	are_code	are_lev_code	are_wkg_code	are_ismarine	geom
1	1	Baltic	Stock	WGBAST	NA	NA
2	1	Baltic marine	Stock	WGBAST	TRUE	0106000020E61000000200000001030000002500000021080000304CA60A46C5254011363CBD522E4B40B515FBCBEEA92540984C158C4A2A4B40D7A3703D0A9725409CA223B9FC274B40EC51B81E858B254065AA605452274B40A8C64B37896125408F53742497274B40B459F5B9DA4A2540D0D556EC2F2B4B400EBE30992A382540A01A2FDD242E4B40CD3B4ED191BC24409D11A5BDC1374B408CB96B09F9402440F46C567DAE3E4B4011363CBD52362440CA54C1A8A43E4B4033C4B12E6E032440B1E1E995B23C4B409A081B9E5EC92340C3D32B65193A4B4008AC1C5A64BB2340FB3A70CE883A4B40C5FEB27BF2B0234069006F81043D4B405917B7D100BE234085EB51B81E3D4B407E1D386744C92340A1F831E6AE3D4B40C5FEB27BF2F02340295C8FC2F5404B40E0BE0E9C330224402575029A08434B40713D0AD7A310244002BC051214474B4014AE47E17A1424407E8CB96B09514B40A5BDC11726132440F697DD9387554B408B6CE7FBA9F12340FE65F7E461614B409031772D21DF23406ABC749318644B4076711B0DE0CD2340CCEEC9C342654B40A779C7293A9223405BB1BFEC9E644B40A779C7293A522340D5E76A2BF6674B40B98D06F0162823408B6CE7FBA9694B408CB96B09F9E022408195438B6C674B40DC4603780BE42240280F0BB5A6694B40F31FD26F5FE72240A69BC420B06A4B408CDB68006F012340F38E5374246F4B4052B81E85EB312340499D8026C2764B404FAF94658843234091ED7C3F35764B40613255302A492340EB73B515FB734B400A68226C783A2340A7E8482EFF714B408F537424973F2340CCEEC9C3426D4B407F6ABC749378234027C286A7576A4B4063EE5A423E8823403411363CBD724B40AED85F764F7E23400C93A98251794B4088F4DBD781732340BB270F0BB57E4B404B598638D6452340C3D32B6519824B40D42B6519E21823405C8FC2F528844B40A4703D0AD7032340917EFB3A70864B403F355EBA49EC2240BA6B09F9A08F4B40265305A392FA224096218E7571934B402E90A0F831262340C74B378941984B40273108AC1C3A2340713D0AD7A3984B402F6EA301BC4523408104C58F31974B40423EE8D9AC5A2340637FD93D79984B405DFE43FAED6B23406688635DDC9E4B40D3DEE00B936923407D3F355EBAA14B402B8716D9CE57234016FBCBEEC9A34B40E02D90A0F8312340643BDF4F8DAF4B40637FD93D793823406DC5FEB27BB24B40A4DFBE0E9C332340A5BDC11726B34B4004E78C28ED2D2340F697DD9387B54B40B8AF03E78C682340EB73B515FBC34B4033C4B12E6EA3234013F241CF66CD4B40E10B93A9827123404182E2C798D34B4026E4839ECD4A23408E06F01648D84B40EBE2361AC03B2340295C8FC2F5D84B40F241CF66D5272340B84082E2C7D84B4024B9FC87F41B2340448B6CE7FBD94B4027A089B0E1492340EC2FBB270FDB4B40CE1951DA1B5C2340B537F8C264DA4B40849ECDAACF752340728A8EE4F2D74B40E9482EFF219D234092CB7F48BFD54B40992A1895D4A923406744696FF0D54B4007F0164850FC23400AD7A3703DDA4B404850FC18731724402B1895D409E04B4082E2C798BB16244071AC8BDB68E84B400000000000002440613255302AE94B40EFC9C342ADE923409A99999999E94B40637FD93D79D8234099BB96900FEA4B40BC0512143FC62340C286A757CAEA4B40053411363CBD2340B22E6EA301EC4B40D7A3703D0AF723402BF697DD93EF4B404A7B832F4C062440728A8EE4F2EF4B4023DBF97E6A1C24409CC420B072F04B4089D2DEE00B3324404694F6065FF04B40E09C11A5BD4124409FCDAACFD5EE4B407E1D38674449244096B20C71ACEB4B40E6AE25E4835E24400B46257502EA4B4071AC8BDB68802440DAACFA5C6DF54B40E561A1D6348F24407CF2B0506B024C40DC68006F8184244097900F7A360B4C4099BB96900F7A24408104C58F310F4C403A234A7B836F244052499D8026124C40265305A3927A244012A5BDC117164C40228E75711BCD2440A1D634EF38254C40A1F831E6AEE52440E8D9ACFA5C254C403480B74082022540A69BC420B0224C40A167B3EA73F52440B1BFEC9E3C1C4C40728A8EE4F2DF2440F931E6AE251C4C40D95F764F1EB62440E17A14AE47194C4048E17A14AEC72440789CA223B9144C4060764F1E160A2540A2B437F8C20C4C40F0164850FC1825405B423EE8D90C4C40F5B9DA8AFD252540736891ED7C0F4C40ED9E3C2CD41A254095D4096822144C4004E78C28ED2D254098DD9387851A4C404182E2C7983B2540BE9F1A2FDD1C4C40D49AE61DA7482540E8D9ACFA5C1D4C4003780B24285E2540075F984C151C4C4003780B24285E2540D42B6519E2184C408313D1AFAD692540B461660272174C40EE0E63B1D168254046E4C30583154C40F668E4D27C6E254013FB1DED2C124C4011DF0D92D38D264037F56847FD004C4018EF0199218E26403356B49CF8004C40C126A57FCD90264013EFDF4071004C40E1F2F5087795264072DFACB90AFF4B404CB1DD1C329A2640FDC35402FBFD4B405DFE43FAED8B2640BBB88D06F0FE4B4020D26F5F078E26403EE8D9ACFAFC4B400EBE30992AB826407DAEB6627FF94B401895D40968C226408C4AEA0434F94B40F5B9DA8AFDE52640713D0AD7A3F84B40DE718A8EE4F22640B98D06F016F84B40EBE2361AC0FB264002BC051214F74B40F853E3A59B0427405A643BDF4FF54B4063EE5A423E082740265305A392F24B40F5B9DA8AFD0527401B0DE02D90F04B4088635DDC46032740AC8BDB6800EF4B4002BC051214FF26406891ED7C3FED4B4045D8F0F44AF9264088855AD3BCEB4B40006F8104C5AF264048E17A14AEDF4B40075F984C156C26405396218E75D94B4082734694F666264036AB3E575BD94B4070CE88D2DE6026407DAEB6627FD94B4011363CBD525626407DD0B359F5D94B4058A835CD3B4E2640ED0DBE3099DA4B4082734694F64626407A36AB3E57DB4B406E3480B740422640F8C264AA60DC4B409CA223B9FCE725405839B4C876DE4B405227A089B0E12540D881734694DE4B4041F163CC5DCB2540029A081B9EDE4B401895D40968C22540F5B9DA8AFDDD4B40CC5D4BC807FD2540713D0AD7A3D84B4011C7BAB88D262640D95F764F1ED64B40B1BFEC9E3C2C2640F7065F984CD54B40B515FBCBEE49264052B81E85EBC94B405C2041F1634C2640713D0AD7A3C84B4063EE5A423E6826405396218E75B94B401D386744696F26403411363CBDB24B40AA60545227602640A54E401361AB4B4076E09C11A57D2640C442AD69DE994B404ED1915CFE832640B7D100DE02994B4096B20C71AC8B26409B559FABAD984B40789CA223B99C2640711B0DE02D984B40DAACFA5C6DA52640AAF1D24D62984B40A4DFBE0E9CB326400BB5A679C7994B40772D211FF4CC2640091B9E5E299B4B40A167B3EA73D52640083D9B559F9B4B402575029A08DB2640083D9B559F9B4B400E4FAF9465E8264033333333339B4B40EFC9C342AD0927409A99999999994B404BEA0434111627409B559FABAD984B40D0D556EC2F1B2740D50968226C984B40EF38454772392740D5E76A2BF6974B40E2E995B20C512740D5E76A2BF6974B40AEB6627FD95D2740EEEBC039239A4B40DD24068195632740265305A3929A4B403A234A7B836F2740A60A4625759A4B4067D5E76A2B76274099BB96900F9A4B4096218E75717B27405396218E75994B40637FD93D79982740C05B2041F1934B40C5FEB27BF2902740295C8FC2F5904B404BEA0434117627409D11A5BDC1874B4058A835CD3BAE274097FF907EFB824B400EBE30992AB827400BB5A679C7814B40CCEEC9C342CD274093A98251497D4B40986E1283C0CA2740917EFB3A707E4B40FB5C6DC5FED227408D976E1283804B40FD87F4DBD7E127401C7C613255804B40D734EF3845E72740728A8EE4F27F4B40865AD3BCE3F42740211FF46C567D4B403D0AD7A370FD2740CEAACFD5567C4B40CCC6D675A0FF2740574C7C203F7C4B40D712F241CF062840C05B2041F17B4B4030BB270F0B1528405D6DC5FEB27B4B40302AA913D0242840EB73B515FB7B4B408C4AEA043451284002BC0512147F4B408126C286A7572840C7BAB88D06804B402497FF907E5B2840BA490C022B8F4B40780B24287E4C28409B559FABAD904B40894160E5D0422840705F07CE19914B407B14AE47E13A284054742497FF904B40022B8716D92E2840545227A089904B4066666666662628402A3A92CB7F904B401DC9E53FA41F284070CE88D2DE904B40D656EC2FBB072840A4DFBE0E9C934B40228E75711B0D2840A1F831E6AE954B406DC5FEB27B122840CA32C4B12E964B40B5A679C7295A2840B1BFEC9E3C9C4B40B98D06F01668284077BE9F1A2F9D4B4022FDF675E07C284011363CBD529E4B40EC51B81E858B28406744696FF09D4B40BC96900F7A9628404B598638D69D4B40A8C64B3789A128402041F163CC9D4B40F085C954C1A82840925CFE43FA9D4B403333333333D328408D28ED0DBEA04B400B46257502DA28407DAEB6627FA14B40DC68006F81E42840DE02098A1FA34B40226C787AA5EC2840F775E09C11A54B40060EAA05DDE9284030B58C7151A84B40F241CF66D5E72840A69BC420B0AA4B4033C4B12E6EE3284031992A1895AC4B40AEB6627FD9DD284020D26F5F07AE4B40780B24287ECC2840B84082E2C7B04B4070CE88D2DEC02840ED0DBE3099B24B405F07CE1951BA2840EC2FBB270FB34B40ABCFD556ECAF2840CF66D5E76AB34B40637FD93D799828404182E2C798B34B404D840D4FAF742840AD69DE718AB64B40992A1895D46928401CEBE2361AB84B40BEC1172653652840C520B07268B94B4052499D8026622840174850FC18BB4B403480B7408262284092CB7F48BFBD4B409A081B9E5E6928408E06F01648C04B40E9482EFF217D2840840D4FAF94C54B405227A089B08128401F85EB51B8C64B40B515FBCBEE89284048E17A14AEC74B40E7FBA9F1D2AD2840B30C71AC8BCB4B401C7C613255B0284096438B6CE7CB4B4034A2B437F8C22840AE47E17A14CE4B400E4FAF9465C82840AD69DE718ACE4B4074B515FBCBCE2840BBB88D06F0CE4B404772F90FE9D7284090A0F831E6CE4B403D0AD7A370DD284074B515FBCBCE4B40516B9A779CE22840598638D6C5CD4B40956588635DFC2840CCEEC9C342CD4B408CDB68006F012940E25817B7D1D04B405C2041F1630C2940FD87F4DBD7D14B40865AD3BCE31429404F1E166A4DD34B409A9999999919294032772D211FD44B40E5F21FD26F1F2940E8D9ACFA5CD54B401904560E2D322940B515FBCBEED94B407AA52C431C2B2940D95F764F1EDE4B40DE9387855A3329401B0DE02D90E04B403333333333332940E0BE0E9C33E24B4052B81E85EB3129403D2CD49AE6E54B40F5DBD781732629405C2041F163EC4B406E3480B740222940F6285C8FC2ED4B4003780B24281E2940AC8BDB6800EF4B40992A1895D409294086C954C1A8F44B407FD93D7958082940AE47E17A14F64B4058A835CD3B0E2940454772F90FF94B404ED1915CFE232940AC1C5A643BFF4B407AA52C431C2B2940ABCFD556ECFF4B4055302AA913302940462575029A004C4040A4DFBE0E3C2940E9482EFF21054C408C4AEA043411294027C286A7570A4C40D734EF38450729405E4BC8073D0B4C40EFC9C342ADA92840E4141DC9E50F4C400000000000A028408E06F01648104C400A68226C789A2840FF21FDF675104C407725340F8197284032A5BB0C7B104C407BA5C91861E42840EC7AB3179B264C401100FB6C0DEA2840F2F211846D264C40E22BB28452EB28407318C4EE58264C40C50C228C7EFC284025C4019942254C400AD87170A7FE2840F18D7448E2244C407AC7293A92EB2840925CFE43FA254C4044696FF085292940EEEBC03923124C4092CB7F48BF3D2940E7FBA9F1D20D4C404F401361C353294099BB96900F0A4C4022FDF675E05C2940ABCFD556EC074C40DE9387855AD3294088635DDC46EB4B40D0D556EC2FDB294036AB3E575BE94B403A92CB7F481F2A40F38E537424D74B40DDB5847CD0132A4063EE5A423ED04B4082E2C798BBF62940B1E1E995B2CC4B4096B20C71ACEB29403255302AA9CB4B408126C286A7D729407E1D386744C94B40857CD0B359D5294076711B0DE0C54B40DF51A55E9EF32940429348CB16B54B402D431CEBE2F6294088635DDC46B34B40107A36AB3E972A405D6DC5FEB2AB4B40C6DCB5847CB02A40CF66D5E76AAB4B4055C1A8A44EC02A40EC51B81E85AB4B400C022B8716D92A40AF94658863AD4B401FF46C567DEE2A408FE4F21FD2AF4B40C9E53FA4DFFE2A40FE43FAEDEBB04B4012A5BDC117462B40211FF46C56B54B4096B20C71AC6B2B404A7B832F4CB64B404BEA043411762B40BC96900F7AB64B400E4FAF9465C82B40D6C56D3480B74B40DE718A8EE4D22B4001DE02098AB74B409031772D21DF2B4048BF7D1D38B74B401C7C613255F02B403D2CD49AE6B54B401895D40968022C40C05B2041F1B34B403333333333132C40FD87F4DBD7B14B4003780B24281E2C40287E8CB96BB14B401F85EB51B83E2C401B0DE02D90B04B40FA7E6ABC74532C409B559FABADB04B40508D976E12632C40287E8CB96BB14B400000000000A02C404A7B832F4CBE4B405C2041F163AC2C404703780B24C04B407A36AB3E57BB2C4068226C787AC54B4074B515FBCBAE2C4099BB96900FCA4B40598638D6C58D2C4050FC1873D7D24B40401361C3D36B2C40B6847CD0B3D94B40516B9A779C622C40304CA60A46DD4B40A4703D0AD7632C4034A2B437F8E24B407FFB3A70CE682C4082734694F6E64B40E561A1D6346F2C4035EF384547EA4B40D1915CFE437A2C40F5B9DA8AFDED4B4082734694F6862C40287E8CB96BF14B40D3BCE3141DA92C40ABCFD556ECF74B40E9482EFF21BD2C403411363CBDFA4B40F5B9DA8AFD052D40CDCCCCCCCC044C40DA1B7C6132152D40AD69DE718A064C40EA043411361C2D408104C58F31074C4030BB270F0B352D40CA54C1A8A4064C40AEB6627FD93D2D404F401361C3034C40304CA60A46452D40295C8FC2F5004C40713D0AD7A3702D402B1895D409004C400B462575027A2D40FE65F7E461014C407FFB3A70CE882D40787AA52C43044C4071AC8BDB68802D401DC9E53FA4074C403F575BB1BF6C2D40B0726891ED0C4C403D0AD7A3705D2D40006F8104C50F4C40516B9A779C622D40151DC9E53F144C40E561A1D6346F2D40B1E1E995B2144C40A779C7293AB22D4086C954C1A8144C40933A014D842D2E4014AE47E17A144C40A4DFBE0E9C732E407A36AB3E57134C40AA60545227C02E40EFC9C342AD114C40A779C7293A322F40FE43FAEDEB184C4036AB3E575B512F4090A0F831E6164C403E7958A8358D2F40A52C431CEB124C404F401361C3932F4011363CBD520E4C40D0D556EC2FBB2F407958A835CD0B4C4037894160E500304088635DDC461B4C40B3EA73B5150B30407CF2B0506B224C405917B7D1000E30404703780B24284C408104C58F31173040C3F5285C8F324C40423EE8D9AC1A30405839B4C876364C40022B8716D91E3040C364AA60543A4C4065AA605452373040CB10C7BAB84D4C40FE43FAEDEB40304009F9A067B3524C40F2D24D6210483040EA04341136544C403D2CD49AE64D304040A4DFBE0E544C405305A3923A613040E9B7AF03E7544C400000000000603040787AA52C435C4C400E4FAF94656830404B598638D6654C40053411363C6D3040EB73B515FB6B4C4054E3A59BC47030407CF2B0506B724C406A4DF38E5374304035EF3845477A4C403A92CB7F487F30405B423EE8D9844C404BC8073D9B953040925CFE43FA854C4039D6C56D34903040158C4AEA048C4C4062A1D634EF88304057EC2FBB278F4C40166A4DF38E833040AC8BDB68008F4C40F5B9DA8AFD753040AEB6627FD9954C4031992A1895743040E02D90A0F8994C409EEFA7C64B773040B4C876BE9FA24C40D42B6519E2783040B003E78C28A54C40764F1E166A8D3040E25817B7D1B04C40D26F5F07CE993040B81E85EB51B04C401AC05B2041A13040637FD93D79B04C4089D2DEE00BA330404182E2C798B34C4043AD69DE71AA30403F355EBA49B44C40C5FEB27BF2B0304093A9825149BD4C4052B81E85EBA13040F46C567DAEC64C40AA60545227A0304065AA605452CF4C40D95F764F1EB63040B515FBCBEED94C40A60A462575B2304032E6AE25E4DB4C40D3BCE3141DA930406688635DDCDE4C407DAEB6627F993040C3F5285C8FE24C40B9FC87F4DB773040787AA52C43EC4C40B37BF2B0506B3040D26F5F07CEF14C403B70CE88D28E30409A081B9E5EE94C40F085C954C198304058A835CD3BE64C4092CB7F48BF9D304041F163CC5DE34C40E02D90A0F8B13040BBB88D06F0DE4C40E5F21FD26FAF3040B7627FD93DE14C40FF21FDF675903040211FF46C56ED4C4068B3EA73B5853040E3361AC05BF04C40363CBD5296813040E5D022DBF9FE4C40D122DBF97E9A3040499D8026C2F64C40CA32C4B12E9E304060764F1E16F24C40F085C954C1A83040454772F90FF14C40B1E1E995B2BC3040AB3E575BB1EF4C40C139234A7BC330401B9E5E29CBF04C40BB270F0BB5C630402041F163CCF54C4075029A081BBE3040C4B12E6EA3F94C4007F0164850BC30404FAF946588FB4C40B22E6EA301AC304068226C787AFD4C402B8716D9CEA73040B003E78C28FD4C40ADFA5C6DC59E30407593180456FE4C403255302AA9A330406666666666FE4C40E561A1D634BF3040C520B07268014D407A36AB3E57BB30404A7B832F4C064D4003780B2428BE3040C2172653050B4D40C1CAA145B6C33040C7BAB88D06104D40933A014D84CD304044FAEDEBC0114D406C09F9A067D33040029A081B9E164D40DE02098A1FD3304044696FF085194D407DD0B359F5C93040B7627FD93D294D4085EB51B81EC53040AC8BDB68002F4D4036AB3E575BB130406A4DF38E53344D406D567DAEB692304064CC5D4BC8374D40B1BFEC9E3C6C3040B003E78C283D4D4044FAEDEBC079304013F241CF663D4D405396218E75813040780B24287E3C4D407CF2B0506B8A3040EC2FBB270F3B4D40FBCBEEC9C392304060E5D022DB394D407D3F355EBA9930408C4AEA0434394D40AC8BDB6800BF3040F4FDD478E9364D40166A4DF38ED3304038F8C264AA384D40AC8BDB6800EF30408351499D803E4D40D7A3703D0AC730407CF2B0506B4A4D40F2D24D6210B83040CCEEC9C3424D4D40BC0512143FA630408195438B6C4F4D4073D712F2416F3040C442AD69DE514D4061C3D32B656930408351499D804E4D40022B8716D95E304068226C787A4D4D40B3EA73B5154B3040AD69DE718A4E4D406F8104C58F313040B81E85EB51504D40E9B7AF03E73C30403EE8D9ACFA544D40956588635D4C304022FDF675E0544D40EFC9C342AD693040CEAACFD556544D4072F90FE9B76F30405C8FC2F528544D40D95F764F1E76304096438B6CE7534D40933A014D84AD304044696FF085514D40EA95B20C71BC30408D976E1283504D40B5A679C729CA30408FE4F21FD24F4D402AA913D044E83040BBB88D06F04E4D407FFB3A70CE0831406F8104C58F514D40F9A067B3EA233140C58F31772D594D4030BB270F0B1531406ADE718A8E5C4D400EBE30992A0831400F9C33A2B45F4D402DB29DEFA71631408B6CE7FBA9614D408048BF7D1D2831402041F163CC5D4D40B7627FD93D393140933A014D845D4D4013F241CF6645314011363CBD525E4D409A999999995931408E06F01648604D40A2B437F8C294314014AE47E17A6C4D40E2E995B20CA13140DC68006F81744D40BADA8AFD65973140B84082E2C7784D40DC4603780B9431408B6CE7FBA9794D405BD3BCE3149D3140A2B437F8C27C4D4004560E2DB29D314024287E8CB9834D40C7293A92CB9F314044696FF085894D40265305A392AA3140764F1E166A954D404F401361C3C33140ACADD85F768F4D40F7E461A1D6C43140F8C264AA608C4D4019E25817B7C1314027C286A757824D4060764F1E16CA3140C66D3480B7784D4030BB270F0BE531406744696FF06D4D406A4DF38E53E43140E4141DC9E56F4D40BE9F1A2FDDE43140DDB5847CD0734D4068226C787A053240E8D9ACFA5C854D40F9A067B3EA233240083D9B559F8B4D40D26F5F07CE39324002BC0512148F4D4072F90FE9B74F3240295C8FC2F5904D408FC2F5285C4F324032772D211F9C4D40DB8AFD65F744324051DA1B7C61A24D4082734694F646324064CC5D4BC8A74D40F853E3A59B5432406FF085C954A94D4062105839B4583240C8073D9B55A74D40C3D32B6519623240F46C567DAEA64D40A1F831E6AE85324012143FC6DCA54D402B8716D9CE973240029A081B9EA64D4086C954C1A8A432408C4AEA0434A94D40B1BFEC9E3C9C32408FC2F5285CAF4D40499D8026C28632403D9B559FABB54D40C898BB96907F3240C976BE9F1AB74D40B5A679C7297A324072F90FE9B7B74D4091ED7C3F356E3240105839B4C8B64D4088635DDC46733240A60A462575B24D40D5E76A2BF677324074B515FBCBAE4D40EF38454772793240FAEDEBC039AB4D40BF7D1D386774324051DA1B7C61AA4D40FF21FDF6757032408638D6C56DAC4D4012143FC6DC6532403B014D840DAF4D40B7627FD93D59324064CC5D4BC8AF4D409EEFA7C64B4732406688635DDCAE4D40C976BE9F1A3F3240E9B7AF03E7AC4D40DB8AFD65F7343240B5A679C729AA4D40E0BE0E9C3322324062105839B4A84D40DE02098A1F133240545227A089A84D403D0AD7A3700D32402A3A92CB7FA84D402FDD2406810532400C022B8716A94D40C976BE9F1AFF314018265305A3AA4D40B29DEFA7C6FB31405F984C158CAA4D408B6CE7FBA9F131408D28ED0DBEA84D40637FD93D79E83140832F4CA60AA64D40D42B6519E2D83140FD87F4DBD7A14D40933A014D84BD31406DC5FEB27BA24D402EFF21FDF695314032772D211FA44D40F1F44A59867831406744696FF0A54D4036AB3E575B713140AC8BDB6800A74D4044FAEDEBC0693140D5E76A2BF6A74D4027C286A7575A31409A779CA223A94D4041F163CC5D5B3140AF94658863A54D401EA7E8482E5F3140A69BC420B0A24D403108AC1C5A6431408E06F01648A04D40022B8716D95E31409D11A5BDC19F4D40D0B359F5B94A3140FE43FAEDEBA04D40EF38454772493140613255302AA14D408D28ED0DBE403140992A1895D4A14D40166A4DF38E433140A1D634EF38A54D40931804560E4D31400C93A98251A94D40AC8BDB68004F31405C2041F163AC4D402B8716D9CE473140D93D7958A8AD4D40226C787AA52C31409D11A5BDC1AF4D4094F6065F981C3140ABCFD556ECAF4D40A1D634EF38F530406666666666B64D40E63FA4DFBEEE30406FF085C954B94D40F6285C8FC2E53040A4703D0AD7BB4D402E90A0F831D63040910F7A36ABBE4D406EA301BC05B230404E62105839BC4D40D200DE0209BA304099BB96900FBA4D40772D211FF4CC30405839B4C876B64D40D0B359F5B9DA3040CE1951DA1BB44D40C0EC9E3C2CE43040D044D8F0F4B24D40C66D3480B7E03040D42B6519E2B04D40569FABADD8CF3040EFC9C342ADB14D400000000000C030404A7B832F4CB64D40C6DCB5847CB03040DE9387855ABB4D40B5A679C729AA30405BB1BFEC9EBC4D40F6285C8FC25530406EA301BC05BA4D401EA7E8482E4F3040E09C11A5BDB94D40499D8026C246304046B6F3FDD4B84D4022FDF675E02C30408E06F01648B84D4096218E75711B3040B7D100DE02B94D40C364AA605412304035EF384547BA4D40B81E85EB51083040E63FA4DFBEBE4D4054742497FF103040F6285C8FC2BD4D40F085C954C1183040EA95B20C71BC4D40956588635D2C30404FAF946588BB4D4014AE47E17A543040CE88D2DEE0BB4D40B3EA73B5157B304039454772F9BF4D405EBA490C028B304082734694F6C64D40BD5296218E853040C58F31772DC94D40006F8104C57F304032E6AE25E4CB4D40B6847CD0B3893040AEB6627FD9CD4D40AC8BDB68008F30404BEA043411CE4D40CA54C1A8A49E3040A69BC420B0CA4D4062A1D634EFA83040917EFB3A70C64D40EF38454772C9304001DE02098AC74D40166A4DF38ED33040A52C431CEBCA4D403D0AD7A370DD304050FC1873D7CA4D40FD87F4DBD7F1304092CB7F48BFC54D40BC96900F7A063140143FC6DCB5C44D40713D0AD7A31031405A643BDF4FC54D405917B7D1001E3140D95F764F1EC64D40E5F21FD26F2F314014D044D8F0C44D40E0BE0E9C334231401B9E5E29CBC04D4017D9CEF7535331405839B4C876BE4D409D8026C28667314040A4DFBE0EBC4D4025068195436B31404A0C022B87BE4D402B8716D9CE6731403C4ED1915CC64D40575BB1BFEC5E314018265305A3CA4D407E1D386744593140CA54C1A8A4CE4D40A7E8482EFF61314088F4DBD781D34D40736891ED7C5F314011363CBD52CE4D4036AB3E575B7131409D8026C286C74D4074B515FBCB7E3140CCEEC9C342C54D407958A835CD8B3140DBF97E6ABCC44D40849ECDAACF853140295C8FC2F5C04D40787AA52C438C314082E2C798BBBE4D405E4BC8073DBB3140C5FEB27BF2B84D40613255302AC93140C0EC9E3C2CC44D406C787AA52CC33140992A1895D4C94D405917B7D100BE31404182E2C798CB4D4019E25817B7B13140F6285C8FC2CD4D4082734694F6963140FD87F4DBD7D14D40E4839ECDAA8F3140BD5296218ED54D40CB10C7BAB89D31407E8CB96B09D94D409487855AD39C314092CB7F48BFDD4D40499D8026C296314011363CBD52DE4D409A99999999893140CB10C7BAB8DD4D408AB0E1E995823140C58F31772DD94D40F90FE9B7AF833140986E1283C0D24D40BD5296218E8531402063EE5A42CE4D4048BF7D1D38873140A2B437F8C2CC4D402575029A088B3140D34D621058C94D404BEA043411863140355EBA490CCA4D40FD87F4DBD7713140AD69DE718AD64D406D567DAEB6723140A01A2FDD24DE4D40166A4DF38E8331408FC2F5285CDF4D4060E5D022DB893140006F8104C5DF4D40DBF97E6ABC9431404182E2C798E34D409EEFA7C64B97314074B515FBCBE64D408C4AEA0434A13140E9263108ACE44D400F0BB5A679A7314096438B6CE7DB4D40107A36AB3EA73140014D840D4FD74D40DA1B7C6132A531406891ED7C3FD54D4054E3A59BC4A03140A2B437F8C2D44D40F0164850FC983140A3923A014DD44D40B4C876BE9F9A314088635DDC46D34D4014AE47E17AA431409A99999999D14D4080B74082E2B7314072F90FE9B7CF4D402A3A92CB7FB83140A3923A014DD44D40A089B0E1E9B53140A1F831E6AED54D4014D044D8F0B4314058CA32C4B1D64D40462575029AB831400C022B8716D94D40E8D9ACFA5CBD3140A245B6F3FDD44D408B6CE7FBA9C13140F38E537424CF4D406FF085C954D13140508D976E12CB4D400E2DB29DEFD731405DFE43FAEDC34D4057EC2FBB27CF3140C364AA6054C24D400D71AC8BDBC8314057EC2FBB27BF4D40A60A462575C23140228E75711BB54D400E4FAF9465C8314036CD3B4ED1B14D403B014D840DCF3140ABCFD556ECAF4D40DA1B7C6132D531401F85EB51B8AE4D4024B9FC87F4DB3140840D4FAF94AD4D404B598638D6E53140158C4AEA04AC4D40CA54C1A8A4EE3140508D976E12AB4D408BFD65F7E40132405C8FC2F528AC4D40BADA8AFD6517324026E4839ECDAA4D40499D8026C2163240AC8BDB6800AF4D40CA54C1A8A40E3240C3D32B6519B24D4011363CBD52163240BA6B09F9A0B74D401F85EB51B81E3240C3D32B6519BA4D401973D712F2313240FDF675E09CB94D40A9A44E4013313240D8F0F44A59B64D40B537F8C2642A324031992A1895B44D40DC4603780B5432406DE7FBA9F1B24D409A081B9E5E4932400612143FC6B44D401895D409684232409A779CA223B94D402D211FF46C463240E9B7AF03E7BC4D408C4AEA04345132405BB1BFEC9EBC4D40C898BB96905F32404F401361C3BB4D406891ED7C3FA532405F07CE1951CA4D40ED0DBE3099AA32405D6DC5FEB2CB4D40226C787AA5BC32407C61325530D24D40A5BDC11726B3324043AD69DE71D24D40F2B0506B9AB732406744696FF0D54D40022B8716D9BE32409C33A2B437D84D40BADA8AFD65D732403333333333DB4D4032772D211FF432405C8FC2F528DC4D40CEAACFD556FC324088855AD3BCDB4D4063EE5A423E0833405C8FC2F528DC4D408AB0E1E9951233401FF46C567DDE4D40F8C264AA60143340A779C7293AE24D40D5E76A2BF6F73240A3923A014DE44D403A92CB7F48EF3240A3923A014DE44D40E8D9ACFA5CDD3240A01A2FDD24E64D401DC9E53FA4EF32408FC2F5285CEF4D408048BF7D1DF8324074B515FBCBEE4D402A3A92CB7F08334060764F1E16EA4D4054E3A59BC4103340C3F5285C8FEA4D40FC1873D71212334027A089B0E1F14D4096B20C71AC0B3340174850FC18F34D40FD87F4DBD7013340F7E461A1D6F44D40A245B6F3FDF432402041F163CCF54D405917B7D100EE32403C4ED1915CF64D40F163CC5D4BE832408E75711B0DF84D40A4703D0AD7E3324018265305A3FA4D408C4AEA0434D132408B6CE7FBA9094E4054742497FFD03240CEAACFD5560C4E409FCDAACFD5C63240CA32C4B12E0E4E40D95F764F1EB63240AAF1D24D62104E40A7E8482EFFA13240C3F5285C8F124E408048BF7D1D8832404182E2C798134E408BFD65F7E4813240B30C71AC8B134E4024B9FC87F46B3240D5E76A2BF6174E4013F241CF66653240B4C876BE9F1A4E40C58F31772D5132404850FC1873274E40423EE8D9AC5A3240A1D634EF38254E408B6CE7FBA9613240B4C876BE9F224E4070CE88D2DE703240C976BE9F1A1F4E40F1F44A5986783240AE47E17A141E4E40D5E76A2BF687324030BB270F0B1D4E40742497FF908E3240789CA223B91C4E407DAEB6627F99324003780B24281E4E40FB5C6DC5FE9232400000000000204E40107A36AB3E77324011363CBD52264E401CEBE2361A703240EC51B81E852B4E40E3361AC05B503240F7065F984C2D4E401361C3D32B45324087A757CA322C4E40211FF46C563D3240273108AC1C2A4E409FCDAACFD536324096438B6CE72B4E409A99999999193240A7E8482EFF394E40E71DA7E848FE314001DE02098A474E40BC74931804F6314016FBCBEEC94B4E40ABCFD556ECEF31406ADE718A8E4C4E40FBCBEEC9C3E23140B1506B9A774C4E4048E17A14AED73140EC51B81E854B4E40D9CEF753E3C53140705F07CE19494E400A68226C78BA3140B1E1E995B2444E40B29DEFA7C6BB314064CC5D4BC83F4E401CEBE2361AB03140E4839ECDAA3F4E40D50968226CA83140FFB27BF2B0404E4036CD3B4ED1A13140516B9A779C424E40401361C3D39B3140022B8716D9464E407AA52C431C9B3140EE7C3F355E4A4E4011C7BAB88DA63140B0726891ED4C4E40FBCBEEC9C3A231401EA7E8482E4F4E4024B9FC87F49B3140FE43FAEDEB504E40DC4603780B94314052B81E85EB514E4003780B24288E314027C286A757524E406891ED7C3F8531405F07CE1951524E406688635DDC663140FFB27BF2B0504E40545227A089603140AC1C5A643B4F4E40075F984C155C3140A7E8482EFF514E40BC96900F7A463140AD69DE718A564E407E8CB96B09493140A1F831E6AE5D4E40462575029A4831405F29CB10C7624E40F46C567DAE46314096438B6CE76B4E40CA32C4B12E3E3140EE7C3F355E724E406B9A779CA2333140E8D9ACFA5C754E40D7A3703D0A2731409B559FABAD784E40280F0BB5A6293140925CFE43FA854E403C4ED1915C2E3140E3361AC05B984E4032772D211F3431405D6DC5FEB2A34E40BD5296218E353140C442AD69DEA94E4085EB51B81E2531402041F163CCAD4E40D044D8F0F41A3140D0D556EC2FB34E408195438B6C2731403B70CE88D2B64E409C33A2B437383140BA6B09F9A0B74E4027C286A7572A31403480B74082C24E4003780B24281E3140C976BE9F1AC74E40B7627FD93D193140B003E78C28CD4E40174850FC18233140363CBD5296D14E40431CEBE2362A3140D42B6519E2D04E403A234A7B832F3140B7627FD93DD14E40C1CAA145B6433140D656EC2FBBD74E40105839B4C836314052499D8026DA4E40FF21FDF6753031405F07CE1951DA4E4012A5BDC117263140DDB5847CD0DB4E40431CEBE2363A31403FC6DCB584DC4E40F7065F984C55314017D9CEF753DB4E40EA95B20C715C3140355EBA490CDA4E4017D9CEF7536331402AA913D044D84E40075F984C156C3140E0BE0E9C33D24E40A7E8482EFF7131402A3A92CB7FD04E40ADFA5C6DC57E31405305A3923AD14E40832F4CA60A8631405396218E75D94E40C8073D9B557F3140B0726891EDDC4E4099BB96900F7A31401283C0CAA1DD4E40C66D3480B770314030BB270F0BDD4E403411363CBD62314046B6F3FDD4E04E40D95F764F1E563140C66D3480B7E84E40D26F5F07CE59314054E3A59BC4F84E400F9C33A2B4673140F46C567DAEFE4E40C66D3480B7703140BBB88D06F0FE4E40F46C567DAE763140E25817B7D1004F405D6DC5FEB27B3140645DDC4603104F40FCA9F1D24D823140B515FBCBEE194F404F401361C3833140DAACFA5C6D1D4F409A999999998931405AF5B9DA8A1D4F4039454772F98F3140F697DD93871D4F4096438B6CE78B3140EE5A423EE8194F409A9999999999314026E4839ECD1A4F40D95F764F1EA63140D93D7958A81D4F4072F90FE9B79F3140CA54C1A8A41E4F40C1CAA145B69331401DC9E53FA41F4F40E8D9ACFA5C8D3140C7BAB88D06204F40713D0AD7A38031400C022B8716214F402CD49AE61D773140A7E8482EFF214F4072F90FE9B75F3140992A1895D4294F40075F984C155C3140D95F764F1E2E4F404D840D4FAF543140917EFB3A703E4F409D11A5BDC1673140BF0E9C33A2444F40AE47E17A146E3140A3923A014D444F4044696FF085793140E25817B7D1404F408A1F63EE5A823140D93D7958A83D4F40462575029A883140B5A679C7293A4F40AE47E17A148E31408D28ED0DBE384F40CDCCCCCCCC9C3140BA6B09F9A0374F403FC6DCB584AC314070CE88D2DE384F40F1F44A5986A8314096438B6CE73B4F40C6DCB5847CB0314082734694F63E4F40B7627FD93DB93140B9FC87F4DB3F4F404703780B24C83140AA60545227404F406F8104C58FD13140F6285C8FC23D4F402D431CEBE2D63140A01A2FDD243E4F40EE7C3F355EFA314002BC051214474F40CE88D2DEE00B324087A757CA324C4F40D1915CFE430A32400000000000504F40BADA8AFD65F731408C4AEA0434514F40A9A44E4013E13140696FF085C9544F406B9A779CA2F33140CB10C7BAB8554F40B84082E2C7F8314079E9263108544F40917EFB3A70FE3140401361C3D3534F40C58F31772D013240B6F3FDD478594F40CA54C1A8A4FE3140933A014D845D4F40A857CA32C4E13140705F07CE19714F40A1F831E6AED53140ACADD85F76774F40091B9E5E29CB31408B6CE7FBA9794F40174850FC18C331406D567DAEB67A4F402575029A08BB31403FC6DCB5847C4F4017D9CEF753B331409E5E29CB107F4F40C217265305D33140014D840D4F7F4F4080B74082E2D73140091B9E5E297B4F40E78C28ED0DDE31408351499D80764F403D0AD7A370ED3140A4703D0AD76B4F40B3EA73B515FB314054E3A59BC4684F4044696FF085093240CF66D5E76A6B4F40FBCBEEC9C3123240174850FC186B4F40DFE00B93A9223240C898BB9690674F40D0B359F5B91A3240BBB88D06F0664F40DA1B7C6132153240EB73B515FB634F408E06F016482032406EA301BC05624F404E6210583934324088F4DBD781634F40D50968226C7832403CBD5296216E4F40C1A8A44E409332407AA52C431C7B4F40CA54C1A8A48E3240933A014D847D4F40F163CC5D4B88324012A5BDC1177E4F40DAACFA5C6D653240F38E5374247F4F405917B7D1005E324090A0F831E67E4F40F085C954C15832405839B4C8767E4F40D200DE02094A32401DC9E53FA47F4F40FD87F4DBD7613240029A081B9E864F40371AC05B20913240D7A3703D0A8F4F40A1F831E6AEA53240A7E8482EFF914F400612143FC6BC3240598638D6C5954F405F29CB10C7BA324051DA1B7C619A4F40B7D100DE02C93240FE65F7E461994F40363CBD5296C132401D5A643BDF974F409D11A5BDC1C73240696FF085C9944F40F6285C8FC2E53240545227A089984F4061C3D32B65E93240A69BC420B09A4F4035EF384547E23240F775E09C119D4F40D42B6519E2D832402EFF21FDF69D4F401FF46C567DCE324048E17A14AE9F4F40BD5296218EE5324034A2B437F8A24F4065AA605452F73240A3923A014D9C4F40D0B359F5B90A3340F5DBD78173964F400000000000103340CA54C1A8A4964F40B22E6EA3011C33405EBA490C029B4F404B598638D6153340CF66D5E76A9B4F405917B7D1000E3340401361C3D39B4F40075F984C150C3340C9E53FA4DF9E4F40F54A5986382633402B1895D409A84F402575029A083B334052B81E85EBA94F409FCDAACFD5463340CC5D4BC807B54F40448B6CE7FB4933405F984C158CBA4F4057EC2FBB274F33407A36AB3E57BB4F4054E3A59BC4503340A857CA32C4B94F40401361C3D35B3340AB3E575BB1B74F406DC5FEB27B723340E3361AC05BB84F40B7627FD93D793340A7E8482EFFB94F4005A3923A016D334004E78C28EDC54F409D8026C28677334048E17A14AEC74F40386744696F80334011363CBD52C64F402041F163CC9D334065AA605452BF4F404E62105839A4334016FBCBEEC9BB4F40FA7E6ABC74A333409BE61DA7E8B84F40772D211FF4AC3340C976BE9F1AB74F4014AE47E17AB433403A92CB7F48B74F4066F7E461A1C633407B14AE47E1BA4F40A913D044D8C0334029CB10C7BAC04F40BC96900F7AC63340787AA52C43C44F40DAACFA5C6DE533402041F163CCCD4F4089D2DEE00B033440FE65F7E461D14F409A99999999193440C05B2041F1D34F40166A4DF38E433440A1D634EF38D54F40EA95B20C714C34403411363CBDD24F40A9A44E401351344014AE47E17AD44F401A51DA1B7C613440E71DA7E848D64F407CF2B0506B6A3440D42B6519E2D84F4018265305A3723440454772F90FE14F40713D0AD7A38034407E8CB96B09E94F40295C8FC2F58834404A7B832F4CE64F406C09F9A067A334401CEBE2361AE84F4098DD938785BA34406ADE718A8EEC4F40A01A2FDD24C63440492EFF21FDEE4F405E4BC8073DCB34405396218E75F14F406891ED7C3FE534405C8FC2F52800504048E17A14AEE73440091B9E5E2903504082E2C798BBF63440E9482EFF21095040E5F21FD26F3F35403255302AA913504068B3EA73B5453540AC8BDB680013504036AB3E575B51354039B4C876BE1350404ED1915CFE6335405396218E751550406891ED7C3F7535407AA52C431C175040F6285C8FC29535404703780B241C504076711B0DE08D3540AE47E17A142250402CD49AE61D77354046B6F3FDD4245040211FF46C565D354027C286A75726504068226C787A453540A54E401361275040E6AE25E4831E35407958A835CD2B504026E4839ECD1A35404BEA0434112E50401B2FDD240621354066666666662E50402CD49AE61D273540AED85F764F2E5040B003E78C283D3540B9FC87F4DB2B5040BC74931804463540ADFA5C6DC52A504095D40968224C35402D211FF46C2A50403D9B559FAB4D35403F575BB1BF3050408A8EE4F21F423540F6285C8FC2315040D42B6519E2383540FC1873D7123250407FFB3A70CE283540C4B12E6EA33150401AC05B20412135401283C0CAA13150408048BF7D1D183540D95F764F1E32504060764F1E160A3540789CA223B934504026E4839ECD0A35404BEA04341136504017D9CEF753133540029A081B9E365040B8AF03E78C183540B0726891ED3450401B0DE02D90203540AAF1D24D62345040022B8716D92E354085EB51B81E35504068226C787A3535403333333333375040DBF97E6ABC34354037894160E5385040569FABADD83F3540143FC6DCB53C504005A3923A014D35405BD3BCE3143D5040C898BB96905F3540DF4F8D976E3E50400F0BB5A679773540FF21FDF67540504029CB10C7BA783540D88173469442504023DBF97E6A7C3540401361C3D34350400DE02D90A08835404F401361C343504032E6AE25E4933540F241CF66D5435040742497FF909E35405227A089B0495040CB10C7BAB88D3540A857CA32C44D504036AB3E575B8135404772F90FE94F504026E4839ECD6A35408F537424975350404E62105839543540226C787AA5545040DDB5847CD0433540280F0BB5A6555040F931E6AE255435405D6DC5FEB25750404850FC18737735408716D9CEF7535040917EFB3A707E3540D8F0F44A59525040EEEBC039238A354000917EFB3A505040022B8716D98E3540492EFF21FD4E504011C7BAB88D963540C2172653054F5040B84082E2C7A83540D656EC2FBB4F5040FB5C6DC5FEB2354003780B2428525040925CFE43FA9D3540B84082E2C7545040F697DD93879535403FC6DCB584545040EE5A423EE8893540B0726891ED545040FF21FDF675803540B537F8C264565040D49AE61DA77835401B0DE02D9058504096218E75718B3540D95F764F1E5A50405F07CE19519A35408351499D805A5040E09C11A5BDA13540DAACFA5C6D5950408126C286A7A735405BD3BCE31459504005A3923A01AD3540CC5D4BC80759504004E78C28EDED3540B6F3FDD478595040DD2406819503364088635DDC465B50400E2DB29DEF073640287E8CB96B5D5040423EE8D9ACFA35407A36AB3E575F5040CCEEC9C342ED3540F1F44A59866050401B0DE02D90F03540EC2FBB270F5F5040EC51B81E85EB35406C787AA52C5F5040F46C567DAEE6354016FBCBEEC95F504096218E7571DB354004E78C28ED61504088855AD3BCE33540E0BE0E9C33625040F085C954C1E8354068B3EA73B5615040917EFB3A70FE3540FE43FAEDEB6050402041F163CC0D3640D3BCE3141D615040D200DE02092A3640D95F764F1E625040A857CA32C4313640BB270F0BB5625040E5D022DBF91E36403E7958A83565504074B515FBCB0E3640B6847CD0B3655040F6285C8FC205364035EF3845476650409A779CA223D93540F5B9DA8AFD6950404ED1915CFED33540D8817346946A5040DC4603780BC43540D3DEE00B936D5040FB5C6DC5FED23540F085C954C16C5040B515FBCBEED9354010E9B7AF036B5040BF7D1D38671436406688635DDC665040302AA913D034364030BB270F0B6550403D2CD49AE63D3640C5FEB27BF2645040567DAEB6625F3640333333333363504066666666666636408A1F63EE5A62504095D40968226C3640FB5C6DC5FE6250406D567DAEB6623640151DC9E53F6850406D567DAEB65236407C613255306A5040D122DBF97E4A36405F984C158C6A5040C66D3480B75036403F355EBA4968504029ED0DBE30493640AA60545227685040006F8104C53F364014AE47E17A685040A7E8482EFF41364000917EFB3A6C5040DCD7817346543640705F07CE197550402063EE5A425E3640492EFF21FD765040091B9E5E296B3640AC8BDB6800775040166A4DF38E733640C286A757CA765040423EE8D9AC7A36400A68226C787650407958A835CD9B3640AC8BDB68007350404850FC1873A736407F6ABC74937050403D2CD49AE6AD3640E9263108AC705040BF7D1D3867B4364048E17A14AE735040228E75711BAD364079E9263108785040DC68006F81A43640933A014D8479504005A3923A01AD3640E09C11A5BD795040613255302AB93640143FC6DCB57850405F07CE1951CA3640D712F241CF7650403255302AA9D33640E3C798BB9674504055302AA913003740711B0DE02D705040DDB5847CD01337405B423EE8D96C5040A52C431CEB22374076E09C11A56D5040B22E6EA3013C374070CE88D2DE705040ABCFD556EC3F3740C1A8A44E40735040DB8AFD65F764374085EB51B81E7150403A92CB7F486F3740E9263108AC7050404D840D4FAF843740C1A8A44E40735040BADA8AFD65A73740A4DFBE0E9C7350402EFF21FDF6C5374090A0F831E6725040CAC342AD69EE3740E9263108AC705040158C4AEA04F4374052B81E85EB715040EA95B20C71FC37401F85EB51B8725040401361C3D30B3840E4839ECDAA7350404D840D4FAF1438408F537424977350405BD3BCE3141D38406C787AA52C73504012A5BDC11726384033C4B12E6E735040986E1283C02A38406ABC749318745040CDCCCCCCCC2C38402DB29DEFA77250400E4FAF94653838402FDD240681715040B5A679C7296A3840D3BCE3141D7150406DE7FBA9F17238409FCDAACFD572504027A089B0E189384090A0F831E6725040E4141DC9E58F3840B84082E2C7745040AAF1D24D62A038407FFB3A70CE785040386744696FB03840857CD0B359795040E17A14AE47A13840F46C567DAE76504057EC2FBB278F3840742497FF9072504004560E2DB28D384054E3A59BC4705040E561A1D6348F38403480B740826E5040B37BF2B050AB38403D2CD49AE66950401DC9E53FA4BF38401A51DA1B7C695040BD5296218EC5384020D26F5F076A50407958A835CDCB38403CBD5296216A5040C0EC9E3C2CE438407DAEB6627F6950404D840D4FAF1439405F29CB10C7665040D656EC2FBB17394027A089B0E165504099BB96900F1A3940386744696F64504036CD3B4ED12139400F0BB5A679635040D34D6210582939407AC7293A92635040545227A089303940DDB5847CD06350409E5E29CB103739400F0BB5A67963504092CB7F48BF4D3940DB8AFD65F7605040EE5A423EE859394018265305A35E504096218E75715B39404D840D4FAF5C5040D044D8F0F45A3940984C158C4A5A504033333333335339403CBD5296215650405BD3BCE3144D3940C286A757CA5250402575029A084B3940E2E995B20C51504014AE47E17A4439407B14AE47E14A5040ED0DBE30994A3940AC8BDB6800475040ED0DBE30995A3940A089B0E1E9455040022B8716D95E39405BD3BCE31445504044FAEDEBC06939407C6132553042504019E25817B771394069006F81043D5040D200DE02096A3940EA95B20C713C504091ED7C3F355E3940857CD0B3593D5040C364AA6054523940499D8026C23E50409F3C2CD49A363940107A36AB3E3F5040FF21FDF67530394004560E2DB23D504042CF66D5E73A3940D6C56D34803B5040567DAEB6624F39402041F163CC39504064CC5D4BC857394075029A081B3A504005C58F31775D3940B6F3FDD478395040E9263108AC5C39405BD3BCE314355040D7A3703D0A573940462575029A345040A857CA32C4513940CEAACFD5563450405C8FC2F5284C394032772D211F345040FE43FAEDEB4039408048BF7D1D34504097FF907EFB3A3940386744696F345040C4B12E6EA3313940273108AC1C36504003780B24282E3940C139234A7B37504092CB7F48BF1D3940AE47E17A143A50404BC8073D9B153940D8F0F44A593A50400000000000003940705F07CE1939504076E09C11A5CD3840BA6B09F9A0375040091B9E5E29BB3840F46C567DAE365040EEEBC039238A3840A5BDC11726335040454772F90F893840C7293A92CB2F5040CDCCCCCCCC6C384048BF7D1D382B5040744694F6065F38406B9A779CA2275040006F8104C55F38402FDD24068125504024B9FC87F45B3840499D8026C22250406688635DDC563840AF946588632150408104C58F314738407958A835CD1F5040A1F831E6AE3538402063EE5A421E50401F85EB51B81E38405DFE43FAED1B5040575BB1BFECFE3740068195438B18504093A9825149ED3740B4C876BE9F125040053411363CED37405305A3923A115040D044D8F0F4DA3740A4DFBE0E9C0F5040D42B6519E2B8374016FBCBEEC90B50402E90A0F831B637402D211FF46C0A504019E25817B7B1374052499D80260A504082734694F6A63740B7D100DE0209504024287E8CB99B3740984C158C4A065040B5A679C7299A374032772D211F045040A52C431CEB823740C364AA605402504005C58F31776D3740091B9E5E290350408AB0E1E995623740098A1F63EE02504002BC0512145F374011363CBD52FE4F403B70CE88D25E3740637FD93D79F84F40C4B12E6EA35137405F29CB10C7F24F40A54E401361333740C520B07268F14F4012143FC6DC253740AAF1D24D62F04F4054742497FF103740E9B7AF03E7EC4F40956588635DEC3640AD69DE718AE64F40E9482EFF21ED3640C4B12E6EA3E14F406666666666E63640287E8CB96BD94F4089D2DEE00BD33640C3D32B6519D24F402B8716D9CEC736401CEBE2361AD04F4005C58F3177BD36402C6519E258CF4F40DB8AFD65F7B43640BF7D1D3867D44F404FAF946588B33640736891ED7CD74F4043AD69DE71AA36408CDB68006FD94F409FCDAACFD59636407E8CB96B09D94F40E17A14AE478136407593180456CE4F4036AB3E575B81364009F9A067B3CA4F4093A98251497D3640D50968226CC84F4004E78C28ED6D3640B9FC87F4DBBF4F4068226C787A65364014AE47E17ABC4F408E06F01648603640F46C567DAEBE4F40BEC1172653553640C3F5285C8FC24F400BB5A679C7493640ECC039234AC34F401CEBE2361A3036408CB96B09F9B84F4017D9CEF7533336403D9B559FABB54F404BC8073D9B453640287E8CB96BB14F40EA95B20C714C36401CEBE2361AB04F4017B7D100DE523640C976BE9F1AAF4F402575029A085B3640C139234A7BAB4F40029A081B9E5E3640B98D06F016A84F40D7A3703D0A57364008AC1C5A64A34F409D8026C2863736402063EE5A429E4F405917B7D1002E36405AF5B9DA8A9D4F40832F4CA60A1636403FC6DCB5849C4F400F0BB5A679F735408195438B6C974F406ADE718A8EE4354051DA1B7C619A4F40C66D3480B7E0354054E3A59BC4A04F40C66D3480B7B035405F29CB10C79A4F40C05B2041F1A33540713D0AD7A3984F401AC05B20419135406A4DF38E539C4F4060E5D022DB89354068226C787A9D4F40E561A1D6347F3540355EBA490C9A4F40386744696F803540EA95B20C71944F40A2B437F8C28435402B1895D409904F406F1283C0CA91354099BB96900F8A4F403FC6DCB5849C3540C66D3480B7884F400F9C33A2B4A73540D95F764F1E864F40933A014D84AD3540CEAACFD556844F4074B515FBCBAE35407958A835CD834F403E7958A835AD3540265305A392824F401F85EB51B89E354051DA1B7C61824F40E9482EFF218D35402F6EA301BC854F4065AA6054528735402B1895D409884F4039D6C56D348035408D976E1283884F403A234A7B836F3540EA95B20C71844F40DE9387855A733540E25817B7D1804F408A8EE4F21F723540A4703D0AD77B4F40575BB1BFEC6E35408D28ED0DBE784F4068226C787A6535407DAEB6627F714F40C898BB96905F35403C4ED1915C6E4F40F1F44A5986583540AF946588636D4F4007F01648504C3540CA32C4B12E6E4F4035EF384547223540228E75711B654F402575029A081B3540A779C7293A624F4037894160E5103540068195438B4C4F4055302AA913203540AE47E17A14464F403D0AD7A3701D354090A0F831E63E4F4094F6065F981C354036CD3B4ED1394F402B8716D9CE273540F7E461A1D6344F408A1F63EE5A4235401CEBE2361A304F4030BB270F0B553540BE9F1A2FDD2C4F4057EC2FBB275F3540E17A14AE47214F403255302AA9633540B537F8C2641A4F4036AB3E575B6135402C6519E258174F4068226C787A5535405D6DC5FEB2134F40E71DA7E8484E35406744696FF00D4F40D3BCE3141D493540D42B6519E2084F40A913D044D840354062105839B4004F400D71AC8BDB4835409A779CA223F94E40B1E1E995B24C3540B8AF03E78CF84E4005A3923A015D354082734694F6F64E40D3BCE3141D693540A7E8482EFFF14E40E9482EFF217D3540F5DBD78173E64E4062A1D634EF783540C520B07268E14E407E8CB96B09893540083D9B559FD34E407DD0B359F599354069006F8104CD4E407DD0B359F5A93540B003E78C28C54E4043AD69DE719A3540CB10C7BAB8C54E40C898BB96907F3540C520B07268C94E402B1895D409783540AC1C5A643BC74E40C21726530583354061545227A0C14E402B8716D9CE873540B7627FD93DB94E4004E78C28ED8D3540F2B0506B9AA74E4041F163CC5D8B3540A54E401361A34E40917EFB3A707E3540A01A2FDD249E4E40A857CA32C471354013F241CF66954E402B8716D9CE67354096438B6CE7834E4088635DDC46633540D50968226C784E40DC4603780B543540499D8026C26E4E402575029A085B3540B81E85EB51684E4014AE47E17A64354062105839B4604E4050FC1873D7623540B9FC87F4DB574E4008AC1C5A645B35401E166A4DF3564E407958A835CD5B35403255302AA9534E4094F6065F986C354052499D80264A4E40F9A067B3EA7335400000000000484E40B37BF2B0507B35400DE02D90A0484E40A69BC420B082354044696FF085494E4077BE9F1A2F8D35409BE61DA7E8484E40F90FE9B7AF9335400000000000484E4011C7BAB88D9635408C4AEA0434414E40D200DE0209AA35401F85EB51B8464E40F7065F984CC535403EE8D9ACFA4C4E4089D2DEE00BD3354073D712F2414F4E40BF7D1D3867D4354017D9CEF7534B4E40CCEEC9C342CD35401C7C613255484E40E86A2BF697CD3540F6285C8FC23D4E40B0726891EDDC3540BF7D1D38673C4E40D656EC2FBBD73540B537F8C264424E40CA54C1A8A4DE3540174850FC18434E408A8EE4F21FF23540273108AC1C424E40462575029AF835405839B4C8763E4E40F90FE9B7AF033640166A4DF38E3B4E4013F241CF66153640E25817B7D1384E40E8D9ACFA5C1D36405396218E75394E4005A3923A012D364048BF7D1D38374E4094F6065F983C3640787AA52C43344E408A1F63EE5A4236406DE7FBA9F1324E402D431CEBE2463640FE65F7E461314E407958A835CD7B3640B5A679C729324E4071AC8BDB68A03640FFB27BF2B0304E40B7627FD93D8936400000000000284E4024B9FC87F47B3640151DC9E53F244E40BC96900F7A7636400BB5A679C7214E40C217265305733640744694F6061F4E4054E3A59BC4903640DE02098A1F1B4E40EFC9C342ADA93640EA95B20C711C4E40FB5C6DC5FEB23640211FF46C561D4E4040A4DFBE0EBC36409FABADD85F1E4E406D567DAEB6C2364073D712F2411F4E405BD3BCE314CD36400C022B8716214E40B37BF2B050EB3640BBB88D06F0264E4072F90FE9B70F37403E7958A8352D4E40A1F831E6AE153740234A7B832F2C4E40BF7D1D386704374032772D211F244E409D11A5BDC1F736402B1895D409204E40CCEEC9C342ED364021B07268911D4E40D6C56D3480E73640083D9B559F1B4E40CA54C1A8A4DE3640BBB88D06F0164E40569FABADD8DF3640B4C876BE9F124E40D5E76A2BF6E73640711B0DE02D104E4048E17A14AEF7364011363CBD520E4E402D211FF46C063740FF21FDF675104E40D044D8F0F40A37408104C58F310F4E408638D6C56D043740772D211FF40C4E403A234A7B83FF364089D2DEE00B0B4E4092CB7F48BFFD36402C6519E258074E40226C787AA50C37405BB1BFEC9E044E4087A757CA32143740A301BC0512044E402575029A081B37405B423EE8D9044E40E3361AC05B203740D93D7958A8054E40BD5296218E2537403D2CD49AE6054E40371AC05B20313740D95F764F1E064E401AC05B20414137403F575BB1BF044E4029CB10C7BA3837409C33A2B437004E40A4703D0AD7333740F38E537424FF4D40D0B359F5B92A3740D95F764F1EFE4D40EA95B20C711C374087A757CA32FC4D4024B9FC87F41B3740BC96900F7AF64D400AD7A3703D3A3740F4FDD478E9F64D40AA6054522740374054742497FFF84D409D11A5BDC1473740E8D9ACFA5CFD4D4079E92631084C37400000000000004E40386744696F503740D1915CFE43024E406744696FF0553740508D976E12034E40DE9387855A5337400000000000004E4024B9FC87F44B374009F9A067B3FA4D40FD87F4DBD7413740A1F831E6AEF54D40613255302A2937407E1D386744F14D403D0AD7A3701D37408C4AEA0434F14D40BC0512143F163740545227A089F04D403A92CB7F48EF36404182E2C798EB4D40BC96900F7AE63640D734EF3845E74D40C4B12E6EA33137407DD0B359F5E94D408FE4F21FD23F3740C1A8A44E40EB4D4076E09C11A55D3740462575029AF84D40917EFB3A707E3740ACADD85F76074E409A99999999893740D49AE61DA7084E400AD7A3703D8A3740A4DFBE0E9C034E404BC8073D9B853740A60A462575024E40D0B359F5B97A37402BF697DD93FF4D401FF46C567D6E374060764F1E16FA4D403480B740828237408716D9CEF7FB4D4072F90FE9B78F3740151DC9E53FFC4D4001DE02098A9F3740C442AD69DEF94D40F90FE9B7AFB33740B5A679C729FA4D40075F984C15CC3740DE02098A1FFB4D40FB5C6DC5FE1238407DD0B359F5014E409D8026C28637384030BB270F0B054E40B6847CD0B3493840E3361AC05B084E406891ED7C3F5538408C4AEA0434094E40956588635D5C384070CE88D2DE084E40CE88D2DEE06B3840C139234A7B034E407958A835CD6B38403C4ED1915CFE4D40295C8FC2F5783840ADFA5C6DC5FE4D406688635DDC963840744694F606074E40B29DEFA7C69B3840A4703D0AD70B4E4043AD69DE719A3840F2B0506B9A0F4E408E06F01648C0384044696FF085114E40D26F5F07CED938407DAEB6627F114E40DCD7817346F43840287E8CB96B114E40A913D044D8103940EB73B515FB134E40A9A44E40133139408F53742497174E4036CD3B4ED131394009F9A067B31A4E402D211FF46C263940787AA52C431C4E40FCA9F1D24D323940022B8716D91E4E4003780B24285E39408D28ED0DBE204E4024B9FC87F47B3940BA6B09F9A01F4E404182E2C7988B39408FE4F21FD2274E40C139234A7B933940C3D32B65192A4E40B81E85EB51A8394058A835CD3B2E4E408D976E1283B039405BB1BFEC9E2C4E400F9C33A2B4A73940508D976E122B4E40BB270F0BB5A63940BDE3141DC9254E4094F6065F98AC39405E4BC8073D234E40637FD93D79B839409D11A5BDC11F4E409FABADD85FC639403D2CD49AE61D4E4012A5BDC117D63940DF4F8D976E224E402506819543CB3940984C158C4A224E40E5D022DBF9BE3940F4FDD478E9264E40E561A1D634DF39405F29CB10C7224E40DE02098A1FE3394065AA6054521F4E4024B9FC87F4EB39408104C58F311F4E407D3F355EBAE93940A1F831E6AE2D4E40E5D022DBF9DE3940B7D100DE02314E40D881734694D639405EBA490C02334E40166A4DF38EE3394027A089B0E1314E40AC8BDB6800FF394032772D211F2C4E40423EE8D9AC0A3A409031772D21274E40C0EC9E3C2C143A401283C0CAA1254E4042CF66D5E71A3A4062A1D634EF284E4075029A081B0E3A40D95F764F1E364E4013F241CF66253A407CF2B0506B324E40B4C876BE9F6A3A40EEEBC03923324E409A779CA223793A4082E2C798BB364E400EBE30992A783A407B832F4CA63A4E40F7E461A1D6743A408F537424973F4E402D431CEBE2863A40E3361AC05B484E407958A835CD8B3A406DC5FEB27B4A4E403FC6DCB5849C3A40E09C11A5BD514E4010E9B7AF03A73A403411363CBD524E40371AC05B20B13A400AD7A3703D524E407FFB3A70CEB83A4002BC0512144F4E40ABCFD556ECBF3A40C4B12E6EA3494E40C3D32B6519B23A404F401361C34B4E40462575029AA83A40AEB6627FD94D4E403A234A7B839F3A40A1F831E6AE4D4E40B81E85EB51983A406ADE718A8E4C4E4052B81E85EB913A40A779C7293A4A4E407CF2B0506B7A3A401361C3D32B3D4E40C7BAB88D06803A40295C8FC2F5384E408E06F01648903A4074B515FBCB364E40D0B359F5B9AA3A40029A081B9E364E4093A9825149BD3A405396218E75394E401DC9E53FA4CF3A403FC6DCB5843C4E409D11A5BDC1E73A4014D044D8F03C4E40F7065F984CF53A409BE61DA7E8384E4083C0CAA145063B4079E9263108444E408048BF7D1D383B406D567DAEB64A4E4068226C787A453B40A3923A014D444E4096B20C71AC4B3B402A3A92CB7F404E40D3BCE3141D793B403EE8D9ACFA3C4E40EF38454772793B401B0DE02D90404E40B37BF2B0509B3B40B84082E2C7404E407958A835CDAB3B401B0DE02D90404E40B0726891EDBC3B405396218E75414E4024B9FC87F4BB3B406ADE718A8E444E4075029A081BBE3B408D976E1283484E4048BF7D1D38C73B40371AC05B20494E40E6AE25E483CE3B40F54A598638464E4074B515FBCBCE3B406744696FF0454E40A52C431CEBD23B407A36AB3E57434E40DA1B7C6132053C40DD24068195434E40BD5296218E253C404F401361C3434E4065AA605452573C4040A4DFBE0E4C4E401904560E2D623C404772F90FE94F4E40166A4DF38E633C40508D976E12534E40D3BCE3141D693C40E9482EFF21554E40C139234A7B833C4002BC051214574E40053411363CAD3C40CA32C4B12E5E4E404260E5D022BB3C4096B20C71AC5B4E40EC51B81E85BB3C40F46C567DAE564E405AF5B9DA8AAD3C404A7B832F4C4E4E40386744696FA03C4048BF7D1D384F4E405BB1BFEC9E9C3C40613255302A514E40DE02098A1F933C40C4B12E6EA3514E403D2CD49AE68D3C40D9CEF753E34D4E40A857CA32C4913C40A52C431CEB4A4E4042CF66D5E79A3C406F1283C0CA494E40705F07CE19A13C406B2BF697DD4B4E4098DD938785AA3C40355EBA490C4A4E4086C954C1A8B43C40FB5C6DC5FE3A4E401F85EB51B8AE3C40C3D32B65193A4E40D5E76A2BF6A73C40091B9E5E293B4E40DB8AFD65F7943C40499D8026C23E4E40E3361AC05B903C40D5E76A2BF63F4E40849ECDAACF853C40C217265305434E40C520B07268813C403BDF4F8D97464E408A1F63EE5A723C4083C0CAA145464E40613255302A993C406F8104C58F314E404F1E166A4DA33C40C8073D9B552F4E40B98D06F016B83C40A3923A014D2C4E4093A9825149BD3C405D6DC5FEB22B4E4044696FF085C93C40B459F5B9DA2A4E4048E17A14AED73C4001DE02098A274E408E06F01648E03C4069006F8104254E40789CA223B9FC3C404260E5D0221B4E40A913D044D8003D40E17A14AE47194E40499D8026C2063D4064CC5D4BC8174E40CE88D2DEE00B3D4073D712F241174E40D3DEE00B93293D40840D4FAF94154E4011C7BAB88D663D403108AC1C5A144E40FD87F4DBD7713D403D0AD7A370154E407F6ABC7493783D401D38674469174E40A2B437F8C2843D40B84082E2C7184E40CB10C7BAB88D3D40A835CD3B4E194E4014AE47E17A943D40287E8CB96B194E40075F984C15AC3D40705F07CE19194E40F085C954C1B83D40462575029A184E401DC9E53FA4BF3D40ABCFD556EC174E40FB5C6DC5FED23D405AF5B9DA8A154E40637FD93D79D83D403FC6DCB584144E401AC05B2041E13D40ED0DBE3099124E40F4FDD478E9E63D404850FC18730F4E40DBF97E6ABCE43D408716D9CEF70B4E408AB0E1E995F23D40C139234A7B034E409B559FABADF83D405F07CE1951024E40B6F3FDD478093E401B2FDD2406014E406B9A779CA2133E40545227A089004E40EC51B81E852B3E40F241CF66D5FF4D40CE88D2DEE03B3E40849ECDAACFFD4D4057EC2FBB273F3E404F401361C3FB4D402B8716D9CE373E4026E4839ECDF24D40C8073D9B552F3E402BF697DD93EF4D402B8716D9CE273E40D881734694EE4D404FAF946588133E403B014D840DEF4D4060764F1E160A3E4072F90FE9B7EF4D408A1F63EE5A023E40B84082E2C7F04D40E71DA7E848EE3D40EC51B81E85F34D40423EE8D9ACCA3D4064CC5D4BC8F74D4088635DDC46C33D40E3361AC05BF84D4003780B24287E3D4086C954C1A8FC4D4036CD3B4ED1313D4070CE88D2DE004E407A36AB3E571B3D404A0C022B87FE4D4055302AA913103D4040A4DFBE0EFC4D40107A36AB3E073D40C976BE9F1AF74D407E1D386744093D40265305A392F24D4010E9B7AF03073D40BBB88D06F0EE4D40091B9E5E29FB3C40E0BE0E9C33EA4D40BE9F1A2FDDF43C400D71AC8BDBE84D4030BB270F0BD53C403F355EBA49E44D40EEEBC03923BA3C4079E9263108E44D4019E25817B7B13C4005C58F3177E54D407CF2B0506BAA3C4055C1A8A44EE84D4014D044D8F0843C4067D5E76A2BEE4D40575BB1BFEC7E3C4068226C787AED4D40E4141DC9E56F3C400C93A98251E94D40ED9E3C2CD46A3C40865AD3BCE3E44D4099BB96900F6A3C40295C8FC2F5E04D40EB73B515FB6B3C405917B7D100DE4D4097900F7A366B3C4099BB96900FDA4D4063EE5A423E683C408104C58F31D74D40A4703D0AD7633C40E8D9ACFA5CD54D403D2CD49AE65D3C40BF0E9C33A2D44D40E6AE25E4833E3C4074B515FBCBD64D404A0C022B87363C40D42B6519E2D84D40BF7D1D3867343C40401361C3D3DB4D408A8EE4F21F323C40F241CF66D5DF4D4096218E75712B3C400AD7A3703DE24D4099BB96900F1A3C405AF5B9DA8AE54D40174850FC18133C4068B3EA73B5E54D40E0BE0E9C33023C409A779CA223E14D40CA32C4B12EFE3B408E06F01648D84D408D28ED0DBE003C4020D26F5F07D64D402C6519E258073C402575029A08D34D40C74B378941103C401361C3D32BCD4D4036CD3B4ED1113C408CB96B09F9C84D40C5FEB27BF2103C40A1F831E6AEC54D4060764F1E160A3C408E06F01648C04D404BC8073D9B053C404B598638D6BD4D40DC4603780B043C40CCEEC9C342BD4D40E5D022DBF9FE3B407C61325530BA4D40EE7C3F355EFA3B40AAF1D24D62B84D4087A757CA32F43B40917EFB3A70B64D40575BB1BFECEE3B4013F241CF66B54D404F1E166A4DE33B40A3923A014DB44D40D3BCE3141DD93B4040A4DFBE0EB44D40E86A2BF6978D3B406688635DDCB64D40C3F5285C8F823B40F2B0506B9AB74D4096B20C71AC7B3B4063EE5A423EB84D406C09F9A067733B40D3BCE3141DB94D40E8D9ACFA5C6D3B40FE65F7E461B94D4032772D211F643B40287E8CB96BB94D40E5F21FD26F2F3B408C4AEA0434B94D406666666666163B40462575029AB84D40F1F44A5986083B40713D0AD7A3B84D407E8CB96B09F93A40D42B6519E2B84D40CE88D2DEE0EB3A40B6847CD0B3B94D40E0BE0E9C33E23A405F29CB10C7BA4D407B14AE47E1DA3A4007CE1951DABB4D408C4AEA0434D13A405AF5B9DA8ABD4D404B598638D6C53A402A3A92CB7FC04D40226C787AA5BC3A407B14AE47E1C24D4005A3923A01AD3A40AE47E17A14C64D4011C7BAB88DA63A403B70CE88D2C64D4057EC2FBB279F3A4065AA605452C74D4088635DDC46933A4058CA32C4B1C64D408048BF7D1D883A40F7E461A1D6C44D40772D211FF47C3A40787AA52C43C44D40BEC1172653753A40CCEEC9C342C54D404F401361C3633A40E4141DC9E5C74D4017D9CEF753433A4097FF907EFBCA4D404ED1915CFE133A40BE9F1A2FDDCC4D4044696FF085093A4001DE02098ACF4D407B14AE47E1FA394054742497FFD04D403A234A7B83DF394086C954C1A8CC4D40F6285C8FC2C53940B6F3FDD478D14D40BBB88D06F0B639405BD3BCE314D54D4091ED7C3F35AE3940B1E1E995B2D44D4054E3A59BC4B03940744694F606CF4D408351499D80B639404F401361C3CB4D4031992A1895B439407E8CB96B09C94D40B5A679C729AA39402B1895D409C84D406C787AA52CA33940E3C798BB96C84D40D3BCE3141D993940A323B9FC87CC4D40431CEBE2367A39404E62105839D44D40B003E78C287D394055C1A8A44EC84D402E90A0F8316639401FF46C567DBE4D40CE88D2DEE05B394074B515FBCBBE4D40C4B12E6EA3513940B9FC87F4DBBF4D400E4FAF9465483940C58F31772DC14D408A8EE4F21F42394036CD3B4ED1C14D409D8026C28627394040A4DFBE0EC44D40B30C71AC8B1B39404F401361C3C34D40772D211FF40C394044696FF085C14D404260E5D022EB384070CE88D2DEC04D40DE02098A1FE338406DE7FBA9F1C24D40211FF46C56DD3840933A014D84C54D40B29DEFA7C6CB3840B81E85EB51C84D402E90A0F831C638402B8716D9CEC74D40295C8FC2F5C838407C61325530C24D407E8CB96B09B938407DD0B359F5B94D4061C3D32B6589384098DD938785BA4D402CD49AE61D673840B1E1E995B2BC4D40CF66D5E76A5B384014AE47E17ABC4D40A3923A014D543840B3EA73B515BB4D40A60A46257552384090A0F831E6B64D4086C954C1A8343840158C4AEA04AC4D40E6AE25E4832E384007F0164850AC4D40D9CEF753E32538404A7B832F4CAE4D401DC9E53FA41F38409C33A2B437B04D40D044D8F0F41A384036AB3E575BB14D406D567DAEB61238405F07CE1951B24D4097900F7A360B3840EEEBC03923B24D40BC74931804063840499D8026C2AE4D4082734694F6063840F7E461A1D6A44D401DC9E53FA4EF374050FC1873D7A24D406744696FF0E53740ED9E3C2CD4A24D4041F163CC5DDB3740F8C264AA60A44D404D840D4FAFD437405BD3BCE314A54D405F29CB10C7BA3740A52C431CEBA24D403FC6DCB584BC37408E06F01648A04D40AE47E17A14BE374001DE02098A9F4D403D2CD49AE6BD374091ED7C3F359E4D404703780B24B83740CC7F48BF7D9D4D405BD3BCE3149D3740F46C567DAE9E4D4036AB3E575B813740CC5D4BC8079D4D40107A36AB3E7737408BFD65F7E4994D40EC51B81E857B374096218E75718B4D40E63FA4DFBE6E37403A92CB7F48874D40454772F90F6937400B46257502824D40E09C11A5BD7137401E166A4DF37E4D40BADA8AFD65773740925CFE43FA7D4D406B9A779CA2833740840D4FAF947D4D40917EFB3A708E3740917EFB3A707E4D40CF66D5E76A9B374044FAEDEBC0814D40FED478E926913740E8D9ACFA5C854D408104C58F319737403FC6DCB584844D408FE4F21FD29F3740F0A7C64B37814D40FB5C6DC5FEA23740143FC6DCB57C4D404CA60A46259537405396218E75794D4052B81E85EB813740D5E76A2BF6774D40B5A679C7297A37400000000000784D4039D6C56D34703740B81E85EB51784D402575029A086B37405C2041F163744D4096B20C71AC7B374048E17A14AE674D400F0BB5A679973740BE9F1A2FDD644D40956588635DCC37403C4ED1915C664D404F401361C3D33740CCEEC9C342654D40053411363CDD37406DE7FBA9F1624D407D3F355EBAB9374065AA6054525F4D402BF697DD939737402041F163CC5D4D40A857CA32C4913740A01A2FDD245E4D407E8CB96B098937401E166A4DF35E4D40022B8716D97E3740A913D044D8584D408E06F01648803740FD87F4DBD7494D4011363CBD52863740ACADD85F76474D4072F90FE9B79F37406C787AA52C434D4076711B0DE0AD37405839B4C876364D4098DD938785BA3740ACADD85F762F4D4082E2C798BBC637405BB1BFEC9E2C4D40166A4DF38ED3374013F241CF662D4D40FDF675E09CF137407DAEB6627F294D40613255302AF93740D50968226C284D4091ED7C3F35FE3740BADA8AFD65274D40FBCBEEC9C3023840F54A598638264D40CB10C7BAB80D38407B832F4CA6224D4098DD9387851A3840A01A2FDD241E4D40925CFE43FA3D3840508D976E12234D40D26F5F07CE4938409CC420B072284D405C8FC2F5284C3840BE30992A182D4D404D840D4FAF5438407DAEB6627F314D4097FF907EFB5A3840C3D32B6519324D404ED1915CFE6338406EA301BC05324D4096218E75716B38407DAEB6627F314D4068226C787A753840D50968226C304D406ADE718A8E84384077BE9F1A2F2D4D40CBA145B6F38D38406F8104C58F294D40E63FA4DFBE8E3840F5DBD78173264D400D71AC8BDB883840EBE2361AC0234D4024B9FC87F47B384038F8C264AA204D400EBE30992A783840029A081B9E1E4D40A4703D0AD7733840BADA8AFD65174D40B81E85EB5178384088F4DBD781134D40F1F44A598678384055C1A8A44E104D404D158C4AEA743840E09C11A5BD094D40AE47E17A146E384021B0726891054D402B1895D409683840A52C431CEB024D40A4703D0AD7633840E3361AC05B004D40B29DEFA7C65B38407AA52C431CFB4C409D8026C28657384073D712F241F74C40713D0AD7A35038408F53742497EF4C40448B6CE7FB493840A69BC420B0EA4C40B5A679C7294A38403D0AD7A370E54C40AEB6627FD94D3840D93D7958A8DD4C404D840D4FAF54384044696FF085D94C40789CA223B95C384027C286A757CA4C40F085C954C1683840857CD0B359AD4C400BB5A679C7693840C139234A7BA34C400F0BB5A679673840AA60545227A04C4001DE02098A5F384079E92631089C4C40613255302A3938402A3A92CB7F904C409D11A5BDC127384014AE47E17A8C4C405C8FC2F528FC374050FC1873D7824C406ADE718A8EF437405396218E75814C401DC9E53FA4DF3740D8817346947E4C40EFC9C342ADD937406744696FF07D4C40613255302AC9374087A757CA327C4C40F9A067B3EAC33740C05B2041F17B4C409D11A5BDC1A737406B9A779CA27B4C40832F4CA60A96374069006F81047D4C40F085C954C1583740BBB88D06F0864C40A4703D0AD7533740D5E76A2BF6874C40E71DA7E8484E3740FE65F7E461894C406F8104C58F413740B0726891ED8C4C40C7293A92CB3F3740F46C567DAE8E4C40CB10C7BAB83D3740E86A2BF697954C407B14AE47E12A37402B1895D409A84C403411363CBD223740CA54C1A8A4AE4C4098DD9387851A374048E17A14AEAF4C4013F241CF661537401CEBE2361AB04C4094F6065F980C37400C93A98251B14C406B9A779CA2033740C217265305B34C40C5FEB27BF2F036409031772D21B74C409D11A5BDC1E73640E0BE0E9C33BA4C408E06F01648C036403FC6DCB584C44C40075F984C15AC3640705F07CE19C94C404D840D4FAFA43640A52C431CEBCA4C409C33A2B437983640FDF675E09CD14C402FDD240681953640D578E92631D84C4041F163CC5D9B364080B74082E2DF4C40DA1B7C61329536409B559FABADE04C4042CF66D5E77A3640910F7A36ABDE4C402B1895D4094836401DC9E53FA4D74C40613255302A1936409A779CA223D14C4094F6065F980C3640BBB88D06F0CE4C4030BB270F0B05364077BE9F1A2FCD4C40E7FBA9F1D2FD3540B1BFEC9E3CCC4C40B1BFEC9E3CEC3540CFF753E3A5CB4C40F46C567DAEE63540EB73B515FBCB4C4097900F7A36DB3540787AA52C43CC4C40E17A14AE47C13540DFE00B93A9CA4C40B4C876BE9FBA35406F8104C58FC94C404D840D4FAFB435401DC9E53FA4C74C4093A9825149AD3540CEAACFD556C44C40EE7C3F355EAA3540EEEBC03923C24C40A4703D0AD7A33540D881734694BE4C40462575029A9835408D28ED0DBEB84C40EC51B81E857B35403411363CBDAA4C4013F241CF667535401B0DE02D90A84C4057EC2FBB276F3540ADFA5C6DC5A64C40ED9E3C2CD46A3540075F984C15A44C409A999999996935403BDF4F8D979E4C400C93A98251693540ABCFD556EC8F4C4027C286A7576A3540EB73B515FB8B4C40EE7C3F355E6A3540D42B6519E2884C407FFB3A70CE68354011363CBD52864C40F7065F984C653540CF66D5E76A834C40083D9B559F5B354003098A1F637E4C40A52C431CEB423540D95F764F1E764C40EF3845477239354040A4DFBE0E744C4085EB51B81E25354072F90FE9B76F4C40728A8EE4F20F35404F401361C36B4C40E8D9ACFA5C0D3540713D0AD7A3684C40C74B378941103540075F984C15644C40713D0AD7A31035408126C286A75F4C403C4ED1915C0E35402A3A92CB7F584C40226C787AA50C35405917B7D100564C40B5A679C7290A3540A60A462575524C409FCDAACFD5063540029A081B9E4E4C4074B515FBCB0E354036AB3E575B414C406F8104C58F11354039B4C876BE374C4041F163CC5D0B3540DFE00B93A93A4C409FABADD85F063540E2E995B20C414C40567DAEB662FF34408C4AEA0434414C40D5E76A2BF6F73440AD69DE718A2E4C40B7627FD93DF93440F5DBD781731E4C40022B8716D9FE3440BA6B09F9A0174C40FAEDEBC0390335406744696FF0154C409F3C2CD49A063540EB73B515FB134C40D044D8F0F40A3540F38E5374240F4C40053411363C0D35408BFD65F7E4094C4057EC2FBB270F3540BA490C022B074C40371AC05B2011354020D26F5F07FE4B408FC2F5285C0F354044FAEDEBC0F94B4041F163CC5D0B3540865AD3BCE3F44B4096218E75710B354054742497FFF04B40575BB1BFEC0E354040A4DFBE0EE44B4088855AD3BC1335402041F163CCDD4B40234A7B832F1C35406C09F9A067DB4B404D158C4AEA24354082E2C798BBD64B40C3F5285C8F3235409A99999999C94B40575BB1BFEC3E35404F401361C3BB4B40448B6CE7FB493540E9263108ACA44B40166A4DF38E4335401D5A643BDF9F4B402E90A0F831463540228E75711B9D4B4033333333334335408BFD65F7E4994B40D1915CFE433A35406F8104C58F994B40A913D044D8303540174850FC18934B400F0BB5A67937354051DA1B7C61824B4027C286A7573A35405839B4C8767E4B40E63FA4DFBE3E35406E3480B7407A4B40295C8FC2F53835403A92CB7F48774B402B8716D9CE1735407B832F4CA6724B40B22E6EA301FC34400AD7A3703D724B4005C58F3177DD3440EC51B81E85734B40EC2FBB270FCB3440B81E85EB51784B4036AB3E575BC1344062105839B4784B4048E17A14AEA73440713D0AD7A3784B40D95F764F1E96344055C1A8A44E784B40C898BB96908F34409C33A2B437784B408048BF7D1D88344096218E75717B4B40DAACFA5C6D953440645DDC4603804B401A51DA1B7CA1344009F9A067B3824B405C8FC2F528AC34405AF5B9DA8A854B40BEC1172653B53440711B0DE02D884B402B8716D9CED734407AA52C431C934B401904560E2DE23440C58F31772D994B40F1F44A5986E83440A1D634EF389D4B40CE1951DA1BFC34406C09F9A067A34B4024B9FC87F40B3540E0BE0E9C33AA4B40EE7C3F355E1A354012143FC6DCB54B4054E3A59BC42035409A99999999C94B406DC5FEB27B223540545227A089D04B40C7BAB88D062035408195438B6CD74B40789CA223B91C35408B6CE7FBA9D94B40643BDF4F8D17354008AC1C5A64DB4B40F1F44A5986183540F5B9DA8AFDD54B40F1F44A598618354040A4DFBE0ECC4B400E2DB29DEF17354085EB51B81EC54B402EFF21FDF6153540FE43FAEDEBC04B40C6DCB5847C10354036CD3B4ED1B94B407AA52C431C0B3540CEAACFD556B44B40713D0AD7A300354085EB51B81EAD4B404182E2C798FB344027C286A757AA4B4036AB3E575BF13440A2B437F8C2A44B403A234A7B83EF34404F401361C3A34B40DB8AFD65F7E43440C976BE9F1A9F4B40F9A067B3EAD334401DC9E53FA4974B401895D40968B234400B462575028A4B405C8FC2F528AC34400000000000884B400000000000A03440EA95B20C71844B4026E4839ECD9A3440C217265305834B40EA95B20C718C344001DE02098A7F4B40234A7B832F6C344045D8F0F44A794B40226C787AA55C34401B0DE02D90784B4064CC5D4BC80734409A779CA223794B405AF5B9DA8AFD334098DD9387857A4B40C5FEB27BF2F03340499D8026C2764B40053411363CED3340174850FC18734B40780B24287EEC334091ED7C3F356E4B40C6DCB5847CF03340DE02098A1F6B4B40A1F831E6AEF53340F54A598638664B402FDD240681F53340FD87F4DBD7614B403F355EBA49EC334097900F7A365B4B402E90A0F831E63340711B0DE02D584B401D38674469DF3340A7E8482EFF514B409F3C2CD49AE63340D50968226C504B4004E78C28EDED3340713D0AD7A3504B40DCD7817346F43340E02D90A0F8514B4019E25817B7F13340F8C264AA60544B40BC74931804F63340D49AE61DA7584B405E4BC8073DFB3340FCA9F1D24D5A4B40A60A46257502344095D40968225C4B406F8104C58F3134408C4AEA0434594B4068B3EA73B5653440CAC342AD69564B40022B8716D93E3440AED85F764F4E4B409D8026C286373440A1D634EF384D4B400AD7A3703D1A34407DAEB6627F494B40B7627FD93D093440711B0DE02D484B40FB3A70CE88D233405D6DC5FEB23B4B4079E9263108CC3340645DDC4603384B40F46C567DAEB63340454772F90F314B40C5FEB27BF2A03340BE9F1A2FDD2C4B401904560E2D923340BF7D1D38672C4B40265305A3927A33409C33A2B437284B40AC8BDB68005F334051DA1B7C61224B40A913D044D8403340A4DFBE0E9C234B40B3EA73B5153B33406D567DAEB62A4B409E5E29CB10473340A3923A014D2C4B408E06F0164860334085EB51B81E2D4B40637FD93D796833406744696FF02D4B4076E09C11A56D3340499D8026C22E4B40BC0512143F8633405F29CB10C7324B4036AB3E575BA13340C66D3480B7384B40F46C567DAEA6334027C286A7573A4B4018265305A3B23340E8D9ACFA5C3D4B4036AB3E575BC1334052B81E85EB414B406ADE718A8ED433400C93A98251494B40D95F764F1ED63340CDCCCCCCCC4C4B4068226C787AC533403D2CD49AE6454B407A36AB3E57BB3340C3F5285C8F424B4017B7D100DEB23340E4141DC9E53F4B40E3C798BB96A033404F1E166A4D3B4B4005A3923A019D3340E0BE0E9C333A4B4055302AA91390334090A0F831E6364B40A3923A014D843340CE1951DA1B344B401B0DE02D90703340FE65F7E461314B407B14AE47E16A3340C66D3480B7304B40569FABADD85F33408FE4F21FD22F4B406666666666363340F7065F984C2D4B40166A4DF38E233340F8C264AA602C4B40CE88D2DEE00B33407958A835CD2B4B404B598638D60533407958A835CD2B4B40637FD93D79F83240C0EC9E3C2C2C4B4087A757CA32F4324069006F81042D4B40764F1E166AED3240CEAACFD5562C4B4086C954C1A8E43240B1BFEC9E3C2C4B40645DDC4603D83240CC5D4BC8072D4B40A7E8482EFFC13240ACADD85F762F4B40BEC1172653B5324054742497FF304B404694F6065F983240499D8026C2364B40C217265305933240FED478E926394B40C442AD69DE91324069006F81043D4B40A9A44E40139132401FF46C567D464B40B1BFEC9E3C8C3240FAEDEBC0394B4B40A52C431CEB823240B84082E2C7504B4080B74082E2773240A1D634EF38554B40D5E76A2BF66732401FF46C567D5E4B409D8026C286773240AED85F764F5E4B40EBE2361AC07B3240613255302A614B4054742497FF8032409CC420B072604B401895D4096892324013F241CF665D4B40696FF085C9B43240B84082E2C7584B40B3EA73B515BB32403B70CE88D2564B408CDB68006FC13240EB73B515FB534B40D93D7958A8C53240705F07CE19514B40B7627FD93DC932401EA7E8482E4F4B401904560E2DD232404ED1915CFE4B4B401361C3D32BD53240736891ED7C4F4B4011363CBD52C63240F7065F984C554B403D0AD7A370BD324072F90FE9B7574B4087A757CA32B43240C4B12E6EA3594B40B3EA73B5159B324003780B24285E4B40158C4AEA04943240107A36AB3E5F4B406688635DDC863240FE65F7E461614B40C217265305733240E9B7AF03E7644B40E5F21FD26F5F3240FF21FDF675684B40832F4CA60A5632405EBA490C026B4B40B3EA73B5150B3240986E1283C06A4B405E4BC8073DEB314036CD3B4ED1694B404BC8073D9BE531405396218E75694B40BC0512143FC6314048BF7D1D38674B40006F8104C5AF314005A3923A01654B4064CC5D4BC8973140C3F5285C8F624B402B8716D9CE8731406F8104C58F614B4057EC2FBB276F3140E3361AC05B604B40789CA223B95C314072F90FE9B75F4B408E06F0164840314068226C787A5D4B40A913D044D810314091ED7C3F35564B40D34D6210580931406ADE718A8E544B404D158C4AEA0431407B14AE47E1524B40C6DCB5847CF03040CC7F48BF7D4D4B405F07CE1951EA30403FC6DCB5844C4B408A1F63EE5AE23040C139234A7B4B4B40B3EA73B515CB3040B6847CD0B3494B40F90FE9B7AFB330407FFB3A70CE484B405F07CE1951AA30401B0DE02D90484B40C139234A7BA330404703780B24484B408A1F63EE5A923040AD69DE718A464B40091B9E5E298B3040CB10C7BAB8454B404ED1915CFE833040F8C264AA60444B405A643BDF4F7D3040B537F8C264424B40D9CEF753E3753040D5E76A2BF63F4B402A3A92CB7F683040508D976E123B4B401A51DA1B7C51304099BB96900F324B40E78C28ED0D4E30408126C286A72F4B406A4DF38E535430403D9B559FAB2D4B409D11A5BDC1373040C217265305234B40789CA223B92C3040C58F31772D214B40BE9F1A2FDD2430404694F6065F204B40F7E461A1D6143040B8AF03E78C204B40B1BFEC9E3C0C30407DAEB6627F214B40C66D3480B7C02F4001DE02098A1F4B40CB10C7BAB8AD2F4090A0F831E61E4B4066F7E461A1962F4014D044D8F01C4B405305A3923A812F40CF66D5E76A1B4B401E166A4DF34E2F40E25817B7D1184B40E9263108ACFC2E405A643BDF4F154B4007CE1951DADB2E40A2B437F8C2144B4009F9A067B3CA2E406ADE718A8E144B40DA1B7C6132B52E40158C4AEA04144B402575029A089B2E40A52C431CEB124B40DBF97E6ABC742E401B0DE02D90104B404BC8073D9BF52D409A779CA223094B408CB96B09F9A02D4069006F8104054B40E86A2BF6977D2D403255302AA9034B40CAC342AD691E2D4069006F8104FD4A40E0BE0E9C33022D405F29CB10C7FA4A404850FC1873F72C40B7627FD93DF94A40D26F5F07CED92C40AE47E17A14F64A40C66D3480B7C02C40A2B437F8C2F44A40DE9387855AB32C406ADE718A8EF44A40F241CF66D5A72C40CDCCCCCCCCF44A40728A8EE4F27F2C404BEA043411F64A40FA7E6ABC74732C406688635DDCF64A403D9B559FAB6D2C408FC2F5285CF74A4091ED7C3F355E2C409A779CA223F94A40F6285C8FC2352C408FE4F21FD2FF4A4038F8C264AA002C404694F6065F084B40E71DA7E848EE2B409A081B9E5E094B4040A4DFBE0EDC2B407C613255300A4B409BE61DA7E8C82B40166A4DF38E0B4B4096218E7571BB2B40D93D7958A80D4B400F0BB5A679A72B406E3480B740124B40AA60545227A02B40211FF46C56154B40B6F3FDD478892B40A1D634EF38154B40F931E6AE25842B4017B7D100DE124B409031772D219F2B404C378941600D4B40E78C28ED0D9E2B40613255302A094B40304CA60A46A52B40B1E1E995B2044B402B8716D9CEB72B40ABCFD556ECFF4A4005A3923A01CD2B4044696FF085014B40D881734694D62B40DC4603780B044B409A779CA223D92B40643BDF4F8D074B402A3A92CB7FE82B409D11A5BDC1074B4068B3EA73B5F52B40107A36AB3E074B405227A089B0012C40151DC9E53F044B40BA490C022B072C4036CD3B4ED1014B40D50968226C182C40FFB27BF2B0004B40E9263108AC1C2C404BEA043411FE4A40DCD7817346142C40D49AE61DA7F84A408351499D80062C4044696FF085F94A409FCDAACFD5F62B4097900F7A36FB4A40984C158C4AEA2B40FF21FDF675F84A40A9A44E4013E12B40228E75711BF54A40EBE2361AC0DB2B407B14AE47E1F24A405839B4C876BE2B40AAF1D24D62F04A40B8AF03E78CA82B40BC74931804EE4A40304CA60A46C52B404FAF946588EB4A40F5B9DA8AFDE52B40EB73B515FBEB4A40AA60545227002C40CC5D4BC807ED4A40C0EC9E3C2C142C40C8073D9B55EF4A400AD7A3703D2A2C4064CC5D4BC8EF4A40508D976E12632C400000000000F04A4055302AA913702C40AC1C5A643BEF4A4063EE5A423E882C40174850FC18EB4A4014AE47E17A942C400C93A98251E94A40598638D6C5AD2C40744694F606E74A40C1A8A44E40D32C404BEA043411EE4A40A301BC0512F42C40910F7A36ABEE4A4008AC1C5A641B2D402041F163CCED4A409A081B9E5E292D4014AE47E17AEC4A400A68226C783A2D402A3A92CB7FE84A40E86A2BF6973D2D40984C158C4AE24A40B22E6EA3011C2D406688635DDCD64A40CDCCCCCCCC0C2D406ADE718A8ED44A4035EF384547D22C40C976BE9F1AD74A4096218E7571BB2C4044696FF085D94A40228E75711B8D2C401A51DA1B7CD94A4088635DDC46832C407E1D386744D94A4018265305A3722C40B6847CD0B3D94A407AA52C431CEB2B403333333333E34A40CBA145B6F39D2B40764F1E166AED4A40BF7D1D3867A42B40711B0DE02DF04A402575029A08BB2B4088855AD3BCF34A407FFB3A70CEA82B402063EE5A42FE4A406DC5FEB27B922B406666666666064B400C022B8716992B405F29CB10C70A4B40F9A067B3EA932B40832F4CA60A0E4B405DFE43FAED6B2B4012143FC6DC154B402B8716D9CEF72A403A92CB7F480F4B40569FABADD8FF2A40C2172653050B4B4061C3D32B65F92A40D0D556EC2F0B4B40F085C954C1E82A408638D6C56D0C4B4052499D8026C22A4011363CBD52164B40E02D90A0F8912A404BEA0434111E4B40CDCCCCCCCC6C2A4063EE5A423E204B40789CA223B93C2A4008AC1C5A64234B40B1BFEC9E3C0C2A40091B9E5E29334B409D8026C28687294048E17A14AE2F4B40E86A2BF6975D29404703780B24304B40569FABADD83F294072F90FE9B72F4B40B515FBCBEE292940F38E5374242F4B40EF38454772192940A01A2FDD242E4B40849ECDAACFF528406DC5FEB27B2A4B4066F7E461A1D6284079E9263108244B400C022B8716D928409A779CA223214B4048BF7D1D38E72840D5E76A2BF61F4B4054742497FFD02840F241CF66D51F4B40AB3E575BB1BF2840363CBD5296214B405E4BC8073DBB28409E5E29CB10274B400B46257502DA284048E17A14AE2F4B40933A014D84ED2840265305A392324B400C93A982514929403EE8D9ACFA344B40BADA8AFD65572940F775E09C11354B407FD93D7958682940AC8BDB6800374B40857CD0B3597529402E90A0F831364B40151DC9E53F842940BE9F1A2FDD344B40894160E5D0C22940789CA223B9344B402B8716D9CED729401F85EB51B8364B40B003E78C28CD29400DE02D90A0384B40ECC039234A9B2940713D0AD7A3384B40CD3B4ED1915C2940C5FEB27BF2384B408A8EE4F21F522940705F07CE19394B4082734694F6462940B6F3FDD478394B40DBF97E6ABC1429403411363CBD3A4B40598638D6C50D2940B1E1E995B23C4B404BEA043411F628400D71AC8BDB384B40AB3E575BB1DF284099BB96900F324B40A5BDC11726B32840D712F241CF264B408195438B6CA72840B1E1E995B2244B401895D40968422840FF21FDF675184B4015719DF311002840B9D89BB567164B40D42B6519E2F82740CA32C4B12E164B401F85EB51B87E2740158C4AEA04144B400000000000602740083D9B559F134B401B0DE02D9040274058A835CD3B0E4B40C66D3480B74027405F984C158C0A4B4065AA6054522727405C2041F163044B40B22E6EA3011C274096218E7571034B402D431CEBE2162740A54E401361034B40B37BF2B0500B2740787AA52C43044B4007F0164850FC2640014D840D4FFF4A4067D5E76A2BF62640A52C431CEBFA4A40DE9387855AD32640CB10C7BAB8F54A40E25817B7D18026400EBE30992AF84A40CBA145B6F35D264044696FF085014B40C6DCB5847C302640E09C11A5BD014B40956588635D1C2640C5FEB27BF2004B402DB29DEFA7062640BADA8AFD65FF4A4004E78C28EDED254068226C787AFD4A403B014D840DCF2540FB3A70CE88FA4A40B1BFEC9E3CCC25401EA7E8482EF74A40508D976E12A32540B515FBCBEEF14A40B5A679C729BA2540E8D9ACFA5CF54A40D656EC2FBBC7254052499D8026FA4A40A60A462575C225402E90A0F831FE4A407E8CB96B09B9254048BF7D1D38FF4A40DB8AFD65F7A4254073D712F241FF4A402C6519E2589725408195438B6CFF4A4076711B0DE08D25400D71AC8BDB004B4016FBCBEEC983254031992A1895044B40BC0512143F862540AC1C5A643B074B405F07CE19519A254096218E75710B4B4060764F1E16AA2540083D9B559F0B4B40894160E5D0C22540083D9B559F0B4B401F85EB51B81E2640F46C567DAE164B40E3A59BC420302640FCA9F1D24D1A4B404B598638D6252640711B0DE02D304B402D431CEBE216264065AA6054522F4B40D49AE61DA70826408FC2F5285C2F4B4045D8F0F44AF92540F2B0506B9A2F4B407B832F4CA6EA25408D976E1283304B40A167B3EA73F52540C66D3480B7304B40F853E3A59BE42540F0A7C64B37314B40304CA60A46C5254011363CBD522E4B4005000000053411363CFD2540B81E85EB51304B409CC420B072082640545227A089304B403D0AD7A370FD2540D42B6519E2304B40A167B3EA73F52540C66D3480B7304B40053411363CFD2540B81E85EB51304B4030000000CAC342AD69FE27405D6DC5FEB2734B407B14AE47E1BA274045D8F0F44A794B4088F4DBD781B32740431CEBE2367A4B40E10B93A982B1274051DA1B7C617A4B40D49AE61DA7882740174850FC187B4B4091ED7C3F357E27407B14AE47E17A4B402D431CEBE2762740B5A679C7297A4B4079E92631086C27409C33A2B437784B408BFD65F7E4812740A1F831E6AE6D4B401C7C61325590274086C954C1A86C4B40A089B0E1E995274032772D211F6C4B40265305A3929A2740CF66D5E76A6B4B4011363CBD52B62740174850FC18634B405C2041F163CC2740EB73B515FB5B4B40D49AE61DA7C82740E09C11A5BD594B408CDB68006FC127402B1895D409584B405917B7D100BE274013F241CF66554B405839B4C876BE2740A54E401361534B40D1915CFE43DA2740A8C64B3789494B40F90FE9B7AFE32740713D0AD7A3484B40AC8BDB6800EF2740ABCFD556EC474B4091392221C2F227406FAF16E98F484B406891ED7C3FF52740F0164850FC484B40DE718A8EE4F22740DE02098A1F4B4B403A234A7B83EF27405917B7D1004E4B40228E75711BED2740462575029A504B40EC51B81E85EB2740A52C431CEB524B4026E4839ECDEA2740E9B7AF03E7544B40780B24287EEC2740C976BE9F1A574B40C6DCB5847CF0274044696FF085594B40BC96900F7AF62740A52C431CEB5A4B4007F01648501C2840091B9E5E29634B40F5DBD78173262840CDCCCCCCCC644B40E8D9ACFA5C2D2840840D4FAF94654B402D431CEBE23628403C4ED1915C664B4093A98251493D284048BF7D1D38674B40E2E995B20C51284099BB96900F6A4B40D95F764F1E562840FAEDEBC0396B4B40F5B9DA8AFD4528401EA7E8482E6F4B40E9482EFF213D2840FE43FAEDEB704B407F6ABC74933828409A99999999714B40FB5C6DC5FE322840C3D32B6519724B407C613255302A284051DA1B7C61724B409031772D211F284099BB96900F724B4088F4DBD7811328400AD7A3703D724B400AD7A3703D0A28403411363CBD724B40DC31AF0200002840922F780392734B40CAC342AD69FE27405D6DC5FEB2734B40360000009A081B9E5EC92640C976BE9F1A6F4B40D42B6519E2B826409A779CA223714B40C286A757CAB22640DF4F8D976E724B40EA95B20C71AC264030BB270F0B754B40A4DFBE0E9C93264062105839B4784B40228E75711B8D26406F8104C58F794B40D0B359F5B97A264098DD9387857A4B4017B7D100DE6226405F984C158C7A4B40C0EC9E3C2C542640A60A4625757A4B404B598638D6452640C3D32B65197A4B4022FDF675E03C2640B6847CD0B3794B406ADE718A8E242640D50968226C784B40E86A2BF6971D264048E17A14AE774B404BC8073D9B152640A01A2FDD24764B40E561A1D6340F2640CDCCCCCCCC744B40BA490C022B0726405F07CE1951724B40772D211FF40C264063EE5A423E684B40ED9E3C2CD4FA2540A1D634EF38654B4023DBF97E6AFC254014AE47E17A644B4060764F1E160A2640C4B12E6EA3614B4074B515FBCB0E2640C5FEB27BF2604B40C286A757CA322640D7A3703D0A5F4B4055C1A8A44E402640499D8026C25E4B40B515FBCBEE49264011363CBD525E4B40A8C64B378961264022FDF675E05C4B40AEB6627FD97D2640174850FC185B4B40BC0512143F86264098DD9387855A4B4003098A1F638E26406F8104C58F594B408CDB68006FA126405839B4C876564B40DE9387855AB32640CEAACFD556544B40371AC05B20C126405F29CB10C7524B409BE61DA7E8E826402C6519E2584F4B401C7C613255F0264001DE02098A4F4B40EC51B81E850B2740B5A679C729524B4026E4839ECD2A2740B0726891ED544B409CA223B9FC472740CDCCCCCCCC544B4088635DDC4663274096438B6CE7534B4052B81E85EB7127407A36AB3E57534B407E8CB96B09792740ED0DBE3099524B403A234A7B838F2740E02D90A0F8514B40D044D8F0F4AA27405BB1BFEC9E544B4000917EFB3AB02740A1F831E6AE554B402D431CEBE2B62740B9FC87F4DB574B4045D8F0F44AB92740014D840D4F5F4B40A857CA32C4B12740984C158C4A624B40637FD93D797827403D0AD7A3706D4B4026E4839ECD6A27402B1895D409704B401C7C6132555027405D6DC5FEB2734B407D3F355EBA4927404F401361C3734B40295C8FC2F5482740E3361AC05B704B408CB96B09F92027408C4AEA0434694B402CD49AE61D072740E0BE0E9C336A4B4036AB3E575BD1264066666666666E4B409A081B9E5EC92640C976BE9F1A6F4B4011000000B1BFEC9E3C8C26406D567DAEB63A4B400612143FC67C26403A92CB7F483F4B402D431CEBE2762640AAF1D24D62404B40AED85F764F5E2640C3F5285C8F424B40E0BE0E9C332226408638D6C56D444B40BC96900F7A16264027C286A757424B4058CA32C4B10E2640E3C798BB96404B4044696FF0850926403B70CE88D23E4B404B598638D6052640931804560E3D4B40DE02098A1F032640265305A3923A4B40F853E3A59B042640B7627FD93D394B40986E1283C00A26400EBE30992A384B40E5F21FD26F3F26406ABC749318344B40D734EF384547264040A4DFBE0E344B400000000000A02640787AA52C43344B40D0B359F5B99A264083C0CAA145364B40B1BFEC9E3C8C26406D567DAEB63A4B401D000000933A014D84AD25408D28ED0DBE784B408104C58F31B72540CE88D2DEE07B4B408CB96B09F9C0254012143FC6DC7D4B40A1F831E6AEC52540D8817346947E4B40091B9E5E29CB2540ABCFD556EC7F4B40A779C7293AD22540083D9B559F834B40789CA223B9DC2540B6847CD0B3894B40AB3E575BB1DF2540EC51B81E858B4B40EC2FBB270FEB2540098A1F63EE924B400F0BB5A679E725405C2041F163944B406FF085C954E12540F853E3A59B944B40CFF753E3A5DB2540CE1951DA1B944B40FDF675E09CD1254099BB96900F924B409E5E29CB10C725406744696FF08D4B4088F4DBD781B32540F6285C8FC2854B40B81E85EB519825408351499D807E4B407C613255308A2540A1D634EF387D4B4029ED0DBE3079254050FC1873D77A4B40DE02098A1F632540832F4CA60A764B40E9482EFF215D2540A3923A014D744B40598638D6C54D2540091B9E5E296B4B40560E2DB29D4F25401F85EB51B8664B409A999999995925402A3A92CB7F604B4071AC8BDB6860254066666666665E4B409F3C2CD49A662540228E75711B5D4B4004E78C28ED6D2540CDCCCCCCCC5C4B404FAF946588832540C139234A7B634B40DE718A8EE4922540158C4AEA046C4B40933A014D84AD25408D28ED0DBE784B403200000020D26F5F07CE2440091B9E5E29CB4B40508D976E12A32440CA54C1A8A4CE4B40F46C567DAE9624403B70CE88D2CE4B40CDCCCCCCCC6C244086C954C1A8CC4B408126C286A7572440DF4F8D976ECA4B40075F984C154C24407DAEB6627FC94B40569FABADD83F2440C66D3480B7C84B404B598638D625244073D712F241C74B40AA6054522700244093A9825149C54B402497FF907EDB2340363CBD5296C14B405B423EE8D9CC234029CB10C7BAC04B40736891ED7CBF2340B7D100DE02C14B40E4839ECDAAAF2340ED0DBE3099C24B4082734694F6A6234092CB7F48BFC54B403A92CB7F487F2340B003E78C28C54B40B22E6EA3015C2340BA6B09F9A0BF4B40DA1B7C6132752340B1E1E995B2BC4B407E1D3867448923404F401361C3BB4B409031772D219F2340E4141DC9E5B74B4011C7BAB88DC6234096218E7571AB4B40091B9E5E29CB234087A757CA32A44B4034A2B437F80224408D28ED0DBE984B4046B6F3FDD43824408195438B6C974B40598638D6C54D2440AA8251499D904B40304CA60A46452440AC8BDB68008F4B4058A835CD3B4E244026E4839ECD8A4B40C9E53FA4DFFE24405D6DC5FEB2834B40B515FBCBEE0925403255302AA9834B403B014D840D0F25404F401361C3834B40022B8716D92E25406519E25817874B403C4ED1915C3E2540006F8104C5874B40DBF97E6ABC5425409D11A5BDC1874B402AA913D044782540E3361AC05B884B40E2E995B20C91254080B74082E28F4B40AA8251499DA02540454772F90F994B407D3F355EBAA9254011363CBD52A64B40DBF97E6ABC942540917EFB3A70AE4B40E86A2BF6977D2540BBB88D06F0BE4B40FC1873D71252254097900F7A36CB4B4008AC1C5A643B2540A01A2FDD24CE4B405C8FC2F5283C2540711B0DE02DC84B400A68226C783A25406DC5FEB27BC24B40DA1B7C6132352540F54A598638BE4B40FB3A70CE88F22440645DDC4603B84B40B6847CD0B3D924408D976E1283B84B4046B6F3FDD4D824404F1E166A4DBB4B402F6EA301BCE52440A167B3EA73BD4B406DC5FEB27BF2244052B81E85EBC14B400EBE30992AF824405C8FC2F528C44B4020D26F5F07CE2440091B9E5E29CB4B40090000000B462575025A254082734694F67E4B40371AC05B20412540840D4FAF94854B40FE43FAEDEB0025400D71AC8BDB804B4048E17A14AE072540840D4FAF947D4B407B14AE47E11A2540C520B07268794B408CDB68006F212540705F07CE19794B407B14AE47E13A2540C4B12E6EA3794B40BBB88D06F05625405BD3BCE3147D4B400B462575025A254082734694F67E4B4014000000CC5D4BC8071D254032772D211FFC4B40AED85F764F1E2540AED85F764FFE4B4003780B24281E2540BA6B09F9A0FF4B4066F7E461A1162540D734EF3845FF4B40075F984C150C2540AF25E4839EFD4B400E4FAF9465082540787AA52C43FC4B40F5B9DA8AFD052540D7A3703D0AE74B4058A835CD3B0E25400AD7A3703DE24B402D431CEBE2162540287E8CB96BE14B403D9B559FAB2D2540295C8FC2F5E04B40DE9387855A332540295C8FC2F5E04B4046B6F3FDD4382540B6F3FDD478E14B404CA60A4625552540BA6B09F9A0EF4B40A167B3EA735525406E3480B740F24B40DCD781734654254064CC5D4BC8EF4B40014D840D4F4F2540F46C567DAEEE4B40304CA60A46452540910F7A36ABEE4B40CAC342AD693E2540736891ED7CEF4B40744694F6061F25407DD0B359F5F94B40CC5D4BC8071D254032772D211FFC4B400A000000CC5D4BC8079D2440CA54C1A8A4764B40A779C7293A9224408FE4F21FD2774B408F537424975F2440789CA223B97C4B405227A089B0612440B4C876BE9F7A4B402041F163CC7D24408716D9CEF7734B40780B24287ECC24407DAEB6627F694B40C6DCB5847CD024409A779CA223694B40E86A2BF697DD244088855AD3BC6B4B405227A089B0E12440D8817346946E4B40CC5D4BC8079D2440CA54C1A8A4764B402400000052499D802682234026E4839ECD8A4B403F575BB1BF6C2340A60A4625758A4B406A4DF38E535423409B559FABAD884B40F31FD26F5F472340D6C56D3480874B4017D9CEF75343234083C0CAA145864B40B3EA73B5155B234052499D8026824B402F6EA301BC65234045D8F0F44A814B40560E2DB29D6F23408C4AEA0434814B4007CE1951DA7B23406F8104C58F814B401B9E5E29CB902340A7E8482EFF814B408126C286A79723408B6CE7FBA9814B40CDCCCCCCCCAC2340F853E3A59B7C4B401E166A4DF3AE2340B8AF03E78C784B40B8AF03E78CA823401B0DE02D90784B4033C4B12E6EA32340C58F31772D794B4055302AA91390234040A4DFBE0E7C4B40F697DD9387852340DAACFA5C6D7D4B40151DC9E53F842340EC2FBB270F7B4B40EC51B81E858B234090A0F831E6764B40A913D044D890234023DBF97E6A744B40EBE2361AC09B2340DE02098A1F734B407B832F4CA6CA23402BF697DD936F4B40CC5D4BC807DD23402063EE5A426E4B40EFC9C342ADE923404B598638D66D4B4030BB270F0BF523403D2CD49AE66D4B405F07CE1951FA2340AE47E17A146E4B40C139234A7B232440B9FC87F4DB6F4B4085EB51B81E2524406FF085C954714B4033C4B12E6E232440ED0DBE3099724B404BEA043411162440D578E92631784B401895D4096802244013F241CF667D4B400000000000002440A089B0E1E97D4B40E3A59BC420F023409A081B9E5E814B40F6285C8FC29523409A99999999894B4097900F7A368B234027C286A7578A4B4052499D802682234026E4839ECD8A4B4017000000F085C954C1D8384000917EFB3A405040832F4CA60AF638406F8104C58F4150400000000000003940D93D7958A8415040BC96900F7A0639404B598638D6415040B81E85EB51083940F5DBD78173425040F7E461A1D6043940C286A757CA425040D3BCE3141DD9384014D044D8F044504013F241CF66D53840CC5D4BC807455040AC8BDB6800CF384054742497FF4450406C09F9A067B33840CD3B4ED191445040E86A2BF697AD384071AC8BDB68445040BEC1172653A53840A301BC051244504074B515FBCB9E38405D6DC5FEB2435040AC8BDB68008F3840A01A2FDD24425040053411363C8D38409A99999999415040C520B07268913840BADA8AFD653F50402EFF21FDF69538400AD7A3703D3E50407DAEB6627F9938405227A089B03D504085EB51B81EB53840CD3B4ED1913C50407E8CB96B09B93840D50968226C3C50405C8FC2F528BC38409CC420B0723C5040A7E8482EFFD138405227A089B03D5040F085C954C1D8384000917EFB3A4050400F000000226C787AA58C374050FC1873D7FA4D40D26F5F07CE893740A52C431CEBFA4D40D9CEF753E38537405F29CB10C7FA4D4035EF3845476237408351499D80F64D403B70CE88D25E374069006F8104F54D40386744696F6037407958A835CDF34D4032772D211F643740A54E401361F34D4082734694F66637404F1E166A4DF34D40FD87F4DBD781374077BE9F1A2FF54D40F085C954C19837402063EE5A42F64D402041F163CC9D3740BC96900F7AF64D40BC0512143FA6374012143FC6DCF54D404D840D4FAFB43740F46C567DAEF64D407DD0B359F5B93740E561A1D634F74D40226C787AA58C374050FC1873D7FA4D401500000068226C787A6537408351499D80464D40D93D7958A8653740D5E76A2BF6474D40B9FC87F4DB573740355EBA490C524D408C4AEA04345137404F1E166A4D534D4068226C787A45374030BB270F0B554D408FC2F5285C3F37403D2CD49AE6554D40BADA8AFD65373740499D8026C2564D406C09F9A067333740BBB88D06F0564D4072F90FE9B72F374074B515FBCB564D40EA95B20C712C37408351499D80564D4011363CBD522637402041F163CC554D40F9A067B3EA233740A1D634EF38554D401B0DE02D90103740AC1C5A643B4F4D40C976BE9F1A0F3740AF946588634D4D40FE65F7E46111374096B20C71AC4B4D4065AA60545217374035EF3845474A4D402CD49AE61D373740B1BFEC9E3C444D40B5A679C7293A37407958A835CD434D403D0AD7A3705D3740A4703D0AD7434D408E06F01648603740B1BFEC9E3C444D4068226C787A6537408351499D80464D40160000001F85EB51B85E3740068195438B7C4D406F8104C58F6137403EE8D9ACFA7C4D40DDB5847CD063374058A835CD3B7E4D40F90FE9B7AF633740711B0DE02D804D40CB10C7BAB85D3740C1A8A44E40834D4060764F1E165A3740075F984C15844D4082734694F65637403FC6DCB584844D406C09F9A067533740CDCCCCCCCC844D408C4AEA04343137404C37894160854D40917EFB3A702E374013F241CF66854D409F3C2CD49A26374077BE9F1A2F854D4088635DDC46233740E9B7AF03E7844D40386744696F20374014AE47E17A844D405917B7D1001E3740C05B2041F1834D40B1E1E995B21C3740A69BC420B0824D4017D9CEF75323374069006F81047D4D40BC74931804263740C05B2041F17B4D40B5A679C7292A37405E4BC8073D7B4D4091ED7C3F352E3740C2172653057B4D4082734694F646374088635DDC467B4D40D0B359F5B94A3740CF66D5E76A7B4D401F85EB51B85E3740068195438B7C4D403900000057EC2FBB27DF364002BC0512144F4D40E25817B7D1D03640BBB88D06F04E4D401283C0CAA1C536403E7958A8354D4D40C7293A92CB9F3640C05B2041F14B4D40386744696F8036405BD3BCE3144D4D404F401361C353364052B81E85EB494D40FDF675E09C313640832F4CA60A464D40CDCCCCCCCC1C3640AC8BDB68003F4D4075029A081BFE35400B46257502424D40CDCCCCCCCCEC3540E0BE0E9C33424D409FABADD85FD635400D71AC8BDB404D4040A4DFBE0EDC35404B598638D63D4D4054E3A59BC4E03540B1E1E995B23C4D400C93A98251E93540508D976E123B4D4002BC051214FF3540E25817B7D1304D40A857CA32C4013640D93D7958A82D4D40BF0E9C33A2F4354096438B6CE72B4D408A8EE4F21FE23540C1CAA145B62B4D40D578E92631D8354069006F8104254D40545227A089E03540C5FEB27BF2204D4050FC1873D7123640D95F764F1E164D40FA7E6ABC74233640EA95B20C71144D405227A089B03136408AB0E1E995124D40F46C567DAE263640EC2FBB270F0B4D4025068195431B3640ED9E3C2CD40A4D40BC7493180416364052499D80260A4D405D6DC5FEB2FB35407958A835CDFB4C40C8073D9B55FF354092CB7F48BFF54C40C0EC9E3C2C04364086C954C1A8F44C40EFC9C342AD093640787AA52C43F44C40053411363C0D3640EA95B20C71F44C40FBCBEEC9C3123640211FF46C56F54C4061C3D32B65193640C976BE9F1AF74C40A4703D0AD7333640F54A598638FE4C406C09F9A0674336406ADE718A8E0C4D40A167B3EA734536404182E2C798134D409FCDAACFD5463640C976BE9F1A174D40ACADD85F765F3640CEAACFD5561C4D4097900F7A367B3640492EFF21FD1E4D406891ED7C3F853640AC8BDB68001F4D40DB8AFD65F79436406688635DDC1E4D40D95F764F1EA6364068226C787A1D4D40A857CA32C4C13640499D8026C21E4D4065AA6054520737402041F163CC2D4D40386744696F103740FE65F7E461314D40F90FE9B7AF1337403EE8D9ACFA344D4014AE47E17A243740B84082E2C7384D4088855AD3BC3337407E1D386744394D4027A089B0E139374080B74082E2374D406E3480B740423740744694F606374D40158C4AEA045437408C4AEA0434394D40D5E76A2BF6473740D8F0F44A593E4D4017D9CEF753433740D6C56D34803F4D40107A36AB3E17374063EE5A423E484D4095D4096822FC3640789CA223B94C4D407E1D386744E936401E166A4DF34E4D4057EC2FBB27DF364002BC0512144F4D4013000000569FABADD8CF36405C8FC2F528FC4D402041F163CCCD364044FAEDEBC0F94D40295C8FC2F5D83640A1F831E6AEF54D40FDF675E09CE136403255302AA9F34D407B14AE47E1EA364097FF907EFBF24D40933A014D84ED3640C217265305F34D408A8EE4F21FF23640C139234A7BF34D40BF0E9C33A20437403333333333F34D40EE7C3F355E0A374050FC1873D7F24D40E86A2BF6970D374017D9CEF753F34D40E0BE0E9C3312374044696FF085F94D4076711B0DE00D374009F9A067B3FA4D40C6DCB5847C0037406DE7FBA9F1FA4D4041F163CC5DFB364009F9A067B3FA4D40C66D3480B7F03640A60A462575FA4D409F3C2CD49AD63640A779C7293AFA4D406ADE718A8ED43640A54E401361FB4D4088855AD3BCD33640F853E3A59BFC4D40569FABADD8CF36405C8FC2F528FC4D404700000063EE5A423EB83640FF21FDF675804D406C09F9A067B3364052499D8026824D401895D40968B23640D95F764F1E864D406C09F9A067933640B84082E2C7884D40832F4CA60A8636407B14AE47E1824D4022FDF675E07C3640AC8BDB68007F4D402B8716D9CE77364077BE9F1A2F7D4D40E561A1D6346F36405D6DC5FEB27B4D40AA6054522760364036CD3B4ED1794D40FE43FAEDEB303640E17A14AE47794D40713D0AD7A310364027A089B0E1794D40E8D9ACFA5C0D36406F8104C58F794D407A36AB3E570B3640D49AE61DA7784D4094F6065F980C36403B014D840D774D408E06F01648103640F54A598638764D400AD7A3703D2A3640B5A679C729724D40E8D9ACFA5C2D36400BB5A679C7714D40D0B359F5B93A3640AAF1D24D62704D40022B8716D93E3640FF21FDF675704D402D431CEBE2463640FD87F4DBD7714D40832F4CA60A563640E0BE0E9C33724D40CA54C1A8A45E36407DD0B359F5714D4050FC1873D76236405305A3923A714D4048E17A14AE6736402BF697DD936F4D405BD3BCE3146D364068226C787A6D4D4018265305A372364050FC1873D76A4D407FFB3A70CE783640E09C11A5BD614D400E2DB29DEF7736402575029A085B4D407B14AE47E17A36406F8104C58F594D406ADE718A8E843640FF21FDF675584D408048BF7D1D8836404703780B24584D405C8FC2F5288C3640D5E76A2BF6574D40C66D3480B790364080B74082E2574D40637FD93D799836401CEBE2361A584D40C7293A92CB9F3640FF21FDF675584D4068226C787AA536400C93A98251594D40D0B359F5B9AA364035EF3845475A4D40933A014D84AD36407A36AB3E575B4D40FE43FAEDEBB03640D95F764F1E5E4D40166A4DF38EB336402A3A92CB7F604D40462575029AB8364040A4DFBE0E644D40E71DA7E848BE3640ADFA5C6DC5664D409F3C2CD49AC63640B84082E2C7684D403F355EBA49CC36409A99999999694D400000000000D036400BB5A679C7694D404FAF946588D33640FE43FAEDEB684D402041F163CCCD36406688635DDC664D40D044D8F0F4CA3640917EFB3A70664D409D11A5BDC1C73640933A014D84654D40645DDC4603C8364096B20C71AC634D40956588635DCC3640C217265305634D40A913D044D8D03640A52C431CEB624D40D3BCE3141DD93640C217265305634D40FBCBEEC9C3E2364007F0164850644D40DFE00B93A9E23640AA60545227684D4004E78C28EDDD3640C58F31772D694D401895D40968E23640B4C876BE9F6A4D40FBCBEEC9C3F23640075F984C156C4D402EFF21FDF6F536405C8FC2F5286C4D4036AB3E575B0137405F07CE19516A4D40BD5296218E0537407E8CB96B09694D404182E2C7980B3740EC2FBB270F6B4D4024287E8CB90B37403FC6DCB5846C4D40A4703D0AD7F336404F401361C37B4D405917B7D100EE3640F6285C8FC27D4D40956588635DDC36409C33A2B437804D402B8716D9CED736408E06F01648804D405BD3BCE314CD36401DC9E53FA47F4D40ABCFD556ECBF36400000000000804D40CE88D2DEE0BB3640711B0DE02D804D4063EE5A423EB83640FF21FDF675804D401200000060764F1E16EA36400D71AC8BDBE84F4042CF66D5E7EA3640984C158C4AEA4F40BE30992A18E53640AC1C5A643BEF4F40C520B07268E1364072F90FE9B7EF4F4076711B0DE0DD3640E4141DC9E5EF4F403C4ED1915CCE364063EE5A423EF04F404ED1915CFEB336407E8CB96B09E94F4003780B2428AE36403A92CB7F48E74F403FC6DCB584AC3640211FF46C56E54F404D158C4AEAB436405E4BC8073DE34F4039D6C56D34C036407958A835CDE34F40DA1B7C6132C53640F8C264AA60E44F40371AC05B20D1364096438B6CE7E34F4043AD69DE71DA364096218E7571E34F40E71DA7E848DE3640B4C876BE9FE24F400000000000E0364074B515FBCBE64F4035EF384547E236401CEBE2361AE84F4060764F1E16EA36400D71AC8BDBE84F4018000000DA1B7C6132A5364097FF907EFB1A4E40BC96900F7A96364052B81E85EB194E40D3BCE3141D7936405D6DC5FEB21B4E4085EB51B81E753640CEAACFD5561C4E408A1F63EE5A723640083D9B559F1B4E4003780B24286E3640D42B6519E2184E405F07CE19516A3640C139234A7B134E4094F6065F986C364012143FC6DC0D4E40462575029A7836405305A3923A094E40462575029A7836402B8716D9CEFF4D40FB5C6DC5FE8236409C33A2B437004E406666666666863640E3C798BB96004E40C3F5285C8F923640EC51B81E85034E40D26F5F07CE993640B81E85EB51004E4027A089B0E199364012143FC6DCFD4D408A8EE4F21FA23640462575029A004E4076711B0DE0AD3640D5E76A2BF6FF4D405E4BC8073DBB364055C1A8A44E004E406666666666D63640B22E6EA3010C4E40F0164850FCD83640BA6B09F9A00F4E40F6285C8FC2D53640E25817B7D1184E4089D2DEE00BD336405BD3BCE3141D4E40EC51B81E85BB36405C8FC2F5281C4E40DA1B7C6132A5364097FF907EFB1A4E401700000004E78C28ED3D364048BF7D1D38274E40F2B0506B9A37364001DE02098A274E404D840D4FAF3436402BF697DD93274E40462575029A183640BADA8AFD65274E40C3F5285C8F1236404C37894160254E406DC5FEB27B123640A7E8482EFF214E40A9A44E40132136408D28ED0DBE204E40F46C567DAE263640B84082E2C7204E40956588635D2C3640B6F3FDD478214E40AE47E17A142E3640A69BC420B0224E40BF0E9C33A22436404F401361C3234E40BD5296218E3536407B14AE47E1224E402B1895D40938364098DD938785224E409F3C2CD49A363640BBB88D06F01E4E40B3EA73B5153B3640AA60545227204E40F2D24D621048364099BB96900F224E40075F984C154C3640B5A679C729224E403A234A7B834F364052B81E85EB214E40A7E8482EFF51364044696FF085214E40BEC1172653553640E02D90A0F8214E400F0BB5A679573640CD3B4ED191244E40C520B072685136405AF5B9DA8A254E4004E78C28ED3D364048BF7D1D38274E401100000057EC2FBB27FF35406744696FF02D4E40575BB1BFECEE3540211FF46C56354E4068226C787AE53540D42B6519E2384E406B9A779CA2D335405D6DC5FEB23B4E40FE43FAEDEBD03540CE88D2DEE03B4E4003098A1F63CE354033333333333B4E40D42B6519E2C83540C4B12E6EA3314E407E8CB96B09C9354063EE5A423E304E40EC2FBB270FCB35408195438B6C2F4E40C4B12E6EA3D135404B598638D62D4E40BC0512143FE63540A52C431CEB2A4E4035EF384547F23540FD87F4DBD7294E40A1F831E6AEF535406F8104C58F294E40D42B6519E2F83540287E8CB96B294E404182E2C798FB35407DAEB6627F294E40C898BB9690FF354026E4839ECD2A4E4057EC2FBB27FF35406744696FF02D4E400E000000E8D9ACFA5CDD3540A4703D0AD7134E40C3F5285C8FE23540AED85F764F164E40E5F21FD26FDF35401DC9E53FA4174E405E4BC8073DDB3540545227A089184E404F401361C3D33540D50968226C184E40613255302AB93540B9FC87F4DB174E4011363CBD52B635401EA7E8482E174E406A4DF38E53B43540E8D9ACFA5C154E40C217265305B33540029A081B9E0E4E405F07CE1951BA3540D95F764F1E0E4E40053411363CBD3540AE47E17A140E4E4072F90FE9B7BF35402063EE5A420E4E40295C8FC2F5C8354048BF7D1D380F4E40E8D9ACFA5CDD3540A4703D0AD7134E4012000000A7E8482EFF213540462575029A984F406744696FF0253540D881734694964F405F07CE19512A3540F54A598638964F4005A3923A012D35406666666666964F401F85EB51B82E3540BADA8AFD65974F4054E3A59BC43035409C33A2B437984F40C7BAB88D064035404F401361C3934F40E561A1D6343F354098DD938785924F409D8026C28647354097FF907EFB924F4003780B24284E3540EC51B81E85934F40068195438B6C35409BE61DA7E8984F40D122DBF97E6A3540E4141DC9E59F4F40022B8716D95E3540E25817B7D1A04F40613255302A593540545227A089A04F40B29DEFA7C64B354090A0F831E69E4F40F853E3A59B1435402497FF907EA34F400C93A98251193540F54A5986389E4F40A7E8482EFF213540462575029A984F403B0000003C4ED1915CDE33403255302AA9334E4096218E7571DB33403255302AA9334E403333333333D33340E0BE0E9C33324E40575BB1BFECCE3340B81E85EB51304E402B1895D409C83340CC5D4BC8072D4E4067D5E76A2BD633400C022B8716294E403D0AD7A370DD3340B81E85EB51284E40560E2DB29DDF33400BB5A679C7294E4036AB3E575BE1334008AC1C5A642B4E40A1D634EF38E53340C1CAA145B62B4E403C4ED1915CEE334090A0F831E6264E4039454772F9EF3340857CD0B359254E40022B8716D9EE3340401361C3D3234E401CEBE2361AD03340E4141DC9E5174E40B37BF2B050CB3340D578E92631184E404F1E166A4DC333403B014D840D1F4E402D431CEBE2C63340287E8CB96B214E400E4FAF9465C8334005C58F3177254E4072F90FE9B7BF3340AC8BDB6800274E40BC74931804A633405305A3923A214E408AB0E1E995B23340BF0E9C33A2144E40F085C954C1B833404703780B24104E4039D6C56D34C033405C8FC2F5280C4E40BD5296218EC5334027C286A7570A4E40295C8FC2F5D83340B6847CD0B3094E405BD3BCE314DD33406F1283C0CA094E4057EC2FBB27DF334025068195430B4E4030BB270F0BE53340EA95B20C710C4E40637FD93D79E8334087A757CA320C4E402575029A08EB3340CE88D2DEE00B4E40E9482EFF21ED3340508D976E120B4E408E06F01648F033402BF697DD93074E40C4B12E6EA3F1334068226C787A054E4018265305A3F2334099BB96900F0A4E40F8C264AA60F43340D0D556EC2F0B4E4080B74082E2F73340166A4DF38E0B4E40569FABADD8FF334096218E75710B4E40BD5296218E05344097FF907EFB0A4E40234A7B832F0C344097900F7A360B4E40211FF46C560D3440F853E3A59B0C4E409F3C2CD49A063440EBE2361AC0134E40CDCCCCCCCCFC33402041F163CC154E40A9A44E4013F133408FC2F5285C174E402041F163CCED3340E3361AC05B184E40448B6CE7FBE9334096218E75711B4E40BC96900F7A0634409D11A5BDC1274E40EF384547720934409D11A5BDC1274E40B22E6EA3010C3440BADA8AFD65274E40386744696F103440F54A598638264E4074B515FBCB1E34401F85EB51B81E4E40E5F21FD26F1F344014D044D8F01C4E40D122DBF97E1A34408716D9CEF71B4E406744696FF01534405839B4C876164E40D0B359F5B92A3440BE9F1A2FDD144E409D11A5BDC1373440DFE00B93A91A4E40D7A3703D0A473440174850FC18234E40A1F831E6AE153440F7E461A1D62C4E4080B74082E2E733403333333333334E403C4ED1915CDE33403255302AA9334E400D000000C520B072683134408E06F01648084E4027A089B0E1293440B84082E2C7084E403A234A7B831F34405F07CE19510A4E405C8FC2F5281C344088635DDC460B4E404260E5D0221B3440EB73B515FB0B4E408048BF7D1D18344096438B6CE70B4E40C520B0726801344063EE5A423E084E40386744696F003440D712F241CF064E40C3D32B6519223440F38E537424FF4D4069006F81043534406891ED7C3FFD4D4099BB96900F3A3440711B0DE02D004E4013F241CF663534404772F90FE9074E40C520B072683134408E06F01648084E40100000003FC6DCB584AC3340A01A2FDD24164E4042CF66D5E7AA33409CC420B072184E401B0DE02D90A0334048BF7D1D381F4E40226C787AA59C3340AAF1D24D62204E4060764F1E169A33408D28ED0DBE204E40A1F831E6AE9533408D28ED0DBE204E401A51DA1B7C91334055302AA913204E40D26F5F07CE893340B003E78C281D4E40A52C431CEB8233403B70CE88D2164E402EFF21FDF68533404F1E166A4D134E40B22E6EA3018C33409C33A2B437104E4055302AA91390334072F90FE9B70F4E4088635DDC46933340F2B0506B9A0F4E40D0B359F5B99A33404182E2C798134E400612143FC6AC33403255302AA9134E403FC6DCB584AC3340A01A2FDD24164E4013000000CE88D2DEE04B334069006F8104FD4C40613255302A49334069006F8104FD4C4026E4839ECD1A3340F7E461A1D6FC4C408126C286A7173340BF7D1D3867FC4C407E8CB96B09093340DA1B7C6132F54C40F0A7C64B3709334089D2DEE00BF34C404F1E166A4D1333403CBD529621EE4C4080B74082E2173340931804560EED4C408E06F01648203340C139234A7BEB4C40DC4603780B2433407958A835CDF34C402EFF21FDF6253340CCEEC9C342F54C40423EE8D9AC2A3340D712F241CFF64C40B0726891ED3C33408C4AEA0434F94C4019E25817B741334044696FF085F94C40DAACFA5C6D45334029ED0DBE30F94C409C33A2B437483340711B0DE02DF84C40BD5296218E55334026E4839ECDFA4C401F85EB51B84E3340789CA223B9FC4C40CE88D2DEE04B334069006F8104FD4C40330000002CD49AE61D5732407D3F355EBA814C4064CC5D4BC85732408B6CE7FBA9894C40BF0E9C33A26432408D28ED0DBE904C4026E4839ECD6A3240A60A462575924C40C3F5285C8F723240401361C3D3934C408B6CE7FBA9813240DAACFA5C6D954C4093A98251498D3240BADA8AFD65974C40637FD93D79A83240787AA52C439C4C40D881734694B63240D734EF38459F4C40D26F5F07CEC93240FE65F7E461B94C40696FF085C9C4324032772D211FBC4C406DC5FEB27BC232407E8CB96B09C14C40B3EA73B515CB3240F8C264AA60D44C4057EC2FBB27CF324099BB96900FDA4C400C93A98251D93240A3923A014DDC4C4004E78C28EDED3240F46C567DAEDE4C40F1F44A5986083340FD87F4DBD7E94C4054742497FF003340EC2FBB270FF34C40FE65F7E461013340EB73B515FBF34C40F1F44A5986E83240CC7F48BF7DF54C40A857CA32C4E13240CB10C7BAB8F54C40637FD93D79B832405917B7D100F64C40560E2DB29DAF3240228E75711BF54C408195438B6C77324072F90FE9B7E74C4096218E75716B32400C93A98251E14C409FCDAACFD5663240AD69DE718ADE4C40A857CA32C4613240EB73B515FBDB4C40B3EA73B5155B3240454772F90FD94C40499D8026C256324073D712F241D74C4096218E75714B3240DFE00B93A9D24C40F46C567DAE463240C58F31772DD14C4090A0F831E63E324073D712F241CF4C40F163CC5D4B3832403D2CD49AE6CD4C40E5F21FD26F2F32407958A835CDCB4C40C139234A7B233240AD69DE718AC64C40933A014D841D32405E4BC8073DC34C40234A7B832F1C3240E3C798BB96C04C40933A014D841D32405AF5B9DA8ABD4C4014AE47E17A243240BF7D1D3867B44C40637FD93D79283240D50968226CA84C402EFF21FDF6253240D95F764F1E9E4C40956588635D2C3240B5A679C729924C40FED478E926413240EEEBC039238A4C4019E25817B741324032772D211F844C40BD5296218E353240029A081B9E7E4C40F1F44A5986283240545227A089784C4069006F8104253240BE30992A18754C405E4BC8073D2B324014AE47E17A744C402EFF21FDF6353240CDCCCCCCCC744C40780B24287E4C32408FE4F21FD2774C402CD49AE61D5732407D3F355EBA814C400F000000FD87F4DBD78132406ADE718A8E2C4E40A2B437F8C27432401F85EB51B8364E405C8FC2F5286C32404A7B832F4C3E4E409B559FABAD683240D5E76A2BF63F4E4018265305A3623240462575029A404E40ACADD85F765F3240F2B0506B9A3F4E40A857CA32C4613240F7065F984C354E404D840D4FAF6432408E06F01648304E40F46C567DAE663240499D8026C22E4E406A4DF38E53843240713D0AD7A3284E4094F6065F988C32401EA7E8482E274E40C7293A92CB8F324002BC051214274E4035EF3845479232409D11A5BDC1274E4044696FF085893240A4703D0AD72B4E40FD87F4DBD78132406ADE718A8E2C4E400F00000076E09C11A5BD3140273108AC1CB24D4011C7BAB88DA631404A7B832F4CB64D40166A4DF38EA33140AD69DE718AB64D4001DE02098A9F3140917EFB3A70B64D4096B20C71AC9B3140D93D7958A8B54D40AE47E17A149E3140A52C431CEBB24D40EF38454772A9314096438B6CE7AB4D40B22E6EA301AC314050FC1873D7AA4D40569FABADD8AF3140A7E8482EFFA94D40F085C954C1B83140FF21FDF675A84D40371AC05B20C13140F2B0506B9AA74D402CD49AE61DC73140D6C56D3480A74D407D3F355EBAC931402AA913D044A84D4011C7BAB88DC631401D38674469AF4D4076E09C11A5BD3140273108AC1CB24D400A000000F90FE9B7AF83314030BB270F0B354F400F9C33A2B47731407B14AE47E13A4F40575BB1BFEC5E31405C2041F1633C4F4098DD9387856A3140AAF1D24D62304F4019E25817B7713140D7A3703D0A2F4F404D158C4AEA743140499D8026C22E4F40075F984C157C3140BC96900F7A2E4F408A1F63EE5A8231408351499D802E4F407B14AE47E18A31406688635DDC2E4F40F90FE9B7AF83314030BB270F0B354F400E000000C58F31772D51314096438B6CE7B34D409D11A5BDC1473140ABCFD556ECB74D406A4DF38E534431400D71AC8BDBB84D40A857CA32C4413140C58F31772DB94D40EA95B20C713C31406F8104C58FB94D4011C7BAB88D263140A60A462575BA4D4082734694F61631407B14AE47E1BA4D401973D712F2113140A60A462575BA4D40499D8026C226314045D8F0F44AB14D40091B9E5E293B314055302AA913B04D40C4B12E6EA341314064CC5D4BC8AF4D4087A757CA32443140ABCFD556ECAF4D40C74B37894150314009F9A067B3B24D40C58F31772D51314096438B6CE7B34D40340000003FC6DCB5847C3040AF25E4839E1D4C40AC8BDB68007F30403B70CE88D21E4C40E86A2BF6978D30400BB5A679C7294C408A1F63EE5A9230403D0AD7A3702D4C404F1E166A4D93304066F7E461A12E4C40E0BE0E9C339230404772F90FE92F4C4052B81E85EB91304060764F1E16324C406B9A779CA29330406ABC749318344C406D567DAEB6A230407CF2B0506B424C40D200DE0209BA304027A089B0E1594C40DA1B7C6132C5304012143FC6DC654C4063EE5A423EC830401D38674469674C40C7BAB88D06D030409A99999999694C404ED1915CFED33040C4B12E6EA3694C40499D8026C2D630407C613255306A4C40613255302AD93040158C4AEA046C4C4036AB3E575BE13040AC8BDB6800774C40053411363CED304022FDF675E0844C40832F4CA60AF63040C4B12E6EA3894C4035EF384547023140D42B6519E2904C40B0726891ED0C3140E4141DC9E5974C40C7293A92CB1F3140454772F90FA94C40EEEBC039231A3140789CA223B9AC4C40E6AE25E4830E3140925CFE43FAAD4C40D3BCE3141D09314030BB270F0BAD4C40A1F831E6AE0531405C8FC2F528AC4C40C3F5285C8F0231403333333333AB4C4091ED7C3F35FE30406F8104C58FA94C404182E2C798FB3040AAF1D24D62A84C40613255302AF9304002BC051214A74C4065AA605452F73040840D4FAF94A54C402F6EA301BCF53040C139234A7BA34C404B598638D6F53040D42B6519E2A04C408104C58F31F73040BBB88D06F09E4C4012A5BDC117F63040A245B6F3FD9C4C406666666666E63040E17A14AE47914C40D3BCE3141DD930409C33A2B437884C40569FABADD8BF30408E06F01648784C4085EB51B81EA53040705F07CE19714C40A9A44E4013A13040545227A089704C40CCEEC9C3429D3040736891ED7C6F4C407B14AE47E18A3040A54E401361634C404182E2C7986B3040EC2FBB270F4B4C4069006F81046530405C8FC2F528444C40DCD7817346643040EC51B81E853B4C40107A36AB3E673040174850FC18234C40613255302A693040CDCCCCCCCC1C4C404260E5D0226B3040ECC039234A1B4C405917B7D1006E30403411363CBD1A4C40ABCFD556EC6F3040EC2FBB270F1B4C40B7627FD93D793040789CA223B91C4C403FC6DCB5847C3040AF25E4839E1D4C4019000000A089B0E1E9352E4034A2B437F8824B405C2041F1634C2E4097FF907EFB8A4B40075F984C154C2E40545227A089904B40F5DBD78173462E40C3D32B6519924B40744694F6063F2E40EC2FBB270F934B401FF46C567D2E2E40CE88D2DEE0934B40C139234A7B232E405BB1BFEC9E944B401F85EB51B8FE2D40D5E76A2BF6974B40BC0512143FC62D40E8D9ACFA5C9D4B40E8D9ACFA5CAD2D4055302AA913A04B401361C3D32BA52D409A779CA223A14B40AA60545227A02D40A60A462575A24B40423EE8D9AC9A2D40C1CAA145B6A34B40D578E92631882D4002BC051214A74B40E86A2BF6977D2D401283C0CAA1A54B404B598638D6652D40EC51B81E859B4B402497FF907E5B2D40E9B7AF03E78C4B40A167B3EA73752D400C93A98251894B40CAC342AD697E2D40E3361AC05B884B404FAF946588C32D405D6DC5FEB2834B40C3F5285C8FE22D40B6847CD0B3814B40F6285C8FC2152E4065AA6054527F4B40B0726891ED1C2E404850FC18737F4B40D734EF3845272E404703780B24804B40A089B0E1E9352E4034A2B437F8824B405E000000EE5A423EE8D92A40A3923A014D544B40C3D32B6519E22A409FABADD85F564B40423EE8D9ACDA2A408FC2F5285C574B406ADE718A8EC42A40F38E537424574B40265305A392BA2A4082734694F6564B404BC8073D9B952A40CA32C4B12E564B40CA54C1A8A48E2A406744696FF0554B40D712F241CF862A40211FF46C56554B4093A98251497D2A4040A4DFBE0E544B408716D9CEF7732A40FC1873D712524B40567DAEB6627F2A40BADA8AFD65474B405396218E75912A4000917EFB3A484B40744694F6069F2A40FE65F7E461494B407FFB3A70CEA82A40ED0DBE30994A4B402EFF21FDF6B52A40E8D9ACFA5C4D4B40569FABADD8BF2A4012A5BDC1174E4B403108AC1C5AC42A40D200DE02094A4B40B5A679C729DA2A40295C8FC2F5484B4097900F7A36EB2A408351499D80464B406FF085C954012B409FABADD85F464B402A3A92CB7F082B4019E25817B7414B40363CBD5296012B402CD49AE61D3F4B409D11A5BDC1F72A40A089B0E1E93D4B4085EB51B81EE52A40D8817346943E4B40107A36AB3ED72A405F29CB10C7424B407C61325530CA2A404C37894160454B408CDB68006FC12A40E5F21FD26F474B40CE1951DA1BBC2A40984C158C4A4A4B40772D211FF4AC2A402A3A92CB7F484B40CFF753E3A59B2A40917EFB3A70464B4017B7D100DE822A40742497FF90464B40BC96900F7A762A401F85EB51B8464B4025068195434B2A404B598638D6454B404BC8073D9B552A4019E25817B7414B402D431CEBE2762A40CA32C4B12E3E4B4030BB270F0B752A40EC51B81E853B4B40DCD7817346542A408F53742497374B40B537F8C2646A2A4074B515FBCB364B40EE7C3F355E7A2A40E9263108AC344B402CD49AE61D872A40713D0AD7A3304B4029CB10C7BA782A402BF697DD932F4B40FFB27BF2B0702A4065AA6054522F4B402DB29DEFA7662A4072F90FE9B72F4B40022B8716D94E2A402B1895D409304B40F853E3A59B442A4001DE02098A2F4B409487855AD33C2A40174850FC182B4B40ABCFD556EC4F2A40C976BE9F1A274B405227A089B0812A40FE43FAEDEB204B407E1D386744892A40713D0AD7A3204B40DE02098A1FA32A408FE4F21FD21F4B4043AD69DE71CA2A40F8C264AA601C4B40265305A392DA2A40CA32C4B12E1E4B40098A1F63EEDA2A40D656EC2FBB1F4B40643BDF4F8DD72A400C93A98251214B401C7C613255D02A40B515FBCBEE214B40D7A3703D0AB72A40B7627FD93D214B406519E25817B72A4034A2B437F8224B401C7C613255F02A406EA301BC052A4B4088635DDC46032B407958A835CD2B4B40AB3E575BB15F2B40BF0E9C33A22C4B40CDCCCCCCCC6C2B4045D8F0F44A294B405DFE43FAED6B2B40BC74931804264B40DC4603780B642B406ABC749318244B40228E75711B6D2B4026E4839ECD224B40857CD0B359752B4097900F7A36234B4022FDF675E07C2B4051DA1B7C612A4B4011363CBD52762B4013F241CF662D4B40CE88D2DEE06B2B4054E3A59BC4304B4048E17A14AE672B40EFC9C342AD314B409D11A5BDC1572B4008AC1C5A64334B40D044D8F0F44A2B40A54E401361334B40DE02098A1F432B407A36AB3E57334B40B0726891ED3C2B40083D9B559F334B4026E4839ECD2A2B40545227A089384B402BF697DD93272B406DC5FEB27B3A4B40BA6B09F9A0272B40401361C3D33B4B403F575BB1BF2C2B40AEB6627FD93D4B40992A1895D4492B4036CD3B4ED1414B4011363CBD52562B40986E1283C0424B40EE7C3F355E5A2B404F401361C3434B40EBE2361AC05B2B403D0AD7A370454B40D34D621058592B40F1F44A5986484B40F4FDD478E9462B4017B7D100DE4A4B4007F01648503C2B40C2172653054B4B40560E2DB29D2F2B4050FC1873D74A4B4063EE5A423E082B40992A1895D4494B40F46C567DAEF62A406F8104C58F494B4061545227A0E92A409A081B9E5E494B403D2CD49AE6DD2A407DD0B359F5494B40E3C798BB96D02A4041F163CC5D4B4B407FD93D7958C82A40A1F831E6AE4D4B40A2B437F8C2C42A40B5A679C729524B40EFC9C342ADC92A40ECC039234A534B40EE5A423EE8D92A40A3923A014D544B401800000033C4B12E6E0329406EA301BC05824B409D8026C286E7284096218E7571834B40B537F8C264CA28405C8FC2F528844B40AB3E575BB19F28403FC6DCB584844B402575029A085B2840F6285C8FC27D4B40F853E3A59B442840273108AC1C7A4B40ECC039234A3B284088855AD3BC734B403E7958A8354D2840287E8CB96B714B404F401361C35328407E8CB96B09714B405917B7D1005E28409B559FABAD704B40D734EF3845872840D93D7958A8754B40575BB1BFEC9E28406F8104C58F794B40228E75711BAD28403411363CBD7A4B4029ED0DBE30B92840091B9E5E297B4B40371AC05B20C1284088635DDC467B4B401E166A4DF3CE284088635DDC467B4B40006F8104C5EF2840A60A4625757A4B4070CE88D2DE002940B7627FD93D794B40D122DBF97E0A29400D71AC8BDB784B40C286A757CA122940295C8FC2F5784B409D11A5BDC11729409A081B9E5E794B40B0726891ED1C2940DE9387855A7B4B40E2E995B20C11294038F8C264AA804B4033C4B12E6E0329406EA301BC05824B4001030000000100000008000000772D211FF42C2840D881734694DE4B40022B8716D92E2840F7E461A1D6DC4B4055302AA913302840C217265305DB4B40E561A1D6342F2840FE65F7E461D94B40E6AE25E4831E28405DFE43FAEDD34B400F123F5286182840A8C08F8BE6D34B408D955EBC9B2A284078F75B968ADF4B40772D211FF42C2840D881734694DE4B40

Figure 4: The nested structure layed out in table tr_area_area, dotted box indicate that the level is optional. This table will be created specifically for each group.

Data access (tr_dataaccess_dta)

Type of data Public, or Restricted

dbExecute(con_diaspara_admin,
"DROP TABLE IF EXISTS ref.tr_dataaccess_dta CASCADE;")

dbExecute(con_diaspara_admin,
"CREATE TABLE ref.tr_dataaccess_dta(
   dta_code TEXT PRIMARY KEY,
   dta_description TEXT  
);")


tr_dataaccess_dta <- dbGetQuery(con_diaspara_admin, 
"SELECT * FROM refwgeel.tr_dataaccess_dta")

dbExecute(con_diaspara_admin,
  "INSERT INTO ref.tr_dataaccess_dta 
  SELECT * FROM refwgeel.tr_dataaccess_dta
  ;")#2

dbExecute(con_diaspara_admin, "GRANT ALL ON ref.tr_dataaccess_dta 
          TO diaspara_admin;")
dbExecute(con_diaspara_admin, "GRANT SELECT ON ref.tr_dataaccess_dta 
          TO diaspara_read;")

dbExecute(con_diaspara_admin, "COMMENT ON TABLE ref.tr_dataaccess_dta 
IS 'Table with two values, Public or Restricted access.';")

Missing data (tr_missvalueqal_mis)

This comes from wgeel.

dbExecute(con_diaspara_admin,
"DROP TABLE IF EXISTS ref.tr_missvalueqal_mis CASCADE;")

dbExecute(con_diaspara_admin,
"CREATE TABLE ref.tr_missvalueqal_mis(
   mis_code TEXT PRIMARY KEY,
   mis_description TEXT NOT NULL,  
   mis_definition TEXT);")

dbExecute(con_diaspara_admin,
"INSERT INTO ref.tr_missvalueqal_mis 
SELECT
'NR',
'Not reported', 
'Data or activity exist but numbers are not reported to authorities (for example for commercial confidentiality reasons).';")
dbExecute(con_diaspara_admin,
"INSERT INTO ref.tr_missvalueqal_mis 
SELECT
'NC',   
'Not collected',    
'Activity / habitat exists but data are not collected by authorities (for example where a fishery exists but the catch data are not collected at the relevant level or at all).';")
dbExecute(con_diaspara_admin,
"INSERT INTO ref.tr_missvalueqal_mis 
SELECT
'NP',   
'Not pertinent',
'Where the question asked does not apply to the individual case (for example where catch data are absent as there is no fishery or where a habitat type does not exist in a stock unit).';")

dbExecute(con_diaspara_admin, "GRANT ALL ON ref.tr_missvalueqal_mis 
          TO diaspara_admin;")
dbExecute(con_diaspara_admin, "GRANT SELECT ON ref.tr_missvalueqal_mis 
          TO diaspara_read;")

dbExecute(con_diaspara_admin, "COMMENT ON TABLE ref.tr_missvalueqal_mis 
IS 'Table showing the qualification when value is missing, NC, NP, NR.';")

Table 16: Code for missing values

mis_code	mis_description	mis_definition
NR	Not reported	Data or activity exist but numbers are not reported to authorities (for example for commercial confidentiality reasons).
NC	Not collected	Activity / habitat exists but data are not collected by authorities (for example where a fishery exists but the catch data are not collected at the relevant level or at all).
NP	Not pertinent	Where the question asked does not apply to the individual case (for example where catch data are absent as there is no fishery or where a habitat type does not exist in a stock unit).

Life stages (tr_lifestage_lfs)

Life stages cannot easily be shared among all species, they are species specific, probably similar between Sea trout and Salmon, but there is a large gap between a leptocephalus and a parr.

Creating a life stage referential is a challenging issue that will require inputs from the working groups for validation.

Considerations about the database structure

For life stage, unlike in other referentials, using working group specific life stage would lead to confusion. WGBAST and WGNAS would share the same stages for salmon. So unlike in many other table, the referentials will not use inheritance (see paragraph Section 1 for more details on inheritance). This means that we will create a table grouping all life stages and then we will only select the relevant ones at working group levels. For instance currently WGEEL does not use the Egg or Parr stages. It will be listed in the ref.tr_lifestage_lfs table but not in the refeel.tr_lifestage_lfs table. So the working group referentials, refnas, refbast, refeel … will have tr_lifestage_lfs tables with a foreign key to ref.tr_lifestage_lfs, and a subset of values used by the working group.

The lifestages in working group databases

The creation of life stage is discussed in the issue 16 in git github link to issue

Stages use are described in WGNAS metadata paragraph life stage of the WGNAS description report and WGBAST reports (though for the young fish this concept is mixed with age) paragraph life stage of the WGBAST description report. So they are not used as a separate column to describe the data. But in the eel database they are and so we will need to add this dimension to the table structure.

Adding a stage dimension

Currently the stage is used by WGEEL and not by WGNAS or WGBAST landings data. For WGBAST it is used to describe the numbers release from hatchery at various stages. Spatial information, and information about the age are used to split the dataset, not the stage. We have two solutions : 1 / ignore the stage for WGBAST and WGNAS and only have the stage as a new column in the wgeel database when the inherited table is created. 2/ Add it everywhere and populate the column using the metadata (which should not be too hard) but with no practical use in the table since the information is not used currently. Our preference would go for the second because having a single data structure should make the common shiny development more easy, what do you think ?

Use of lifestages to include other information.

The different steps in the models are identified using spatio temporal units and stages (e.g; post smolt and location at sea) ICES (2024), a location and stage to descibe the different steps in the return migration (for instance returning adult in seawater, in freshwater…). Some of the elements added in the stage column are not stage per-se, but elements used to describe the spatio-temporal elements within the life-cycle, for instance the use PFA (pre-fishery-abundance) which correspond to the number at sea of different stages before fishery. To deal with those, we will simplify the stages table, remove elements which are not stages, and still refer to spatio temporal parameters from their definition. Here we are focusing on the stock DB which will group information, but the individual metric database might require more details that the simple adult stage. For this reason, we will add the maturity scale from ICES in the DB.

Adding a bit more complexity with the eel.

In some cases mixture of stages are used. Many fyke net fisheries for eel will not distinguish between yellow and silver eel stage and WGEEL uses the YS stage. Some historical trap series did not distinguish between small yellow eels and glass eel, or the glass eel ascending are in a late stage. In that case the GYstage is used to count the recruits.

QUESTION TO WGEEL

The new database will have to include a source WILD/HATCHERY/AQUACULTURE shouldn’t we use that opportunity to get rid of OG (ongrown) and QG (quarantine glass eel) which are cumbersome when we try to make simple graphs ?

Code to create the stage table.

The code for creating tr_lifestage_lfs is shown below, it also includes the import of the WGEEL stage table.

SQL code to create table tr_lifestage_lfs

-- Table for lifestage
-- there is one table for all working groups 
-- so this table is not inherited (otherwise two wkg could create referential for the same species)

DROP TABLE IF EXISTS ref.tr_lifestage_lfs CASCADE;
CREATE TABLE ref.tr_lifestage_lfs (
  lfs_id SERIAL PRIMARY KEY,
  lfs_code TEXT NOT NULL,
  lfs_name TEXT NOT NULL,
  lfs_spe_code character varying(3) NOT  NULL,    
  lfs_description TEXT,
  lfs_icesvalue character varying(4),  
  lfs_icesguid uuid,
  lfs_icestablesource text,
CONSTRAINT fk_lfs_spe_code FOREIGN KEY (lfs_spe_code)
  REFERENCES ref.tr_species_spe(spe_code) 
  ON DELETE CASCADE
  ON UPDATE CASCADE,
CONSTRAINT uk_lfs UNIQUE (lfs_code, lfs_spe_code)
);

COMMENT ON TABLE ref.tr_lifestage_lfs IS 'Table of lifestages';
COMMENT ON COLUMN ref.tr_lifestage_lfs.lfs_id IS 'Integer, primary key of the table';
COMMENT ON COLUMN ref.tr_lifestage_lfs.lfs_code IS 'The code of lifestage';
COMMENT ON COLUMN ref.tr_lifestage_lfs.lfs_name IS 'The english name of lifestage';
COMMENT ON COLUMN ref.tr_lifestage_lfs.lfs_spe_code IS 'The code of the species referenced from
tr_species_spe : one of SAL, ELE, TRT, ALA, ALF, SLP, RLP ';
COMMENT ON COLUMN ref.tr_lifestage_lfs.lfs_description IS 'Definition of the lifestage';
COMMENT ON COLUMN ref.tr_lifestage_lfs.lfs_icesvalue IS 'Code for the lifestage in the ICES database';
COMMENT ON COLUMN ref.tr_lifestage_lfs.lfs_icesguid IS 'GUID in the ICES database';
COMMENT ON COLUMN ref.tr_lifestage_lfs.lfs_icestablesource IS 'Source table in ICES vocab';


GRANT ALL ON ref.tr_lifestage_lfs TO diaspara_admin;
GRANT SELECT ON ref.tr_lifestage_lfs TO diaspara_read;

Existing stages in ICES dictionaries.

types <- icesVocab::getCodeTypeList()
types[grep('stage', tolower(types$Description)),]%>% kable()
TS_MATURITY <- icesVocab::getCodeList('TS_MATURITY')
TS_DevStage <- icesVocab::getCodeList('TS_DevStage')
# Devstage is 1 to 15 => no use
DevScale <- icesVocab::getCodeList('DevScale')
# At the present the codetypes target Eggs and Larvae surveys
# This is a description of scales types using different publications => not for use()
kable(TS_MATURITY, caption = "TS_MATURITY") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed")) 
kable(TS_DevStage, caption = "Devstage scale, this seems to be about shrimp (berried seems to an egg which looks at you with eyes in shrimps....)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed")) 

Table 17: ICES vocabularies for life stages

(a) Possible match for stage in ICES vocab.

	GUID	Key	Description	LongDescription	Modified
30	76fab5ff-5f3e-4488-8167-17d360f47c35	STAGE	Developmental Stage of Specimens	NA	2024-10-22
216	dcc61865-822a-47a4-a740-a85a5702d0c5	TS_DevStage	Development stage or maturity	NA	2024-04-11
324	eac72d6a-219e-42a7-99b3-f50ac258b607	DevScale	Scale applied for reporting developmental stages	At present, the codetype targets the Eggs and Larvae surveys	2023-09-21
325	d943050b-a4e3-4508-b0ca-10b298f308f5	DevStage	Developmental stage codes	NA	2023-09-21
426	2e045807-14b1-48b0-bb2e-43967cec51e7	POFStaging	Stages of POF (description to be updated)	NA	2023-09-21
448	8b86cce6-b742-4865-986c-13932874d9df	LifeStage	Life stage based on plumage (birds)	NA	2023-09-21
464	14cbcbfe-f084-40a8-80e3-052e69b06334	DigestionStage	Digestion stage of stomach content.	Visual-based assessment of digestion stage, in relation to diet studies.	2023-09-21

(b) TS_MATURITY table

GUID	Key	Description	LongDescription	Modified	Deprecated
7c946d9d-fe30-48fa-a19d-54e72a4160da	-9	Missing Value	NA	2024-04-04	FALSE
0da7235e-9a5f-471f-8685-cdcceb3259c0	1	Juvenile/Immature (4-stage scale)	NA	2024-04-04	FALSE
781f7cd8-2e68-4590-9124-f9d936e31f52	2	Maturing (4-stage scale)	NA	2024-04-04	FALSE
ebb2258d-81fd-47d0-8383-7bfe88af82cd	3	Spawning (4-stage scale)	NA	2024-04-04	FALSE
5e364aed-fe87-494e-81ff-195c6363f191	4	Spent (4-stage scale)	NA	2024-04-04	FALSE
02c6638c-9a7d-49d1-b06f-942ee5d95a39	5	Resting/Skip of spawning (4-stage scale, additional option)	NA	2024-04-04	FALSE
37d204b2-f8a6-48cd-a244-728ab4010111	51	Immature (5 stage scale)	NA	2024-04-04	FALSE
46d154b0-61f7-414a-b1d1-4275839afdfe	52	Developing/Resting (5-stage scale)	NA	2024-04-04	FALSE
54992917-0bbd-4dd6-8044-32bb70754279	53	Pre-spawning ((5-stage scale)	NA	2024-04-04	FALSE
4146842c-eb45-49cd-8674-8d69ee6b81c1	54	Spawning (5-stage scale)	NA	2024-04-04	FALSE
52a5be9c-c656-496f-adba-b6af23ba88a3	55	Post-spawning (5-stage scale)	NA	2024-04-04	FALSE
17e59dde-46b3-4764-80f1-01e1134c8ba0	6	Abnormal (4-stage scale, additional option)	NA	2024-04-04	FALSE
9dab155a-95e6-4dcc-adaf-c8b67febbb60	61	Juvenile/Immature (6-stage scale)	NA	2024-04-04	FALSE
32e854ae-0e44-4523-855d-92b8bccdabbe	62	Maturing (6-stage scale)	NA	2024-04-04	FALSE
85c79865-4505-4b6e-96bb-37e5a18b9c95	63	Spawning (6-stage scale)	NA	2024-04-04	FALSE
2637e1c0-8dba-4da8-beba-7c865acfd7b4	64	Spent (6-stage scale)	NA	2024-04-04	FALSE
f682c57c-f70e-4c1b-859c-ab77f865c3f6	65	Resting/Skip of spawning (6-stage scale)	NA	2024-04-04	FALSE
8165075c-ae3b-49e4-b8ed-0dad8f215e78	66	Abnormal (6-stage scale)	NA	2024-04-04	FALSE
a25f81c6-91af-47fe-b2a1-6f76a8643ac3	A	Immature	NA	2024-04-04	FALSE
62b08142-2f9d-4b8c-b53f-d9bce26b1c35	B	Developing	NA	2024-04-04	FALSE
4b4643bd-0255-42bc-a473-0b936492ae9f	Ba	Developing, but functionally immature (first-time developer)	NA	2024-04-04	FALSE
5fb2d514-c4f0-4911-8eb4-f4e9c692041d	Bb	Developing and functionally mature	NA	2024-04-04	FALSE
1ad95503-f3e6-46a6-997f-eefcba1c9bd7	Be	Berried (crustaceans)	Used to be "B", changed to leave code for maturity scale SMSF	2024-04-04	FALSE
c5e12291-673e-4ad2-b7b8-6f2adf52ec21	C	Spawning	NA	2024-04-04	FALSE
a9bc29b4-4ee3-4b1b-bf31-76d58170d75e	Ca	Actively spawning	NA	2024-04-04	FALSE
8d8784d6-9795-43c0-927b-f404a18f3491	Cb	Spawning capable	NA	2024-04-04	FALSE
5af75fd2-06df-4d60-a843-2a3977cd6e5c	D	Regressing / Regenerating	NA	2024-04-04	FALSE
c11149a2-e084-4af0-b737-4d41d59d3943	Da	Regressing	NA	2024-04-04	FALSE
ecb4cdea-3ac1-42cd-a279-c9b20e360999	Db	Regenerating	NA	2024-04-04	FALSE
a1cf8684-f842-4f2f-9912-aefefea6d9d3	E	Omitted spawning	NA	2024-04-04	FALSE
1dce569f-2d2d-43a9-8041-d90950ade023	Eg	Elasmobranch egg	Used to be "E", changed it to use the code for maturity scale SMSF	2024-04-04	FALSE
1736cf72-fbca-4389-868d-e996159664e3	F	Abnormal	NA	2024-04-04	FALSE
429ad685-ee68-4781-84eb-90f0cd8f0634	I	Immature / National BITS scale step	NA	2024-04-04	FALSE
ad5c19e1-3c59-4bd6-b001-f1cfbcab0b6d	II	National BITS scale step	NA	2020-06-03	FALSE
595124ad-a831-4ce0-ba88-26fb4183022c	III	National BITS scale step	NA	2020-06-03	FALSE
52e89979-b100-4cee-93c9-206d132a4c2a	IV	National BITS scale step	NA	2024-04-04	FALSE
60f82148-71f3-4f0f-98c1-a6cd59918f25	IX	National BITS scale step	NA	2024-04-04	FALSE
9ddbe93e-b920-46fc-8f0d-b1bc94cacace	M	Mature	NA	2024-04-04	FALSE
4b9a6671-aae4-4731-8a20-6bfa6764cc9c	R1_1	Immature	IMARES RIVO 4 scale code for Immature used before 2000 for cod, haddock, whiting, Norway pout and saithe	2024-04-04	FALSE
542d1576-cb99-42b4-a046-c5af124c7812	R1_2	Maturing	IMARES RIVO 4 scale code for Maturing, used before 2000 for cod, haddock, whiting, Norway pout and saithe	2024-04-04	FALSE
e5dba8d3-daf0-49b7-9a5e-0deaeae94591	R1_3	Spawning	IMARES RIVO 4 scale used before 2000 for Spawning, for cod, haddock, whiting, Norway pout and saithe	2024-04-04	FALSE
35caf0f4-b23e-4114-b004-53ceed7dc5ae	R1_4	Spent	IMARES PIVO 4 scale code for Spent, used before 2000 for cod, haddock, whiting, Norway pout and saithe	2024-04-04	FALSE
cc410273-2714-4253-bfa6-fadd39e83099	R2_2	Immature	IMARES RIVO new 4 scale code for Immature, used from 2000 til 1-1-2011, for all species	2024-04-04	FALSE
24e63a36-bbcc-422e-9b42-1938d816574c	R2_4	Maturing	IMARES RIVO new 4 scale code for Maturing, used between 2000 and 1-1-2011, for all species	2024-04-04	FALSE
6024da49-355d-41e0-9c35-91c92133adef	R2_6	Spawning	IMARES RIVO new 4 scale for Spawning, used between 2001 and 1-1-2011, for all species	2024-04-04	FALSE
8f5b8ad1-f335-487a-bb17-58936b4bae8b	R2_8	Spent/Recovering	IMARES RIVO new 4 scale code for Spent/Recovering, used between 2001 and 1-1-2011, for all species	2024-04-04	FALSE
193d1307-7594-4e61-bddb-5ed722448fc1	RF1	Immature redfish	NA	2024-04-04	FALSE
2bd20b2d-41f7-4ce4-944c-c124c2ee25bc	RF2	Maturing_mature redfish	NA	2024-04-04	FALSE
34288e8c-0586-48e9-bce9-2ea62580d278	RF3	Mature_fertilized redfish	NA	2024-04-04	FALSE
8891c3b8-f440-4eca-8cb9-32c3f544cb82	RF4	Parturition redfish	NA	2024-04-04	FALSE
94165bd1-2c96-485e-92f7-f8fdbec294d0	RF5	Postspawning redfish	NA	2024-04-04	FALSE
b725388b-288c-4895-819e-029e9625d0a4	RF6	Recovery redfish	NA	2024-04-04	FALSE
add7abf9-c812-4073-afd5-6168efc473da	S1	Immature (6-stage scale)	NA	2024-04-04	FALSE
d94cc258-cf59-41df-8e12-e917b8c8753d	S2	Maturing (6-stage scale)	NA	2024-04-04	FALSE
ab5a79b8-c948-41d0-ba0e-2d048c9c2d65	S3	Pre-spawning (6-stage scale)	NA	2024-04-04	FALSE
185d00c7-b55b-4b6f-ad48-bc02494e7c3e	S4	Spawning (6-stage scale)	NA	2024-04-04	FALSE
d7b6f253-9fa6-41bb-96ff-4e4ee3440858	S5	Post-partial spawning (6-stage scale)	NA	2024-04-04	FALSE
2fad9d30-d07a-4ffb-b665-1bd687131d5b	S6	Spent/Recovery (6-stage scale)	NA	2024-04-04	FALSE
7d6b8aeb-1019-410c-9377-e03dd45bb06c	V	National BITS scale step	NA	2020-06-03	FALSE
61f27c0b-f86f-4d44-b1fc-e695a071ff76	VI	National BITS scale step	NA	2024-04-04	FALSE
94bbebde-ffe9-41e9-b0d8-d712018b81e6	VII	National BITS scale step	NA	2024-04-04	FALSE
304ebd7a-7749-4b94-b155-301e786d8f6a	VIII	National BITS scale step	NA	2024-04-04	FALSE
39a755c5-f830-49f0-8ca1-d1ea31989843	X	National BITS scale step	NA	2024-04-04	FALSE

(c) DevScale table

GUID	Key	Description	LongDescription	Modified	Deprecated
16f8c30a-b724-4636-bd78-0a9a1776039e	-9	No information	NA	2024-04-04	FALSE
c1566f6d-c099-44c3-bd39-222fe4c033de	B	Berried	NA	2024-04-04	FALSE
0424ae90-03aa-4e73-8cda-e8745d0b8158	E	Egg	NA	2024-04-04	FALSE
3ea553fe-66cf-47ff-b4ff-eb83d1c28643	J	Juvenile	NA	2024-04-04	FALSE

QUESTION TO ICES

Does it make sense to use a code outside from its scope (I seems to me that yes). If so can we use the TS_DevStage and add values in it and propose definitions ?

Importing the lifestages for Salmon.

Some of the definitions come from Ontology portal.

lfs <- tribble(
  ~lfs_code, ~lfs_name, ~lfs_spe_code, ~lfs_description, 
  ~lfs_icesvalue, ~lfs_icesguid, ~lfs_icestablesource,
  "E",   "Egg", "SAL" , "In fish, the term egg usually refers to female haploid gametes.", 
  "E",   "0424ae90-03aa-4e73-8cda-e8745d0b8158", "TS_DevStage",
  "EE",   "Eyed egg", "SAL" , "Eyed eggs are fertilized eggs that have developed to the stage where the eyes of the fish can easily be seen with naked eyes through the translucent egg shell. They might be used for stocking purpose.", 
  NA,  NA , NA,
  "ALV",   "Alevin with the yolk sac", "SAL" , "Larval salmon that have hatched but have not yet completely absorbed their yolk sacs and usually have not yet emerged from the gravel.  http://purl.dataone.org/odo/SALMON_00000403", 
  NA,  NA , NA,
  "FR",   "Fry", "SAL" , "A young salmonid at the post-larval stage who has resorbed the yolk sac but remains buried in the gravel.  The stage starts at the end of dependence on the yolk sac as the primary source of nutrition to dispersal from the redd.", 
  NA,  NA , NA,
  "P", "Parr", "SAL", 
  "A young salmonid with parr-marks before migration to the sea and after dispersal from the redd.  http://purl.dataone.org/odo/SALMON_00000649",
  NA, NA, NA,
  "SM", "Smolt", "SAL", "1) A young salmonid which has developed silvery coloring on its sides, obscuring the parr marks, and which is about to migrate or has just migrated into the sea, 2) to undergo the transformation from parr to smolt.http://purl.dataone.org/odo/SALMON_00000404",
  NA, NA, NA,
  "PS", "Post Smolt", "SAL", "A young salmonid after
  the smolt freshwater or estuarine migration. [TODO check this]",
  NA, NA, NA,
 "A", "Adult", "SAL", "Salmonids that have fully developed morphological and meristic characters and that have attained sexual maturity, for salmon this might refer to fishes during their spawning migration in coastal waters, or spawning adults in freshwater. More details can be given on the maturity of the fish using the maturity scale.", NA, NA, NA
)
dbWriteTable(con_diaspara_admin, "temp_lfs", lfs, overwrite = TRUE)
    dbExecute(con_diaspara_admin, "INSERT INTO ref.tr_lifestage_lfs 
    ( lfs_code, lfs_name, lfs_spe_code, lfs_description, lfs_icesvalue, lfs_icesguid, lfs_icestablesource)
    SELECT 
     lfs_code, 
    lfs_name, 
    lfs_spe_code,
    lfs_description,
    lfs_icesvalue, 
    lfs_icesguid::uuid, 
    lfs_icestablesource
    FROM temp_lfs;")
dbExecute(con_diaspara_admin, "DROP TABLE temp_lfs")

Import lifestages for eel.

dbExecute(con_diaspara_admin,"DELETE FROM ref.tr_lifestage_lfs WHERE lfs_spe_code ='ANG';")
dbExecute(con_diaspara_admin, "INSERT INTO ref.tr_lifestage_lfs (lfs_code,lfs_name,lfs_description, lfs_spe_code)
SELECT lfs_code,initcap(lfs_name),lfs_definition, 'ANG' 
FROM refwgeel.tr_lifestage_lfs ;") # 8

Import lifestages for trutta.

  dbExecute(con_diaspara_admin, "INSERT INTO ref.tr_lifestage_lfs 
    (lfs_code, lfs_name, lfs_spe_code, lfs_description, lfs_icesvalue, lfs_icesguid, lfs_icestablesource)
    SELECT 
    lfs_code, 
    lfs_name, 
    'TRT' AS lfs_spe_code,
    lfs_description,
    lfs_icesvalue, 
    lfs_icesguid::uuid, 
    lfs_icestablesource
    FROM ref.tr_lifestage_lfs WHERE lfs_spe_code = 'SAL';")

Content of the tr_lifestage_lfs table

dbGetQuery(con_diaspara, "SELECT * FROM ref.tr_lifestage_lfs;") %>% 
knitr::kable() %>% 
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))

Code for missing values

lfs_id	lfs_code	lfs_name	lfs_spe_code	lfs_description	lfs_icesvalue	lfs_icesguid	lfs_icestablesource
1	E	Egg	SAL	In fish, the term egg usually refers to female haploid gametes.	E	0424ae90-03aa-4e73-8cda-e8745d0b8158	TS_DevStage
2	EE	Eyed egg	SAL	Eyed eggs are fertilized eggs that have developed to the stage where the eyes of the fish can easily be seen with naked eyes through the translucent egg shell. They might be used for stocking purpose.	NA	NA	NA
3	ALV	Alevin with the yolk sac	SAL	Larval salmon that have hatched but have not yet completely absorbed their yolk sacs and usually have not yet emerged from the gravel. http://purl.dataone.org/odo/SALMON_00000403	NA	NA	NA
4	FR	Fry	SAL	A young salmonid at the post-larval stage who has resorbed the yolk sac but remains buried in the gravel. The stage starts at the end of dependence on the yolk sac as the primary source of nutrition to dispersal from the redd.	NA	NA	NA
5	P	Parr	SAL	A young salmonid with parr-marks before migration to the sea and after dispersal from the redd. http://purl.dataone.org/odo/SALMON_00000649	NA	NA	NA
6	SM	Smolt	SAL	1) A young salmonid which has developed silvery coloring on its sides, obscuring the parr marks, and which is about to migrate or has just migrated into the sea, 2) to undergo the transformation from parr to smolt.http://purl.dataone.org/odo/SALMON_00000404	NA	NA	NA
7	PS	Post Smolt	SAL	A young salmonid after the smolt freshwater or estuarine migration. [TODO check this]	|NA	|NA	|NA
8	A	Adult	SAL	Salmonids that have fully developed morphological and meristic characters and that have attained sexual maturity, for salmon this might refer to fishes during their spawning migration in coastal waters, or spawning adults in freshwater. More details can be given on the maturity of the fish using the maturity scale.	NA	NA	NA
9	G	Glass Eel	ANG	Young, unpigmented eel, recruiting from the sea into continental waters. WGEEL consider the glass eel term to include all recruits of the 0+ cohort age. In some cases, however, also includes the early pigmented stages.	NA	NA	NA
10	S	Silver Eel	ANG	Migratory phase following the yellow eel phase. Eel in this phase are characterized by darkened back, silvery belly with a clearly contrasting black lateral line, enlarged eyes. Silver eel undertake downstream migration towards the sea, and subsequently westwards. This phase mainly occurs in the second half of calendar years, although some are observed throughout winter and following spring.	NA	NA	NA
11	GY	Glass Eel + Yellow Eel	ANG	A mixture of glass and yellow eel, some traps have historical set of data where glass eel and yellow eel were not separated, they were dominated by glass eel	|NA	|NA	|NA
12	OG	Ongrown Eel	ANG	Eel that have been held in water tanks for some days or months between first capture and then release to a new water basin, and they have been fed and grown during that time.	NA	NA	NA
13	QG	Quarantined Eel	ANG	Ongrown eel (see definition above) that have been held in isolation between capture and restocking.	NA	NA	NA
14	AL	All Stages	ANG	All stages combined	NA	NA	NA
15	YS	Yellow Eel+ Silver Eel	ANG	Yellow and Silver eel defined below	NA	NA	NA
16	Y	Yellow Eel	ANG	Life-stage resident in continental waters. Often defined as a sedentary phase, but migration within and between rivers, and to and from coastal waters occurs and therefore includes young pigmented eels (‘elvers’ and bootlace). Sometimes is also called Brown eel.	|NA	|NA	|NA
17	E	Egg	TRT	In fish, the term egg usually refers to female haploid gametes.	E	0424ae90-03aa-4e73-8cda-e8745d0b8158	TS_DevStage
18	EE	Eyed egg	TRT	Eyed eggs are fertilized eggs that have developed to the stage where the eyes of the fish can easily be seen with naked eyes through the translucent egg shell. They might be used for stocking purpose.	NA	NA	NA
19	ALV	Alevin with the yolk sac	TRT	Larval salmon that have hatched but have not yet completely absorbed their yolk sacs and usually have not yet emerged from the gravel. http://purl.dataone.org/odo/SALMON_00000403	NA	NA	NA
20	FR	Fry	TRT	A young salmonid at the post-larval stage who has resorbed the yolk sac but remains buried in the gravel. The stage starts at the end of dependence on the yolk sac as the primary source of nutrition to dispersal from the redd.	NA	NA	NA
21	P	Parr	TRT	A young salmonid with parr-marks before migration to the sea and after dispersal from the redd. http://purl.dataone.org/odo/SALMON_00000649	NA	NA	NA
22	SM	Smolt	TRT	1) A young salmonid which has developed silvery coloring on its sides, obscuring the parr marks, and which is about to migrate or has just migrated into the sea, 2) to undergo the transformation from parr to smolt.http://purl.dataone.org/odo/SALMON_00000404	NA	NA	NA
23	PS	Post Smolt	TRT	A young salmonid after the smolt freshwater or estuarine migration. [TODO check this]	|NA	|NA	|NA
24	A	Adult	TRT	Salmonids that have fully developed morphological and meristic characters and that have attained sexual maturity, for salmon this might refer to fishes during their spawning migration in coastal waters, or spawning adults in freshwater. More details can be given on the maturity of the fish using the maturity scale.	NA	NA	NA

Maturity (ts_maturity_mat)

Working on stages, and looking at the ICES vocab, we have decided to include information on maturity.

SQL code to create table tr_maturity_mat

-- maturity table code


DROP TABLE IF EXISTS ref.tr_maturity_mat CASCADE;
CREATE TABLE ref.tr_maturity_mat (
  mat_id SERIAL PRIMARY KEY,
  mat_code TEXT NOT NULL CONSTRAINT uk_mat_code UNIQUE, 
  mat_description TEXT,
  mat_icesvalue character varying(4),  
  mat_icesguid uuid,
  mat_icestablesource text
);

COMMENT ON TABLE ref.tr_maturity_mat IS 'Table of maturity corresponding to the 6 stage scale of the ICES vocabulary';
COMMENT ON COLUMN ref.tr_maturity_mat.mat_id IS 'Integer, primary key of the table';
COMMENT ON COLUMN ref.tr_maturity_mat.mat_code IS 'The code of maturity stage';
COMMENT ON COLUMN ref.tr_maturity_mat.mat_description IS 'Definition of the maturity stage';
COMMENT ON COLUMN ref.tr_maturity_mat.mat_icesvalue IS 'Code (Key) of the maturity in ICES db';
COMMENT ON COLUMN ref.tr_maturity_mat.mat_icesguid IS 'UUID (guid) of ICES, you can access by pasting ';

GRANT ALL ON ref.tr_maturity_mat TO diaspara_admin;
GRANT SELECT ON ref.tr_maturity_mat TO diaspara_read;

The information about the stage mixes information on stage and maturity. So here we add a table of maturity following the 6 stage scale of ICES vocab (Table (tbl_tr_maturity_mat?)).

maturity <- icesVocab::getCodeList('TS_MATURITY')

maturity <- maturity |> 
rename("mat_icesguid"="GUID",  "mat_icesvalue" = "Key", "mat_description" = "Description") |> 
select ( mat_icesvalue, mat_icesguid, mat_description) |>
filter(mat_icesvalue %in% c(61,62,63,64,65,66)) |>
mutate(mat_icestablesource = "TS_MATURITY",
       mat_id = 1:6,
      mat_code = c("Juvenile", "Maturing", "Spawning", "Spent", "Skip", "Abnormal")) |>
select(mat_id, mat_code, mat_description, mat_icesvalue, mat_icesguid, mat_icestablesource)

DBI::dbWriteTable(con_diaspara_admin, "temp_maturity", maturity, overwrite = TRUE)


DBI::dbExecute(con_diaspara_admin, "INSERT INTO ref.tr_maturity_mat
(mat_id, mat_code, mat_description, mat_icesvalue, mat_icesguid, mat_icestablesource)
SELECT mat_id, mat_code, mat_description, mat_icesvalue, mat_icesguid::uuid, mat_icestablesource
FROM temp_maturity")


DBI::dbExecute(con_diaspara_admin, "DROP table temp_maturity")

dbGetQuery(con_diaspara, "SELECT * FROM ref.tr_maturity_mat;") %>% 
knitr::kable() %>% 
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))

Code for missing values

mat_id	mat_code	mat_description	mat_icesvalue	mat_icesguid	mat_icestablesource
1	Juvenile	Juvenile/Immature (6-stage scale)	61	9dab155a-95e6-4dcc-adaf-c8b67febbb60	TS_MATURITY
2	Maturing	Maturing (6-stage scale)	62	32e854ae-0e44-4523-855d-92b8bccdabbe	TS_MATURITY
3	Spawning	Spawning (6-stage scale)	63	85c79865-4505-4b6e-96bb-37e5a18b9c95	TS_MATURITY
4	Spent	Spent (6-stage scale)	64	2637e1c0-8dba-4da8-beba-7c865acfd7b4	TS_MATURITY
5	Skip	Resting/Skip of spawning (6-stage scale)	65	f682c57c-f70e-4c1b-859c-ab77f865c3f6	TS_MATURITY
6	Abnormal	Abnormal (6-stage scale)	66	8165075c-ae3b-49e4-b8ed-0dad8f215e78	TS_MATURITY

ICES areas

Download shapefiles from NAFO => create table following ICES_Areas for GUID FAO major fishing area (27, 21, 37, 34, 31) 27 Atlantic, Northeast 21 Atlantic, Northwest 37 Mediterranean and Black Sea 34 Atlantic, Eastern Central 31 Atlantic, Western Central subarea division subdivision

code 27.9.b.2 area_full area_27 9.b.2

source : GFCM geographical subareas https://www.fao.org/gfcm/data/maps/gsas/fr/ https://gfcmsitestorage.blob.core.windows.net/website/5.Data/ArcGIS/GSAs_simplified_updated_division%20(2).zip

source : NAFO divisions https://www.nafo.int/Data/GIS https://www.nafo.int/Portals/0/GIS/Divisions.zip

source : ICES statistical areas https://gis.ices.dk/shapefiles/ICES_areas.zip https://gis.ices.dk/geonetwork/srv/eng/catalog.search#/metadata/c784a0a3-752f-4b50-b02f-f225f6c815eb

The rest of the word was somewhere on my computer. Cannot trace the source, it’s exaclty the same for NAFO but changed in the med and ICES. For some reasons was not complete in table from wgeel so have to download it again to postgres.

Values for geom have been updated from ICES areas, the new boundaries are different, however, more than the previous ones. The values for Areas and Subareas have not been updated but these are for wide maps so we’ll leave it as it is.

dbExecute(con_diaspara_admin, "DROP TABLE IF EXISTS ref.tr_fishingarea_fia 
CASCADE;")
dbExecute(con_diaspara_admin,
"
  CREATE TABLE ref.tr_fishingarea_fia
  (  
    fia_level TEXT,
    fia_code TEXT,
    fia_status numeric,
    fia_ocean TEXT,
    fia_subocean TEXT,
    fia_area TEXT,
    fia_subarea TEXT,
    fia_division TEXT,
    fia_subdivision TEXT,
    fia_unit TEXT,
    fia_name TEXT NULL,
    geom geometry(MultiPolygon,4326),
    CONSTRAINT tr_fishingarea_fia_pkey PRIMARY KEY (fia_code),
    CONSTRAINT uk_fia_subdivision UNIQUE (fia_unit)
  )
  ;
")


# start with initial FAO dataset

#area_all <- dbGetQuery(con_diaspara_admin, "SELECT * FROM area.\"FAO_AREAS\"
# WHERE f_area IN ('21','27','31','34','37') ;")

# In this table all geom are mixed from unit to division.
# It only make sense to extract for a unique f_level


# TODO add species, wk
dbExecute(con_diaspara_admin, "INSERT INTO ref.tr_fishingarea_fia
SELECT
   initcap(f_level) AS fia_level, 
   f_code AS  fia_code,
   f_status AS  fia_status,
   ocean AS  fia_ocean,
   subocean AS  fia_subocean,
   f_area AS  fia_area,
   f_subarea AS  fia_subarea,
   f_division AS  fia_division,
   f_subdivis AS  fia_subdivision,
   f_subunit AS  fia_unit,
   NULL as fia_name,
   geom 
  FROM area.\"FAO_AREAS\"
  WHERE f_area IN ('21','27','31','34','37') 
") # 187

# Replace values ices
dbExecute(con_diaspara_admin, "UPDATE ref.tr_fishingarea_fia
    set geom = st_transform(are.geom, 4326)
    FROM
    area.\"ICES_Areas_20160601_cut_dense_3857\" are
    WHERE area_full = fia_code;") # 66
# Replace values NAFO (nothing to do ...)



# Replace values GFCM
dbExecute(con_diaspara_admin, "INSERT INTO ref.tr_fishingarea_fia
SELECT 
'Subdivision' AS fia_level, 
f_gsa AS fia_code,
1 AS fia_status,
'Atlantic' AS fia_ocean, 
3 AS fia_subocean, 
f_area, 
f_subarea, 
f_division, 
f_gsa AS fia_subdivision,
NULL AS fia_unit,
smu_name AS fia_name,
geom
FROM area.\"GSAs_simplified_division\";") # 32

dbExecute(con_diaspara_admin, "GRANT ALL ON ref.tr_fishingarea_fia 
          TO diaspara_admin;")
dbExecute(con_diaspara_admin, "GRANT SELECT ON ref.tr_fishingarea_fia 
          TO diaspara_read;")

dbExecute(con_diaspara_admin, "COMMENT ON TABLE ref.tr_fishingarea_fia 
IS 'Table of fishing areas, attention, different levels of geometry
details are present in the table, area, subarea, division, subdivision, unit,
most query will use WHERE 
 fia_level = ''Subdivision''';")

library(rnaturalearth)
world <- ne_countries(scale = "small", returnclass = "sf")


if (file.exists("data/fishingareas_major.Rdata")) 
load("data/fishingareas_major.Rdata") else {
fishing_areas_major <- sf::st_read(con_diaspara,
                          query = "SELECT fia_code, ST_MakeValid(geom) 
                          from ref.tr_fishingarea_fia
                          WHERE fia_level = 'Major'") %>%
  sf::st_transform(4326) 
save(fishing_areas_major, file="data/fishing_areas_major.Rdata")
}
load("data/country_sf.Rdata")
crs_string <- "+proj=ortho +lon_0=-30 +lat_0=30"

ocean <- sf::st_point(x = c(0,0)) %>%
  sf::st_buffer(dist = 6371000) %>%
  sf::st_sfc(crs = crs_string)
area_sf2 <-  fishing_areas_major %>% 
  sf::st_intersection(ocean %>% sf::st_transform(4326)) %>% 
  sf::st_transform(crs = crs_string) 

country_sf2 <-  country_sf %>% 
  sf::st_intersection(ocean %>% sf::st_transform(4326)) %>% 
  sf::st_transform(crs = crs_string) 



g <- ggplot() + 
  geom_sf(data = ocean, fill = "deepskyblue4", color = NA) + 
  geom_sf(data = area_sf2, aes(fill = fia_code), color="white", lwd = .1) + 
  geom_sf(data = world, fill="black", color="grey20") + 
  geom_sf(data= country_sf2 , fill= "grey10",color="grey30")  +
  theme_void()
  #scale_fill_discrete(guide = "none") +

  
# this part is used to avoid long computations
png(filename="images/fig-fishingareas_major.png",width=600, height=600, res=300, bg="transparent")
print(g)
dev.off()

Figure 5: Map of ICES fishing areas at Major level, source NAFO, FAO, ICES, GFCM.

if (file.exists("data/fishingareas_subarea.Rdata")) load("data/fishingareas_subarea.Rdata") else {
fishing_areas_subarea <- sf::st_read(con_diaspara,
                          query = "SELECT fia_code, ST_MakeValid(geom) 
                          from ref.tr_fishingarea_fia
                          WHERE fia_level = 'Subarea'") %>%
  sf::st_transform(4326) 
save(fishing_areas_subarea, file="data/fishing_areas_subarea.Rdata")
}
crs_string <- "+proj=ortho +lon_0=-30 +lat_0=30"

area_sf2 <-  fishing_areas_subarea %>% 
  sf::st_intersection(ocean %>% sf::st_transform(4326)) %>% 
  sf::st_transform(crs = crs_string) # reproject to ortho

g <- ggplot() + 
  geom_sf(data = ocean, fill = "deepskyblue4", color = NA) + 
  geom_sf(data = area_sf2, aes(fill = fia_code), color="white", lwd = .1) + 
  geom_sf(data = world, fill="black", color="grey20") + 
  geom_sf(data= country_sf2 , fill= "grey10",color="grey30")  +
  scale_fill_discrete(guide = "none")  +
  theme_void()

# this part is used to avoid long computations
png(filename="images/fig-fishingareas_subarea.png", bg="transparent")
print(g)
dev.off()

Figure 6: Map of ICES fishing areas at Subarea level, source NAFO, FAO, ICES, GFCM.

if (file.exists("data/fishingareas_division.Rdata")) load("data/fishingareas_division.Rdata") else {
fishing_areas_division <- sf::st_read(con_diaspara,
                          query = "SELECT fia_code, ST_MakeValid(geom) 
                          from ref.tr_fishingarea_fia
                          WHERE fia_level = 'Division'") %>%
  sf::st_transform(4326) 
save(fishing_areas_division, file="data/fishing_areas_division.Rdata")
}

crs_string <- "+proj=ortho +lon_0=-30 +lat_0=30"


ocean <- sf::st_point(x = c(0,0)) %>%
  sf::st_buffer(dist = 6371000) %>%
  sf::st_sfc(crs = crs_string)
  
area_sf2 <-  fishing_areas_division %>% 
  sf::st_intersection(ocean %>% sf::st_transform(4326)) %>% 
  sf::st_transform(crs = crs_string) 

g <- ggplot() + 
  geom_sf(data = ocean, fill = "deepskyblue4", color = NA) + 
  geom_sf(data = area_sf2, aes(fill = fia_code), color="white", lwd = .1) + 
  geom_sf(data = world, fill="black", color="grey20") + 
  geom_sf(data= country_sf2 , fill= "grey10",color="grey30")  +
  scale_fill_discrete(guide = "none") +
  theme_void()

png(filename="images/fig-fishingareas_division.png", bg="transparent")
print(g)
dev.off() 

Figure 7: Map of ICES fishing areas at division level, source NAFO, FAO, ICES, GFCM.

if (file.exists("data/fishingareas_subdivision.Rdata")) load("data/fishingareas_subdivision.Rdata") else {
fishing_areas_subdivision <- sf::st_read(con_diaspara,
                          query = "SELECT fia_code, ST_MakeValid(geom) 
                          from ref.tr_fishingarea_fia
                          WHERE fia_level = 'Subdivision'") %>%
  sf::st_transform(4326) 
save(fishing_areas_subdivision, file="data/fishing_areas_subdivision.Rdata")
}

crs_string <- "+proj=ortho +lon_0=-30 +lat_0=30"


ocean <- sf::st_point(x = c(0,0)) %>%
  sf::st_buffer(dist = 6371000) %>%
  sf::st_sfc(crs = crs_string)
  
area_sf2 <-  fishing_areas_subdivision %>% 
  sf::st_intersection(ocean %>% sf::st_transform(4326)) %>% 
  sf::st_transform(crs = crs_string) 

g <- ggplot() + 
  geom_sf(data = ocean, fill = "deepskyblue4", color = NA) + 
  geom_sf(data = area_sf2, aes(fill = fia_code), color= "white", lwd = .1) + 
  geom_sf(data = world, fill="black", color="grey20") + 
  geom_sf(data= country_sf2 , fill= "grey10",color="grey30")  +
  scale_fill_discrete(guide = "none") +
  theme_void()

png(filename="images/fig-fishingareas_subdivision.png", bg="transparent")
print(g)
dev.off() 

Figure 8: Map of ICES fishing areas at subdivision level, source NAFO, FAO, ICES, GFCM.

SCHEMA datnas and refnas

Create referential for WGNAS

Creating the referential for WGNAS

DROP TABLE IF EXISTS refnas.tr_version_ver CASCADE;
CREATE TABLE refnas.tr_version_ver() inherits (ref.tr_version_ver);

ALTER TABLE refnas.tr_version_ver ADD CONSTRAINT ver_code_pkey PRIMARY KEY (ver_code);
ALTER TABLE refnas.tr_version_ver ADD CONSTRAINT  fk_ver_spe_code FOREIGN KEY (ver_spe_code) 
REFERENCES ref.tr_species_spe(spe_code)
ON UPDATE CASCADE ON DELETE CASCADE;

COMMENT ON TABLE refnas.tr_version_ver
IS 'Table of data or variable version, essentially one datacall or advice, inherits ref.tr_version_ver';

COMMENT ON COLUMN refnas.tr_version_ver.ver_version 
IS 'Version code, stockkey-year-version.';
COMMENT ON COLUMN refnas.tr_version_ver.ver_year 
IS 'Year of assessement.';
COMMENT ON COLUMN refnas.tr_version_ver.ver_spe_code 
IS 'Species code e.g. 'SAL' references tr_species_spe.';
COMMENT ON COLUMN refnas.tr_version_ver.ver_stockkeylabel 
IS 'Ver_stockkeylabel e.g. ele.2737.nea.';
COMMENT ON COLUMN refnas.tr_version_ver.ver_datacalldoi 
IS 'Data call DOI, find a way to retrieve that information 
and update this comment';
COMMENT ON COLUMN refnas.tr_version_ver.ver_version 
IS 'Version code in original database, eg 2,4 for wgnas, dc_2020 for wgeel.';
COMMENT ON COLUMN refnas.tr_version_ver.ver_description 
IS 'Description of the data call / version.';
GRANT ALL ON refnas.tr_version_ver TO diaspara_admin;
GRANT SELECT ON refnas.tr_version_ver TO diaspara_read;


-- values inserted in chunk tr_version_ver insert


DROP TABLE IF EXISTS refnas.tr_metadata_met;

CREATE TABLE refnas.tr_metadata_met(met_oldversion numeric)
INHERITS (ref.tr_metadata_met);


-- ADDING CONSTRAINTS

ALTER TABLE refnas.tr_metadata_met ADD
 CONSTRAINT t_metadata_met_pkey PRIMARY KEY(met_var, met_spe_code);
 
ALTER TABLE refnas.tr_metadata_met ADD 
  CONSTRAINT fk_met_spe_code FOREIGN KEY (met_spe_code)
  REFERENCES ref.tr_species_spe(spe_code) 
  ON DELETE CASCADE
  ON UPDATE CASCADE;

 ALTER TABLE refnas.tr_metadata_met ADD
    CONSTRAINT ck_met_spe_code CHECK (met_spe_code='SAL'); 

 ALTER TABLE refnas.tr_metadata_met ADD
    CONSTRAINT fk_met_wkg_code FOREIGN KEY (met_wkg_code)
  REFERENCES ref.tr_icworkinggroup_wkg(wkg_code) 
  ON DELETE CASCADE
  ON UPDATE CASCADE;

ALTER TABLE refnas.tr_metadata_met ADD
    CONSTRAINT ck_met_wkg_code CHECK (met_wkg_code='WGNAS');

ALTER TABLE refnas.tr_metadata_met ADD
  CONSTRAINT fk_met_ver_code FOREIGN KEY (met_ver_code)
  REFERENCES refnas.tr_version_ver(ver_code) 
  ON DELETE CASCADE
  ON UPDATE CASCADE;

ALTER TABLE refnas.tr_metadata_met ADD
  CONSTRAINT fk_met_oty_code FOREIGN KEY (met_oty_code) 
  REFERENCES ref.tr_objecttype_oty (oty_code) ON DELETE CASCADE
  ON UPDATE CASCADE;

ALTER TABLE refnas.tr_metadata_met ADD
  CONSTRAINT fk_met_nim_code FOREIGN KEY (met_nim_code) 
  REFERENCES ref.tr_nimble_nim (nim_code) ON DELETE CASCADE
  ON UPDATE CASCADE;

ALTER TABLE refnas.tr_metadata_met ADD  
  CONSTRAINT fk_met_mtr_code FOREIGN KEY (met_mtr_code)
  REFERENCES ref.tr_metric_mtr(mtr_code)
  ON DELETE CASCADE
  ON UPDATE CASCADE;

ALTER TABLE refnas.tr_metadata_met ADD
  CONSTRAINT fk_met_uni_code FOREIGN KEY (met_uni_code)
  REFERENCES ref.tr_units_uni(uni_code)
  ON DELETE CASCADE
  ON UPDATE CASCADE;

ALTER TABLE refnas.tr_metadata_met ADD
  CONSTRAINT fk_met_cat_code FOREIGN KEY (met_cat_code)
  REFERENCES ref.tr_category_cat(cat_code)
  ON DELETE CASCADE
  ON UPDATE CASCADE;

ALTER TABLE refnas.tr_metadata_met ADD
  CONSTRAINT fk_met_des_code FOREIGN KEY (met_des_code)
  REFERENCES ref.tr_destination_des(des_code)
  ON DELETE CASCADE
  ON UPDATE CASCADE;
--  COMMENTS FOR WGNAS


COMMENT ON TABLE refnas.tr_metadata_met IS 
'Table (metadata) of each variable (parameter) in the wgnas database.';
COMMENT ON COLUMN refnas.tr_metadata_met.met_var 
IS 'Variable code, primary key on both met_spe_code and met_var.';
COMMENT ON COLUMN refnas.tr_metadata_met.met_spe_code 
IS 'Species, SAL primary key on both met_spe_code and met_var.';
COMMENT ON COLUMN refnas.tr_metadata_met.met_ver_code 
IS 'Code on the version of the model, see table tr_version_ver.';
COMMENT ON COLUMN refnas.tr_metadata_met.met_oty_code 
IS 'Object type, single_value, vector, matrix see table tr_objecttype_oty.';
COMMENT ON COLUMN refnas.tr_metadata_met.met_nim_code 
IS 'Nimble type, one of data, constant, output, other.';
COMMENT ON COLUMN refnas.tr_metadata_met.met_dim 
IS 'Dimension of the Nimble variable, use {10, 100, 100} 
to insert the description of an array(10,100,100).';
COMMENT ON COLUMN refnas.tr_metadata_met.met_dimname 
IS 'Dimension of the variable in Nimble, use {''year'', ''stage'', ''area''}.';
COMMENT ON COLUMN refnas.tr_metadata_met.met_modelstage 
IS 'Currently one of fit, other, First year.';
COMMENT ON COLUMN refnas.tr_metadata_met.met_type 
IS 'Type of data in the variable, homewatercatches, InitialISation first year,
abundance ....';
COMMENT ON COLUMN refnas.tr_metadata_met.met_location 
IS 'Describe process at sea, e.g. Btw. FAR - GLD fisheries, or Aft. Gld fISheries.';
COMMENT ON COLUMN refnas.tr_metadata_met.met_fishery 
IS 'Description of the fishery.';
COMMENT ON COLUMN ref.tr_metadata_met.met_des_code 
IS 'Destination of the fish, e.g. Released (alive), Seal damage,
Removed (from the environment), references table tr_destination_des., this is currently only used by WGBAST,
so can be kept NULL';
COMMENT ON COLUMN refnas.tr_metadata_met.met_uni_code 
IS 'Unit, refnaserences table tr_unit_uni.';
COMMENT ON COLUMN refnas.tr_metadata_met.met_cat_code 
IS 'Broad category of data or parameter, 
catch, effort, biomass, mortality, count ...refnaserences table tr_category_cat.';
COMMENT ON COLUMN refnas.tr_metadata_met.met_mtr_code 
IS 'Code of the metric, refnaserences tr_metric_mtr, Estimate, Bound, SD, CV ....';
COMMENT ON COLUMN refnas.tr_metadata_met.met_definition 
IS 'Definition of the metric.';
COMMENT ON COLUMN refnas.tr_metadata_met.met_deprecated
IS'Is the variable still used ?';


GRANT ALL ON refnas.tr_metadata_met TO diaspara_admin;
GRANT SELECT ON refnas.tr_metadata_met TO diaspara_read;

Import the metadata table

# TODO create tr_area_are
# tr_metadata_met

metadata <- dbGetQuery(con_salmoglob, "SELECT * FROM metadata")

res <- dbGetQuery(con_diaspara, "SELECT * FROM datnas.tr_metadata_met;")
clipr::write_clip(colnames(res))

# unique(metadata$metric)
# dbGetQuery(con_diaspara, "SELECT * FROM ref.tr_metric_mtr")

tr_metadata_met <-
    data.frame(
        met_var = metadata$var_mod,
        met_spe_code = "SAL",
        met_wkg_code = "WGNAS",
        met_ver_code = "SAL-2024-1", # no data on version in metadata
        met_oty_code = metadata$type_object,
         met_nim_code =  case_when(
            "Data_nimble"== metadata$nimble ~ "Data",
            "Const_nimble" == metadata$nimble ~ "Parameter constant",
            "Output" == metadata$nimble ~ "Output",
            "other" == metadata$nimble ~ "Other",
            .default = NA),
        met_dim = paste0(
            "{", metadata$dim1, ",",
            replace_na(metadata$dim2, 0), ",",
            replace_na(metadata$dim3, 0), "}"
        ),
        met_dimname = paste0(
            "{'", metadata$name_dim1, "',",
            ifelse(metadata$name_dim2 == "", "NULL", paste0("'", metadata$name_dim2, "'")), ",",
            ifelse(metadata$name_dim3 == "", "NULL", paste0("'", metadata$name_dim3, "'")), "}"
        ),
        met_modelstage = metadata$model_stage,
        met_type = metadata$type,
        met_location = metadata$locations,
        met_fishery = metadata$fishery,
        met_mtr_code = case_when(metadata$metric == "Standard deviation" ~ "SD",
            metadata$metric == "Coefficient of variation" ~ "CV",
            .default = metadata$metric
        ),
        met_des_code = NA,
        met_uni_code = NA, # (TODO)
        met_cat_code = case_when(
            grepl("Origin distribution in sea catches", metadata$type) ~ "Other",
            grepl("catch", metadata$type) ~ "Catch",
            grepl("harvest rates", metadata$type) ~ "Mortality",
            grepl("Survival rate", metadata$type) ~ "Mortality",
            grepl("Returns", metadata$type) ~ "Count",
            grepl("Fecundity", metadata$type) ~ "Life trait",
            grepl("Sex ratio", metadata$type) ~ "Life trait",
            grepl("Maturation rate", metadata$type) ~ "Life trait",
            grepl("Proportion", metadata$type) ~ "Other",
            grepl("Stocking", metadata$type) ~ "Count",
            grepl("Smolt age structure", metadata$type) ~ "Life trait",
            grepl("Time spent", metadata$type) ~ "Life trait",
            grepl("Conservation limits", metadata$type) ~ "Conservation limit",
            grepl("Abundance", metadata$type) ~ "Count",
            grepl("Demographic transitions", metadata$type) ~ "Other",
            grepl("year", metadata$type) ~ "Other",
            grepl("Number of SU", metadata$type) ~ "Other",
            grepl("Prior", metadata$type) ~ "Other",
            grepl("Number of SU", metadata$type) ~ "Other",
            .default = NA
        ),
        met_definition = metadata$definition,
        met_deprecated = NA
        
    )

res <- dbWriteTable(con_diaspara_admin, "tr_metadata_met_temp", 
tr_metadata_met, overwrite = TRUE)
dbExecute(con_diaspara_admin, "INSERT INTO refnas.tr_metadata_met 
SELECT 
 met_var,
 met_spe_code,
 met_wkg_code,
 met_ver_code,
 upper(substring(met_oty_code from 1 for 1)) ||
          substring(met_oty_code from 2 for length(met_oty_code)), 
 met_nim_code,
 met_dim::INTEGER[], 
 met_dimname::TEXT[], 
 met_modelstage, 
 met_type,
 met_location, 
 met_fishery, 
 met_mtr_code, 
 met_des_code, 
 met_uni_code,
 met_cat_code, 
 met_definition, 
 met_deprecated
FROM tr_metadata_met_temp")

dbExecute(con_diaspara_admin, "DROP TABLE tr_metadata_temp CASCADE;")

TODO diaspara: Integrate new data from REFNAS

Collect the latest version of the db from Jerome, there are new variables there.

After integration,, the table of metadata from WGNAS is not changed much, apart from adapting to the new referentials. The table is show in Table Table 13.

Table 18: Content of the refnas metadata table

met_var	met_spe_code	met_wkg_code	met_ver_code	met_oty_code	met_nim_code	met_dim	met_dimname	met_modelstage	met_type	met_location	met_fishery	met_mtr_code	met_des_code	met_uni_code	met_cat_code	met_definition	met_deprecated	met_oldversion
log_C5_NAC_1_lbnf_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	neNF fishery	SD	NA	NA	Catch	Standard-deviation of observed catches (log scale) of NAC 1 SW by early Newfoundland fishery	NA	NA
log_C5_NAC_2_lbnf_lab_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	LB fishery	SD	NA	NA	Catch	Standard-deviation of observed catches (log scale) of NAC Labrador-origin 1 SW by late Newfoundland, Labrador and Saint Pierre et Miquelon fisheries	NA	NA
log_C5_NAC_2_lbnf_oth_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	LB/SPM/swNF fishery	SD	NA	NA	Catch	Standard-deviation of observed catches (log scale) of NAC Labrador-excluded 1 SW by late Newfoundland, Labrador and Saint Pierre et Miquelon fisheries	NA	NA
log_C5_NEC_1_far_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	FAR fishery	SD	NA	NA	Catch	Standard-deviation of observed total catches (log scale) of 1SW adults from the NEC complex in Faroe islands	NA	NA
log_C8_2_gld_tot_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Gld fisheries	GLD fishery	SD	NA	NA	Catch	Standard-deviation of observed total catches (log scale) of 2SW adults (mixed complexes) in Greenland	NA	NA
log_C8_NAC_1_lbnf_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	neNF fishery	SD	NA	NA	Catch	Standard-deviation of observed catches (log scale) of NAC 1 SW by Labrador/Newfoundland fishery	NA	NA
log_C8_NAC_3_lbnf_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	neNF fishery	SD	NA	NA	Catch	Standard-deviation of observed catches (log scale) of NAC 2 SW by early Newfoundland fishery	NA	NA
log_C8_NAC_4_lbnf_lab_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{47,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	LB fishery	SD	NA	NA	Catch	Standard-deviation of observed catches (log scale) of NAC Labrador-origin 2 SW by late Newfoundland, Labrador and Saint Pierre et Miquelon fisheries	NA	NA
log_C8_NAC_4_lbnf_oth_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{52,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Aft. Gld fisheries	LB/SPM/swNF fishery	SD	NA	NA	Catch	Standard-deviation of observed catches (log scale) of NAC Labrador-excluded 2 SW by late Newfoundland, Labrador and Saint Pierre et Miquelon fisheries	NA	NA
log_C8_NEC_1_far_sd	SAL	WGNAS	SAL-2024-1	Vector	Parameter constant	{50,0,0}	{'Year',NULL,NULL}	Fit	Sea catches	Bef. Gld fisheries	FAR fishery	SD	NA	NA	Catch	Standard-deviation of observed total catches (log scale) of 2SW individuals from the NEC complex before maturation in Faroe islands	NA	NA

Import the database table from the salmoglob (WGNAS) database

For data we first need to create the table.

The code for creating t_stock_sto is listed below

SQL code to create tables (TODO HERE work in progress ….)

-- before working there should have been these constraints in the salmoglob DB

DBI::dbGetQuery(con_salmoglob, ALTER TABLE "database" 
ADD CONSTRAINT c_uk_area_varmod_year_location_age UNIQUE  (area, var_mod, "year", "location", age);
ALTER TABLE "database_archive" ADD CONSTRAINT c_uk_archive_version_area_varmod_year_location_age 
UNIQUE  ("version", area, var_mod, "year", "location", age);

-- For the archive db, the constraint is not working meaning that we have duplicated values

SELECT DISTINCT met_nim_code FROM refnas.tr_metadata_met
JOIN refsalmoglob."database" ON var_mod = met_var

WHERE  met_cat_code ='Other'


-- This will create the main table to hold the stock data
-- I'm currenlty putting foreign key to ref but this is just for show because this table
-- will only contain inherited valeus




CREATE TABLE dat.t_stock_sto (
  sto_id serial4 NOT NULL,
  sto_met_var NOT NULL CONSTRAINT fk_met_var FOREIGN KEY REFERENCES "ref".tr_metadata_met(met_var) ,
  sto_year int4 NULL,
  sto_value numeric NULL,
  sto_are_code TEXT NOT NULL 
  CONSTRAINT fk_sto_are_code FOREIGN KEY REFERENCES "ref".tr_area_are(are_code) 
  ON UPDATE CASCADE ON DELETE CASCADE,
  -- NOTE : here I'm referencing the code because it's more easy to grasp than a number, but the id is the primary key.
  -- should work stil but requires a unique constraint on code (which we have set).
  eel_emu_nameshort varchar(20) NOT NULL,
  eel_cou_code varchar(2) NULL,
  eel_lfs_code varchar(2) NOT NULL,
  eel_hty_code varchar(2) NULL,
  eel_area_division varchar(254) NULL,
  eel_qal_id int4 NOT NULL,
  eel_qal_comment text NULL,
  eel_comment text NULL,
  eel_datelastupdate date NULL,
  eel_missvaluequal varchar(2) NULL,
  eel_datasource varchar(100) NULL,
  eel_dta_code text DEFAULT 'Public'::text NULL,
  sto_wkg_code text NOT NULL
  CONSTRAINT ck_eel_missvaluequal CHECK ((((eel_missvaluequal)::text = 'NP'::text) OR ((eel_missvaluequal)::text = 'NR'::text) OR ((eel_missvaluequal)::text = 'NC'::text) OR ((eel_missvaluequal)::text = 'ND'::text))),
  CONSTRAINT ck_notnull_value_and_missvalue CHECK ((((eel_missvaluequal IS NULL) AND (eel_value IS NOT NULL)) OR ((eel_missvaluequal IS NOT NULL) AND (eel_value IS NULL)))),
  CONSTRAINT ck_qal_id_and_missvalue CHECK (((eel_missvaluequal IS NULL) OR (eel_qal_id <> 0))),
  CONSTRAINT ck_removed_typid CHECK (((COALESCE(eel_qal_id, 1) > 5) OR (eel_typ_id <> ALL (ARRAY[12, 7, 5])))),
  CONSTRAINT t_eelstock_eel_pkey PRIMARY KEY (eel_id),
  CONSTRAINT c_fk_area_code FOREIGN KEY (eel_area_division) REFERENCES "ref".tr_faoareas(f_division) ON UPDATE CASCADE,
  CONSTRAINT c_fk_cou_code FOREIGN KEY (eel_cou_code) REFERENCES "ref".tr_country_cou(cou_code),
  CONSTRAINT c_fk_eel_dta_code FOREIGN KEY (eel_dta_code) REFERENCES "ref".tr_dataaccess_dta(dta_code) ON UPDATE CASCADE,
  CONSTRAINT c_fk_emu FOREIGN KEY (eel_emu_nameshort,eel_cou_code) REFERENCES "ref".tr_emu_emu(emu_nameshort,emu_cou_code),
  CONSTRAINT c_fk_hty_code FOREIGN KEY (eel_hty_code) REFERENCES "ref".tr_habitattype_hty(hty_code) ON UPDATE CASCADE,
  CONSTRAINT c_fk_lfs_code FOREIGN KEY (eel_lfs_code) REFERENCES "ref".tr_lifestage_lfs(lfs_code) ON UPDATE CASCADE,
  CONSTRAINT c_fk_qal_id FOREIGN KEY (eel_qal_id) REFERENCES "ref".tr_quality_qal(qal_id) ON UPDATE CASCADE,
  CONSTRAINT c_fk_typ_id FOREIGN KEY (eel_typ_id) REFERENCES "ref".tr_typeseries_typ(typ_id) ON UPDATE CASCADE





);

COMMENT ON TABLE dat.t_stock_sto IS 
'Table including the stock data from the different schema, dateel, datnas.... This table should be empty, as there are no
constraints set and it''s getting its content by inheritance from other tables in other schema';
COMMENT ON COLUMN dat.t_stock_sto.sto_id IS 'Integer serial identifying. Only unique in this table
when looking at the pair, sto_id, sto_wkg_code';
COMMENT ON COLUMN dat.t_stock_sto.sto_met_var IS 'Name of the variable in the database, this was previously named
var_mod in the salmoglob database and eel_typ_id in the wgeel database';
COMMENT ON COLUMN dat.t_stock_sto.sto_year IS 'Year';
COMMENT ON COLUMN dat.t_stock_sto.sto_value IS 'Value';
COMMENT ON COLUMN dat.t_stock_sto_sto_wkg_code IS 'Code of the working group, one of
WGBAST, WGEEL, WGNAS, WKTRUTTA';

IS 'Variable code, primary key on both met_spe_code and met_var.';
COMMENT ON COLUMN ref.tr_metadata_met.met_spe_code 
COMMENT ON COLUMN sto_id



-- HERE IS THE CODE TO CREATE THE MAIN STOCK TABLE FOR WEEL

-- DROP TABLE datawg.t_eelstock_eel;

CREATE TABLE datawg.t_eelstock_eel (
  eel_id serial4 NOT NULL,
  eel_typ_id int4 NOT NULL,
  eel_year int4 NOT NULL,
  eel_value numeric NULL,
  eel_emu_nameshort varchar(20) NOT NULL,
  eel_cou_code varchar(2) NULL, -- we don't need that one IF we have an emu
  eel_lfs_code varchar(2) NOT NULL,
  eel_hty_code varchar(2) NULL,
  eel_area_division varchar(254) NULL,
  eel_qal_id int4 NOT NULL,
  eel_qal_comment text NULL,
  eel_comment text NULL,
  eel_datelastupdate date NULL,
  eel_missvaluequal varchar(2) NULL,
  eel_datasource varchar(100) NULL,
  eel_dta_code text DEFAULT 'Public'::text NULL,
  CONSTRAINT ck_eel_missvaluequal CHECK ((((eel_missvaluequal)::text = 'NP'::text) OR ((eel_missvaluequal)::text = 'NR'::text) OR ((eel_missvaluequal)::text = 'NC'::text) OR ((eel_missvaluequal)::text = 'ND'::text))),
  CONSTRAINT ck_notnull_value_and_missvalue CHECK ((((eel_missvaluequal IS NULL) AND (eel_value IS NOT NULL)) OR ((eel_missvaluequal IS NOT NULL) AND (eel_value IS NULL)))),
  CONSTRAINT ck_qal_id_and_missvalue CHECK (((eel_missvaluequal IS NULL) OR (eel_qal_id <> 0))),
  CONSTRAINT ck_removed_typid CHECK (((COALESCE(eel_qal_id, 1) > 5) OR (eel_typ_id <> ALL (ARRAY[12, 7, 5])))),
  CONSTRAINT t_eelstock_eel_pkey PRIMARY KEY (eel_id),
  CONSTRAINT c_fk_area_code FOREIGN KEY (eel_area_division) REFERENCES "ref".tr_faoareas(f_division) ON UPDATE CASCADE,
  CONSTRAINT c_fk_cou_code FOREIGN KEY (eel_cou_code) REFERENCES "ref".tr_country_cou(cou_code),
  CONSTRAINT c_fk_eel_dta_code FOREIGN KEY (eel_dta_code) REFERENCES "ref".tr_dataaccess_dta(dta_code) ON UPDATE CASCADE,
  CONSTRAINT c_fk_emu FOREIGN KEY (eel_emu_nameshort,eel_cou_code) REFERENCES "ref".tr_emu_emu(emu_nameshort,emu_cou_code),
  CONSTRAINT c_fk_hty_code FOREIGN KEY (eel_hty_code) REFERENCES "ref".tr_habitattype_hty(hty_code) ON UPDATE CASCADE,
  CONSTRAINT c_fk_lfs_code FOREIGN KEY (eel_lfs_code) REFERENCES "ref".tr_lifestage_lfs(lfs_code) ON UPDATE CASCADE,
  CONSTRAINT c_fk_qal_id FOREIGN KEY (eel_qal_id) REFERENCES "ref".tr_quality_qal(qal_id) ON UPDATE CASCADE,
  CONSTRAINT c_fk_typ_id FOREIGN KEY (eel_typ_id) REFERENCES "ref".tr_typeseries_typ(typ_id) ON UPDATE CASCADE
);
CREATE UNIQUE INDEX idx_eelstock_1 ON datawg.t_eelstock_eel USING btree (eel_year, eel_lfs_code, eel_emu_nameshort, eel_typ_id, eel_hty_code, eel_qal_id, eel_area_division) WHERE ((eel_hty_code IS NOT NULL) AND (eel_area_division IS NOT NULL));
CREATE UNIQUE INDEX idx_eelstock_2 ON datawg.t_eelstock_eel USING btree (eel_year, eel_lfs_code, eel_emu_nameshort, eel_typ_id, eel_qal_id, eel_area_division) WHERE ((eel_hty_code IS NULL) AND (eel_area_division IS NOT NULL));
CREATE UNIQUE INDEX idx_eelstock_3 ON datawg.t_eelstock_eel USING btree (eel_year, eel_lfs_code, eel_emu_nameshort, eel_typ_id, eel_hty_code, eel_qal_id) WHERE ((eel_hty_code IS NOT NULL) AND (eel_area_division IS NULL));
CREATE UNIQUE INDEX idx_eelstock_4 ON datawg.t_eelstock_eel USING btree (eel_year, eel_lfs_code, eel_emu_nameshort, eel_typ_id, eel_qal_id) WHERE ((eel_hty_code IS NULL) AND (eel_area_division IS NULL));

-- Table Triggers

CREATE TRIGGER trg_check_no_ices_area AFTER
INSERT
    OR
UPDATE
    ON
    datawg.t_eelstock_eel FOR EACH ROW EXECUTE FUNCTION datawg.check_no_ices_area();
CREATE TRIGGER trg_check_the_stage AFTER
INSERT
    OR
UPDATE
    ON
    datawg.t_eelstock_eel FOR EACH ROW EXECUTE FUNCTION datawg.check_the_stage();
CREATE TRIGGER update_eel_time BEFORE
INSERT
    OR
UPDATE
    ON
    datawg.t_eelstock_eel FOR EACH ROW EXECUTE FUNCTION datawg.update_eel_last_update();
CREATE TRIGGER trg_check_emu_whole_aquaculture AFTER
INSERT
    OR
UPDATE
    ON
    datawg.t_eelstock_eel FOR EACH ROW EXECUTE FUNCTION datawg.checkemu_whole_country();

While creating the table, we stumbled on some issues (which we have put on the github site for details):

• duplicated values in archive DB

Some of the variables in salmoglob have no year dimension, this leads to dropping the non null constraint on year. We need to check for possible impact in the eel db.

• NULL values allowed for year

Import the stock_sto

# TODO create tr_area_are
# tr_metadata_met

stock <- dbGetQuery(con_salmoglob, "SELECT * FROM database")

# TODO HERE .....

WORK IN PROGRESS….

This document is still in construction

Acknowledgements

• Data source : EuroGeographics and UN-FAO for countries

References

ICES. 2024. “The Second ICES/NASCO Workshop on Salmon Mortality at Sea (WKSalmon2; Outputs from 2022 Meeting).” ICES Scientific Reports. https://doi.org/10.17895/ICES.PUB.22560790.