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Record W7133279500

Sentinel Surveys 1995-2020 – Catch rates and biological information on Atlantic Cod (Gadus morhua) in NAFO Divisions 2J3KL

2023· other· en· W7133279500 on OpenAlexafffund
L. G. S. Mello, M. R. Simpson, D.‏ Maddock Parsons

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsGovernment of CanadaFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsStratumFishingFish <Actinopterygii>BycatchAtlantic cod
DOInot available

Abstract

fetched live from OpenAlex

Catch rates and biological information of Atlantic Cod from the Sentinel survey program in Northwest Atlantic Fisheries Organization (NAFO) Divisions (Divs.) 2J3KL are updated for 2018. Temporal trends in gillnet (3¼ and 5½ inch mesh) and linetrawl unstandardized catch rates were initially similar for all gears, with relatively high values at the beginning of each time-series, followed by sharp declines in the late-1990s, early-2000s. Catch rates for small mesh gillnet and linetrawl oscillated around or below the historical mean catch rate thereafter, and increased for large mesh gillnet until 2014–15. Catch rates for all gears declined since then. Mean catch rate for small mesh gillnet was consistently higher than that of large mesh gillnet for most of the time-series. Standardized age-disaggregated catch rate for large mesh gillnet in the Northern area was stable at low levels in 1995–2004 (mostly ≤6 year-old fish), then increased rapidly and peaked in 2015 before declining over 2016–17. The contribution of ≥7 year-old fish increased considerably since 2012. Catch rates in the Central area were higher at the beginning of the time-series (mostly 6–8 year-old fish), declined rapidly to their lowest values in 2002, and then followed a pattern similar to that of the Northern area. Catch rates in the Southern area declined rapidly over 1998–2002, then remained stable at low levels. Catch rates for small mesh gillnet in Northern and Central areas indicated patterns similar to those of large mesh size gillnet. In the Southern area, catch rates declined until 2014, then increased by several folds over 2015–16. Temporal trend for linetrawl (Central area) was also similar to those of gillnets in Northern and Central areas (mostly 3–8 year-old fish). Three to five year-old fish were well-represented in 1995–2008, but declined thereafter. Age-aggregated catch rates showed patterns similar to those of age-disaggregated estimates in all cases. Large mesh gillnet and linetrawl captured larger fish from specific size ranges; whereas the small mesh gillnet retaining small and large fish from multiple length-classes. Indices of physiological condition for both males and females cod (Fulton’s condition factor, Hepatosomatic Index, and Gonadosomatic Index) varied seasonally and annually. Total removals (control plus experimental sites, all gears combined) of Atlantic Cod caught in Divs. 2J3KL Sentinel surveys (1995–2017) peaked at 388 t in 1998, declined to 92 t in 2003, reached 270 t annually over 2012–15, and then declined to 173 t in 2017. Several fish species were recorded as Sentinel bycatch in 1995–2017: American Plaice and Winter Flounder were the most common in large mesh gillnet.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.455

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.250
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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Same venueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du CanadaFrench-language works237,207