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Record W7133281622

Gulf of St. Lawrence (4RST) Atlantic Halibut Stock Assessment in 2024

2025· other· en· W7133281622 on OpenAlexfundno aff
Fisheries and Oceans Canada, Pêches et Océans Canada

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersFisheries and Oceans Canada
KeywordsFishingStock (firearms)Maximum sustainable yieldStock assessmentClimate changeBiomass (ecology)EcosystemOverfishing
DOInot available

Abstract

fetched live from OpenAlex

Status The 2024 exploitable biomass is estimated at 81,392 t, and is above the proposed upper stock reference (USR) with a very high probability, placing the stock in the Healthy Zone of the Precautionary Approach. The fishing mortality rate (F) in 2024 is estimated at 0.03 and is below the maximum sustainable yield exploitation rate (Fmsy) estimated by the model with a very high probability. Trends Exploitable biomass has been increasing since the early 2000s, and is currently at its highest level since 1983. Recruitment has been stable at high levels since 2010. Fishing mortality rate (F) has been stable since 2012, at around 30% of Fmsy. Ecosystem and Climate Change Considerations The observed rise in water temperatures in the Gulf of St. Lawrence does not appear to be adversely affecting the survival and development of Atlantic halibut. Warming could continue to improve habitat conditions. Stock Advice Constant catch scenarios (2,466 to 4,932 t) indicate, with probabilities greater than 99%, that exploitable biomass will remain above the proposed USR over the two projected years (2025-26 and 2026-27). Under these catch scenarios, exploitable biomass would vary between a 4% increase and a 3% decrease over two years. The fishing mortality rates (F) corresponding to the catch scenarios evaluated all remain under Fmsy.

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 score0.278
Threshold uncertainty score0.560

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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
Published2025
Admission routes1
Has abstractyes

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