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Record W7133273347

Sea Scallop Southern Gulf of St. Lawrence

2024· other· en· W7133273347 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 · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersFisheries and Oceans Canada
KeywordsScallopFishingStock (firearms)Climate changeEcosystemMarine ecosystemFish stock
DOInot available

Abstract

fetched live from OpenAlex

Status The exploitable biomass estimate of the southern Gulf of St. Lawrence (sGSL) Sea Scallop stock is in the Critical Zone of the Precautionary Approach (PA) Framework and has a very high (greater than 99%) probability of being in the Critical Zone. Trends Initial biomass of the sGSL Sea Scallop stock was high (~4,000 tonnes of meat weight, t) prior to the onset of commercial fishing and rapidly declined below the limit reference point (LRP) over a period of approximately 10 years; biomass has remained stable below the LRP since 1982. Biomass estimates have shown a slight increase toward the LRP in the last three years (2021-2023). Recruit numbers peaked in 2021 for the survey time series (2019-2023). Clapper index (proxy of natural mortality) has also been increasing in recent years. Ecosystem and Climate Change Considerations Because Sea Scallops are mostly sedentary molluscs, the sGSL Sea Scallop stock is susceptible to climate change stressors. Sea Scallops are vulnerable to temperatures exceeding 21 °C. Maximum summer bottom temperatures in the Northumberland Strait have increased steadily since 1995. The number of days exceeding 21 °C per year has also been increasing. Stock Advice The estimated biomass of the sGSL Sea Scallop stock dropped below the LRP in 1982 and has remained there since. Stock projections are not available.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.116
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.004

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 head0.237
Teacher spread0.227 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
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→