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Record W7133276602

Shrimp stock status indicators in the Eastern Assessment Zone

2024· other· en· W7133276602 on OpenAlexaboutno 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
Fundersnot available
KeywordsShrimpStock assessmentStock (firearms)Fish stockFisheries management
DOInot available

Abstract

fetched live from OpenAlex

Fisheries and Oceans Canada (DFO) Resource Management (RM) has requested Science advice on the status of the two species of shrimp, Northern Shrimp (Pandalus borealis) and Striped Shrimp (Pandalus montagui) in the waters adjacent to Nunavut and Nunavik. Both species in the Eastern Assessment Zone (EAZ) were last fully assessed in 2023 (DFO 2023). Full assessments are carried out every two years with stock status updates in the intervening years. The next full assessment is scheduled for 2025. This assessment follows the framework developed in 2007 for Northern Shrimp off Labrador and the northeastern coast of Newfoundland (DFO 2007). The Limit Reference Point (LRP) was updated and an updated Upper Stock Reference point (USR) was proposed in 2020 (DFO 2020). A series of fishery independent surveys and fishery data formed the basis of the current assessment. This Science Response Report results from the regional peer review of February 6, 2024 on the Stock Update of Northern Shrimp (Pandalus borealis) and Striped Shrimp (P. montagui) in the Eastern Assessment Zone and Western Assessment Zone, February 2024. (IFMP). Additional publications from this meeting will be posted on the Fisheries and Oceans Canada (DFO) Science Advisory Schedule as they become 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)0.001
metaresearch head score (Gemma)0.002
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 score0.732
Threshold uncertainty score0.539

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
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 head0.267
Teacher spread0.257 · 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
Published2024
Admission routes1
Has abstractyes

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