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Record W7133274121

2018 4X5Y Atlantic Cod Framework Data Inputs

2022· other· en· W7133274121 on OpenAlexaboutno aff
I. Andrushchenko, D. Clark, F. Irvine, E. MacEachern, R. Martin, Y. Chen Wang

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsFishingStock assessmentStock (firearms)EcosystemFish stockVariety (cybernetics)Cod fisheries
DOInot available

Abstract

fetched live from OpenAlex

In 2018, the Maritimes Region of Fisheries and Oceans Canada will be undertaking a framework assessment of 4X5Y Atlantic Cod (Gadus morhua). Such assessments are intended to be a comprehensive review of the biology, stock structure, the fishery, abundance indices, current assessment methodology and approaches for determining acceptable harvest limits. This document explores a variety of data sources available for 4X5Y Cod, including tagging projects (1980s–2000s), commercial fishery information (1980–2017) and departmental research surveys (1983–2017). The results of stock structure analyses, spatial and temporal patterns in distribution, bycatch, biological attributes (length and weight at age, condition factor, growth and maturity) and updates to data inputs for stock assessment (fishery catch at age, survey indices of abundance, relative fishing mortality, total mortality) are also described here, as well as any persistent data gaps. Finally, a preliminary review of ecosystem indicators and accompanying data considered relevant to the 4X5Y Cod assessment is summarized.

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)0.005
metaresearch head score (Gemma)0.015
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.203
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.010
Science and technology studies0.0020.000
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0540.027

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 head0.017
GPT teacher head0.258
Teacher spread0.241 · 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
GenreDataset

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
Published2022
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