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Record W7133288143

Summary of factors that affect survey and fishing catchability and data available regarding the NAFO Subarea 0+1 (offshore) Greenland Halibut (Reinhardtius hippoglossoides) stock and fishery

2023· other· en· W7133288143 on OpenAlexaboutno aff
Kevin J. Hedges, Dayanne Raffoul

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersPinngortitaleriffik
KeywordsFishingStock assessmentTrawlingHalibutRepresentativeness heuristicBottom trawlingStock (firearms)HaddockBycatch
DOInot available

Abstract

fetched live from OpenAlex

Fishery-independent stock assessment surveys rely on consistent sampling methods, equipment and strategies to minimize variability in the data that could mask or confound trends in the assessed stock. Surveys typically use the same vessel and fishing gear each year as changes in either can affect the catchability of species during the survey, and therefore, the representativeness of the survey data. An unrecognized decline or increase in catchability could be interpreted as a decline or increase, respectively, in stock status. These observations could then lead to ill-informed advice to reduce or increase the level of harvest, respectively, producing hidden economic or conservation consequences. Factors that affect catchability, particularly during trawling surveys, are summarized here to help inform data considerations when there are changes in the vessel or gear used to conduct surveys. Data pertaining to the Greenland Halibut (Reinhardtius hippoglossoides) stock in the Northwest Atlantic Fisheries Organization (NAFO) Subareas 0+1 (offshore) (GH-0+1) are collected during several fishery-independent surveys, as well as through commercial logbooks and at-sea observer records. Fisheries and Oceans Canada (DFO) and the Greenland Institute of Natural Resources (GINR) use data from groundfish-focused surveys conducted in Divisions 0A-South and 1CD and a shrimp and fish focused survey in Divisions 1A to F to assess the status of the GH-0+1 stock. However, additional data are available from the Canadian Northern Shrimp Research Foundation survey and a few less frequent surveys conducted by DFO and the Greenland Institute of Natural Resources (GINR) in Divisions 0A-North, 0B and 1AB. This document summarizes the methods used for each survey, the types of data collected and the years and areas where surveys occurred, to provide an overview of data available for assessment and modelling of the GH-0+1 stock.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.964
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.039
GPT teacher head0.263
Teacher spread0.224 · 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 routes1
Has abstractyes

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