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Record W2023474632 · doi:10.1139/f09-057

Mixed-fishery or ecosystem conundrum? Multispecies considerations inform thinking on long-term management of North Sea demersal stocks

2009· article· en· W2023474632 on OpenAlexvenueno aff
Steven Mackinson, Barrie Deas, Doug Beveridge, John Casey

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsWhitingMaximum sustainable yieldFisheryHaddockDemersal zoneFisheries managementFish stockContext (archaeology)SustainabilityEnvironmental resource managementFishingBusinessEnvironmental scienceGeographyEcologyFish <Actinopterygii>Biology

Abstract

fetched live from OpenAlex

Signatories of the 2002 World Summit on Sustainable Development declaration committed to maintain or restore fish stocks to levels that can produce the maximum sustainable yield (MSY), a goal that has been challenged on a number of grounds. The European Commission has stated an objective to manage fisheries (independently) to achieve MSY by 2015, which has catalysed the Regional Advisory Councils’ (RACs) thinking on MSY and how it relates to their goal of developing long-term management plans. This study uses an ecosystem model of the North Sea to investigate questions relating to MSY in the context of mixed demersal fisheries for cod, haddock, and whiting. Results suggest that it is not possible to simultaneously achieve yields corresponding to MSYs predicted from single-species assessments and that the contradictory response of whiting is central to the trade-offs in yield and value for mixed demersal fisheries. Incompatibility between mixed-fishery and ecosystem-scale considerations exemplifies the difficult conceptual and practical challenges faced when moving toward an ecosystem approach.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0040.009
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.247
Teacher spread0.209 · 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 designTheoretical or conceptual
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

Citations92
Published2009
Admission routes1
Has abstractyes

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