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Record W2155026017 · doi:10.1111/cjag.12009

Viability of Transboundary Fisheries and International Quota Allocation: The Case of the Bay of Biscay Anchovy

2013· article· en· W2155026017 on OpenAlexvenueno aff
Richard Curtin, Vincent Martinet

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersCurtin University of Technology
KeywordsFisheryFishingAnchovyGeographyEngraulisStock (firearms)BayFisheries managementStock assessmentWelfare economicsEconomicsFish <Actinopterygii>Biology

Abstract

fetched live from OpenAlex

This paper examines the viability of the management of a transboundary fishery. In a deterministic dynamic framework, we consider a fish stock with two age groups, juveniles and adults, exploited by two countries having a different impact on the age groups. A regulatory agency defines an annual total allowable catch and its allocation between the two countries. We consider allocations satisfying a set of constraints representing viability conditions. The constraints include a minimal quota and minimal profit for each country. We compare intertemporal viable trajectories with the trajectory resulting from a cooperative game in which the two countries agree on the maximization of the fishery's total profit. While the cooperative solution consists in allocating the whole fishing effort to the most efficient country, the viable solution ensures a limitation of inequalities but reduces the overall economic outcome. The results are illustrated for the Bay of Biscay anchovy (Engraulis encrasicolus L.), exploited by Spain and France, with two different types of gear, the purse seine for Spain and the pelagic trawl for France.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.168
Teacher spread0.153 · 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 designSimulation or modeling
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

Citations4
Published2013
Admission routes1
Has abstractyes

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