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Record W1998890679 · doi:10.1561/102.00000023

Can Cooperative Management of Tuna Fisheries in the Western Pacific Solve the Growth Overfishing Problem?

2013· article· en· W1998890679 on OpenAlexaff
Megan Bailey, U. Rashid Sumaila

Bibliographic record

VenueStrategic Behavior and the Environment · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOverfishingTunaFisheryFisheries managementFishingFish <Actinopterygii>Biology

Abstract

fetched live from OpenAlex

Tuna fisheries in the western and central Pacific Ocean are important globally for both food and economic security. Yellowfin and bigeye tuna stocks in this region are declining, in part due to the juvenile bycatch of these species by the purse seine fishery using floating objects and fish aggregating devices (FADs). This leads to a conflict between the longline and handline fisheries, which target adult fish, and the purse seine fishery, whose bycatch is leading to growth overfishing. This paper develops a bioeconomic game-theoretic equilibrium model to determine if, at equilibrium, the elimination of juvenile fishing could bring economic benefits to the region. Specifically, we examine non-cooperative and cooperative outcomes for a threeplayer game: purse seine, longline and handline, incorporating skipjack, yellowfin and bigeye as target species. Our results suggest that the reduction or elimination of fishing on floating objects could result in increased net benefits to the region from US $100 million to US $350 million per year; however, this gain is not shared equitably among the three fisheries. Notably, purse seine fisheries stand to lose economically through this type of management decision. The realization of potential economic gains for this region will require the formation of a cooperative sharing system.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.219
Teacher spread0.199 · 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 teacher head, not a consensus.

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

Citations30
Published2013
Admission routes1
Has abstractyes

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