Age-structured bioeconomic model for strategic interaction: an application to pomfret stock in the Arabian/Persian Gulf
Bibliographic record
Abstract
Abstract When fish stocks migrate across multiple exclusive economic zones (EEZs), they compel managers to examine management at both national and international levels. A strategic interaction emerges when the fishing activity of one country impacts fishing opportunities available for other countries sharing the stock. Left unaddressed, strategic interaction could lead to overexploitation and suboptimal payoffs. Here, we develop and apply a bioeconomic model to address the competitive fishing for silver pomfret in the Arabian/Persian Gulf—a highly commercial fish stock shared between Kuwait and Iran—and evaluate biological–economic trade-offs under competition, cooperation, and country-independent management using maximum sustainable yield (MSY) and fishing mortality that maintain MSY (Fmsy) policies. When cooperation involves an equal share of the overall Fmsy or a share based on the proportion of the stock available in each EEZ, countries are expected to cooperate given the substantially higher catch, biomass, and relative profits compared to other management regimes. However, other than these two arrangements, countries would favour different regimes. Besides providing policy insights to improve the perilous status of silver pomfret, our approach could be useful in exploring alternative fishing arrangements to sustainably harvest a transboundary fish stock while maximizing average yields.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".