Trade-Based Estimation of Bluefin Tuna Catches in the Eastern Atlantic and Mediterranean, 2005–2011
Bibliographic record
Abstract
The Eastern Atlantic and Mediterranean stock of Bluefin tuna Thunnus thynnus (BFTE) has long been considered overfished and at risk of collapse. Although ICCAT quotas for this stock have decreased considerably over the past years, uncertainty exists about the degree of catch beyond this quota. The extent of such catch is an important piece of information in stock assessment models as well as being an indicator of the effectiveness of fisheries management. We present a model using Bluefin tuna trade data to infer actual catches. Basing our calculations on 25 countries involved in BFTE trade, we estimate that between 2005 and 2011, allowable quotas were exceeded by 44 percent. This gap between catch and quotas has slightly increased over past years, leading to estimated excess catches of 57 percent for the period between 2008 and 2011. To improve assessments, preparation and design of BFTE management, we suggest that the estimated total removals reported in this paper be included in stock assessment models for BFTE. An implication of our findings is that ICCAT member states should take stronger measures to monitor and enforce compliance with quotas.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".