Evaluation of the robustness of maximum sustainable yield based management strategies to variations in carrying capacity or migration pattern of Atlantic bluefin tuna (<i>Thunnus thynnus</i>)
Bibliographic record
Abstract
In this study, we examine the performances of current stock assessment methods with respect to their ability to (i) provide estimates of maximum sustainable yield (MSY), FMSY, and BMSY and (ii) assess stock status and exploitation level relative to MSY targets. The robustness of the current International Commission for the Conservation of Atlantic Tunas (ICCAT) management strategy is then evaluated with respect to uncertainty about the true population dynamics and contrasted with a simpler management strategy based solely on a size limit. Reference points are more robust to dynamic uncertainty than the estimates of absolute values and trends in F and spawning stock biomass. However, their performances depend on the underlying dynamics (they perform better when fluctuations come from changes in the carrying capacity than migration) and on when they are implemented relative to the intrinsic cycle of the population. Reference points based on F were less biased and more precise than those based on biomass and (or) yield. Although F0.1 appeared to be the best proxy for FMSY, it cannot indicate past and current levels of exploitation relative to FMSY when there is uncertainty about the dynamics. Finally, the F0.1 management strategy of ICCAT performed only slightly better than a simpler strategy based on size limit and led to lower catch levels.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".