Consequences of variations in carrying capacity or migration for the perception of Atlantic bluefin tuna (<i>Thunnus thynnus</i>) population dynamics
Bibliographic record
Abstract
Spectacular long-term cycles (around 110 years), independent of human exploitation, have been seen in historical catches of Atlantic bluefin tuna (Thunnus thynnus). Previous studies indicated that such variations could have been generated by contrasting but equally plausible dynamic processes, i.e., changes in carrying capacity or migration. A simulation framework was therefore used to test whether the International Commission for the Conservation of Atlantic Tuna stock assessment model, i.e., a virtual population analysis (VPA), can capture such dynamics. The main outcome is that knowledge of the underlying process is crucial, because distinct hypotheses lead to different population dynamics and contrasting performances of the stock assessment model. The VPA is indeed able to reconstruct accurately the historical stock parameters under the carrying-capacity hypothesis, but not under the migratory hypothesis, for which there is often strong bias (up to 500%) in absolute values and in trends of spawning stock biomass and F. Furthermore, it was shown that (i) different phases between exploitation and long-term cycle can induce contrasting terminal F for a same effort and (ii) that there was considerable confounding between the dynamics and increasing effort (as currently seen). We conclude that it is difficult to infer the actual dynamics on the basis of commercial catch data and that novel fishery-independent observation is needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".