Multiple lines of evidence highlight the dire straits of yellowfin tuna in the Indian Ocean.
Bibliographic record
Abstract
Yellowfin tuna (Thunnus albacares) are highly valued pelagic fisheries target species. Regional fisheries management organizations (RFMOs) are the principal mechanism that manage yellowfin tuna fisheries. Determining changes in population abundances is crucial for effective conservation and management. We use multiple methods for monitoring biomass trends and evaluating the status of yellowfin tuna in each ocean basin and show how additional, multiple lines of evidence can enhance our understanding of the conservation and exploitation status of this species. Our analysis of regional biomass trajectories and Catch-MSY++ assessments corroborate the findings of the most recent RFMO stock assessments suggesting yellowfin tuna in the Indian Ocean are in critical condition, while the Eastern Pacific yellowfin tuna population shows the lowest levels of exploitation. These results are supported by fisheries-independent data from baited remote underwater video systems (BRUVS), showing that the Indian Ocean yellowfin tuna population is the least common, least abundant, and smallest across all oceans. Our findings support previous claims of systematic and widespread overfishing of yellowfin tuna in the Indian Ocean and thus confirm calls to reduce current fishing levels to ensure the long-term viability of the species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".