High overexploitation risk due to management shortfall in highly traded requiem sharks
Bibliographic record
Abstract
Abstract Most of the international trade in fins (and likely meat too) is derived from requiem sharks (family Carcharhinidae), yet trade in only two of the 56 species is currently regulated. Here, we quantify catch, trade, and the shortfall in national and regional fisheries management (M‐Risk) for all 56 requiem shark species based on 831 assessments across 30 countries and four Regional Fisheries Management Organizations (RFMOs). Requiem sharks comprise over half (60%) of the annual reported global Chondrichthyan catch with most species (86%) identified in the international fin trade. Requiem sharks are inadequately managed by fisheries, with an average M‐Risk of half (50%) of an ideal score, consequently 70% of species are threatened globally. The high catch and trade volume and shortfall in management of these iconic species require worldwide fisheries management for sustainable catch, supported by full implementation of CITES regulations for international trade of this newly listed family.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".