A global review of species‐specific shark‐fin‐to‐body‐mass ratios and relevant legislation
Bibliographic record
Abstract
In this review, shark-fin-to-body-mass ratios, which have been legislated by several countries as a means of regulating and monitoring shark fisheries, have been compiled and reviewed. Observed and legislated wet-fin-mass-to-round-mass (M(fw) :M(r) ) ratios have been collected for 50 species and eight countries. Wet to dry-fin mass conversion factors have also been reviewed. Existing shark fishery legislation was compiled by political entity and regional fishery management organizations (RFMO). The mean observed M(fw) :M(r) ratio for all species was 3·0%, but actual fin to body-mass ratios varied considerably by species and location. Species-specific mean ratios ranged from 1·1 to 10·9%, and estimated mean ratios ranged from 1·5 to 6·1% by country, depending on fin-cutting practices and the mix of exploited species. The mean conversion factor for wet to dry-fin mass was 0·43. Shark-related legislation was found to exist in 37 countries and the 22 maritime members of the European Union, and shark-related regulations have been designated by nine RFMOs. Results suggest that currently regulated ratios may not be appropriate for all species and fin-cutting practices, and regulations based on generalized ratios for all sharks may be inadequate. Alternative policies may be necessary for the effective management of global shark fisheries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".