Shark fin trade bans and sustainable shark fisheries
Bibliographic record
Abstract
Abstract The U.S. Congress is currently discussing the Shark Fin Sales Elimination Act to eliminate shark fin trade at the federal level. This bill was introduced in 2017 and has been proceeding very slowly in Congress because of mixed reviews from the scientific community. Debate exists on whether shark conservation and management are effectively addressed with tightened trade controls for imported shark products or blanket bans that outright end U.S. participation in the shark fin trade. Here we contribute to this debate with a review and analysis of economic, nutritional, ethical, and legal arguments, as well as of the shark fisheries status and shark fin trade. We show that the United States has a limited commercial interest in shark fisheries and contributes to the shark fin trade mainly as a facilitator. A fin trade ban has few tangible economic drawbacks but would have a considerable conservation impact. While making all shark fisheries sustainable is the ultimate goal, in practice this objective is far from achievable everywhere in the world. Conversely, banning shark fin trade is an interim measure that nations like the United States can take with negligible cost and can truly impact the biggest driver of shark exploitation globally.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".