The EU shark finning ban at the beginning of the new millennium: the legal framework
Bibliographic record
Abstract
Abstract The strong international market for shark fins, but the often relatively low value of shark meat and the practical constraints for preserving it on board, have led to the practice whereby fins are removed from any shark caught by a fishing vessel and retained on board while the remainder of the shark is discarded at sea. This practice, known as “shark finning”, has raised increasing concerns, at both the international and European levels, due to the killing of large quantities of sharks, with devastating and unsustainable effects on shark populations. Despite the importance of shark fisheries for EC fleets, to date shark fisheries are not subject to a comprehensive management framework at the European level. A number of measures aiming directly or indirectly at the conservation and management of sharks have been adopted over time. Considering that, the range of existing measures must be strengthened to ensure the rebuilding of many depleted stocks fished by the EU fleet both in and outside EU waters. At the EC level a new regulation has recently been implemented. This paper carries out an overview of the legal framework for the finning bans in the EU to improve current knowledge about this topic and to aid in focusing future research.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.014 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".