Are spatial closures better than size limits for halting the decline of the North Sea thornback ray, Raja clavata?
Bibliographic record
Abstract
A key challenge of the ecosystem approach to fisheries management is to sustain viable populations of large-bodied less-productive vulnerable elasmobranchs that are the by-catch of fisheries that target more productive species. The North Sea population of the thornback ray (Raja clavata) is now mainly confined to the Thames Estuary and surrounding SW North Sea, which is subject to a flatfish trawl fishery. We explored the relative effectiveness of seasonal closures versus size-based landing restrictions using a four-season age-structured model. More than a third of adult thornback rays are currently removed by fishing each year, and without effective management, a further 90% decline within 30 years is likely. A three-season closure of the Thames Estuary was the shortest closure that ensured thornback ray recovery and minimal loss of fishery yield. Minimum and maximum landing size restrictions are nearly as effective at recovering thornback rays but less so at improving yield. While long seasonal closures and full marine protected areas are more effective at ensuring the recovery of thornback rays, length restrictions may be simpler to implement under the current institutional framework and may have less impact on the multispecies trawl fisheries operating in the area.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".