Addressing ecosystem effects of fishing using marine protected areas
Bibliographic record
Abstract
This article is a synthesis of the current literature on the potential of marine protected areas (MPAs) a useful management tool for limiting the ecosystem effects of fishing, including biological and socio-economic aspects. There is sufficient evidence that fishing may negatively affect ecosystems. Modelling and case studies show that the establishment of MPAs, especially for overexploited populations, can mitigate ecosystem effects of fishing. Although quantitative ecosystem modelling techniques incorporating MPAs are in their infancy, their role in exploring scenarios is considered crucial. Success in implementing MPAs will depend on how well the biological concerns and the socio-economic needs of the fishing community can be reconciled. Cet article fait la synthèse de la littérature sur la possibilité d'utiliser les zones marines protégées (MPAs) comme outils de gestion afin de limiter les effets de la pêche sur les écosystèmes, en incluant les aspects biologiques et socio-économiques. La littérature fournit suffisamment d'évidences à l'effet que la pêche peut avoir un effet négatif. Les MPAs établies dans divers habitats à travers le monde ainsi que les modélisations montrent que MPAs offrent une certaine protection contre ces effets négatifs. Les techniques quantitatives de modélisation des écosystèmes, bien que cruciales pour l'exploration de scénarios de gestion, n'en sont encore qu'à leurs débuts et mériteraient encore plus d'attention. Finalement, le succès des MPAs dépendra de la manière dont on réussira à allier les aspects biologiques et les intérêts socio-économiques.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".