Threats to the ocean: on the role of ecosystem approaches to fisheries
Bibliographic record
Abstract
English It is becoming increasingly clear that humans impact marine ecosystems and their biodiversity to a very considerate degree, and evidence of the scale of impact is growing. An enabling factor for this has been a change in focus from local-level studies to increased emphasis on meta-analysis of global or regional-level analysis of fisheries' impact. Results include the facts that the world's total fish catches have been decreasing over the last decade or more; that larger, predatory fishes (table fish) are becoming increasingly scarcer; and that we are appropriating the ocean shelves' primary productivity to the same level as we are for terrestrial ecosystems. Ecosystems are being eroded in countries throughout the world, and though one might get the impression from the IUCN Red List that it is mainly a developed-country problem, it is alarming that the impact of severe overfishing may be on an even larger scale for developing countries. We describe aspects of the risks overfishing poses to marine ecosystems, and point out how ecosystem approaches to fisheries can be used to evaluate the potential impact of alternative fishing policy scenarios. French Il devient de plus en plus clair que les activités humaines altèrent les écosystèmes marins et leur biodiversité à un degré considérable, et les preures de l'échelle des impacts anthropiques s'accumulent. Un des facteurs déterminants de cette prise de conscience réside dans le changement d'échelle des études de l'impact des pêches qui sont passées d'un niveau local à un niveau global ou régional au travers d'études de type méta-analyse. Les résultats obtenus les plus évocateurs incluent le fait que la totalité des captures mondiales de poissons a diminué pendant la dernière décennie ou plus; que les grands prédateurs (l'essentiel des poissons "de table") deviennent de plus en plus rares; et que nous nous approprions la productivité primaire des plateaux continentaux au même titre que nous nous sommes appropriés celle des écosystèmes terrestres. Les écosystèmes sont érodés dans les pays du monde entier, et bien que la liste rouge de l'IUCN laisse penser que c'est principalement le problème des pays développés, il est alarmant de constater que l'impact de la surpêche excessive pourrait être une échelle bien plus grande encore pour les pays en voie de développement. Nous décrivons certains aspects des risques que pose la surpêche pour les écosystèmes marins, et mettons en exergue comment l'approche écosystémique des pêches peut être employée pour évaluer l'impact potentiel de scénarios alternatifs de règlementation des pêches.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".