Ecosystems at Risk: The Contribution of Ecosystem Approaches to Fisheries to Identify Problems and Evaluate Potential Solutions
Bibliographic record
Abstract
The wide expanse of the sea, the inter-linkages among, and the productivity of its resources have until recently led most researchers to consider it unrealistic that humans could have more than local impact on marine ecosystems and their biodiversity. This perception is changing, however, as more evidence of the scale of impact becomes available. 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 that the world’s total fish catches are no longer increasing, but rather 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 at 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.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| 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".