Extinction Risk and Overfishing: Reconciling Conservation and Fisheries Perspectives on the Status of Marine Fishes
Bibliographic record
Abstract
Anthropogenic disturbances are ubiquitous in the ocean, but their impacts on marine species are hotly debated. We evaluated marine fish statuses using conservation (Red List threatened or not) and fisheries (above or below reference points) metrics, compared their alignment, and diagnosed why discrepancies arise. Whereas only 13.5% of Red Listed marine fishes (n = 2952) are threatened, 40% and 21% of populations with stock assessments (n = 166) currently are below their more conservative and riskier reference points, respectively. Conservation and fisheries metrics aligned well (70.5% to 80.7%), despite their mathematical disconnect. Red Listings were not biased towards exaggerating threat status, and egregious errors, where populations were categorized at opposite extremes of fisheries and conservation metrics, were rare. Our analyses suggest conservation and fisheries scientists will agree on the statuses of exploited marine fishes in most cases, leaving only the question of appropriate management responses for populations of mutual concern still unresolved.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".