Conservation biology of marine fishes: perceptions and caveats regarding assignment of extinction risk
Bibliographic record
Abstract
Quantitative criteria used to assign species to categories of extinction risk may seriously overestimate these risks for marine fishes. Contemporary perception is that marine fishes may be less vulnerable to extinction than other taxa, because of great natural variability in abundance, high fecundity, rapid population growth, and an intrinsically high capability of recovering from low population size. Contrary to perception, however, there appears to be generally little theoretical or empirical support for the hypotheses that marine fish are more likely to experience large reductions in population size, to produce unusually high levels of recruitment, to have higher reproductive rates, or to recover more rapidly from prolonged population declines than nonmarine fishes. Although existing population-decline criteria may not accurately reflect probabilities of biological extinction, they do appear to reflect the converse-population recovery. Insufficient support for contemporary perceptions of their susceptibility to extinction, coupled with caveats associated with the assignment of extinction risk, suggest that significant increases in the population-decline thresholds used to assign marine fishes to at-risk categories would be inconsistent with a precautionary approach to fisheries management and the conservation of marine biodiversity.
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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 | 0.098 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".