When control rules collide: a comparison of fisheries management reference points and IUCN criteria for assessing risk of extinction
Bibliographic record
Abstract
Abstract Rice, J. C., and Legacè, È. 2007. When control rules collide: a comparison of fisheries management reference points and IUCN criteria for assessing risk of extinction. – ICES Journal of Marine Science, 64: 718–722. The quantitative criteria used by the International Union for the Conservation of Nature (IUCN) to assess risk-of-extinction are compared with reference points used by ICES and other fisheries organizations for advising on fisheries management. Criteria based on numbers of individuals and geographic range appear to be in harmony with limit reference points and control rules used in fisheries management, with reference points indicating that fisheries should be closed well before there is any risk of extinction. However, there is huge potential for conflict between fisheries and risk-of-extinction approaches when considering the extent of population declines. Of 89 species examined, the decline criterion suggested a serious risk-of-extinction in 87%, whereas most of the stocks were still within a zone that allowed fisheries management reference points to indicate that exploitation could continue. Much of the conflict seems rooted in different types of tolerance to risk between the two disciplines. The conservation-biology community acknowledges a high tolerance for “false alarms”, to keep the probability of a “miss” very low, whereas tolerance in fisheries management is comparable for both types of error.
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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 | 0.174 | 0.409 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".