Evaluating Benchmarks of Population Status for Pacific Salmon
Bibliographic record
Abstract
Abstract Canada is developing an indicator approach for assessing the biological status of conservation units of Pacific salmon Oncorhynchus spp. under its Wild Salmon Policy that is based, in part, on the abundance of adult fish. Two benchmarks will be used to place populations in one of three abundance categories. The lower benchmark is proposed to be at a level that allows for a substantial buffer between it and the abundance that would result in a population's being assessed as at risk of extirpation based on quantitative criteria used by the Committee on the Status of Endangered Wildlife in Canada. Using Monte Carlo simulation, we evaluated eight candidate lower benchmarks calculated from parameters of the stock–recruit relationship against two criteria, the probability of extirpation over 100 years and the probability of recovery to spawner abundances that result in the maximum sustainable yield in one or three generations. For modeled populations of moderate size (unfished equilibrium abundances >25,000) and moderate productivity (∼four adult recruits produced per spawner at low spawner abundances), all benchmarks protected populations from extirpation when harvest restrictions were imposed at the lower benchmark. For small or unproductive populations, none of the benchmarks was adequate to prevent populations from being at risk of extirpation. However, those benchmarks that covaried with the productivity parameter of the stock–recruitment relationship performed better and are preferred to those that were derived mainly from estimates of habitat capacity. Received May 31, 2010; accepted February 4, 2011
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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 | 0.019 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".