Will depleted populations of Pacific salmon recover under persistent reductions in survival and catastrophic mortality events?
Bibliographic record
Abstract
Abstract Holt, C. A. 2010. Will depleted populations of Pacific salmon recover under persistent reductions in survival and catastrophic mortality events? – ICES Journal of Marine Science, 67: 2018–2026. Under Canada's Wild Salmon Policy, benchmarks between zones of biological status are required to distinguish populations requiring conservation attention (Red and Amber zones) from those that can be managed for production (Green zone). The recovery of depleted populations (i.e. from Red to Green) will depend in part on the choice of the lower benchmark. At a minimum, that benchmark should be set high enough to allow recovery within an acceptable time-frame in the absence of targeted fishing. Currently, benchmarks are evaluated and selected using simulation models that assess the probability of recovery to spawner abundance associated with the maximum sustainable yield within a specified time-frame. Guided by case examples, the evaluation is extended to include two scenarios of future conditions: persistent reductions in survival rates below the replacement level; and increased frequency of catastrophic mortality (die-off) events. Probabilities of recovery appear to be more sensitive to persistent reductions in survival than to increased probability of die-off events. The current lower benchmarks on spawner abundance and fishing mortality might not be sufficiently precautionary to allow recovery under those conditions.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".