A fisheries risk-assessment framework to evaluate trade-offs among management options in the presence of time-varying productivity
Bibliographic record
Abstract
Empirically based simulation models can help fisheries managers make difficult decisions involving trade-offs between harvests and maintaining spawner abundance, especially when data contain uncertainties. We developed such a general risk-assessment framework and applied it to chum salmon ( Oncorhynchus keta ) stocks in the Arctic–Yukon–Kuskokwim region of Alaska, USA. These stocks experienced low abundance in the 1990s, which led to declarations of economic disaster and calls for changes in harvest strategies. Our stochastic model provides decision makers with quantitative information about trade-offs among commercial harvest, subsistence harvest, and spawner abundance. The model included outcome uncertainty (the difference between target and realized spawner abundances) in the subsistence and commercial catch modules. We also used closed-loop simulations to investigate the utility of time-varying management policies in which target spawner abundance changed in response to changes in the Ricker productivity parameter (a), as estimated with a Kalman filter. Time-varying policies resulted in higher escapements and catches and reduced risk across a range of harvest rates. The resulting generic risk-assessment framework can be used to evaluate harvest guidelines for most salmon stocks.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".