Optimal in-season management of pink salmon (<i>Oncorhynchus gorbuscha</i>) given uncertain run sizes and seasonal changes in economic value
Bibliographic record
Abstract
In this study, we developed a stochastic simulation model that simulates the in-season abundance dynamics of pink salmon (Oncorhynchus gorbuscha) stocks, the fleet dynamics, and management of purse seine fisheries in the northern Southeast Alaska inside waters. Uncertainties in annual stock size and run timing, fleet dynamics, and both preseason and in-season forecasts were accounted for explicitly in this simulation. The simulation model was applied to evaluating four kinds of management strategies with different fishing opening schedules and decision rules. The ranking of the management strategies is apparently determined by the evaluation criteria applied. When only flesh quality is concerned, both the current and a more aggressive strategy, as long as they adapted themselves to the run strength, were able to provide higher quality fish without compromising the escapement objectives. When the value of the eggs is also a concern, the management strategies that have more intensive late opening schedules might be preferable. When both flesh quality and the value of eggs are considered, the ranking of the management strategies depends on the timing of the 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".