Using model simulations to compare performance of two commercial salmon management strategies in Bristol Bay, Alaska
Bibliographic record
Abstract
In this paper, we discuss the costs and benefits, in relation to economics and Alaska’s salmon management policies, associated with two management strategies on the Egegik and Togiak sockeye salmon (Oncorhynchus nerka) fisheries in Bristol Bay, Alaska. “Daily management” allows managers to open or close the fishery on a daily basis, whereas a fixed fishing schedule allows fishing, to the extent practical, on fixed days of the week. To compare the strategies, we simulated the effects of daily management and a fixed fishing schedule on the two fisheries. Our simulations show that a daily management strategy results in higher yearly catches, less yearly variation in escapement, and fewer years of escapement below the goal range. A fixed fishing schedule results in less yearly variation in catch and a more stable harvest rate, with the harvest stability more pronounced when effort was held constant. Daily management is a desirable strategy for fisheries that are managed for maximum sustainable yield, have high fishing effort, have a small fishing area, or have a more temporally compressed run. A fixed fishing schedule is a desirable strategy for fisheries with less intense effort, budgetary or efficiency concerns, or stock components that differ in run timing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".