Does more fish mean more money? Evaluating alternative escapement goals in the Bristol Bay salmon fishery
Bibliographic record
Abstract
We develop an economically sophisticated management strategy evaluation for four sockeye salmon (Onchorhynchus nerka) fishing districts in Bristol Bay, Alaska, to evaluate whether proposed increases in escapement goals — the number of fish allowed up each river to spawn — could improve fishery outcomes for the industry and the region. Higher escapements increase average runs toward biological maximum sustainable yield, but this is driven by infrequent years of very abundant runs. Our economic model shows processors do not add capacity in response to infrequent abundant runs. Therefore, interannual variance in district-specific catch increases because years with little or no fishing become more frequent to meet higher escapement in low-run years, but industry cannot capture greater value in the high-run years. In abundant runs, processors shift available labor to focus on high-volume, lower-margin products; in very abundant years, insufficient processing capacity allows additional fish to escape. Mobile driftnet vessels that can move to rivers experiencing high runs each year benefit, but district specialists in the small boat and set-net fleets are more vulnerable to years with little or no catch.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".