The effects of salmon abundance and run timing on the performance of management by emergency order
Bibliographic record
Abstract
We examine the effect of uncertainty in salmon run abundance and run timing on the ability of managers to achieve escapement goals using in-season regulation of fishery openings using a detailed model of the arrival of salmon and operation of the fishery, the information available to managers, and managers’ behavior. We supplement this management strategy evaluation by examining historical management performance of sockeye salmon (Oncorhynchus nerka) fisheries from Bristol Bay, Alaska. We find that uncertainty about run timing exacerbates the effects of uncertainty about salmon abundance. Early-arriving small runs and late-arriving large runs are especially problematic, as they produce in-season data that mimic that of a typically sized run with average run timing. Managers faced with an early-arriving small run will tend to overharvest the fish, particularly the earliest-arriving component. Managers faced with a late-arriving large run will tend to underharvest the fish, and harvest the latest-arriving components at a higher rate. This differential harvest of early or late components of the run is important because it might reduce the genetic diversity of the stock, thus reducing its future productivity.
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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 | 0.024 |
| 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 | 0.001 |
| 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".