Effect of forecast skill on management of the Oregon coast coho salmon (<i>Oncorhynchus kisutch</i>) fishery
Bibliographic record
Abstract
Better fisheries management is often given as one justification for research on improving forecasts of fish survival. However, the value gained from expected improvements in forecast skill in terms of achieving management goals is rarely quantified as part of research objectives. Using Monte Carlo simulations of population dynamics, we assessed the effect of forecast skill under two strategies for managing Oregon coast natural (OCN) coho salmon ( Oncorhynchus kisutch ). The first, or status quo, strategy is currently being used to rebuild threatened OCN coho populations. This strategy determines harvest based on both a forecasted marine survival rate and parental spawner abundance. The second strategy relies on a forecast of preharvest adult abundance to achieve a constant spawner escapement target. Performance of the status quo strategy was largely insensitive to forecast skill, while the second strategy showed sensitivity that varied with escapement target and specific performance metric. The results imply that effort towards improving forecasts is not justifiable solely on the basis of improved management under the status quo strategy, though it may be were the management strategy altered.
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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 | 0.015 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".