Retrospective analysis of harvest management performance for Bristol Bay and Fraser River sockeye salmon (<i>Oncorhynchus nerka</i>)
Bibliographic record
Abstract
Given current knowledge of mean stock–recruitment relationships and variation in past recruitment, yield of sockeye salmon (Oncorhynchus nerka) in Bristol Bay, Alaska, and Fraser River, British Columbia, might have been at least 100%–300% larger since 1950 than was actually achieved. Most of these gains would have been due to knowledge of optimum mean spawning stock size rather than specific recruitment anomalies; knowing all future recruitment anomalies at the time of each spawning stock choice would have likely only added 2%–5% to total catches. For some stocks, delayed density dependence (cyclic dominance) might have resulted in somewhat lower yields, but under optimal management would still have been higher than were achieved. Even given only estimates of optimum spawning stock size each year based on data available as of that year, but following fixed escapement harvest policy rules, managers could likely have achieved 30%–40% higher total yield. Key management experiments for the future will involve testing for cyclic dominance effects on two major stocks (Kvichak, Late Shuswap) to determine whether stocks with strong, delayed, density-dependent survival effects should be deliberately managed through fallow rotation strategies for juvenile nursery lakes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".