Test fishery indices for sockeye salmon (<i>Oncorhynchus nerka</i>) as affected by age composition and environmental variables
Bibliographic record
Abstract
In this paper, we explore sources of variability in test fishing indices for sockeye salmon (Oncorhynchus nerka) and illustrate potential solutions. For our analysis, we use the Port Moller test fishery in Bristol Bay, Alaska. The traditional model used to forecast run size using Port Moller test fishery data is based on a simple linear regression of total returns to the fishing districts against the cumulative test fishery catch-per-unit-effort (CPUE) index on a given date. We show that much of the inaccuracy of recent forecasts based on test fishery indices is due to the variable age composition of sockeye runs and the unequal vulnerability of respective age classes to the test fishing gear. We also illustrate solutions to the problems presented by variability in migration route and variability in return timing. In warmer years, we found that there was an increase in effective vulnerability to the test fishing gear and that the strength of this relationship increased linearly with the average body length of age classes. Retrospective analysis shows that correcting for age composition, migration route, and timing provides an in-season index of abundance with an average error of 6.7 million fish or 21%, compared with 33% for previous models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".