A hierarchical model for salmon run reconstruction and application to the Bristol Bay sockeye salmon (<i>Oncorhynchus nerka</i>) fishery
Bibliographic record
Abstract
The goal of spreading the annual catch of a Pacific salmon (Oncorhynchus spp.) run proportionally across all segments of the migration is rendered difficult or impossible because of the interannual variability in both run size and run timing. This problem is particularly acute in the case of the fishery for sockeye salmon (Oncorhynchus nerka) in Bristol Bay, Alaska, for which traditional run reconstruction models are not applicable because of the extreme temporal compression of the run. We develop a run reconstruction model appropriate for sockeye salmon in Bristol Bay by accounting for the hierarchical structure of the problem and by including process error. Our results indicate that the hierarchical structure is, in fact, not necessary, whereas the process error parameters are needed to fit the data. We suggest further model development without the hierarchical structure, including incorporating in-river test fishing data. The results of our method can be used to address questions regarding environmental or intrinsic drivers of run timing and the possibility of artificial selection on run timing.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".