Bayesian information updating procedures for Pacific salmon run size indicators: evaluation in the presence and absence of auxiliary migration timing information
Bibliographic record
Abstract
Preseason forecasts of Pacific salmon run size are notoriously uncertain and are thus often updated using various abundance indices collected during the run. However, interpretation of these in-season indices is confounded by uncertainty in migration timing. We assessed the performance of two Bayesian information-updating procedures for Kuskokwim River Chinook salmon (Oncorhynchus tshawytscha), one that uses auxiliary run timing information and one that does not, and compared the performance with methods that did not involve updating. We found that in-season Bayesian updating provided more accurate run size estimates during the time when harvest decisions needed to be made, but that the incorporation of run timing forecasts had little utility in terms of providing more accurate run size estimates. The latter finding is conditional on the performance of the run timing forecast model we used; a more accurate timing forecast model might yield a different conclusion. The Bayesian approach we developed provided a probabilistic expression of run size beliefs, which could be useful in a transparent risk-assessment framework for setting and altering harvest targets during the season.
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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 | 0.044 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".