Retrospective Evaluation of Preseason Forecasting Models for Pink Salmon
Bibliographic record
Abstract
Abstract Models for making preseason forecasts of adult abundance are an important component of the management of many stocks of Pacific salmon Oncorhynchus spp. Reliable forecasts could increase both the profits from fisheries and the probability of achieving conservation and other management targets. However, the predictive performance of salmon forecasting models is generally poor, in part because of the high variability in salmon survival rates. To improve the accuracy of forecasts, we retrospectively evaluated the performance of eight preseason forecasting models for 43 stocks of pink salmon O. gorbuscha over a total of 783 stock-years. The results indicate that no single forecasting model was consistently the most accurate. Nevertheless, across the 43 stocks we found that two naïve time series models (i.e., those without explicitly modeled mechanisms) most frequently performed best based on mean raw error, mean absolute error, mean percent error, and root mean square error for forecasts of total adult recruits. In many cases, though, the best-performing model depended on the stock and performance measure used for ranking. In 21% of the stocks, a new multistock, mixed-effects stock–recruitment model that included early-summer sea surface temperature as an independent variable along with spawner abundance demonstrated the best performance based on root mean square error. The best-performing model for each pink salmon stock explained on average only 20% of the observed variation in recruitment. Owing to the uncertainty in forecasts, a strong precautionary approach should be taken to achieve conservation and management targets for pink salmon on the West Coast of North America.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".