Retrospective Evaluation of Preseason Forecasting Models for Sockeye and Chum Salmon
Bibliographic record
Abstract
Abstract Using comprehensive data sets for chum salmon Oncorhynchus keta (40 stocks) and sockeye salmon O. nerka (37 stocks) throughout their North American ranges, we compared the retrospective performance of 11 models in preseason forecasting of adult abundance. Chum and sockeye salmon have more complicated age structures than pink salmon O. gorbuscha, which we investigated previously (Haeseker et al. 2005), and this complexity presents new challenges as well as opportunities for forecasting. We extended our previous work to include two new forecasting models that make use of leading indicators: either the survival rate of earlier-maturing pink salmon from the same brood year (Ricker pink salmon index model) or the abundance of earlier-maturing siblings (hybrid sibling model, a new version of the standard sibling model). No single forecasting model was consistently the best for either chum or sockeye salmon, but the hybrid sibling model frequently performed best based on mean absolute error, mean percent error, and root mean square error. As was observed for pink salmon, several naïve models (i.e., simple time series models without explicitly modeled mechanisms) also performed well, as did forecast averaging models composed of two models with the least-correlated forecasting errors. In general, model ranking depended on the particular stock and performance measure used. However, even the top-ranked model for each stock explained on average only 21% of the observed interannual variation in chum salmon recruitment and only 36% of the variation in sockeye salmon recruitment. Although improvements may be possible for some stocks in specific circumstances, a major breakthrough in general forecasting ability seems unlikely given the breadth of stocks and models examined to date. Therefore, better in-season updates and adjustments to fishing regulations and a cautious approach to opening and closing fisheries should remain high priorities.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".