Body size as a leading indicator of run size and application to in-season forecasting of sockeye salmon in Bristol Bay, Alaska
Bibliographic record
Abstract
Bristol Bay supports the world’s largest fishery for sockeye salmon which are harvested during an extremely condensed time period as fish return to their natal rivers. Uncertainties in preseason forecasts of run size challenge managers and the fishing community because of limited time to adapt strategies within a season. Preseason forecast errors from 2000 to 2023 were as large as 29%, with a mean absolute % error (MAPE) of 15%. We used autoregressive models including mean size-at-age of returning sockeye salmon, along with other environmental covariates, and weighted these models by the inverse of their MAPE to produce an ensemble in-season model that was subsequently weighted with preseason forecasts. This methodology improved forecasts of run size substantially earlier in the fishing season than currently possible and had an average MAPE of 12% (∼6 million fish), approximately 1 week into the fishing season. This level of error is not met by current in-season methods until approximately 2 weeks later after the season has peaked, and is better than current preseason methods in most years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".