Assessing the impacts of environmental and ecological variables on the performance of Fraser sockeye salmon forecast
Bibliographic record
Abstract
The Canadian Fraser River sockeye salmon ( Oncorhynchus nerka) is one of the largest stock complexes in North America, supporting major commercial, recreational, and First Nations fisheries. Sockeye fisheries management relies on an annual pre-season forecast of adult returns. In this study, we developed a comprehensive framework for visualization with a Taylor diagram to evaluate pre-season forecast models annually and identify external drivers important for forecasting sockeye returns. Specifically, we incorporated five new covariates, including sea surface temperature in the Gulf of Alaska and the abundance of salmon species, into the existing forecast models. Results revealed better performances by both the Ricker and Power models when coupled with the newly included covariates. Moreover, models selected over a decade ago underperformed compared to those selected based on our recent retrospective analysis from 2009 to 2020. We advocate for continuous evaluation of forecast models in the face of environmental change, emphasizing the necessity of developing models that incorporate non-stationary processes and assess the impacts of environmental and ecological factors on Fraser sockeye dynamics.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".