Pre-season run size forecasts for Fraser River Sockeye (Oncorhynchus nerka) and Pink (Oncorhynchus gorbuscha) Salmon in 2025
Bibliographic record
Abstract
Forecasts for the 2025 Fraser River Sockeye and Pink salmon returns were prepared with Bayesian statistical models and presented as cumulative probability distributions. The total Fraser River Sockeye return for 2025 has a median estimate of 2,947,000 (80% PI: 736,000 to 13,140,000), which is less than half the long term average of 7.0 million. The return is expected mainly from the Chilko and Late Stuart River stocks in the Summer run timing group. Out of 27 forecasts for Sockeye, six stocks included environmental covariates and 12 stocks used sibling models to calculate the age-5 return. Warmer nearshore sea surface temperatures (SST) suggested the potential for reduced salmon productivity, while the Pacific Decadal Oscillation (PDO) suggested that SSTs offshore were more favorable for salmon production. The 2025 forecast for Fraser River Pink salmon is 26,965,000 (12,585,000 to 57,854,000) and represents the highest possible return on record, which is largely driven by the high abundance of juvenile outmigration observed in 2024 (1.35 billion). However, Pink returns in 2023 were underestimated and highlights the uncertainty in Pink population dynamics not captured in our current model infrastructure. The 2025 Fraser Pink forecast should be considered highly uncertain.
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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 | 0.001 | 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) | 0.002 | 0.001 |
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".