Long-term trends in age-specific recruitment of sockeye salmon (<i>Oncorhynchus nerka</i>) in a changing environment
Bibliographic record
Abstract
Sibling age-class (sibling) models, which relate abundance of one age-class of adult sockeye salmon (Oncorhynchus nerka) to abundance of the previous age-class in the previous year, are commonly used to forecast abundance 1 year ahead. Standard sibling models assume constant parameters over time. However, many sockeye salmon populations have shown temporal changes in age-at-maturity. We therefore developed a new Kalman filter sibling model that allowed for time-varying parameters. We found considerable evidence for long-term trends in parameters of sibling models for 24 sockeye salmon stocks in British Columbia and Alaska; most trends reflected increasing age-at-maturity. In a retrospective analysis, the Kalman filter forecasting models reduced mean-squared forecasting errors compared with standard sibling models in 29%39% of the stocks depending on the age-class. The Kalman filter models also had mean percent biases closer to zero than the standard models for 54%94% of the stocks. Parameters of these sibling models are positively correlated among stocks from different regions, suggesting that large-scale factors (e.g., competition among stocks for limited marine prey) may be important drivers of long-term changes in age-at-maturity schedules in sockeye salmon.
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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) | 0.000 | 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".