Spatial Covariation in Survival Rates of Northeast Pacific Chum Salmon
Bibliographic record
Abstract
Using indices of survival rate (residuals from stock-recruitment relationships) across four decades, we examined the spatial patterns of covariation among 40 wild and 27 hatchery stocks of chum salmon Oncorhynchus keta from 15 geographical regions in Washington, British Columbia, and Alaska. We found strong evidence of positive covariation among spawner-to-recruit survival rates of wild stocks within regions and between certain adjacent regions (e.g., correlations from 0.3 to 0.7) but little evidence of covariation between stocks of distant regions (e.g., separated by 1,000 km or more). Similarly, for hatchery stocks from Washington, British Columbia, and southeast Alaska, positive covariation in the indices of fry-to-recruit survival rate occurred only within regions and between certain adjacent regions. These patterns suggest that important environmental processes affecting interannual variation in spawner-to-recruit survival rates of chum salmon operate at local or regional spatial scales rather than at the larger, ocean-basin scale. These results are similar to our previous findings for sockeye salmon O. nerka and pink salmon O. gorbuscha and help identify the spatial characteristics of environmental variables required to improve forecasting models and better understand the effects of climatic changes on salmon productivity. Our finding that local or regional-scale processes primarily affect productivity differs from that of other studies, which suggest that large, ocean-basin-scale processes predominate. However, the latter studies were mostly based on time series of aggregate catch data, which provide limited spatial resolution and are potentially confounded by several factors.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".