The role of coastal ocean variation in spatial and temporal patterns in survival and size of coho salmon (<i>Oncorhynchus kisutch</i>)
Bibliographic record
Abstract
Interannual and decadal variability in ocean survival of salmon are well known, but the mechanisms through which environmental variability exerts its effects are poorly understood. Data on hatchery-reared coho salmon (Oncorhynchus kisutch) from individual releases (19731998) along the species' entire North American range were analyzed to provide information on survival and size. Three geographic regions (north of Vancouver Island, Puget Sound and Strait of Georgia, and the outer coast south of the tip of Vancouver Island) showed coherent trends in survival and size of returning fish. Within each region, multivariate nonlinear models were used to relate coho survival and final size to spatially and temporally tailored environmental variables at time periods of release, jack return, and adult return. The most important environmental variable, as indicated by the highest amount of variance explained, was a calculated proxy for mixed-layer depth, followed by sea level. In all regions, survival and adult size were most influenced by environmental conditions at the release time. A shallow mixed layer was associated with increased survival and decreased size in all regions. Improved understanding of the relationship between environmental conditions and size and survival of coho salmon provides insight into production patterns in the coastal ocean.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".