Influence of breeding habitat on bear predation and age at maturity and sexual dimorphism of sockeye salmon populations
Bibliographic record
Abstract
Age structure and morphology differ among Pacific salmon (Oncorhynchus spp.) populations. Sexual selection and reproductive capacity (fecundity and egg size) generally favor large (old), deep-bodied fish. We hypothesized that natural selection from physical access to spawning grounds and size-biased predation by bears, Ursus spp., opposes such large, deep-bodied salmon. Accordingly, size and shape of salmon should vary predictably among spawning habitats. We tested this hypothesis by measuring the age composition and body depth of sockeye salmon, Oncorhynchus nerka, and the intensity of predation in a range of breeding habitats in southwestern Alaska. Stream width was positively correlated with age at maturity and negatively correlated with predation level. However, salmon spawning on lake beaches were not consistently old, indicating that different factors affect age in riverine- and beach-spawning populations. Body depths of male and female salmon were positively correlated with water depth across all sites, as predicted. However, the mouths of some streams were so shallow that they might select against large or deep-bodied salmon, even in the absence of bear predation. Taken together, the results indicated that habitat has direct and indirect effects (via predation) on life history and morphology of mature 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".