Contrasting Ecology Shapes Juvenile Lake‐Type and Riverine Sockeye Salmon
Bibliographic record
Abstract
Abstract Here we compare the body shape of juvenile (age‐0) sockeye salmon Oncorhynchus nerka that rear in lakes (lake type) with that of those that rear in rivers (riverine) and relate rearing habitat to morphology and ecology. The two habitats present different swimming challenges with respect to water flow, foraging strategy, habitat complexity, and predation level. We present morphological data from three riverine and three lake‐type populations in southwest Alaska. Using multivariate analyses conducted via geometric morphometrics, we determine population‐ and habitat‐specific body shape. As predicted, riverine sockeye salmon have a more robust body shape, whereas lake‐type sockeye salmon have a more streamlined body shape. In particular, we found differences in caudal peduncle depth (riverine deeper), eye size (riverine larger), and overall body depth (riverine deeper). One lake‐type population did not follow the predicted pattern, exhibiting an overall exaggerated riverine body shape. Differences between the habitats in terms of predation, complexity, and foraging ecology are probably drivers of these differences. Allometry differed between life history types, suggesting that there are habitat‐specific developmental differences.
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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 | 0.000 |
| 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".