Interpopulation variation in thermal physiology among seasonal runs of Chinook salmon
Bibliographic record
Abstract
Conservation of species facing environmental change requires an understanding of interpopulation physiological variation. However, physiological data are often scarce and therefore pooled across populations and species, erasing potentially important variability between populations. Interpopulation variation in thermal physiology has been observed within the Salmonidae family, although it has not been associated with seasonally distinct migratory phenotypes (i.e., seasonal runs). To resolve whether thermal physiology is associated with life-history strategy, we acclimated four Sacramento River juvenile Chinook salmon ( Oncorhynchus tshawytscha) populations (Coleman fall-run, Feather River fall-run, Feather River spring-run, and Sacramento River winter-run) exhibiting different seasonal migratory phenotypes (fall-, spring-, and winter-run), at 11, 16, and 20 °C and assessed variation in growth rate, critical thermal maxima, and temperature-dependent metabolic traits. We identified population differences in the physiological parameters measured and found compelling evidence that the critically endangered and endemic Sacramento River winter-run Chinook population exhibits thermal physiology associated with its early-migration life-history strategy. Acclimation to warm temperatures limited the growth and metabolic capacity of winter-run Chinook salmon, highlighting the risk of future environmental warming to this endemic population.
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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".