Elevated incubation temperature improves later‐life swimming endurance in juvenile Chinook salmon, <i>Oncorhynchus tshawytscha</i>
Bibliographic record
Abstract
Abstract The effect of incubation and rearing temperature on muscle development and swimming endurance under a high‐intensity swimming test was investigated in juvenile Chinook salmon (Oncorhynchus tshawytscha) in a hatchery experiment. After controlling for the effects of fork length (LF) and parental identity, times to fatigue of fish were higher when fish were incubated or reared at warmer temperatures. Significant differences among combinations of pre‐ and post‐emergence temperatures conformed to 15–15°C > 15–9°C > 9–9°C > 7–9°C > 7–7°C in 2011 when swimming tests were conducted at 300 accumulated temperature units post‐emergence and 15–9°C > (7–9°C = 7–7°C) in 2012 when swimming tests were conducted at an LF of c. 40 mm. The combination of pre‐ and post‐emergence temperatures also affected the number and size of muscle fibres, with differences among temperature treatments in mean fibre cross‐sectional area persisting after controlling for LF and parental effects. Nonetheless, neither fibre number nor fibre size accounted for significant variation in swimming endurance. Thus, thermal carryover effects on swimming endurance were not mediated by thermal imprinting of muscle structure. This is the first study to test how temperature, body size and muscle structure interact to affect swimming endurance during early development in salmon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".