The influence of energetic requirements on the preferred temperature of overwintering juvenile Atlantic salmon (<i>Salmo salar</i>)
Bibliographic record
Abstract
We used a horizontal temperature gradient to investigate the effect of alternate life history strategies and nutritional state on the preferred temperature of overwintering juvenile Atlantic salmon (Salmo salar L.). Contrary to our prediction, there was no significant difference in final preferred temperature between juvenile Atlantic salmon that will migrate to sea the following spring (early migrants) and those that show reduced growth and delay migration for at least another year (delayed migrants). Both migrant groups preferentially selected relatively low temperatures (<10°C), likely owing to their low appetite and growth rates. Food deprivation resulted in a significant increase, rather than our predicted decrease, in the final preferred temperature of the juvenile Atlantic salmon of approximately 2°C. We suggest that this is due to the need for an increased foraging effort to offset the projected energy deficit later in the winter. The final preferred temperature of delayed migrants increased from winter to spring, as predicted, coincident with increases in natural food availability and endogenous seasonal increases in appetite and growth rates. We conclude that the preferred temperature of overwintering juvenile Atlantic salmon may be influenced by future energetic requirements rather than the current level of energy reserves.
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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".