Do prior diel thermal cycles influence the physiological response of Atlantic salmon (<i>Salmo salar</i>) to subsequent heat stress?
Bibliographic record
Abstract
We designed two environmentally relevant thermal cycling regimes using monitoring data from an Atlantic salmon (Salmo salar) river to determine whether exposure to prior diel cycles stimulated protective mechanisms (e.g., heat hardening) and (or) resulted in physiological and cellular stress. Wild fish were exposed to 3 days of diel cycling in the lab and then exposed to an acute thermal challenge near their upper reported critical temperature. We measured routine metabolic rate across the time course as well as indicators of physiological status (e.g., plasma glucose and osmolality) and cellular stress (e.g., heat shock protein 70). We observed that thermal cycling altered physiological and cellular responses, compared with an acute heat shock, but saw no differences between cycling regimes. Unique temperature regime and tissue-specific responses were observed in heat shock protein induction, metabolites, haematology, and osmotic indicators. Routine metabolic rate was not affected by the thermal cycling and increased according to Q10 predictions. While we report unique physiological and cellular responses among all treatment groups, we did not observe a clear indication of a heat hardening response.
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".