Acclimation to constant and fluctuating temperatures promotes distinct metabolic responses in Arctic char (<i>Salvelinus alpinus</i>)
Bibliographic record
Abstract
The Arctic is warming three times faster than the global average, imposing challenges to cold-adapted fish, such as Arctic char (Salvelinus alpinus). We evaluated stress and metabolic responses of Arctic char to different thermal acclimation scenarios to determine whether responses to thermal variation differed from those to stable exposures. Fish were exposed for 7 days to one of four treatments: (1) control (12°C); (2) mean (16°C), corresponding to the mean temperature of the diel thermal cycle; (3) constant high temperature (20°C); and (4) diel thermal cycling (12 to 20°C every 24 h). Exposure to 20°C causes increases plasma lactate and glucose, an imbalance in antioxidant systems, and oxidative stress in the liver. The 20°C treatment also elevated fractional rates of protein synthesis and caused oxidative stress in the heart. Stress responses were more pronounced in diel thermal cycling than in mean (16°C) fish, indicating that peak exposure temperatures or variation are physiologically important. Cortisol was highest in diel thermal cycling fish and oxidative stress was noted in the liver. Gill Na+/K+-ATPase activity was also significantly reduced in diel thermal cycling fish, suggesting gill remodeling in response to an osmoregulatory stress. Exposure to a constant 20°C was more challenging than a diel thermal cycle, demonstrating the importance of daily cooling to recovery. Arctic char inhabit a thermally variable environment and understanding how this impacts their physiology will be critical for informing conservation strategies in the context of a rapidly warming Arctic.
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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".