Slow heating rates increase thermal tolerance and alter mRNA HSP expression in juvenile white sturgeon (Acipenser transmontanus)
Bibliographic record
Abstract
Abstract Freshwater fish such as white sturgeon (Acipenser transmontanus) are particularly vulnerable to the effects of anthropogenically induced global warming. Critical thermal maximum tests (CTmax) are often conducted to provide insight of the impacts of changing temperatures; however, little is known about how the rate of temperature increase in these assays affects thermal tolerance. To assess the effect of heating rate (0.3°C/min, 0.03°C/min, 0.003°C/min) we measured thermal tolerance, somatic indices, and Hsp mRNA expression. Contrary to what has been observed in most other fish species, white sturgeon thermal tolerance was highest at the slowest heating rate of 0.003°C/min, suggesting an ability to rapidly acclimate to slowly increasing temperatures. Hepatosomatic index decreased in all heating rates relative to control fish, indicative of the metabolic costs of thermal stress. At the transcriptional level, slower heating rates resulted in higher mRNA expression of Hsp90a, Hsp90b, and Hsp70. Hsp70 mRNA expression was increased in all heating rates relative to controls, whereas expression of Hsp90a and Hsp90b mRNA only increased in the two slower trials. Together these data indicate that white sturgeon have a very plastic thermal response, which is likely energetically costly to induce. Acute temperature changes may be more detrimental to sturgeon as they struggle to acclimate to rapid changes in their environment, however under slower warming rates they demonstrate strong thermal plasticity to warming.
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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) | 0.002 | 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".