Thermal performance curve of endurance running at high temperatures in deer mice
Bibliographic record
Abstract
The impacts of warming temperatures associated with climate change on performance are poorly understood in most mammals. Thermal performance curves are a valuable means of examining the effects of temperature on performance traits, but they have rarely been used in endotherms. Here, we examined the thermal performance curve of endurance running capacity at high temperatures in the deer mouse (Peromyscus maniculatus). Endurance capacity was measured using an incremental speed test on a treadmill, and subcutaneous temperature in the abdominal region was measured as a proxy for body temperature (Tb). Endurance time at 20°C was repeatable but varied appreciably across individuals, and was unaffected by sex or body mass. Endurance capacity was maintained across a broad range of ambient temperatures (Ta) but was reduced above 35°C. Tb during running varied with Ta, and reductions in endurance were associated with Tb greater than 40°C when Ta was above 35°C. At the high Ta that limited endurance running capacity (but not at lower Ta), Tb tended to rise throughout running trials with increases in running speed. Metabolic and thermoregulatory measurements at rest showed that Tb, evaporative water loss and breathing frequency increased at Ta of 36°C and above. Therefore, the upper threshold temperatures at which endurance capacity is impaired are similar to those inducing heat responses at rest in this species. These findings help discern the mechanisms by which deer mice are impacted by warming temperatures, and provide a general approach for examining thermal breadth of performance in small mammals.
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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 | 0.001 | 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".