Behavioural microclimate selection and physiological responses to environmental conditions in a hibernating bat
Bibliographic record
Abstract
Hibernators adjust the expression of torpor behaviourally and physiologically to balance the benefits of energy conservation in hibernation against the physiological and ecological costs. Small fat-storing species, like many cave-hibernating bats, have long been thought to be highly constrained in their expression of hibernation because they must survive winter relying only on endogenous energy stores. We evaluated behavioural microclimate selection in tri-colored bats (Perimyotis subflavus (F. Cuvier, 1832)) across a 3-month hibernation experiment under laboratory conditions. We also opportunistically tested for evidence of acclimatization in torpid metabolic rate (TMR). When given access to gradients in microclimate, bats tended to choose the warmest temperature available (11 °C) while almost completely avoiding the driest condition available (85% relative humidity at 8 °C). Furthermore, bats held at different temperatures over the course of the hibernation showed no differences in TMR when measured under common conditions at the end of hibernation. Taken together, our results suggest that selective pressures to conserve energy during hibernation are not overwhelmingly strong and further support the proposition that optimal expression of hibernation is something less than the maximal expression of hibernation unless the animal is nearing starvation.
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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".