Feasting, fasting and freezing: energetic effects of meal size and temperature on torpor expression by little brown bats<i>Myotis lucifugus</i>
Bibliographic record
Abstract
Torpor is an adaptation for energy conservation employed by many species of small-bodied endotherms. However, surprisingly little is known regarding proximate factors influencing day-to-day variation in torpor expression in the wild. We used open-flow respirometry to quantify torpor expression in nine little brown bats (Myotis lucifugus, LeConte 1831) at two ambient temperatures (7 degrees C and 17 degrees C) following either sham feeding or consumption of a high-protein meal (50% or 100% of the mass required to reach satiation for each individual). Food consumption significantly increased the time spent normothermic before torpor entry but did not affect either the rate of body cooling or torpid metabolic rate. Bats did not fully exploit potential energy savings by maximising their use of torpor. Instead they varied torpor expression such that total energy expenditure over the course of each 22-h trial was balanced against gross energy intake immediately before the trial, independent of ambient temperature. This was accomplished by adjusting the timing of entry into torpor (thus altering the time spent torpid), rather than by modulating torpid metabolic rate. However, pre-trial body mass was also a significant predictor of torpor expression, which suggests that energy reserves combine with recent foraging success to influence individuals' decisions about depth and duration of their torpor bouts. We also present evidence that little brown bats use the heat generated through digestion (i.e. the heat increment of feeding) to substitute for active thermogenesis at sub-thermoneutral temperatures, thereby reducing the energetic costs of thermoregulation prior to torpor entry.
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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".