An Energy‐Based Body Temperature Threshold between Torpor and Normothermia for Small Mammals
Bibliographic record
Abstract
Field studies of use of torpor by heterothermic endotherms suffer from the lack of a standardized threshold differentiating torpid body temperatures (T(b)) from normothermic T(b)'s. This threshold can be more readily observed if metabolic rate (MR) is measured in the laboratory. I digitized figures from the literature that depicted simultaneous traces of MR and T(b) from 32 respirometry runs for 14 mammal species. For each graph, I quantified the T(b) measured when MR first began to drop at the onset of torpor (T(b-onset)). I used a general linear model to quantify the effect of ambient temperature (T(a)) and body mass (BM) on T(b-onset). For species lighter than 70 g, the model was highly significant and was described by the equation Tb-onset=(0.055+/-0.014)BM+(0.071+/-0.031)Ta+(31.823+/-0.740). To be conservative, I recommend use of these model parameters minus 1 standard error, which modifies the equation to Tb-onset-1 SE=(0.041)BM+(0.040)Ta+31.083. This approach provides a standardized threshold for differentiating torpor from normothermia that is based on use of energy, the actual currency of interest for studies of torpor in the wild. Few laboratory studies have presented the time-course data required to quantify T(b-onset), so more data are needed to validate this relationship.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".