Torpor and Digestion in Food‐Storing Hibernators
Bibliographic record
Abstract
Many species of hibernating mammals rely on hoarded food rather than body fat to support winter energy requirements. Here, we evaluate whether the associated ingestive and digestive requirements reduce the benefits that food-storing hibernators can accrue from torpor. Using a simple model, we predict (1) that digestive efficiency could either increase or decrease with increased use of torpor, depending on the Q(10) of digestion relative to the Q(10) of whole-animal metabolism and (2) that increased torpor will result in a linear decrease in energy consumption but an exponential increase in euthermic intake requirements. In 16 captive eastern chipmunks (Tamias striatus), the proportion of time that different individuals spent in torpor was highly variable (29.8%+/-5.9%; 0.0%-86.3%), positively correlated with dry matter digestibility (r2=0.53, P=0.02) and negatively correlated with energy consumption (r2=0.72, P=0.002). Thus, by both increasing conversion efficiency and reducing energy requirements, torpor appears to provide a double benefit for energy conservation by food-storing hibernators. Despite this, a comparative analysis shows that the euthermic intervals of food-storing rodents are four times as long and torpor intervals are half as long as that of fat-storing rodents. Given that required euthermic intake rates are expected to increase exponentially at high levels of torpor, the reduced torpor expression of food-storing species may result from constraints on their ability to load enough food into the gut when euthermic to cover the energy requirements of the subsequent torpor cycle.
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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 | 0.001 |
| 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".