Bibliographic record
Abstract
Hibernation is an adaptive strategy that allows animals to survive periods of food scarcity and harsh environmental conditions by entering a state of reduced metabolic activity. A key characteristic of hibernation in many species is the torpor–arousal cycle, where body temperature fluctuates with periodicity ranging from days to weeks. These cycles, while conserved across many mammalian hibernators, exhibit species-specific variation in amplitude. Despite decades of research, the mechanisms underlying torpor–arousal cycles remain unresolved. This review examines current hypotheses, including the hourglass, water balance, clock, molecular, two-process, and frequency-modulation (FM) models, to elucidate the drivers of torpor–arousal cycles. I highlight how the FM model captures key features of body temperature fluctuations in species such as Syrian hamsters ( Mesocricetus auratus (Waterhouse, 1839)) and 13-lined ground squirrels ( Ictidomys tridecemlineatus (Mitchill, 1821)), revealing two hidden periodicities—one spanning a few days and the other about a year—both present in these animals. My review provides perspective on the advantages and limitations of various mathematical models for understanding the mechanisms underlying the torpor–arousal cycles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".