Thermogenic responses to different clamped skin temperatures in cold-exposed men and women
Bibliographic record
Abstract
Despite many decades of research examining thermoregulatory responses under varying cold stresses in humans, very little is known about the variability in metabolic heat production and shivering activity. Here, we used a novel closed-loop mean skin temperature clamping technique with a liquid-conditioned suit to isolate the effects of mean skin temperature on the subjective evaluation of thermal sensation, heat production, shivering responses, and oxidative fuel selection in young, lean, and healthy men ( n = 12) and women ( n = 12). Our results showed a skin temperature-dependent increase in metabolic heat production (5.2 ± 1.2 kJ/min, 5.9 ± 1.5 kJ/min, and 7.0 ± 1.8 kJ/min with skin temperature maintained at 31 ± 0.1°C, 29 ± 0.2°C, and 27 ± 0.1°C, respectively; P < 0.0001) and shivering intensity in both men and women [0.6 ± 0.1% maximal voluntary contraction (MVC), 1.1 ± 0.4% MVC, and 2.5 ± 0.7% MVC, respectively; P < 0.0001], including sex-dependent differences in heat production at all three temperatures ( P < 0.005). Even when controlling for lean body mass and fat mass, sex differences persisted ( P = 0.048 and P = 0.004, respectively), whereas controlling for differences in body surface area eliminated these differences. Interestingly, there were no sex differences in the cold-induced change in thermogenesis. Despite clamping skin temperature, there was tremendous variability in the rate of heat production and shivering intensity. Collectively this data suggests that many of the interindividual differences in thermogenesis and shivering may be explained by differences in morphology and body composition.
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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".