Skin temperature modulation of shivering response in humans
Bibliographic record
Abstract
During cold exposure, increase in heat production is produced via the activation of shivering thermogenesis and nonshivering thermogenesis, the former being the main contributor to compensatory heat production in non‐acclimatized humans. In rats, it has been shown that shivering thermogenesis is modulated solely by skin thermoreceptors. The aim of this study was to determine if cold‐induced shivering is also principally modulated by cutaneous thermoreceptors in humans. Using a liquid‐conditioned suit, six non‐acclimatized men were exposed to cold (4ºC) for four 30‐min periods, each of them separated by 15 min of heat exposure (35ºC). Core temperature remained stable throughout exposures. Skin temperatures significantly decreased by 12% in average during the sequential cold/heat exposures compared to baseline (p<0.001). Shivering intensity increased significantly during cold exposures (3.3 ± 0.7 % MVC) and was significantly reduced during the heat exposures (0.5 ± 0.1 %MVC) (p=0.004). Accordingly, metabolic rate was significantly higher during cold exposures (0.40 ±0.0 LO 2 /min) compared to heat exposures (0.25 ± 0.0 LO 2 /min) (p=0.0003). In conclusion, cutaneous thermoreceptors are a major modulator of shivering in humans. This study was funded by NSERC.
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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".