Human conscious response to thermal input is adjusted to changes in mean body temperature
Bibliographic record
Abstract
OBJECTIVE AND DESIGN: To detect the dependable criteria of behavioural thermoregulation through modelling temperature fluctuations of individuals allowed to freely manipulate inlet water temperature of a liquid conditioning garment (LCG) during 130 min of passive exposure to -20 degrees C interspersed with a 10 min period of moderate exercise at the 65th minute using a double-blind experiment. PARTICIPANTS: Eleven volunteers (5 women; 23.40 (SD 2.09) years; BMI: 23.24 (SD 2.19)) who lacked previous experience with LCG and cold exposure experiments. RESULTS: Despite variations in core and skin temperatures, thermal comfort, thermal sensation, and mean body temperature did not fluctuate significantly over time. Participants were able to find a desired level of LCG inlet temperature within 25 minutes which was maintained at similar levels until the 65th minute of the cold exposure. During exercise, LCG inlet water temperature decreased significantly. Regression models demonstrated that mean skin temperature and change in mean body temperature were significantly associated with thermal comfort and thermal sensation. Subsequent models revealed that, although all temperature variables were associated with LCG inlet water temperature, the coefficient of determination mainly depended on mean skin temperature and change in mean body temperature. The involvement of skin temperature was anticipated as the liquid conditioning garment was in contact with the skin. CONCLUSIONS: Humans generate conscious thermoregulatory responses in resting and exercise conditions during exposures to cold environments that are aimed towards maintaining a threshold mean body temperature, rather than temperature changes in individual body regions.
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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 | 0.002 |
| 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".