Responsiveness of thermal sensors to nonuniform thermal environments and exercise.
Bibliographic record
Abstract
BACKGROUND: We investigated the utility of finger temperature, hand heat flux, and mean skin temperature as indices of overall thermal balance during nonuniform thermal manipulations combined with exercise, with a view to identifying useful feedback sites for input into personal thermal control systems. METHODS: There were 16 subjects who performed 4 x 30 s of 120% VO2peak cycling with a 4-min recovery. During recovery, subjects either received no cooling (CON), upper-body cooling (UC), or upper-body cooling combined with leg heating (UCLH) using a multi-zone liquid conditioning garment. Heat loss during recovery was approximately equal to heat production during exercise. Skin temperature was measured on the mid-medial phalanx of the fourth finger. Heat flux was measured on the dorsum of the hand. RESULTS: Neither hand heat flux or finger temperature distinguished between the two cooling conditions during any of the recovery periods, though hand heat flux was very sensitive to the onset and cessation of exercise. Mean skin temperature was significantly different (p < 0.05) during CON (34.0 +/- 0.1 degrees C), UC (32.5 +/- 0.2 degrees C), and UCLH (33.0 +/- 0.2 degrees C). CONCLUSION: Mean skin temperature may serve as a good indicator of overall heat exchange in the body, even when exposed to nonuniform thermal environments. As hand heat flux was very sensitive to the onset and cessation of exercise, it may be useful as a supplemental thermal feedback to modulate heat exchange in microclimate thermal control systems.
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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) | 0.002 | 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".