A comparison between the technical absorbent and ventilated capsule methods for measuring local sweat rate
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study assessed the accuracy of the technical absorbent (TA) method for measuring local sweat rate (LSR) relative to the well-established ventilated capsule (VC) method during steady-state and nonsteady-state sweating using large and small sample surface areas on the forearm and midback. Forty participants (38 males and two females) cycled at 60% peak oxygen consumption for 75 min in either a temperate [22.3 ± 0.9°C, 32 ± 17% relative humidity (RH)] or warm (32.5 ± 0.8°C, 29 ± 7% RH) environment. Simultaneous bilateral comparisons of 5-min LSR measurements using the TA and VC methods were performed for the back and forearm after 10, 30, 50, and 70 min. LSR values, measured using the TA method, were highly correlated with the VC method at all time points, irrespective of sample surface area and body region (all P < 0.001). On average, ≈ 79% of the variability observed in LSR measured with the VC method was described by the TA method. The mean difference in absolute LSR using the TA method (TA-VC with 95% confidence intervals) was -0.23 [-0.30,-0.16], -0.11 [-0.21,0.00], -0.03 [-0.14,+0.08], and +0.02 [-0.07,+0.11] mg · cm(-2) · min(-1) after 10, 30, 50, and 70 min of exercise, respectively. Duplicate LSR measurements within each method during steady-state sweating were highly correlated (TA: r = 0.96, P < 0.001, n = 20; VC: r = 0.97, P < 0.001, n = 20) with a mean bias of +0.07 ± 0.14 and +0.01 ± 0.10 mg · cm(-2) · min(-1) for TA and VC methods, respectively. The mean smallest detectable difference in LSR was 0.12 and 0.05 mg · min(-1) · cm(-2) for TA and VC methods, respectively. These data support the TA method as a reliable alternative for measuring the rate of sweat appearance on the skin surface.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it