A comparison between the technical absorbent and ventilated capsule methods for measuring local sweat rate
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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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".