The effect of thermal transience on the perception of thermal comfort
Bibliographic record
Abstract
The present study tested the hypothesis that at any given ambient temperature (Ta), thermal comfort (TC) is not only a function of the temperature per se, but is also influenced by the temperatures rate of change and direction. Twelve healthy young (age: 23 ± 3) male participants completed experimental trials where Ta increased from 15° to 40 °C (heating) and then decreased from 40 to 15 °C (cooling). In one trial (FAST), the rate of change in Ta was maintained at 1 °C.min−1, and in the other (SLOW) at 0.5 °C.min−1. During each trial participants provided ratings of TC at 3-min intervals to determine their thermal comfort zone (TCZ). In the FAST trial, participants identified TCZ at an Ta between 22 ± 4 and 30 ± 4 °C during heating and between 25 ± 3 and 33 ± 3 °C during cooling phase (p = .003), and in the SLOW trial between 21 ± 3 and 33 ± 4 °C during heating and between 23 ± 4 and 34 ± 3 °C during cooling phase (p = .012). During the heating phase TCZ was established at a lower range of Ta, compared to cooling phase. The difference between the heating and cooling phases in preferred range of Ta was more pronounced in the FAST compared to SLOW trial. TCZ is influenced not only by the prevailing temperature, but also by the direction and the rate of the change in Ta. Faster changes in Ta (1 °C.min−1) established the TCZ at a higher Ta during cooling and at a lower Ta during heating phase.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".