Clothing Thermophysiological Comfort: A Textile Science Perspective
Bibliographic record
Abstract
Thermophysiological comfort is a crucial aspect of human life, contributing to health and work performance. The current paper aims to enhance the understanding of current research, progress, and remaining challenges regarding clothing thermophysiological comfort from a textile science perspective. It provides a comprehensive review of several facets of clothing thermophysiological comfort, focusing on the history of thermophysiological comfort prediction models, heat and moisture transfer mechanisms in the skin–clothing–environment system, controlling factors of thermophysiological comfort, textile materials for superior thermophysiological comfort, and thermal comfort assessment techniques. The paper shows that previously developed thermophysiological comfort models were mainly based on the human thermoregulation process. However, the effect of the air gap size between the human skin and the cloth layer, i.e., the microclimate, on the heat and moisture transfer in the skin–clothing–environment system has been largely overlooked. In addition, thermophysiological comfort models of skin–clothing–environment systems generally only considered dry thermal resistance and evaporative resistance, yet many other fabric properties have effects on human thermophysiological comfort. Potential future directions are identified to fill some of the current gaps. A conceptual model of clothing comfort to contribute to a better understanding of thermophysiological comfort is also proposed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".