Effects of moisture content and clothing fit on clothing apparent ‘wet’ thermal insulation: A thermal manikin study
Bibliographic record
Abstract
‘Wet’ thermal insulation, defined as the thermal insulation when clothing gets partially or fully wet, is an important physical parameter to quantify clothing thermal comfort. As the water/sweat gradually occupies the intra-yarn and inter-yarn air voids of the clothing material, the clothing intrinsic thermal insulation will be diminished and, hence, contribute to the loss of total insulation. In cold conditions, a loss in total thermal insulation caused by sweating may result in an inadequate thermal insulation to keep thermal balance and eventually leads to the development of hypothermia and cold injuries. Therefore, it is imperative to investigate the effect of clothing fit and moisture content on clothing ‘wet’ insulation. In this study, the ‘wet’ thermal insulation of three two-layer clothing ensembles was determined using a Newton thermal manikin. Four levels of moisture content were added to the underwear: 100, 200, 500 and 700 g. The clothing apparent ‘wet’ thermal insulation under different testing scenarios was calculated and compared. A third-degree polynomial relationship between the reduction in ‘wet’ thermal insulation and the moisture content added to underwear was obtained. Further, it was evident that the clothing fit has a minimal effect on the apparent ‘wet’ thermal insulation. The findings may have important applications in designing and engineering functional cold weather clothing and immersion suits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".