PHYSICAL TECHNIQUES FOR DETERMINING THE RESISTANCE TO HEAT TRANSFER PROVIDED BY CLOTHING
Bibliographic record
Abstract
It is necessary to quantify the thermal insulation and evaporative resistance properties of clothing systems so that the heat exchange between the body and the environment can be determined and a person's performance in that environment can be predicted using biophysical models. This paper reviews the physical methods for directly measuring resistance data on fabrics and clothing ensembles and discusses problems associated with different techniques. In addition, methods for estimating the resistance values for clothing from different fabric and clothing properties will be mentioned. FABRICS The resistance to dry heat transfer (i.e., insulation) can be measured using the rate of cooling method, the constant temperature method (e.g., guarded hot plate), and heat flow meter. Flat plate instruments or cylinders have been used, with each type having advantages and disadvantages over the other. Fabric insulation can be estimated from thickness, so the compressometer, micrometer, and pendulum methods will be discussed. The evaporative resistance of fabrics can be measured using a sweating hot plate device or cylinder. A liquid barrier of known resistance is needed to keep the fabric dry during the test. Measurements can be made with and without a temperature gradient between the hot body and the environment. Other methods for measuring the diffusion of water vapor through a fabric include the ASTM control dish method and the Canadian DND apparatus. [1-10]
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".