A comparative analysis of protocols for measuring heat transmission through flame resistant materials: capturing the effects of thermal shrinkage
Bibliographic record
Abstract
Abstract Bench‐scale tests measuring the thermal protective performance of textile materials do not capture the effect of thermal shrinkage, primarily because of the planar geometry of the test device. The performance of single‐layer fabrics commonly used in protective garments is compared here following several protocols, including the use of a new cylindrical device as well as standard and modified ASTM, CGSB and ISO procedures, with and without a 6.35 mm air gap between the fabric and the sensor. Both the time to reach the second degree burn criterion and the time for the sensor to register a 24°C temperature rise were measured. Fabrics that shrink had reduced thermal protection when measured with the cylindrical device, compared with other tests. Two‐way analysis of variance indicated that, although the dependent measures differ significantly among fabrics, the nature and extent of those differences depend on the test used. One‐way analyses of variance indicate that each method differentiates among the fabrics. However, most tests in which the fabrics are in contact with the sensor rank heaviest fabrics as the most protective. Among the tests incorporating a space between the fabric and sensor, those using the cylindrical device differentiate best between lighter fabrics that shrink and those that do not. Regression analyses of data from bench scale tests with data from instrumented mannequin tests confirm the superiority of the cylindrical device in capturing the effects of both thermal shrinkage and fabric integrity. Copyright © 2002 John Wiley & Sons, Ltd
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".