Characterization and empirical analysis of hot water immersion with compression protective performance of fabrics used in firefighters’ clothing
Why this work is in the frame
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Bibliographic record
Abstract
This study aims to investigate hot water immersion with compression protective performance of textile fabrics used in firefighters’ clothing. This study has two key objectives – firstly, to characterize the protective performance of fabrics under different types of hot water immersion with compression exposures; secondly, to empirically analyze the protective performance of these fabrics under different exposures. To accomplish both the objectives, the physical properties (e.g., thickness, air permeability) of multi-layered fabrics that are commonly used in firefighters’ clothing were measured. Next, the protective performances of these fabrics were evaluated under different exposures. The experimental data obtained were statistically analyzed to identify the effects of the fabrics’ physical properties on the performance. Also, the performances provided by the fabrics were compared, and the nature of heat and mass transfer through the fabrics was explored. Using the significant fabric properties that affected the performance, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) modeling techniques were used to empirically predict the performance of the fabrics. The best prediction models were then employed for saliency testing to understand the relative importance of the significant fabric properties on the performance. The study demonstrates that the protective performance of textile fabrics varies with the exposures, depending upon the mass transfer through fabrics. In these exposures, fabric thickness, air or water-vapor permeability, and evaporative resistance are found to be the primary properties to consider in protecting the wearer. In this study, it has been identified that ANN models can be effectively used in comparison to MLR models for predicting the protective performance. By analyzing the best-fit ANN models, it is identified that different fabric properties play a key role in predicting the protective performance. Overall, this study would enhance the understanding of fabric materials used in firefighters’ clothing. This deeper understanding could be applied to engineer new test standards and fabric materials for clothing to provide optimum occupational health and safety for firefighters.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it