Trapezoidal tearing behavior of laminated fabrics used in <scp>Firefighters</scp>' protective clothing
Bibliographic record
Abstract
Abstract Laminated fabrics offer a unique combination of properties and are widely used in various applications, including as moisture barriers in firefighters' protective clothing. Tearing is a common testing method used to evaluate the mechanical performance of laminated fabrics. However, limited information is available on the tearing behavior of laminated fabrics using the trapezoidal procedure. Using high‐speed imaging, this study examined the tearing process of three moisture barriers commonly used in firefighters' protective clothing. The results revealed that the tearing process varied depending on factors such as the structure of the base fabric, the presence of an extra coating on top of the membrane, and the continuous or discontinuous nature of the adhesive layer. This study provides insight into the effect of each component of a laminated fabric on the tearing process, and can inform the design of better performing moisture barriers. Highlights Study of trapezoidal tearing behavior of moisture barriers. Analysis by high‐speed imaging and field emission scanning electron microscope. Influence of base fabric, adhesive configuration, presence of extra coating. Cyclic tearing pattern for woven base fabric and dot adhesive. Continuous tearing for nonwoven base fabric and continuous adhesive.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".