High-Speed Imaging Analysis of Laminated Fabric Tearing Behaviour
Bibliographic record
Abstract
Abstract Laminated fabrics are widely used in diverse industries because of their ability to adapt to many different functions. Understanding their tear force behaviour is critical to evaluate their serviceability. The tearing behaviour of laminated fabrics can be examined using high-speed imaging and analyzed using image analysis techniques. This technique is an attractive tool to elucidate the tearing behaviour of laminated fabrics. Through high-speed imaging it is possible to observe the tearing process on the fabric (e.g., yarns bearing the load before failure, yarn breaking behaviour) and membrane (e.g., pop-in crack length, crack propagation, formation of wrinkles, thinning) sides of the laminated fabrics to understand the tearing behaviour. In addition, this technique helps quantify different phenomena such as the membrane pop-in crack length, and crack wake bridging width by yarns. High-speed imaging analysis is a promising technique to visualize the tear phenomena in laminated fabrics.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 teacher head, 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".