Study of parameters affecting the strength of yarns
Bibliographic record
Abstract
The purpose of this study is to gain an understanding of parameters that affect the strength of yarns. Quasistatic and dynamic strength of five different yarns were obtained using hydraulic and Hopkinson bar testing methods and the rate dependency of the failure strength of each yarn was quantified (average of 10 repeats). The scaling effect was also studied experimentally in order to relate the effect of specimen size to the failure stress of Kevlar 129. Single fibres were tested at five different gage lengths (5, 16, 25, 50, 100 mm) and multi fibre specimens were also tested at various gage lengths (24, 100, 170 mm). By studying the variation of statistical strength with increasing size of specimen, a decreasing trend for the tensile strength was observed. A three-parameter exponential growth model is proposed to relate the strength of the specimen to its volume in order to incorporate the increase in defect population.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".