Characterizing and modeling of low twist yarn mechanics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Twist in yarns can be used for handling or testing purposes, but it is not necessary when using continuous multifilament yarns as compared to the spinning required for a short fiber yarn. Small amounts of twist have shown to increase the strength of the yarn while decreasing the longitudinal stiffness. Previous models, including Gegauff’s cos 2 θ model and Rao and Farris’ model, are compared and discussed. A para-aramid (Kevlar 49) and a regenerated cellulose (BioMid) yarn are tested at various levels of twist to compare with these models. Twist is manually applied, and the samples are tested under continuous rate of extension to determine chord modulus, breaking tenacity, and elongation and break. The results are then fit to existing prediction models using a minimization of the standard error of the regression. Finally, a linear regression is also applied to the data to contrast the fit compared to traditional models. It was found that while the Gegauff model and the Rao and Farris model may capture the overall trend and decrease in longitudinal stiffness over a large range of twist, the small range over which twist can practically be used is not well represented by these models and is better represented by a simple linear relationship.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it