Algorithmic and Computer Software for Determination of Thread Tension After Guide Large Curvacity
Bibliographic record
Abstract
The conducted studies of the effect of the structure of the threads on the amount of tension when interacting with guides and working bodies of weaving machines and knitting machines, which have a large curvature in the zone of contact with the thread, established a mechanism for the process of increasing the tension of the thread after the guide by changing the radius of curvature of the guide and friction forces in the zone contact. It is proved that the increase in tension is explained by a change in the angle of coverage of the thread of a guide of large curvature, and for complex threads and yarns, the real angle of coverage will be greater than the calculated one, due to deformation of the thread diameter in the contact zone, and for monofilaments it is less than the calculated one due to bending stiffness. The sequential passage of the thread along the guides, from the entry zone to the formation zone of fabric and knitwear, leads to a stepwise increase in tension. In this case, the output parameter of the tension after the previous guide will be the input parameter for the subsequent guide, which makes it possible to use recursion when determining the tension in front of the formation zone. In this regard, research on the computer implementation of the algorithm for determining the thread tension on technological equipment using recursion should be considered relevant.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".