Evaluating surface texturing technologies of rubber composites on ice using a novel high-velocity tribotest method
Bibliographic record
Abstract
This study presents a high-velocity tribotest using Bruker UMT Tribolab to evaluate the ice traction of rubber composites. Lab-scale tribotests are essential for developing anti-slip materials for footwear. Reciprocating and rotational movements were studied on ice, along with the physical phenomena influencing friction, optimizing dwell time and load control parameters. GF-TPU composites and commercial winter boots (Green Diamond® and Arctic Grip Vibram®) were tested for validation. The addition of 10 wt% glass fibers to TPU increased static friction by 300 % and kinetic friction by 400 %, enhancing stick-slip behavior. SiC-based texturing in Green Diamond composites improved static friction by 160 % and kinetic friction by 970 %. Arctic Grip samples showed a 440 % increase in static friction and over 1000 % in kinetic friction.
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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 | 0.000 | 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 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".