The effect of wear on slip-resistance of winter footwear with composite outsoles: A pilot study
Bibliographic record
Abstract
Falls on icy surfaces are among the top causes of injuries for workers exposed to the outdoor environment. Our recent field study showed that a new generation of winter footwear incorporating composite outsoles was able to reduce slips and falls on icy surfaces by 68% and 78%, respectively. The widespread adoption of this type of footwear may lead to substantial reductions in pain, suffering and costs of fall-related injuries. However, these composite materials are sensitive to wear and abrasion, which makes it likely that their slip-resistance performance may degrade with use. The goal of this pilot study was to determine the extent to which the slip-resistance of two types of winter footwear with composite outsoles changed as they wore down with real-world use. Seven participants were recruited for this study and were asked to walk 100K steps with their assigned footwear. Tread depth and slip-resistance performance (using the Maximum Achievable Angle test) were measured at baseline and again after each 25K-step interval up to 100K. Our results showed that the slip-resistance performance of the test footwear dropped significantly after the 75K and 100K step intervals compared to baseline. In addition, significant changes in tread depth were found after only 25K steps. These findings indicate that the performance of this type of footwear degrades relatively quickly with real-world use. Therefore, larger scale study of the slip-resistance of winter footwear with composite outsoles is needed and members of the public should be made aware of the potential loss of slip-resistance of these products.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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 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".