Determining the risk of slipping on level ice using winter footwear with varied maximum achievable angle slip-resistance performance
Bibliographic record
Abstract
Slip-related falls on icy surfaces remain a significant public health concern, largely because of the extremely low coefficient of friction of ice. Although advanced composite outsoles can reduce slips by 68% and falls by 78% compared to conventional winter footwear, our studies suggest that frequent slips on ice persist. We hypothesized that absolute slip risk on ice remains high, motivating the need for additional interventions. The objective of this project was to measure the risk of slipping for participants walking on a level ice surface using winter footwear with varying slip resistance performance and to compare the slip risk on ice to other surfaces reported in the literature. We investigated slip risk by recruiting 27 participants who walked on level ice while wearing 11 different winter boots across five Maximum Achievable Angle (MAA) categories (0°, 3°, 5°, 9°, 10°). After completing the level-ice trials, participants walked on progressively steeper ice surfaces only to determine their Observed MAA. The MAA test defines the steepest icy slope an individual can traverse without slipping. A motion capture system recorded 8,503 steps, of which 999 were slip-steps, corresponding to an overall 11.8% slip probability. Footwear with a 0°MAA exhibited 36% slip risk, while higher-rated 9-10°boots still had a 4%-5% slip probability, approximately one slip every 20-25 steps. Finally, we include an equation that converts MAA ratings into absolute, step-level slip risk on level ice (1 in N steps). These results confirm that, despite technological advances in outsole design, ice remains exceptionally hazardous. Even the best-performing boots did not fully prevent slips. Additional measures - such as slip-prevention training, improved ice-clearing practices, or heated and porous pavements - may thus be required to further reduce winter slip-related injuries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".