Evaluating changes in slip-resistance performance of select IceFX® winter boots in real-world use over a winter season
Bibliographic record
Abstract
Winter boots with specialized outsoles (composite footwear) may be able to reduce the risk of falls on ice by 78%. However, a pilot study has also found that the slip resistance of this composite footwear can diminish relatively quickly with simulated use. The objective of this study was to evaluate the change in slip resistance of popular composite footwear (with IceFX outsoles) over a winter season of real-world use. Eighteen participants were given composite footwear to use over 4 winter months. Changes in slip resistance were measured monthly using the Maximum Achievable Angle (MAA) test while a pedometer was used to track step counts. Over 150,000 steps, MAA scores dropped from 13.86±1.35 (SD) to 8.81 ± 1.32 (SD), with a significant decrease after just 75,000 steps. This drop in slip resistance suggests that the risk of slip-related falls on ice may increase during even the first season of use. • Boots with IceFX composite outsoles had excellent slip resistance on ice when new. • Slip resistance scores of these boots dropped significantly after 75,000 steps. • This suggests a need to improve the wear resistance of these composite outsoles. • Different areas of the composite outsoles may have been worn to different extents.
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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".