Slip resistance and wearability of safety footwear used on icy surfaces for outdoor municipal workers
Bibliographic record
Abstract
BACKGROUND: Outdoor workers experience high injury rates in the winter due to slipping on ice and snow. Our testing program has demonstrated that most safety footwear does not provide adequate slip-resistance and/or comfort in icy conditions. OBJECTIVE: Our objective was to determine which of the most commonly worn safety footwear available to outdoor municipal workers in Toronto, Ontario, Canada would best prevent slips on icy surfaces and which models had good wearability. METHODS: We selected 45 of the most popular types of winter footwear worn by these workers and applied our Maximum Achievable Angle (MAA) test method to rate the slip-resistance of the footwear. A ten-point rating scale was used for recording participants' perceptions of wearability. The MAA test measured the steepest ice-covered incline that participants can walk up and down without experiencing a slip. RESULTS: Of the 45 types of footwear tested, only one model achieved an MAA score of 8 degrees that exceeded our cut-off for acceptable performance set at 7 degrees. Secondary measures of performance including thermal insulation; wearability and heaviness of footwear tested were also ranked. CONCLUSION: Our results demonstrate that footwear manufactures have the opportunity to differentiate their footwear by investing in slip-resistant outsole materials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".