Human Slip Assessment of Candidate Reference Surfaces for Walkway Tribometer Validation: An Update to Standard ASTM F2508
Bibliographic record
Abstract
Abstract Previous studies have reported on the concept of using human subjects to rank and differentiate walkway surfaces that vary in slipperiness. Surfaces identified as having different levels of slipperiness, based on the outcomes of human subject walking trials, are then used to validate tribometer slip resistance measurements. This concept was adopted in the development of ASTM F2508-11, Standard Practice for Validation and Calibration of Walkway Tribometers Using Reference Surfaces. Because of a depleting supply of the reference surfaces cited by ASTM F2508, new reference surfaces are needed. In this study, our objective was to assess new candidate reference surfaces to update the ASTM F2508-16, Standard Practice for Validation, Calibration, and Certification of Walkway Tribometers Using Reference Surfaces. One hundred and forty-eight human subjects walked across four ceramic-based tiles (E, F, G, and H) under contaminated conditions. Our results revealed that, consistent with our prior studies, human subjects were able to rank and differentiate surfaces according to slipperiness. Moreover, the surfaces evaluated in this study demonstrate characteristics that make them suitable replacements for the ASTM F2508 reference surfaces. Based on our findings, we recommend that ASTM F2508 be updated using surfaces E, F, and G and that surface G be considered a candidate to establish a slip resistance threshold for walking.
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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 | 0.006 | 0.001 |
| 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".