Variability of Friction Measurements Using Three Common Walkway Tribometers
Bibliographic record
Abstract
Abstract Walkway tribometers are used to assess the slip resistance of flooring surfaces. To yield valid measurements, the ASTM F2508-16e1, Standard Practice for Validation, Calibration, and Certification of Walkway Tribometers Using Reference Surfaces, standard practice requires an annual calibration to show a tribometer’s test results fall within the 95th percentile confidence intervals (CI) of its supplier’s validation test results on 4 reference surfaces. Many users, however, report difficulty meeting this requirement. Here we examine the variability within and between multiple calibrations to evaluate the current calibration procedure and assess whether field measurements of a surface can be directly compared to calibration test results. We performed multiple calibrations with three common walkway tribometers on one set of reference surfaces. We then simulated field tests on each reference surface and compared these results to the calibration test results. Overall, all three tribometers ranked and differentiated the reference surfaces in all calibrations; however, none of the calibrations fell within the supplier’s CIs for all four surfaces. The between-calibrations variance ranged from 15 % to 90 % of the total variance in the data set, and only 25 % of the “field” test results fell within the 95th percentile CI of our calibration values. Our findings show that the current ASTM F2508 calibration requirement does not adequately account for measurement variability. Moreover, our findings indicate that field measurements should not be compared directly to calibration measurements without factoring in measurement uncertainty. Overall, our results show the need for an improved calibration procedure and more research to establish a valid method for estimating slip risk on field surfaces.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".