Quantifying the uncertainty in tribometer measurements on walkway surfaces
Bibliographic record
Abstract
Properly estimating and reporting the uncertainty of walkway surface friction is key to ensuring pedestrian safety. Here we quantified the amount and sources of uncertainty in friction measurements by having four users of four units of each of two walkway tribometer models (Slip-Test Mark IIIB, English XL) perform 12 measurements on four samples of four different surfaces that ranged from slippery to slip-resistant. We found that 51–82% of the total variance in the measurements was explained by the user, unit, sample and a user–unit interaction, which means that the variance a single user calculates from their own data does not capture most of the uncertainty in their measurements. Based on these data, the minimum uncertainty associated with the mean of a user’s measurements is ±0.064 (Mark IIIB) and ±0.072 (XL) to be 95% confident that their mean captures a surface’s available friction. Practitioner Summary: Walkway surface friction measurements are less accurate than they appear. Based on an experiment quantifying the amount and sources of uncertainty in surface friction measurements using two common tribometers, we quantified and report the minimum uncertainty that users can assign to their walkway surface friction measurements. Abbreviations: ANOVA: analysis of variance; ANSI: American National Standards Institute; CI: confidence interval; E: east; ILS: interlaboratory study; ISO: International Standards Organization; JCGM: joint committee for guides in metrology; N: north; S: south; SBR: styrene-butadiene rubber; SD: standard deviation; TR: test result; W: west
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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".