Exploring pavement friction variability factors using ensemble trees and causal inference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding pavement friction measurement data is necessary to predict future road conditions and determine intervention strategies. Although considerable friction measurement data are collected for these purposes, it is not yet entirely clear how to interpret it. More specifically, there is often unexplained variability associated with these data, which inhibits their use. In this study, we have focused on enhancing the understandability of the data by exploring the causes of the unexplained variability. We constructed a dataset from two decades of friction data on Swiss national roads to explore the influence of different factors, including systematic testing conditions and external factors, on the observed data variations. We used average difference to quantify the degree of variability between consecutive measurements. Explainable ensemble trees and the SHapley Additive exPlanations methods are applied to assess the factors’ contribution to the data variability. Furthermore, a structural causal framework is employed to unravel the factors’ causal effects. Our findings indicate that much of the unexplained variability is related to maintenance interventions, temperature differences, and the speed at which the measurements were taken. These findings demonstrate how the data mining methods confirm the patterns observed in measurements conducted in controlled experiments.
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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 | 0.001 |
| 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 it