Lateral coefficient of friction for characterizing winter road conditions
Bibliographic record
Abstract
Real Time Traction Tool (RT3)-Curve was used in this study to evaluate the effect of ice and snow on tire–road lateral friction coefficient, herein referred to as the Halliday Friction Number (HFN). The field experiments for the study were performed in winter 2012–2013 on the University of Alberta’s test road facility in Edmonton, Alberta. Each run was repeated at three target speeds under varied road conditions, bare dry, dry with ice patches, ice, and three levels of snow accumulation. No considerable correlation was found between vehicle speed and the friction measurements for bare dry, ice- and snow-covered conditions. Expectedly, the bare dry asphalt concrete surface had the highest HFN, the presence of ice reduced the dry surface friction by 55%. The accumulation of snow on the dry surface reduced the HFN further than ice, by 69, 75, and 81% for light, moderate, and heavy snow, respectively. A falling trend was observed for friction as more snow accumulated on the ground. Analysis of the effect of number of truck passes over ice at −3.5 and −5 °C showed that ice can become more slippery after each pass of traffic. A similar analysis for snow revealed that more passes over moderate snow will compact the fresh snow into a slippery surface. For light snow, even at low temperatures (<−10 °C), passes of traffic will melt the snow through frictional heat and result in higher friction values.
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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".