Early-Life, Long-Term, and Seasonal Variations in Skid Resistance in Flexible and Rigid Pavements
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
Skidding contributes to up to 35% of wet weather accidents. Increased temperature and surface wear and polishing may affect the available friction and further increase skid-related accidents. Several studies have attempted to examine and quantify these variations mostly with inadequate or inappropriate conclusions. The surface friction of both port-land cement concrete (PCC) and asphalt concrete (AC) pavements was measured monthly to determine the influencing factors and quantify the seasonal fluctuation. Skid number (SN) and pertinent data of the Long-Term Pavement Performance program were obtained for both PCC and AC pavements, incorporating all geographic and climatic regions of the United States and Canada, to determine the contributing factors and quantify the long-term and early-life variations of surface friction. Surface friction was shown to fluctuate as a result of ambient or pavement temperature fluctuation at 0.35 British pendulum number per 1°C change in temperature. The effect of prior weather was shown to be insignificant. Following the construction, AC and PCC surface friction was shown to increase by 5 SN in about 18 months and 4 SN in about 2½ years. Skid resistance was shown to decrease thereafter at 0.27 SN for AC and at 0.24 SN for PCC pavements per million vehicle passes. Cumulative traffic passes, pavement age, speed, and temperature during the testing and PCC pavement surface texture types were found to be statistically significant for the prediction of long-term surface friction. AC pavement long-term surface friction was shown to be more sensitive, as compared with PCC, to predominant climatic condition.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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