Effectiveness and performance of high friction surface treatments at a national scale
Bibliographic record
Abstract
Although high friction surface treatment (HFST) has been widely installed in recent years, validation efforts considering various materials, installation ages, environmental conditions, and traffic levels are missing primarily due to lacking of high-speed data collection instruments. Utilizing laser imaging technology and fixed-slip friction tester, this study collects comprehensive pavement surface data at 21 HFST sites in 11 states at highway speeds. Measurements on HFST and untreated pavements are compared to determine the effectiveness of HFST. Multivariate analyses are conducted to investigate the impacts of factors on HFST friction. Average temperature and installation age are identified as the significant factors. The HFST sites constructed using calcined bauxite aggregates exhibit better friction performance than those using flints. Subsequently, friction models are developed to aid highway agencies in managing HFST.
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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".