Field performance evaluation of high friction surface treatments (HFST) in Oklahoma
Bibliographic record
Abstract
The high friction surface treatment (HFST) is an effective countermeasure to roadway departure crashes. The field performance of the HFST system was evaluated from seven field monitoring trials on six HFST sites in Oklahoma from 2015 to 2017. HFST sections have statistically significant higher pavement friction and macrotexture in contrast to the adjacent untreated pavements. Meanwhile, distresses including patching, reflective cracking, raveling, and delamination have been observed on these HFST sites. The number of property damage crashes and injured crashes before and after the HFST installation are queried from the Oklahoma safety database. The HFST results in 29% to 100% reductions of annual property damage crashes and 100% reduction of annual injured crashes. The benefit-cost ratios of the HFST sites range from 6.9 to 27.9. In addition, HFST sites constructed with bauxite show higher friction numbers and better polishing resistance performance than sites using the local mine chat aggregates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".