Guidelines for Using Centerline Rumble Strips in Virginia 6. Performing Organization Code 7. Author(s)
Bibliographic record
Abstract
Following the wide and successful use of continuous shoulder rumble strips, many state departments of transportations (DOTs) installed centerline rumble strips (CLRS) on rural two-lane and undivided multilane highways in an effort to reduce cross-over-the-centerline (COCL) crashes. COCL crashes include head-on, sideswipe opposite direction, fixed object run-off-the-road left, and non-collision. The purpose of this research was to develop guidelines for using CLRS in Virginia based on a review of best practices and the analysis of Virginia COCL crash data from 2001 through 2003. The analysis procedures included data query and analyses of crash frequency, density, and rate. Areas and route locations with the highest COCL crashes and densities were identified as potential candidate sites for CLRS. As of 2003, 24 state DOTs and two Canadian provinces were using CLRS. They are generally installed on a case-by-case basis. CLRS design patterns vary greatly among states, but the most commonly used types are continuous grooves 12 to 16 inches in length, 6 to 7 inches in width, and 0.5 inch in depth spaced 12 or 24 inches apart. The optimal CLRS patterns remain unknown. Data analyses revealed that the distribution of COCL crashes in Virginia varied significantly with roadway system, road type, jurisdictional area, and road location. The statewide COCL crash densities were 0.13 and 0.71 crash per mile for secondary and
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.214 | 0.302 |
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".