6. Performing Organization Code
Bibliographic record
Abstract
Over the past two decades, as more knowledge has been gained about driver visibility needs and the aging driver population trends, some highway agencies have begun to use longitudinal pavement markings that are wider than the 4-inch minimum for standard centerline, edge line, and/or lane line applications. The overall goal of this study was to identify information pertaining to the use of wider markings by highway agencies in the United States, Canada and worldwide and combine with the technical literature to provide a comprehensive report detailing the use and benefits of wider markings. Research activities included surveys and reviews of technical literature. A survey was administered to roadway agencies in the United States and Canada in Spring 2001. This survey revealed that a number of these agencies are using wider pavement markings, although levels of implementation and reasons for using them vary. A separate but similar survey was administered to highway personnel worldwide. A review of the research literature has identified five main methods for evaluating the effectiveness of wider pavement markings. Traditional measures of effectiveness have centered on crash evaluations and service life evaluations mainly because the results can readily be used in benefit/cost evaluations. Unfortunately, conclusive crash reduction or improved service life data does not exist in the literature or within highway agencies. Evidence has
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.092 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.797 | 0.807 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".