Orange Work Zone Pavement Marking Midwest Field Test, April 2018
Bibliographic record
Abstract
Roadway lanes are often repositioned to accommodate highway work operations, resulting in a need to alter pavement markings. \nEven the most effective methods for removing old pavement markings sometimes leave “ghost” markings at the old lane line \nlocations. The ghosts can be quite conspicuous under certain lighting conditions and viewing angles. To address this issue, some \ninternational jurisdictions use a special marking color (orange or yellow) to increase the salience of temporary lane lines; this \npractice appears to have originated in Germany in the 1980s and is now routine in several European countries and the Canadian \nprovince of Ontario. Special-color markings have also been used experimentally in Australia, New Zealand, and Quebec. In some \njurisdictions the special-color markings override existing markings (such that the old markings are left in place), while other \njurisdictions use special-color temporary marking but also attempt to remove old lane lines. \nThe Wisconsin Department of Transportation (WisDOT) experimented with orange work zone marking on a high-volume longterm freeway-to-freeway interchange reconstruction project in Milwaukee; surveys indicate good driver acceptance, but the \ncomplex traffic flow characteristics and frequent configuration changes at the site make it difficult to separate the effects of the \norange markings from other aspects of the work zone management strategy. \nTo assess the driver behavior aspects of orange markings in a simpler environment, a matched-pair study was conducted on two \nbridge re-decking projects on I-94 near Oconomowoc, Wisconsin. Evaluation of vehicle positioning and speed data indicated \nvery similar driver behavior with the two colors. Driver surveys and interviews with project field engineers indicated a preference \nfor the orange marking when lateral lane shifts are required. Perhaps the most pragmatic approach is to reserve orange as an \nemphasis color for specific work zone locations that require difficult driving maneuvers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".