Seeing Is Believing: Missouri DOT Convinces Skeptics That Roundabouts Work
Bibliographic record
Abstract
The first roundabout in the State of Missouri, built about 10 years ago, was designed for a golf course community in the Kansas City area. Even in that context, trying to convince the local community that a roundabout would work was difficult. Many people confused modern roundabouts with European-style traffic circles, and assumed they were hard to navigate, and intimidating to drivers, pedestrians, and bicyclists alike. To overcome these misconceptions, traffic engineers from the Missouri Department of Transportation (MoDOT) held local public meetings to explain the difference between roundabouts and traffic circles, how modern roundabouts work, and why they were more desirable than adding a new traffic signal within a quarter mile of an existing signalized intersection. MoDOT's larger goal was to build many roundabouts in the Kansas City area, but the difficulties MoDOT experienced trying to gain public acceptance of this first roundabout became the catalyst for a proactive outreach and education program focused on the benefits of roundabouts. Since the implementation of this first roundabout, MoDOT has installed over two dozen roundabouts in the Kansas City area, increasing community support over time with each new project. For every roundabout project, staff used a variety of outreach techniques to reach all target audiences and age groups who might be affected by the project.\n
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".