Washington State Hosts Roundabout Rodeo
Bibliographic record
Abstract
Although Washington State has been constructing roundabouts in different localities across the state since the mid-1990s, there are still many communities without these types of intersections and citizens who are not familiar with what roundabouts are and how they are navigated. The City of Bellingham, in Whatcom County, WA, is an example of this type of community, and provided the setting for a memorable experience for the Washington State Department of Transportation (WSDOT). The initial proposal for Bellingham's first roundabout was at a four-legged intersection where 18 collisions had occurred in a single year, and officials wanted to build a roundabout to improve conditions at that intersection and along the corridor. Similarly, in another part of Whatcom County, the community was struggling with the idea of roundabouts being constructed on a major highway near a border crossing. Residents voiced concerns about how viable the roundabouts would be for use by not only local farm equipment, but also the large freight trucks that moved on the corridor back and forth across the U.S.-Canadian border. Although meetings and face-to-face question and answer sessions were held, uncertainty persisted, and WSDOT faced stiff resistance from individuals who could not envision the concept or who still did not believe that the larger vehicles would be able to navigate the roundabouts safely. So, WSDOT decided to show them.\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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.484 | 0.217 |
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".