Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it