Transportation Association of Canada
Bibliographic record
Abstract
Many communities in Canada and the U.S. are feeling the effect of too many cars travelling at too high speeds. Such traffic has a profound negative effect on street life. To reduce this effect municipalities are turning to traffic calming. Traffic calming is a combination of policies and physical measures that reduce the negative effects of motorized vehicle use in a community. The key to successful traffic calming lies in changing the design and role of the street in such a fashion that drivers will want to slow down. Depending on the problem on the street, a wide range of traffic calming measures can be applied. The selection of these measures is best done by public consensus. Traffic calming is community-based planning and public participation is essential to its success. The process begins with the establishment of a community-based working group, followed by a thorough analysis of issues and concerns. This may be done by surveys or walk-abouts in the community. With participation of the working group, the problem is then defined, planning principles developed, and a preliminary “traffic calming designation ” is applied to the streets, akin to the traditional street classifications. This information is then presented at a public meeting
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.459 | 0.234 |
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".