Bibliographic record
Abstract
Road Safety Audit is a safety tool that offers promise to help reduce roadway crashes and fatalities. The Transportation Association of Canada (Reference 2) defines a road safety audit as "a formal and independent safety performance review of a road transportation project by an experienced team of safety specialists, addressing the safety of all road users". The purpose of this project was to select a "high risk" corridor in the Region of Waterloo, Ontario and to conduct a Road Safety Audit of the corridor. The audit involved an in-depth study of the accidents that have occurred in the corridor during the last five years. Analysis of the collision data was supported by site visits during which the roadway and intersections were examined in detail to gain an understanding of why collisions occurred and why particular types of collision occurred. Numerous recommendations were developed from the audit process. The recommendations included improving pavement condition, installing new traffic signs, relocating existing traffic signs, reducing the number of driveways at certain locations, improving lighting, installing additional traffic control devices such as red light cameras, and conducting an in-depth study to consider possible geometric improvements. All of the measures suggested are designed to contribute to accident reduction in the corridor. In addition, the report recommends and road safety audit should be widely used in Canada to evaluate and improve the safety of our highway system and to minimize the risk of accidents.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.008 | 0.002 |
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".