Bibliographic record
Abstract
According to Societe de L'Assurance Automobile du Quebec statistics, in Quebec pedestrians comprise 13% of all road deaths, the highest number after car and light truck occupants. Of all pedestrian deaths in Quebec between 2000 and 2004, 27% occurred on the island of Montreal, which has the most pedestrian victims because of its large population. On the road network, on average, 4.4 pedestrians suffer minor, severe, or deadly accident injuries daily, and 24 pedestrians die annually, 44% of all Montreal road network deaths. About 3,500 pedestrians are killed or injured in Quebec traffic accidents annually, with roughly 450 serious injuries, and 100 deaths. The authors argue that safety record analysis should be done in respect to conditions in walking practices, an activity beneficial to health, in order to better understand the pedestrian safety issue. Road network managers will promote walking as a transportation form and improve pedestrian safety by placing priority on pedestrians in public environment design and redesign and harmoniously integrating, within urban areas, transportation networks and offering safe traveling conditions. A provincial pedestrian round table, infrastructure improvements, pedestrian signals, and other issues, including child pedestrians, are discussed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 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".