Bibliographic record
Abstract
OBJECTIVE: To describe and illustrate the geographic distribution of pedestrian crash sites in an urban setting (Montreal, Canada) with an alternative data source. METHODS: Data on pedestrian victims were extracted for a 5-year period (1999-2003) from ambulance services information systems. The locations of crash sites and pedestrian victim density were mapped using a geographic information system. Pedestrian "black spots" were defined as sites where there had been at least eight pedestrian victims. RESULTS: The 22 identified black spots represent only 1% of all city intersections with at least one victim and 4% of all injured pedestrians, whereas 5082 victims were injured at >3500 different crash sites. The number and population rates of injured pedestrians are greater in central boroughs. Accordingly, the density of pedestrian victims is much higher in central boroughs. Over the 5-year period, in some central boroughs, pedestrian crashes occurred in up to 26% of intersections. CONCLUSIONS: Ambulance information systems were relevant to map pedestrian crash sites. Most pedestrians were injured at locations that would have been missed by the black spot approach. This high-risk preventive strategy cannot substantially reduce the total number of injured or the insecurity that many pedestrians experience when walking. Considering the large number and widespread occurrence of pedestrian crashes in Montreal, prevention strategies should include comprehensive environmental measures such as global reduction of traffic volume and speed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".