Motor Vehicle Collisions in London, Ontario: Estimating the influence of the built environment and children’s potential exposure
Bibliographic record
Abstract
Motor vehicle collisions are the leading cause of death for children and youth worldwide. To effectively target interventions to improve child safety, it is necessary to identify where motor vehicle collisions occur most often and what factors make these areas more hazardous. Study #1 maps collisions in London, Ontario (2010-2019) and identifies hotspots using a network kernel density estimation method within a GIS. Logistic regression analysis revealed that bike lanes were negatively associated with hotspots, while sidewalks were positively associated. Study #2 estimated children’s risk of being exposed to a motor vehicle collision while commuting to and from school, by combining collision risk data from study #1 with modelled student pedestrian volumes. Results suggest current crossing guard locations in London are not optimally deployed and should be relocated to the riskiest areas for student pedestrians. The findings of this thesis suggest that certain built environment characteristics have a significant influence on collision hotspots and should be considered in future road safety policy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".