Modelling Fatal Pedestrian Accidents In Montreal's Metropolitan Area 1995-1997
Why this work is in the frame
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Bibliographic record
Abstract
In order to help prevent pedestrian accidents, it is necessaq to identify the environment and circumstances of the accidents and the characteristics of the persons involved. To study these issues a three component model has been elaborated, the first component included characteristic of the environment at the locus of the accident, the second component characteristics related to the driver involved in the accident and the type of vehicle and the third component characteristics relating to the fatally-injured pedestrian. Using a logistic regression as a method of analysis we decompose the variation in fatal pedestrian automobile accidents between the city of Montreal and the periphery of the region of Montreal. Results of the study showed that age of the pedestrian killed in traffic collisions is an important explanatory variable for the tsvo territories. The elderly are more likely to be involved in fatal pedestrian crashes than are the other age groups. Some variables related to the characteristics of the driver and those of the striking vehicle were included in the final model but there are some differences between the two territories for example, high posted speed limits is associated with fatal pedestrian crashed within the city of Montreal but was not statistically significant at the periphery. Variables related to the characteristics of the environment at the site of the accident were roadway alignment and lighting conditions. Roadway alignment (grade-curve and flatstraight categories) is an important explorato~ variable for the periphery and lighting conditions - roadway lighted at night for the city of Montreal. In conclusion, from the demographic and environmental point of view, it is important to make a distinction between geographical areas for exploring the
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it