Logistic Model of Hit and Run Crashes in Calgary
Bibliographic record
Abstract
Hit-and-run cashes refer to traffic collisions in which at least one driver flees from crash scene without reporting the crash. In the City of Calgary, for example, they accounted for 18 percent of total traffic collisions in 2005. The objective of this study is to identify the environment and road characteristics that contribute to the occurrence of hit-and-run crashes in the City of Calgary. A logistic regression model was developed to delineate the likelihood of hit-and-run crashes as opposed to non hit-and-run crashes. Our study showed that compared to weekday and daytime collisions, weekend and night time collisions have significantly higher likelihood of hit-and-run. In terms of weather condition, clear weather exhibited the greatest chance of hit-and-run when compared to any other weather conditions. Moreover, hit-and-run crashes are quite likely to occur on undivided one-way roads and the roads with artificial light. As for driver related factors, female drivers aged at 55 or above showed the greatest likelihood as compared to other age groups. Based on the findings from this study, a set of countermeasures will be proposed in this paper.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".