Investigations of Side Impact Crashes with Control Data
Bibliographic record
Abstract
Side impact crashes account for 25-40% of all crashes, and a high proportion of those involving personal injury. The only driver factor consistently implicated in crash occurrence in the literature is being older, so that vehicle and environmental factors are of primary importance for crash prevention. Crash investigations conducted in Toronto of passenger vehicles in two-vehicle side-impact crashes were included. Control data, obtained at the crash site close to the crash date on the same weekday and time of day, included license numbers of up to four passing vehicles for each vehicle involved in the crash. From the license number we obtained the make, model, year, curb weight, dimensions and safety equipment such as airbags, ABS and traction control. Descriptive and comparative analyses of the crashes were conducted to identify characteristics of crash-involved vehicles relative to control vehicles. Separate effects were estimated for struck (target) and striking (bullet) vehicles where appropriate. Results suggest that safety equipment such as ABS and traction control reduces the risk for striking vehicles; for struck vehicles, traction control may have an effect but there is little evidence for any effect of ABS. Comparisons with control vehicles allow one to examine factors affecting crash avoidance that are not possible by other means.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".