Modeling bicyclist injury severity in bicycle–motor vehicle crashes that occurred in urban and rural areas: a mixed logit analysis
Bibliographic record
Abstract
The purpose of this paper is to identify and compare the contributing factors to bicyclist injury severity in urban and rural areas. Two mixed logit models are developed for both the urban and rural areas separately to identify factors that significantly contribute to the injury severity outcome of bicyclists resulting from bicycle–motor vehicle crashes. Data collected from 2007 to 2014 in North Carolina are utilized for the model development. The model estimation results show that factors including bicyclist age from 25 to 54, driver age under 25, vehicle speed, and divided road are found to significantly affect the injury severity outcome of bicyclists in bicycle–motor vehicle crashes in rural areas only. In contrast, factors including drivers age over 60, van, single unit truck, head-on crash, motorist overtaking bicyclist, two-way roadway, road condition, and crash time are found to have a significant impact on the injury severity of bicyclists in urban areas only.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".