Characterizing commercial vehicle safety in rural
Bibliographic record
Abstract
This investigation focused on characterizing commercial vehicle safety in rural Montana on the basis of driver, vehicle, cargo, carrier, operating environment and crash characteristics using advanced statistical modeling methods. Montana, like other rural states experiences unique challenges in regards to commercial vehicle safety. Crashes typically involve a single vehicle, occur at higher speeds, are more severe and take longer to be detected. In order to address these challenges, researchers utilized statistical modeling to characterize commercial vehicle safety levels on the basis of the various characteristics. Model interpretation was intended to assist public agencies in focusing scarce regulatory and enforcement resources on commercial vehicles highest at-risk for safety-related problems. The agencies would then be able to perform their duties more effectively and efficiently by addressing safety problems in a preventative rather than reactionary manner. An ordered probit model was used to model the 6524 crashes that took place over a seven-year period. The results of this investigation were directly interpretable in regards to magnitude and direction. Independent variables that were found to have a significant influence on the severity of a crash included driver characteristics (age, condition-normal, condition-asleep, driver state residence), vehicle characteristics (vehicle configuration-single unit 2-axle), cargo characteristics (household goods), carrier characteristics (carrier physical address-Canadian Territories, number of trip leased and intrastate drivers, latest review-compliance review), operating environment characteristics (crash years
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 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".