Safety evaluation of cable barriers installation on rural highways in British Columbia
Bibliographic record
Abstract
Objective: The objective of this study was to evaluate the safety effectiveness of cable barrier systems installation on rural highway sections in British Columbia, Canada. Methods: Data on police-attended serious collisions (injury + fatality) on a number of rural highway sections in British Columbia, Canada, were used in the analysis. An empirical Bayes (EB) approach was employed to ensure that the evaluation results were reliable and to account for the regression to the mean artifact. Safety performance functions (SPFs) were developed using data collected at similar sites. For both median cable barrier (MCB) and roadside cable barrier (RCB) sections, the evaluation was undertaken using all serious collisions, truck serious collisions, and off-road serious collisions. Results: For MCB sections, the evaluation results showed statistically significant reductions of 21.7, 53.8, and 34.8% in all serious collisions, truck serious collisions, and off-road left (ORL) combined with head-on (HO) serious collisions. For RCB sections, statistically significant reductions of 74.7, 100, and 100% were found in all serious collisions, truck serious collisions, and off-road right (ORR) serious collisions, respectively. The impact of the after period on the evaluation results was explored. It was found that the changes in safety become more stable using an after period of 2–5 years. Conclusions: Cable barriers were successful in reducing the frequency of serious collisions on provincial highways in British Columbia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".