Bibliographic record
Abstract
Traffic collisions represent a major problem on a worldwide basis. Every year, over 1.2 million people die in traffic collisions across the world and between 20 and 50 million are injured or disabled. In most regions of the world this epidemic of road traffic injuries is still increasing. Highway agencies all over the world are paying more and more attentions on highway safety. Highway safety has been taken into account seriously during the project programming and implementation in British Columbia Ministry of Transportation and Infrastructure, Canada (BCMOTI). This article describes the BCMOTI highway safety practice, which includes general safety analysis procedure to identify the safety problems, the improvement option generation, and economic analysis for improvement options. Historically, collision rates have been used as the basis for safety analysis. Research has shown that there are limitations with this approach due to the non-linear relationship between collision frequency and exposure. Collision prediction modeling is the recommended technique for estimating road safety in the American Association of State Highway and Transpiration Officials (AASHTO) Highway Safety Manual. The BCMOTI developed her own collision prediction models (CPMs) and collision modification factors (CMFs) to be used to assess highway safety improvements on British Columbia highways. It is believed that the CPMs and CMFs have improved the overall safety performance estimate (safety benefits) for road safety analysis and these outputs serve a valuable purpose with respect to option selection, project ranking and programming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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".