Bibliographic record
Abstract
Over a decade ago, a local traffic safety committee in the Fraser Valley area of British Columbia noted a significant and alarming increase in both the collision rate and the collision severity on the highway through the Fraser Canyon. The highway, which was a section of the Trans---Canada Highway (Highway 1) between the communities of Hope and Ashcroft, had road design characteristics that were likely contributing to the poor safety performance on the corridor. Furthermore, it was determined that the commercial vehicle operators were over-represented in the collision occurrence on the corridor and that their driving behavior was also a factor in the poor safety performance on the corridor. While it would not be unusual for a local traffic safety committee to identify a corridor as problematic, what was unique was the approach that would be adopted to address the safety needs of the corridor. Historically, efforts to improve road safety were handled in isolation by public agencies interested in reducing collisions. Individual efforts by enforcement, engineering and education groups were recognized to have a positive impact and can reduce collisions. However, the magnitude of the safety problem on the Fraser Canyon corridor warranted a new approach, which could potentially yield greater overall safety benefits. What evolved was the British Columbia’s High Risk Corridor (HRC) Program, which was created by recognizing that a safe roadway environment is a shared responsibility, involving both public and private agencies that have the ability and desire to act and implement changes that can improve the level of road safety performance. It was felt that a high risk corridor approach, which involved the coordinated and strategic efforts by the various agencies responsible and interested in road safety could yield greater overall safety benefits as compared to individual agency efforts that are undertaken in isolation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".