Bibliographic record
Abstract
The cornerstone of most safety management programs consists of a “collision-prone location” program, where significant collision history must exist and be identified before improvements are recommended. Often, these programs are solely dependent on collision records and thus program success is governed by the data quality. Unfortunately, in many jurisdictions in North America, the quantity and quality of collision data have been degrading for several years. This growing problem is jeopardizing the success and continuance of many road safety programs. To help mitigate this problem, it is believed that a subjective evaluation technique could be developed that does not rely on collision statistics and that could be used to identify and diagnose problematic areas. The development and application of a risk index used for road safety evaluation are described. The risk index is developed as a driver-based, subjective assessment of the potential road safety risks for in-service roadways. The objective of developing the safety risk index is to produce a technique to support road safety analysis that does not rely on deteriorating collision data. The road safety risk index was developed and tested to ensure consistency between observers in their subjective assessment of safety. In addition, the results from the risk index were compared with results from objectively derived road safety measures to evaluate the success of the road safety risk index. The comparison indicates that there is a statistically significant agreement between the results of the risk index and the objectively derived road safety measures.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".