London, Ontario Strategic Road Safety Strategy
Bibliographic record
Abstract
The City of London has initiated a strategic road safety program to reduce the number and severity of motor vehicle collisions. The basic form of the program follows the traditional state/provincial or municipal approach of analysing collision statistics, identifying the nature of the most severe problems, matching countermeasure programs to address the most severe types and developing delivery strategies. The first step in finding the target areas was to conduct a broad-based literature search and compare it to the collision database. The collision data were then analysed looking for traditional and non-traditional areas of high collision frequency. In subsequent stages of the project, there are two elements of the program which may be considered somewhat different from traditional programs. First, the selection of emphasis areas is not solely data-driven. While the basis of the emphasis areas will certainly be the hard data, the choice will be modified by input from an extensive public contact campaign (to determine perceived safety issues) as well as a selection of target areas previously determined by City staff or Council. Second, to maximize the potential for success, the choice of emphasis areas is to be adjusted based on a number of factors, which include the severity of the collisions, the potential effectiveness of the countermeasures and the capacity of the involved agencies to change or add to their current programs to deliver countermeasures specific to the safety strategy. (A) For the covering abstract of this conference see ITRD record 201310RT334E.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.172 | 0.032 |
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".