LOCAL ROAD SAFETY PLANS: GUIDELINES AND BEST PRACTICES
Bibliographic record
Abstract
1 2 Local road practitioners across the country play a critical role in addressing crash risks at the local level 3 and may be able to identify the specific or unique conditions that contribute to crashes within their 4 jurisdictions. The Local Road Safety Plan (LRSP) offers a foundation for consensus and focus. It defines 5 key emphasis areas and strategies that impact local rural roads and provides a framework to accomplish 6 safety enhancements at the local level. The LRSP helps communities take a proactive stance in reducing 7 and preventing local road fatalities and injuries. This paper focuses on the recently completed US 8 guidelines for the development Local Road Safety Plans (LRSP) and how they can potentially be applied 9 in Canadian jurisdictions. These guidelines are summarized in the US Federal Highway Administration 10 (FHWA) report Developing Safety Plans: A Manual for Local Rural Road Owners and how they can be 11 practically applied in Canadian communities. 12
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".