Canadian Guidelines for Establishing Posted Speed Limits
Bibliographic record
Abstract
Canadian Guidelines for Establishing Posted Speed Limits were developed to provide guidance and to enhance consistency in the evaluation of posted speed limits. The guidelines were developed through the review of current domestic and international practices, technical documentation and testing. Road safety may be enhanced through credible posted speed limits that match the expectation of drivers for a given roadway and its surrounding area. The guidelines provide engineers and traffic practitioners with an evaluation tool to assess appropriate posted speed limits based primarily on the classification, function and physical characteristics of a roadway. It is an objective assessment based on engineering factors. The risks associated with the engineering factors determine the appropriate posted speed limit. The higher the level of risk, the lower the recommended posted speed limit. For all categories of roadways, the application of the methodology results in posted speed limits that are consistent with the roadways' physical characteristics. An automated spreadsheet is provided to facilitate the evaluation of posted speed limits. Local policies or procedures for setting and managing posted speed limits may exist, and they govern when in effect. When the recommended speed limit from the guidelines is lower than the posted speed limit dictated by policy, a review of the applicability of the policy may be prudent. There are also unique circumstances related to a roadway's characteristics that warrant more specific guidance with regards to setting posted speed limits.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.223 | 0.099 |
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".