Bibliographic record
Abstract
Roundabouts are a relatively new type of intersection control in Canada, and as such there is growing interest in how they compare relative to established types of control using stop signs and traffic signals. Roundabouts have a clear set of advantages and disadvantages compared to these traditional intersections, which are set out in Section 2 of this paper. Intersections play an critical role in the operation of a road network. Therefore it is important to make sound defensible decisions about type of intersection control, both when implementing a new intersection and when modifying an existing intersection (or road section) due to motor vehicle capacity or user safety issues. When stop and traffic signal control were the only alternatives this process was straightforward and accomplished using traffic signal warrants based on side street delay and historic safety performance. Now that roundabouts are a possible alternative the process is potentially much more complicated. A number of road agencies in Canada and the United States have developed practices to aid in making a decision about type of control. A selected range of these practices is described in Section 3 of this paper as well as practices in Australia, which is a country with similar density and road network characteristics as Canada and the US but a much longer history of roundabout implementation. Section 4 of this paper synthesizes the various roundabout implementation practices descried in Section 3 as being either basic intersection-level, “enhanced” intersection-level, or network-level.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".