Leading Pedestrian Interval Assessment and Implementation Guidelines
Bibliographic record
Abstract
The purpose of a leading pedestrian interval (LPI) is to provide pedestrians with the opportunity to begin crossing the street before adjacent through-movement vehicles are permitted to proceed. This procedure allows pedestrians to establish a presence in the crosswalk; this presence increases the visibility of pedestrians to drivers and therefore reduces conflicts with turning vehicles. The City of Toronto, Ontario, Canada, implemented and formally evaluated an LPI in 2005. LPIs have been added to a few more intersections since then; however, the process has not been streamlined. For LPIs to be implemented at additional locations, an implementation guide and operating standard was needed. In answer to this need, transportation services of the City of Toronto developed an implementation and assessment guideline. The purpose of the guideline is to help traffic engineers identify suitable locations for LPIs by means of a checklist, determine the appropriate length of time for the LPI with a formula, and con...
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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 | 0.060 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".