Bibliographic record
Abstract
York Region covers approximately 1,800 square kilometers and is located in the heart of the Greater Toronto Area (GTA) in Southern Ontario. Since the creation of York Region in 1971, the population has increased dramatically from 169,000 persons to approximately 1.1 million in 2012. York Region is anticipated to grow to a population of 1.5 million by 2031. With this increase in population comes increased exposure between pedestrians and motorists. Understanding and changing social behaviour of pedestrians and motorists through education, enforcement and engineering will change how they interact and increase public safety Region wide. A review of York Region's 2001 to 2010 collision statistics shows that the number of roadway fatalities has remained relatively constant over the past 10 years. However in 2010, a spike in pedestrian fatalities across the Greater Toronto Area is noted. While such a cluster of incidents at first seems alarming, a review of past occurrences indicates that such sudden spikes are likely random events. No particular factors that led to the spike in pedestrian fatalities in 2010 within the Greater Toronto Area could be identified. However, an analysis of our own data does show the percentage of fatalities involving pedestrians have been on the rise in York Region over the last decade. This project was nominated for the TAC 2013 Road Safety Engineering Award. For the covering abstract of this conference see ITRD record number 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.223 | 0.072 |
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".