Bibliographic record
Abstract
York Region is one of the largest Regional municipalities in Canada and the fastest growing Region in the Greater Toronto Area. The Region's Transportation Services is responsible for the operation and maintenance of over 900 kilometres of Regional Roads covering an area of nearly 1800 square kilometres, from the City of Toronto to the south, to Lake Simcoe in the north. With rapid growth has come a need to find innovative methods of managing congestion. Intelligent Transportation System (ITS) technologies provide staff with another tool to monitor, manage traffic flow, manage congestion, and provide alternate route information to travelers, as well as save lives, time and money. As a result Region staff initiated a Intelligent Transportation Systems (ITS) Strategic Plan. York Region includes nine local municipalities. In addition, it is bordered by the City of Toronto, Durham Region, and Peel Region. Therefore, it was essential to perform coalition building with the twenty-five agencies who participated in the strategic planning process. Each regional ITS Strategic Plan is different, and this paper will discuss the process, the identified needs, and the specific deployment plan for York Region.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.013 |
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".