Hurontario/Main Street Corridor Master Plan Missisauga and Brampton - Building a New and Integrated Vision for the Corridor
Bibliographic record
Abstract
Mississauga has successfully grown to be Canada's 6th largest municipality and Brampton is Canada's 10th largest municipality. This paper will focus on the development of an integrated plan for transforming the Hurontario/Main Street Corridor, a major arterial road linking the Cities of Mississauga and Brampton within the Greater Toronto Area. Development of the plan has been built around partnerships, with the study undertaken by the two cities, working within the framework provided by the Province and Metrolinx the regional transportation authority. The plan has established a new vision for a street that is to be fundamentally sustainable - increasing the overall transportation capacity through the provision of rapid transit, being a focus of growth that supports vibrant, mixed use, transit oriented development, while being sensitive to the presence of adjacent stable neighbourhoods. Light Rail Transit (LRT) was selected to build upon an existing successful bus service already in place and one of the key catalysts for transforming land use along the corridor. This paper focuses on key challenges including replacing two existing traffic lanes with LRT, creating a corridor suitable for pedestrians and cyclists, and integration with the regional transit services along the corridor. Another key challenge will be to take a context sensitive approach to design that integrates well with the different land uses along the corridor. This paper will also discuss how both cities are building upon the recently approved LRT Master Plan for the corridor. For the covering abstract of this conference see record control number 201111RT334E.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".