Light Rail as the Catalyst for Ottawa’s Transit-Oriented Development: Planning for Sustainable Growth
Bibliographic record
Abstract
The City of Ottawa, Ontario, Canada, is the nation’s capital city as well as its fourth largest city with a population of nearly 1 million. It is a carefully planned municipality with a strong federal government presence and influence leading to a high quality of life. Ottawa has a broad socioeconomic and age profile, and a continuous demand for high quality municipal services, including public transit. In response to this situation, transportation and mobility was identified as a strategic priority within Ottawa’s 2011–2014 Term of Council priorities and the ensuing City Strategic Plan. This priority formalizes the expectation to provide reliable and sustainable public transit service while also providing an appropriate land use mix in and around transit stations. This paper should be considered as a road map of plans, policies, and steps taken to support Ottawa’s commitment to becoming a leader in Canada’s transit-oriented development (TOD) efforts. It demonstrates Ottawa Light Rail Transit’s (OLRT) role in providing for the city’s growing transit needs through efforts to support and implement modal shift and advancing TOD principles. An overview of the ongoing amendments to planning-related documents in support of the OLRT project will be given. Finally, a high level review of current and proposed land use planning initiatives will be discussed to illustrate how existing and future TODs are influenced by transit choices.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".