Chapter 6 Green roofs: 10 years after City of Toronto Green Roof Bylaw
Bibliographic record
Abstract
Cities around the world are being challenged on how to effectively build infrastructure to support increasing populations, while constrained to a finite amount of space. Our cities are under immense pressure to plan, rethink, and adapt their urban fabric to cope with climate change and rapid urbanization that is shaping our urban future. With a population of over 6 million people, the city of Toronto is the fourth largest city in North America [1]. Toronto has been forced to navigate unprecedented population growth, aging infrastructure, and climate change similar to other global cities. The success of Toronto’s ability to accommodate more people and buildings in challenging times depicts the influence of the policies that govern construction. One policy in particular that is a testament to Toronto’s vision for sustainability is the green roof (GR) bylaw. Reaching its 10-year anniversary in 2020, it is important to look back at the origin of the bylaw and the people who were motivated to change the landscape of the city of Toronto. GRs are now considered a valuable tool of low impact development for their ability to manage stormwater and are utilized around the world. This chapter discusses the current status and future possibilities of GRs in North America and the role that the City of Toronto Green Roof Bylaw has played in it.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 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".