Understanding the Need for Multifunctional and Equitable Green Infrastructure Implementation in Toronto, ON
Bibliographic record
Abstract
Green infrastructure can improve urban resilience by providing ecosystem services to communities. In the City of Toronto, organizational and political barriers have been found to limit equitable planning efforts that promote multifunctionality. This study applies Meerow and Newell's (2017) Green Infrastructure Spatial Planning (GISP) Model to understand and identify (1) the need for green infrastructure across Toronto's 158 Social Planning Neighbourhoods based on six resilience goals; (2) how these goals are prioritized in planning; and (3) the potential for multiple benefits to be delivered to neighbourhoods. This study has identified and mapped hotspots that display a high need for green infrastructure to improve stormwater management, reduce social vulnerability, increase parkland access, mitigate the urban heat island effect, improve air quality, and enhance landscape connectivity. The need for green infrastructure was generally highest in neighbourhoods with major non-residential uses and transportation corridors. Stormwater management was the most valued benefit of green infrastructure, and it was found that neighbourhoods with a high need for this benefit were also likely to exhibit a higher need to reduce heat vulnerability, improve air quality, and enhance landscape connectivity. Using these results, recommendations have been proposed to improve strategic green infrastructure planning in the City of Toronto.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".