Pedestrian-Oriented Communities: Regenerating Critical Neighborhoods using GIS, Multi-Objective Optimization Design, and Simulation Tools to increase Social and Environmental Sustainability Performance
Bibliographic record
Abstract
With cities generating 70% of global greenhouse gas (GHG) emissions and struggling to effectively manage population growth, housing affordability, social equity, and environmental justice, this thesis seeks to assess the capacity data-driven and climate-responsive urban renovations have to address climate change challenges and inequity present in Toronto neighborhoods when implementing green infrastructure and setting transit and pedestrian mobility targets. With the use of multi-objective optimization design algorithms and subsequent digital simulation tools, the intent of this thesis is to virtually simulate the effects of site redevelopment and to discuss the ways in which the domains of civil engineering, urban planning, and architecture are limited in addressing inequity while reflecting on the roles of monetary systems, upfront carbon, social structures, policy, and governance. The outcome is the generation of a decision-making process designed to support GHG emission reduction targets for a neighborhood while promoting its sustainable development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".