The green in-between: the future of public spaces in response to increasing heat waves and urban flood risks
Bibliographic record
Abstract
Downtown low-income neighbourhoods in large Canadian cities are \ndisproportionately affected by heat waves and urban flood risks in the \nface of climate change. They are ill-equipped to protect residents and \ninfrastructures from more frequent and severe extreme weather events \ndue to the predominance of heat-absorbent and impermeable hardscape \nsurfaces. This affects the well-being and physical and psychological health \nof residents. It also has significant economic impacts. Introducing nature inbetween buildings could help better manage heat and water and provide \nbeautiful outdoor spaces where people can seek heat relief. The thesis \ninvestigates creating a new type of public space in downtown Toronto, \nthe Urban Forest Park, learning from soft engineering, passive design, and \nbiophilic design. It aims to offer a beautiful recreational cool microclimate \nthat becomes a destination for residents and a resilient neighbourhood \ninfrastructure that manages stormwater and mitigates the urban heat island \neffect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".