Urban climate adaptation: an interdisciplinary research experience empowering architecture and urbanism education
Bibliographic record
Abstract
The world’s cities are growing in size and number. At the same time, the global climate change rises global average temperatures as well as increase weather extreme events. Sao Paulo, the 5th urbanized region in the world, has currently more than 21 million inhabitants and recent studies alert for the increased frequency of extreme climate/weather events in the city. In this context, this work presents an interdisciplinary research experience, based at the Architecture and Urbanism school in partnership with the Atmospheric Sciences’ Department, approaching the interdependencies between urban morphology, green infrastructure, and microclimate in São Paulo, aiming to discuss planning, urban and building design alternatives to counterbalance urban warming effects in a subtropical changing climate. The research team, led by an architecture researcher and an associate researcher from atmospheric sciences, includes a post-doc researcher, graduate and undergraduate students, engaging architecture, urban design, planning, and meteorology dealing with the role of planning, urban and building design for climate change adaptation. This paper briefly summarizes what we have learnt with remote sensing, measurements and numerical simulation encompassing the metropolitan, the neighbourhood and the building scales and discuss the results of an interdisciplinary research empowering architectural education in different levels.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".