Evaluating climate change impacts on outdoor thermal comfort in a highlyurbanized neighborhood in Montreal
Bibliographic record
Abstract
Climate change has led to more frequent, severe, and prolonged episodes of extreme heat events (EHEs) in Canada and around the globe.EHEs can lead to health issues such as heat stroke, and sometimes fatality, especially among vulnerable populations e.g., seniors living alone, lower-income residents, and homeless in cities.To plan and implement effective extreme heat adaptation and mitigation strategies, it is important to accurately quantify current and future projected outdoor thermal comfort in Canadian citiesespecially of highly urbanized centers, which are usually densely populated and warmer due to Urban Heat Island (UHI) effects.An accurate simulation of these effects requires long-term high spatial resolution climate simulations, which are computationally expensive.This study proposes and implements a computationally efficient process to evaluate the climate change impacts outdoor thermal comfort in a 1250m x 1250m neighborhood in downtown Montreal.The future climate projections from regional climate model (RCM) are generated at building level resolutions by undertaking computational fluid dynamics (CFD) simulations over hot, typical, and cold days falling in the "Extreme Warm Year", "Typical Downscaled Year" and "Extreme Cold Year" prepared from the long-term climate projections from the Coordinated Regional Climate Downscaling experiment (CORDEX) database.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".