High-resolution Assessment of Heat Mitigation Strategies in the City of Toronto
Bibliographic record
Abstract
Toronto is vulnerable to the negative impacts of extreme heat episodes, due to its high density of buildings, regular summertime heat episodes, and complex weather patterns influenced by Lake Ontario. As warming trends are anticipated to continue, the number of extreme heat days (warmer than 30 OC) will also increase. A high frequency of extreme heat days is associated with increased heat related mortality. Despite this, studies assessing strategies of heat mitigation in Toronto, which involve implementing infrastructure to reduce heat hazards, are currently limited. Using the mesoscale (~ 1 km resolution) climate model, Weather Research and Forecasting (WRF), various heat mitigation strategies, including addition of vegetation and high albedo surfaces, are explored. To quantify the impacts of urbanization, we modelled climatic conditions in the Toronto region with and without urban development. This step established that under the same boundary conditions, the presence of urban features significantly increases diurnally averaged temperatures in Toronto by as much as 4 oC. Having established that urbanization has led to higher temperatures, we then modelled the impacts of adding cool roofs and vegetation to urbanized Toronto simulations, finding that having 80% vegetation in all grid cells significantly reduces day-time diurnal temperatures. Lastly, our project will include a novel addition of street trees into WRF, allowing users to calculate tree-building interactions, including evapotranspiration and radiation trapping effects. The results of this study will be used in real time by building simulation teams to determine which mitigation strategies will best prevent overheating.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".