Effects of increasing urban albedo in the Greater Toronto Area
Bibliographic record
Abstract
Abstract Increasing surface reflectivity decreases the skin and air temperature, which potentially reduces cooling energy demands. The state of the art online numerical Weather Research and Forecasting model (WRF) is used to investigate the effect of increasing albedo in Toronto, Ontario, during the 2018 heat wave period (July 2nd through July 5th) on urban climate and building energy consumption. The study couples the WRF with a multi-layer of the Urban Canopy Model (ML-UCM) and Building Energy Model (BEM). The ML-UCM is a part of the land-surface parameterization to predict the heat and moisture fluxes from canopies to atmosphere. The BEM is coupled with Building Effect Parameterization to predict the energy consumption of buildings. BEM simulates the effect of heat generation from buildings on urban climate. The reflectivity of roofs, walls and roads are increased from 0.2 to 0.65, 0.60 and 0.45, respectively. Albedo enhancement leads to a decrease in air temperature by around 1°C and an increase in wind speed which induce a reduction in skin temperature. The combined effect of decreased solar heat gain by buildings and decreased air temperature reduced the energy consumption of HVAC systems by 3-5%, confirming the positive effect of increasing the albedo on urban climate.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".