Effects of increasing urban albedo in the Greater Toronto Area
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 2 nd through July 5 th ) 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.
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.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it