A field study on summertime overheating of six schools in Montreal Canada
Bibliographic record
Abstract
Abstract Due to global climate change, the world has been experiencing significant increases in average temperatures and the frequency and intensity of extreme weather events such as heatwaves. The overheating problem in indoor spaces of buildings has become a concern to the comfort and health of building occupants, especially vulnerable populations such as the elderly, children, or the sick. A field monitoring network consisting of rooftop weather stations and indoor sensors has been set up on 11 buildings of different types in Montreal, Canada. This paper presents the results of field measurements of indoor thermal conditions of six school buildings to assess the risks of summertime overheating. These six primary school buildings were built in 1930-1966 with window-wall-ratios between 10-30% and limited mechanical ventilation. The indoor dry-bulb air temperature, relative humidity, and CO2 concentrations are measured by indoor wireless sensors. The weather conditions, including dry-bulb temperature, relative humidity, solar radiation, rainfall, wind speed, and wind direction, are measured by rooftop weather stations. Measurements presented in this paper are collected from July to September 2020, which include four different time intervals: (a) during two heatwaves, (b) during summer break when schools were closed, and (c) when schools were reopened, and windows were intermittently opened. Data analysis shows that the indoor and outdoor temperature difference has a strong linear correlation with the outdoor temperature observed for all school buildings. This correlation is also affected by building operations, such as opening windows, closing blinds, and the micro-climate of their surroundings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".