A field study of thermal comfort and summertime overheating of six schools in Montreal Canada
Bibliographic record
Abstract
As a result of global climate change, the world has witnessed a noticeable rise in average temperatures along with a surge in the frequency and intensity of extreme weather conditions such as heatwaves. Indoor overheating is a growing concern, particularly for vulnerable populations such as primary school students. To investigate this issue, a field monitoring network was established in six primary school buildings in Montreal, Canada. This network provided field measurements, including indoor and outdoor temperature, humidity, wind speed, and solar radiation, at five-minute intervals. This paper presents a case study that focused on three time intervals during the summer months: two school closed periods in 2020 and 2021 (unoccupied) and one school open period in 2021 (occupied). The study used an adaptive model to analyze the indoor thermal condition between unoccupied and occupied periods, non-heatwave periods, and heatwave periods. \nThe study concluded that natural ventilation in buildings posed a risk for indoor overheating during heatwaves, while buildings with mechanical ventilation systems had better indoor thermal conditions. The correlation analysis showed that the building's response to outdoor weather factors in naturally ventilated buildings is consistent. Multiple linear regression analysis confirmed that outdoor temperature was the most significant factor affecting indoor thermal conditions, followed by solar radiation, wind speed, and relative humidity. Furthermore, the indoor and outdoor temperature difference shows a stronger linear correlation with the indoor temperature than the outdoor temperature in all school buildings.
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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 | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".