Micro-Urban Heat Islands in the City of Montreal
Bibliographic record
Abstract
Heat within a city is not evenly distributed, giving rise to regions of relatively warm and cold temperatures. Regions of very high heat are referred to as micro-urban heat islands (MUHIs) and can be severe enough to harm human health. Despite MUHIs being an important factor in urban health, they are extremely underresearched. In this study we mapped the locations of MUHIs on the island of Montréal and compared them with the locations of vegetation on three clear, sunny days: August 10th, 2021; July 6th, 2020; and June 20th, 2020 using Landsat 8 thermal images with 30 m resolution. We compared two criteria for MUHIs and quantified their composition based on unsupervised classification done on ENVI 5.6.1, and Normalized Difference Vegetation Index (NDVI) calculations. Our results show that MUHIs are mainly associated with the presence of asphalt and concrete, and the absence of dense vegetation. The presence of these materials is not, however, a strong predictor of the formation of MUHIs in themselves. Though variability in unsupervised classifications between images introduces uncertainty in MUHI composition, these results suggest that increasing dense vegetation coverage in Montréal could prevent MUHI development during the summer.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".