Quantifying the Albedo of the Montreal Island and its Potential for Increase
Bibliographic record
Abstract
Urbanization has changed the Earth’s surface, resulting in the urban heat island effect. There has been a recent focus on increasing urban albedo as a strategy to mitigate this phenomenon. Studies on Montreal’s albedo have primarily looked at the impact of albedo manipulations upon the urban heat island effect. However, the current albedo of the island, broken down by land use type, has yet to be quantified. Therefore, previous studies often rely on generalized urban albedo and land use estimates that have not been proven to be generalizable to Montreal. This study attempted to quantify the current albedo of the Island of Montreal through urban land use categorization. The findings were then used to estimate albedo increase under different roof replacement scenarios. Data sets for building footprints, vegetation, and roadways were incomplete in Montreal, requiring the combination of several sources to obtain representative data for analysis. This study found the albedo of Montreal island to be 0.19 ± 0.057. Further, the hypothetical roof change scenarios then aligned with a 0.1 albedo increase, which is the albedo change used in current urban heat island effect mitigation literature. Using the albedo increase potential that resulted from the three scenarios tested here, future research should explore further estimation of the associated surface and air temperature decrease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".