Analysis and Comparison of Shading Strategies to Increase Human Thermal Comfort in Urban Areas
Bibliographic record
Abstract
With the expected increase in warmer conditions caused by climate change, heat-related illnesses are becoming a more pressing issue. One way that humans can protect themselves from this is to seek shade. The design of urban spaces can provide individuals with a variety of ways to obtain this shade. The objective of this study was to perform a detailed evaluation and comparison of three shading strategies that could be used in an urban environment: shade from a building, from a tree, and from an umbrella. This was done through using field measurements to calculate the impact of each strategy on a thermal comfort index (Comfort Formula (COMFA)) in two urban settings during sunny days of the summer of 2013 and 2014 in London, Canada. Building shade was found to be the most effective cooling strategy, followed by the tree strategy and the umbrella strategy. As expected, the main determinant of this ranking was a strategy’s ability to block incoming shortwave radiation. Further analysis indicated that changes in the convective loss of energy and in longwave radiation absorption had a smaller impact that caused variations in the strategy effectiveness between settings. This suggests that under non-sunny days, these rankings could change.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".