Understanding nature based solutions (NBS) on buildings to mitigate urban heat islanding (UHI)
Bibliographic record
Abstract
Buildings contribute to the urban heat island (UHI) phenomenon, but also have vast “un-used” surfaces that can be employed to incorporate innovative nature-based solutions (NBS) to mitigate UHI effects. NBS on buildings can be achieved by increasing surface reflectivity (ISR) on flat building envelop and increasing surface greenery/vegetation (ISG) on both vertical and horizontal building envelope components. This paper presents a snapshot of the current understanding of NBS and how they affect building performance, the simulations conducted to evaluate NBS and UHI effects and modeling approaches used, the primary knowledge gaps as well as the future steps needed to reduce the effects of UHI in urban agglomerations across Canada. The road map consists of; I) providing approaches to lessen the UHI influences with respect to the implications of warming/changing climate, urban canopy/landscape characteristics and building design; II) development of the best management practices (BMP) for NBS – UHI for Canadian communities; and III) guideline for the design of NBS–UHI solutions to mitigate UHI in accordance with Canadian design climatic load.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".