A Review of Green Roof Applications for Managing Urban Stormwater in Different Climatic Zones
Bibliographic record
Abstract
Many regions have turned to low impact development technologies (LIDs), which are implemented to restore the changes in stormwater runoff that have resulted from urbanization. Green roofs are one typical type of LID. Until now, many studies have validated their roles in managing urban stormwater runoff. However, they have also revealed that the performance of green roofs largely varies with their design configuration, as well as their hydro-climatic exposure. The objectives of this review paper are to statistically synthesize the effects of the influential factors, including design and hydrologic variables, on green roof performance and to explore their effects in different climatic zones. The review’s results confirm the differences in the influential variables and, thus, the performance of green roofs in different climatic zones. These are the barriers to knowledge translation among engineering designers, stormwater managers, and policymakers in different climatic zones when implementing green roofs. Consequently, region- or site-specific studies are necessary to implement green roofs with confidence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".