Characterization of Urban Green Roofs’ Stormwater Runoff
Bibliographic record
Abstract
Green roofs are considered as one of the green technologies towards the development of sustainable urban environment and the building of ecofriendly cities.The benefits of implementing green roofs are numerous such as increase of biodiversity and reduction of urban heat island effect, stormwater management, energy saving, air quality improvement, etc. From the perspective of stormwater control, as more and more rooftops in the urban area turns into green spaces, characteristics of stormwater runoff from the drainage area may undergo a significant change which inevitably exerts potential impacts on the existing and yet-to-be-built drainage systems.However, in addition to peak flow reduction, basic understanding of the characteristics of green roof stormwater runoff is either lacking or very limited, and referenced green roof studies are very few in the literature.In this study, based on a set of rooftop-monitored data including the antecedent soil moisture content, rainfall, runoff volume and peak flow rates, stormwater runoff from both the roof garden area and the control area (the original rooftop without greening) was analyzed for its characteristics.It is well known that the antecedent soil moisture content is an important parameter associated with runoff generation, whereas runoff coefficient is a direct measure of the rainfall-runoff transformation process.These fundamental relationships are characterized in an attempt to better understand runoff generation mechanisms from green roofs.As part of this study for stormwater runoff characterization, probability distributions were
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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 | 0.000 | 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) | 0.000 | 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".