Runoff Reduction Effects of Green Roofs in Vancouver, BC, Kelowna, BC, and Shanghai, P.R. China
Bibliographic record
Abstract
This research examines how distinct climatic conditions affect the runoff reduction functions of green roofs by comparing performance in Vancouver, BC, Kelowna, BC and Shanghai, P.R. China. To quantify the reduction in runoff volume effectuated by green roofs, both the Soil Conservation Service Curve Number (SCS-CN), crop coefficient method and the Hargreaves-Samani method are applied in calculating the annual water gains and losses of green roofs during a year of average precipitation, using local climate data such as precipitation, evapotranspiration, and temperature. Using a soil water balance model, the research also analyzes the change in soil water content of a typical green roof with a soil depth of 150 mm, and compares the potential irrigation requirements of plants with low versus high water requirements in each of the three cities. The calculation results show that the typical green roof could reduce annual rooftop runoff by 29% in Vancouver, 55% in Shanghai, and 100% in Kelowna. Furthermore, these results illustrate the important role that soil properties, soil depth, and plant selection play in maintaining growth of plants and minimizing green roof irrigation requirements.
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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 | 0.001 | 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".