Hydrologic impacts of mat-based retention and detention layers within extensive vegetated roof assemblies
Bibliographic record
Abstract
ABSTRACT First-generation extensive green roof systems included only vegetation, growing media (GM), and drainage materials. However, green roof designs are increasingly incorporating lightweight GM alternatives to enhance their retention and detention capabilities. This study evaluates the hydrologic impacts of vegetated roof assemblies (VRAs) that incorporate materials, such as fleece, mineral wool, and a combined reservoir–detention system, under natural precipitation conditions in Toronto, Ontario. Over a year, discharge from testbeds was measured and compared to a traditional green roof and an impervious gravel ballast roof. During the growing season, the VRAs provided similar stormwater retention rates. Green roofs also provided significant detention benefits, reducing peak discharge by 94% and delaying, extending, and increasing discharge delay and duration compared to gravel roofs. Winter performance showed reduced effectiveness, increased peak flows, and shorter discharge delays and durations. Overall, an average VRA runoff coefficient of 0.67 was observed in winter, compared to 0.17 during the warm season. This work demonstrates that although adding retention layers improves the hydrologic performance of green roof systems to varying degrees during warmer months, traditional green roof assemblies may still provide superior annual precipitation volume reduction when winter conditions are considered.
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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 | 0.000 | 0.000 |
| 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".