Hydrologic impacts of mat-based retention and detention layers within extensive vegetated roof assemblies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it