Evapotranspiration Rates of Representative Green Roof Systems and the Implications for Green Roof Policies
Bibliographic record
Abstract
Urban intensification, population growth and climate change are creating multiple issues within cities on a global scale. As more buildings are constructed to support increasing urban populations, cities are having a more difficult time managing sporadic weather events; especially managing stormwater from intense rainfall. Toronto currently has in place a mandatory green roof bylaw, that is designed to utilize shallow green roofs (GRs) on all new buildings as a measure to intercept rainfall and transpire the water back into the atmosphere. Four green roof modules with varying system buildups were constructed and instrumented on top of a downtown building to analyze how the amounts of retained rainfall and evapotranspiration varied amongst the systems. It was determined that the total evapotranspiration (ET) volume of blue/green roofs and vegetable producing green roofs were considerably higher than a traditional extensive green roof; 236% higher in the blue/green roof and 356% higher in the vegetable roof. It was also determined that unvegetated blue roof systems are also a viable method of managing stormwater on rooftops, with the blue roof module showcasing a total evaporation volume of 134 mm over the duration of the study. The results from this research exhibit that higher hydrological benefit can be derived from green roofs using blue-green technology or vegetable producing assemblies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".