Impact of design variables on hydrologic and thermal performance of green, blue-green and blue roofs
Bibliographic record
Abstract
Abstract Blue-green and blue roofs are increasingly promoted to adapt to climate change by providing multiple benefits. However, uncertainties about their design and how they differ from conventional green roofs hinder their implementation. This study investigates the potential of green, blue-green, and blue roofs to control urban stormwater and improve microclimate by monitoring their performance in Toronto, Ontario, Canada. Experimental setups were built and varied with the following design factors: substrate type and thickness, drainage layer thickness and orifice size. The results revealed that blue-green roofs with organic and FLL (blended according to the German Forschungsgesellschaft Landschaftsentiwicklung Landschaftsbau) substrates significantly improved detention compared to green roofs with similar substrates. The organic blue-green roof achieved maximum retention, but FLL blue-green roof did not have higher retention than FLL green roof. The blue roof with smaller orifices had comparable hydrologic performance to vegetated roofs but suffered from long water standing durations. Organic substrates followed by FLL substrates result in the highest air cooling in the noon, but blue roofs had the highest air cooling in the evening. In-substrate temperatures in blue-green roofs were lower than those in green roofs. Trade-offs between the benefits and drawbacks need to be considered in future designs.
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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 | 0.001 | 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".