Optimizing Green Roof Design Parameter and Their Effects on Thermal Performance under Current and Future Climates in the City of Toronto
Bibliographic record
Abstract
Buildings contribute 30% of total energy consumption worldwide and account for 28% of CO2 emissions. Green roofs (GRs) have shown potential in reducing cooling and heating loads of buildings, and thus, the related carbon emissions. This research aimed to analyse the thermal performance of extensive GRs compared to conventional roofs using three design parameters: GR growing media (GM) depth, Leaf Area Index (LAI) and thermal insulation thickness under current and future climates in Toronto, Canada. EnergyPlus was used to model the GR for three building archetypes of secondary school, office, and hospital. In addition, precipitation data for current and future climates was generated to account for GR moisture input. Results show higher GM depth, and LAI provides the highest annual energy savings for uninsulated GRs. However, at highly insulated roofs, the GR thermal performance is impacted and depending on the building type, the GR may require higher energy consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".