Study of environmental and energy performance of vegetative roofs and assessment of their impacts in terms of rainwater management
Bibliographic record
Abstract
Vegetative roofs (VRs) can be classified into two types : Extensive (EGR) and Intensive (IGR). The main differences between the two are the type of vegetation, the depth of the substrate. This research aims to achieve the following objectives : 1. Determine and compare the potential environmental impacts of traditional gravel ballasted roofs (TGBRs), white reflective roofs (WRRs), EGRs, and IGRs ; 2. Evaluate and compare the energy performance and the heating/cooling demand of TGBRs and EGRs ; 3. Determine and compare the water management potential and the runoff dynamics of TGBRs and EGRs. The first objective was covered by performing a comparative Life Cycle Assessment (LCA) on a real EGR of 834m2 and on three fictitious roofs of the sane area : of TGBRs, WRRs, and IGRs. Results indicated that the EGR had the least potential environmental impacts for the 15 impact categories considered. The second and third objectives were achieved by first installing one TGBR mockup and two EGR mockups on the rooftop of the Chemical Engineering Department at the University of Balamand, Lebanon. EGR8 and EGR16 are EGR mockups differed in the roof slope, the depth and the composition of their substrate. Temperature profiles at different substrate depths clearly indicated the reduction of the temperature fluctuations under the substrate layer, the heat storage effect, and the passive cooling effect. The economic study showed that EGR could save up to 45USD/200m2/month compared to TGBR. The water management performance of EGRs illustrated that the soil composition of EGR8 was more efficient than that of EGR16. In contrast, EGR acted as a sink especially for cadmium, iron, calcium, and ammonium.
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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".