Thermal Performance of Green Roof Panels in Sub-Zero Temperatures
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 To date, much of the research on green roof technology has focused on the capacity for these systems to contribute to the cooling of buildings during summer months. The thermal performance of green roofs in cold climate conditions is critical to understanding the potential of these roofs to decrease energy use in buildings during winter. This paper compares the behavior of two green roof systems with that of a conventional built-up roof by making use of a novel hot box testing apparatus. The green roofs tested are classified as extensive systems. Each system included: a 3 mm thick styrene butadiene rubber waterproofing membrane, 0.2 mm thick polyethylene slip sheet, a 76 mm thick extruded polystyrene insulation layer, 2 mm thick filter fabric, a 51 mm drainage layer followed by a 2 mm thick filter cloth, either 100 mm or 150 mm growing medium, and a 25 mm thick wild flower vegetated mat. The conventional roof consisted of a 2 mm thick layer of Kraft™ vapour retarder bonded with insulation adhesive, 51 mm of isocyanurate insulation, 25 mm of fibreboard, a three ply (2 mm) cold-applied built-up roof membrane, and a gravel ballast finish 51 mm thick. Each roof was subjected to temperatures between 0°C and −25°C, while the temperature within the hot box was held at 21°C. The effect of vegetation on a green roof to reduce wind speeds or increase snow cover were not considered in this study. The power required, as well as the temperatures throughout each system at steady state conditions, were monitored for 5 hours. The data collected from thermal testing suggests that the R-value of green roofs with 100 mm or 150 mm thick layers of growing medium is 37% higher than a conventional roof when subjected to temperatures of 0°C to −25°C.
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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 | 0.001 | 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