Hygrothermal behavior of flat cool and standard roofs on residential and commercial buildings in North America
Bibliographic record
Abstract
Installing roofs with high solar reflectance and high thermal emittance, known as “Cool roofs”, are becoming popular because of their cooling energy saving potentials, cost effectiveness and sustainability. Cool roofs may affect the hygrothermal performance of roofing systems and hence their performance should be characterized in different climates. \nWe simulated the performance of several roofing systems including: Typical, smart, and self-drying roofs for residential and commercial buildings. In addition, we proposed vented roofs with smart vapor retarders in different climate regions across North America. We also developed an algorithm to investigate the effect of snow on hygrothermal behaviour of black and white roofs. \n Results showed that office buildings never experience moisture accumulation problem in the simulation period (5 years). In residential buildings, white typical roofing compositions with conventional vapor retarders experienced moisture accumulation problems in cities such as Anchorage, Edmonton and St. John’s. Using smart vapor retarder (smart roofs) or self-drying roofs helped to decrease risk of moisture accumulation. We showed that in these climates, adding a ventilated air space along with using smart vapor retarder eliminated risk of moisture accumulation and prevented excessive OSB (oriented strand board) moisture content. Furthermore, our simulation results showed that risk of mold growth was significantly lower in vented smart roofs than other systems. Simulating the effect of snow on the roof for Anchorage, Montreal and Chicago showed that the hygrothermal performances of white roofs improved with snow accumulation on the roof.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".