Hygrothermal Characterization and Impact of Drainage Positive Fibrous Insulation for Above Grade Exterior Insulation Application
Bibliographic record
Abstract
The push towards high-performance buildings has been the new approach of the Canadian building construction market for environmental sustainability and energy conservation. Generally, high performance buildings are designed with exterior insulation, which needs to be examined through the lens of heat and moisture transport. Currently, there are no integrated simulation models that designers can use to measure the drying rate of drainage positive insulation. This research focused on hygrothermal analysis associated with drainage positive exterior fibrous insulation for the above-grade application. The research occurred in two phases: the first phase investigated characteristics of the drainage positive insulation material, and the second phase investigated long term UV and bulk water performance, and drainage performance of the insulation material. Phase one involved four experiments: temperature-dependent moisture content, complete and partial immersion, temperature and moisture-dependent vapour permeance, and temperature and moisture-dependent thermal conductivity. The results from these experiments showed that fibreglass adsorbed more moisture compared to mineral wool insulation. Additionally, the results indicated that fibreglass had lower permeability and higher thermal conductivity than mineral wool insulation. Phase two included two experiments: UV and bulk water deteriorated material performance, and in-situ drainage test. The results showed that the deteriorated sample showed higher moisture sorption and vapour permeance. The in-situ drainage test revealed that before excessive bulk water exposure, the insulation samples were completely dried in 24 hours.
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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 | 0.000 | 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".