Assessing the Impact of Air Leakage on the Hygrothermal Performance of Wood-Frame Walls Under Historical and Future Climates
Bibliographic record
Abstract
Air leakage is a crucial factor when assessing the hygrothermal performance of wood-frame walls since it can lead to moisture accumulation during the cold season. The seriousness of this prob-lem may change in a warmer climate in the future and hygrothermal simulations are widely used as a tool to predict this effect. However, since 2D models are required for detailed air leakage as-sessment and the high number of input variables leads to having to conduct thousands of simula-tions for a single type of building cladding, downsizing the simulation grid to the lowest number of cells is a crucial task to help ensure reduced computational time. Using a hygrothermal simulation tool, the steps needed to build the smallest 2D grid were explained; as well, convergence and ac-curacy of the results were evaluated and the functional relations between air leakage rate and air permeability of the insulation were clarified. Hygrothermal simulations were performed for wood-frame walls having brick veneer and stucco cladding for three Canadian cities: Whitehorse, Van-couver, and Ottawa. While the air leakage rate has a significant impact on the inner surface of OSB, wind driven rain is the key factor on the outer surface. The performance of stucco cladding is worse than brick in all cases and the future climate may reduce the risk of mould growth on the in-ner surface of OSB in all cities. The results also show that the simulation time can be reduced by 90%, with negligible loss of accuracy, when comparing fine to optimized meshes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".