Effects of air change rate and moisture load related to moisture index and the expected moisture performance of a wall assembly
Bibliographic record
Abstract
Abstract Mould may grow in wood frame wall assemblies when subjected to excessive moisture load over prolonged periods of time. To permit estimating the moisture risk in wood-frame wall assemblies, one approach is to use hygrothermal simulations. This process requires access to relevant climate data and as well, knowledge and experience regarding the use of hygrothermal simulation tools. As well, it may not be practical to undertake such an analysis for each different type of wall being considered for a given location and use. In this regard, the moisture index is usually considered when designing measures for protection from precipitation as it is a useful indicator that reflects the intensity and duration of moisture loads to which a building envelope may be subjected over time. Hence, in the study described in this paper, consideration was given to determining the correlation between mould growth index and moisture index as this would be beneficial to building practitioners in determining the level of protection to mould growth as may be achieved in different climate regions having a particular value for moisture index. The mould growth indices were generated for Oriented Strand Board (OSB) incorporated within a wood-frame vinyl-clad wall assembly located in selected Canadian cities from which moisture indices were derived. Effects of moisture load and air change rate were also taken into account when assessing the correlation between the two indices. The results indicate that when the air change rate and moisture load are favourable to maintain a humid environment, strong correlations were observed between the two indices, vice versa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".