Influence of material properties on the moisture response of an ideal stucco wall : results from hygrothermal simulation
Bibliographic record
Abstract
In a composite wall, each constituent component plays a specific role in effective moisture management. The moisture movement to and from the wall assembly, subjected to climatic forces, such as relative humidity (RH), temperature, rainfall and solar radiation, is influenced by the basic hygrothermal properties of the component materials. Studies conducted at the National Research Council (NRC) Canada, over the years, show that the hygrothermal properties of materials vary over a wide range. Quite naturally, such variation may influence the overall moisture response of the wall. This paper investigates the extent to which each component of the wall can influence the overall drying and wetting characteristics of an ideal (i.e., without any deficiency) wood-frame stucco wall when it is exposed to weather conditions typical of coastal Western Canada. The component materials selected include stucco, sheathing board, a sheathing membrane and a vapour barrier. Hygrothermal material properties compiled in the laboratory at the NRC and the hygrothermal modelling tool, hygIRC, developed by the NRC, are used for this purpose. The findings of this study indicate that, with further work and analysis, practical guidance could be developed to assist designers and engineers to identify the critical components and properties of the stucco wall assembly to achieve a desirable moisture management strategy for the building envelope.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".