Accuracy of using Moisture Reference Years for assessing the Long-Term Moisture Performance of Wall Assemblies
Bibliographic record
Abstract
Abstract Hygrothermal simulation is commonly used to understand the effects of climate loads on wall assemblies. This helps determine the risk of deterioration of wall components due to factors such as mould, wood rot, corrosion, and other modes of degradation. Owing to the lengthy computing time and high cost of undertaking simulations over extended periods of time in excess of 30 years, common practice is to select representative years, generally referred to as moisture reference years (MRYs), amongst the climate data series with the assumption that they would provide results similar to that obtained using the entire climate data series. In this study, simulations were performed for a wood-frame wall having brick veneer and fibreboard as cladding and located in 4 Canadian cities using both a 31-year climate data series and MRYs. The mould growth index on the outer layer of the sheathing panel was used as the performance indicator to permit comparing the moisture performance obtained using the long-term climate series to that obtained using MRYs. It was determined that different strategies should be implemented to analyse the MRY simulation results for different types of wall assemblies to obtain a similar conclusion as that of 31-year simulations.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".