Identifying the critical orientation of wood-frame walls in assessing moisture risks using hygrothermal simulation
Bibliographic record
Abstract
Abstract Hygrothermal simulations can be used as a reliable tool in analysing moisture performance. For an efficient analysis, it is important to appropriately select the wall orientation in the simulations. ASHRAE 160 recommends to using orientation with highest amount of annual wind-driven rain (WDR) and the orientation with the least annual solar radiation. The objective of this work was to identify the orientation which leads to the worst moisture performance of different wall assemblies under historical climate in different Canadian cities. Four cardinal orientations (North, East, South, and West) and orientation receiving the highest amount of annual WDR (Default) were tested in this study. The simulations were carried out assuming three scenarios of moisture loads for four different wood-frame (2×6 wood stud) wall systems that differ by their claddings: brick, fibreboard, stucco, and vinyl. With an assumption of no WDR, north facing wall always leads to the worst moisture performance. In the presence of WDR, with and without water source, default orientation leads to the worst moisture performance with few exceptions. As default orientation was based on total sum of WDR, it sometimes may not lead to worst performance and hence hourly distribution of WDR should be taken into consideration.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".