Impact of High-resolution Regional Climate Conditions on the Moisture Performance of Wood-frame Building Envelopes
Bibliographic record
Abstract
Climate data is one of the most important inputs for hygrothermal simulations and significantly influences the simulation results that are used for moisture performance assessment of building envelopes. Conventionally, climate data from representative weather stations are used for hygrothermal simulations to assess the moisture performance of the buildings. Recently, the Construction Research Centre of the National Research Council Canada generated historical and future climate data for 564 locations across Canada. In this paper, the climate data of four urban locations within Vancouver (a humid and warm coastal city in Canada) were selected for hygrothermal simulations. These include an open area at the airport, a city center, an area surrounded by plants, and an area surrounded by a water body. The 31-year-long simulations were performed for a typical woodframe wall assembly complying with moisture protection designs as provided in the National Building Code Canada (NBCC 2020) but rain penetration was assumed considering the deficiency of exterior cladding. The differences in its hygrothermal response across the four locations were analyzed. It was found that some climate parameters are significantly different among the selected locations, although the values for moisture indices given in the NBCC 2020 for these locations are all similar. The results show that high values of WDR are the dominant factor that drives the moisture performance but there is no indication that the design of the assembly could be different among the tested locations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".