Effects of different climate generation methods on the hygrothermal performance of a wood-frame wall under current and projected future climates
Bibliographic record
Abstract
Climate change will subject the built environment in Canada to unprecedented climatic conditions in the future, which may result in adverse effects on the durability of buildings. Therefore, it is vital to be able to accurately describe future climatic conditions and how buildings will perform under them. Hygrothermal simulation models are important tools for building practitioners to assess the moisture performance of wall assemblies. Hygrothermal simulations require a wide range of climate variables such as cloud cover, wind speed, wind direction, solar radiation, rainfall, snow cover, temperature, and humidity in high temporal frequency. Typically, recorded historical data was used for hygrothermal simulations, but they do not account for changes in climate excepted due to future global warming. Consequently, many climate data generation techniques have been developed to prepare climate data incorporating future projected climate change, which can be used to assess hygrothermal performance of buildings under current and future projected climates. The objective of this work is to evaluate the differences in the various future climate data generation methods and determine their impacts on the hygrothermal performance of a wood-frame wall assembly in the current and future projected climate. The analysis is performed on 6 cities, representing different climates across Canada and future projected climate data is prepared using morphing downscaling method, and two multivariate and a univariate bias correction method. Results indicate that morphed and bias corrected climate data perform better than RCM when compared to the observations. While this is generally true for the hygrothermal performance as well, the bias corrected data often fail to replicate the same degree of mould growth in the simulations with observational data. An analysis of the climate change scenario indicated that all the studied locations will experience warmer and wetter climatic conditions. For instance, the WDR deposited on the OSB in Montreal is expected to increase by at least 14%, resulting in increased MC and mould growth on the OSB. According to morphed climate data, the average MC in the OSB could increase by 54% while mould index exceeds 3.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".