Hygrothermal response of tallwood building enclosures to climate change in different climate zones in Canada
Bibliographic record
Abstract
Within the context of climate change and in order to reduce the carbon footprint of buildings, mass timber products are increasingly used in mid-rise and high-rise buildings. As such, considerable efforts have been invested in developing technical data to support their implementation in North America, with primary emphasis placed on assessing structural, fire, and acoustical performance. Whilst many mass timber buildings have been or are being constructed in many jurisdictions across the country, there are still concerns about the thermal and hygrothermal response and expected moisture performance of mass timber products used in building enclosures. Climate change notwithstanding, tallwood buildings are subjected to increased wind and rain loads given increases in building height. This prolongs the exposure of building enclosures to wind-driven rain and wind loads, and increases the risk of premature deterioration of wood-based wall and roof assemblies. It is also anticipated that future projections of the effects of climate change and extreme weather events will exacerbate the situation. The objective of this study is to assess the potential impacts of climate change on the moisture performance and durability of tallwood building envelopes, using hygrothermal simulations. Deficiencies in the walls that may lead to rain penetration are considered. Potential pathways to adapt design of massive timber to climate change are also explored.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".