Study of gypsum board material properties required to maintain high indoor relative humidity in buildings in cold climates
Bibliographic record
Abstract
Studies have shown that building relative humidity (RH) in the range of 40% to 60% has positive effects on health, including limiting growth and transmission for both bacteria and viruses. These levels are easily obtained in moderate to warm climates but are difficult to maintain in cold climates. There lies a need to research possible advancements in building material properties that can maintain RH within the desired range, higher than what is generally accepted in buildings in cold climates. This report covers the study of gypsum board and the alteration of its material properties in order to achieve the desired range for indoor RH. A 1-dimensional moisture simulation tool, WUFI Pro, is used to analyze a building wall construction with a variety of user-defined gypsum board materials for two cold climate locations: Vancouver, BC and Winnipeg, MB. Material properties that are altered include porosity, specific heat capacity, and the moisture storage function. The simulation results show that changes in porosity and specific heat capacity have insignificant effect on the indoor RH, whereas changes to the moisture storage function do. For Winnipeg, a test material with an increased water content per RH of 20% resulted in an indoor RH minimum and maximum of 45.8% and 72.9%, respectively, compared to 36.3% and 81.5% for the base case.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".