Field Investigation of Moisture Buffering Potential of American Clay and Magnesium Oxide Board in a Mild Climate
Bibliographic record
Abstract
Passive humidity control in buildings can be achieved by incorporating materials that have a moisture-buffering capacity. Such materials absorb moisture at peak-moisture times and give off the stored-up moisture at low moisture times, thereby stabilizing the relative humidity of an interior. The advantages of this phenomenon include energy savings and the improvement of both thermal comfort and perceived air quality. It is necessary to investigate different materials for their moisture-buffering capabilities. In this work, the moisture-buffering potential of American clay and magnesium oxide (MagO) boards was investigated. This was done through a field study that monitored twin buildings under different operation scenarios. One was set as the reference building, and its interior was finished with gypsum, owing to the usage of this material as a common industry practice. The second building was set as the test building and was covered with American clay and MagO boards. The operational congruency of the buildings was checked, and then, three tests were conducted to simulate the interior finishes of a building, ventilation effects, and occupancy density. It was found that the American clay exhibited a better moisture buffering potential than gypsum, especially in the comparison of as-built surface conditions. Further, the experimental results also showed that the moisture-buffering potential of MagO boards may be comparable to that of gypsum, and a coating of vapor-open MagO boards is beneficial for humidity regulation.
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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 | 0.000 | 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".