The effect of structures on indoor humidity - possibility to improve comfort and perceived air quality
Bibliographic record
Abstract
The research presented in this paper shows that moisture transfer between indoor air and hygroscopic building structures can generally improve indoor humidity conditions. This is important because the literature shows that indoor humidity has a significant effect on occupant comfort, perceived air quality (PAQ), occupant health, building durability, material emissions, and energy consumption. Therefore, it appears possible to improve the quality of life of occupants when appropriately applying hygroscopic wood-based materials. The paper concentrates on the numerical investigation of a bedroom in a wooden building located in four European countries (Finland, Belgium, Germany, and Italy). The results show that moisture transfer between indoor air and the hygroscopic structure significantly reduces the peak indoor humidity. Based on correlations from the literature, which quantify the effect of temperature and humidity on comfort and PAQ for sedentary adults, hygroscopic structures can improve indoor comfort and air quality. In all the investigated climates, it is possible to improve the indoor conditions such that, as many as 10 more people of 100 are satisfied with the thermal comfort conditions (warm respiratory comfort) at the end of occupation. Similarly, the percent dissatisfied with PAQ can be 25% lower in the morning when permeable and hygroscopic structures are applied.
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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 | 0.001 |
| 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) | 0.002 | 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".