Evaluation of Parameters Influencing the Moisture Buffering Potential of Hygroscopic Materials with BSim Simulations
Bibliographic record
Abstract
Validated by a large-scale experimental investigation on moisture buffering (MB) effect, a whole building Heat, Air and Moisture (HAM) simulation tool, BSim, is applied to evaluate the impact of a number of parameters on the moisture buffering potential of a full-scale test room finished with hygroscopic materials. The Maximum Accumulated Moisture Buffering Value (MAMBV), developed from the moisture balance analyses in the experimental study, is used in the BSim simulation result analyses to evaluate the impact of various parameters. The parameters investigated include ventilation rates (0.5–5 ACH), types of materials (uncoated gypsum board, wood paneling, orientated strand board, aerated cellular concrete, and telephone book paper), humidity conditions of supply air, volume rates, and steady-state outdoor conditions. It is found that all these parameters have a significant impact on the moisture buffering potential except for the steady-state outdoor conditions. Two material properties, the moisture capacity and vapor permeability, determine the moisture buffering capacities of materials under different moisture generation regimes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".