Moisture parameters and fungal communities associated with gypsum drywall in buildings
Bibliographic record
Abstract
Uncontrolled excess moisture in buildings is a common problem that can lead to changes in fungal communities. In buildings, moisture parameters can be classified by location and include assessments of moisture in the air, at a surface, or within a material. These parameters are not equivalent in dynamic indoor environments, which makes moisture-induced fungal growth in buildings a complex occurrence. In order to determine the circumstances that lead to such growth, it is essential to have a thorough understanding of in situ moisture measurement, the influence of building factors on moisture parameters, and the levels of these moisture parameters that lead to indoor fungal growth. Currently, there are disagreements in the literature on this topic. A literature review was conducted specifically on moisture-induced fungal growth on gypsum drywall. This review revealed that there is no consistent measurement approach used to characterize moisture in laboratory and field studies, with relative humidity measurements being most common. Additionally, many studies identify a critical moisture value, below which fungal growth will not occur. The values defined by relative humidity encompassed the largest range, while those defined by moisture content exhibited the highest variation. Critical values defined by equilibrium relative humidity were most consistent, and this is likely due to equilibrium relative humidity being the most relevant moisture parameter to microbial growth, since it is a reasonable measure of moisture available at surfaces, where fungi often proliferate. Several sources concur that surface moisture, particularly liquid water, is the prominent factor influencing microbial changes and that moisture in the air and within a material are of lesser importance. However, even if surface moisture is assessed, a single critical moisture level to prevent fungal growth cannot be defined, due to a number of factors, including variations in fungal genera and/or species, temperature, and nutrient availability. Despite these complexities, meaningful measurements can still be made to inform fungal growth by making localised, long-term, and continuous measurements of surface moisture. Such an approach will capture variations in a material's surface moisture, which could provide insight on a number of conditions that could lead to fungal proliferation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".