Modeling microbial growth in carpet dust exposed to diurnal variations in relative humidity using the “Time‐of‐Wetness” framework
Bibliographic record
Abstract
Resuspension of microbes in floor dust and subsequent inhalation by human occupants is an important source of human microbial exposure. Microbes in carpet dust grow at elevated levels of relative humidity, but rates of this growth are not well established, especially under changing conditions. The goal of this study was to model fungal growth in carpet dust based on indoor diurnal variations in relative humidity utilizing the time-of-wetness framework. A chamber study was conducted on carpet and dust collected from 19 homes in Ohio, USA and exposed to varying moisture conditions of 50%, 85%, and 100% relative humidity. Fungal growth followed the two activation regime model, while bacterial growth could not be evaluated using the framework. Collection site was a stronger driver of species composition (P = 0.001, R2 = 0.461) than moisture conditions (P = 0.001, R2 = 0.021). Maximum moisture condition was associated with species composition within some individual sites (P = 0.001-0.02, R2 = 0.1-0.33). Aspergillus, Penicillium, and Wallemia were common fungal genera found among samples at elevated moisture conditions. These findings can inform future studies of associations between dampness/mold in homes and health outcomes and allow for prediction of microbial growth in the indoor environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".