Environmental factors influencing fungal growth on gypsum boards and their structural biodeterioration: A university campus case study
Bibliographic record
Abstract
The new era in the design of modern healthy buildings necessitates multidisciplinary research efforts that link principles of engineering and material sciences with those of building biology, in order to better comprehend and apply underlying interactions among design criteria. As part of this effort, there have been an array of studies in relation to the effects of building characteristics on indoor microbiota and their propensity to cause health issues. Despite the abundance of scientific inquiries, limited studies have been dedicated to concomitantly link these effects to the deterioration of 'structural integrity' in the building materials. This study focuses on the observed biodeteriorative capabilities of indoor fungi upon the ubiquitous gypsum board material as a function of building age and room functionality within a university campus. We observed that the fungal growth significantly affected the physical (weight loss) and mechanical (tensile strength) properties of moisture-exposed gypsum board samples; in some cases, tensile strength and weight decreased by more than 80%. Such intertwined associations between the biodeterioration of building material properties due to viable indoor fungi, and as a function of building characteristics, would suggest a critical need towards multi-criteria design and optimization of next-generation healthy buildings. Next to structural integrity measures, with a better understanding of what factors and environmental conditions trigger fungal growth in built environment materials, we can also optimize the design of indoor living spaces, cleaning strategies, as well as emergency management measures during probable events such as flooding or water damage.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".