Characterization of the Airborne Microbiome in Different Indoor and Outdoor Locations of a University Building Using an Innovative Compositional Data Analysis Approach
Bibliographic record
Abstract
In this work, we investigated the structure of the airborne bacterial community obtained by 16S rRNA gene sequencing performed on aerosol samples from different indoor and outdoor locations. The 48-h aerosol samples were collected in two laboratories, in the corridors, and on the roof of the Mathematics and Physics Department of the University of Salento (Italy). The investigation was carried out through the application of an innovative compositional data analysis approach, mainly based on a centered log-ratio transformation as a standardization procedure, the Aitchison distance for data ordination, and the principal component analysis via singular value decomposition for data clustering. This methodology allowed us to explore the main relationships among samples, identifying different results between indoor and outdoor samples both at the genus level and at the species level. Bacillus and Pseudomonas represented the most abundant genera identified in the analyzed samples. Out of the 21 identified bacterial species with the highest abundances in the collected aerosol samples, Acinetobacter lwoffii, Propionibacterium acnes, Diplorickettsia massiliensis, and Corynebacterium tuberculostearicum were the only four commonly classified as human opportunistic pathogens. Among the genera mostly associated with indoor environments, Hymenobacter and Arthrobacter could be noted as including many species that are unique in being radiation resistant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".