Characterizing the bacterial communities in retail stores in the United States
Bibliographic record
Abstract
The microorganisms present in retail environments have not been studied in detail despite the fact that these environments represent a potentially important location for exposure. In this study, HVAC filter dust samples in 13 US retail stores were collected and analyzed via pyrosequencing to characterize the indoor bacterial communities and to explore potential relationships between these communities and building and environmental parameters. Although retail stores contained a diverse bacterial community of 788 unique genera, over half of the nearly 118K sequences were attributed to the Proteobacteria phylum. Streptophyta, Bacillus, Corynebacterium, Pseudomonas, and Acinetobacter were the most prevalent genera detected. The recovered indoor airborne microbial community was statistically associated with both human oral and skin microbiota, indicating occupants are important contributors, despite a relatively low occupant density per unit volume in retail stores. Bacteria generally associated with outdoor environments were present in the indoor communities with no obvious association with air exchange rate, even when considering relative abundance. No significant association was observed between the indoor bacterial community recovered and store location, store type, or season. However, predictive functional gene profiling showed significant associations between the indoor community and season. The microbiome recovered from multiple samples collected months apart from the same building varied significantly indicating that caution is warranted when trying to characterize the bacterial community with a single sampling event.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".