Bacterial Diversity in House Dust: Characterization of a Core Indoor Microbiome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Our indoor microbiome consists of a wide range of microbial taxa. Whilst many of these microbes are benign, some are beneficial, some harmful, yet our knowledge of the spatial heterogeneity of bacterial assemblages in our residential environment remains limited. To investigate the existence of a common core house dust bacterial microbiome we selected household vacuum dusts, collected through a citizen science approach, from homes across two bioclimatic regions (UK, Oceanic/Maritime and Greece, Mediterranean). Following the extraction of DNA from each dust sample, we targeted the bacterial 16S rRNA gene using Illumina NextSeq sequencing. PERMANOVA analysis of the microbial communities at family level grouped samples within their distinct bioclimatic region and SIMPER analysis at genus level identified the statistically significant taxa responsible for driving diversity between these groups. A “common to all” core house dust microbiome consisted of Acinetobacter , Massalia, Rubellimicrobium, Sphingomonas and Staphylococcus ; genera typically associated with human occupancy and common environmental sources. Additionally, a “unique location specific” microbiome was identified, reflective of the bioclimatic region. The Greek dusts indicated a lower average diversity than the UK house dusts, with a high abundance of Rhizobiaceae in the Greek samples. Our study highlights citizen science as a powerful approach to access the indoor residential environment, at scale, and establishes the existence of a “core” house dust microbiome independent of bioclimatic region.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it