Cooking-Derived Organic Compounds Shape Bacterial Communities on Household Surfaces
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Understanding the factors influencing chemical and biological constituent accumulation on indoor surfaces is crucial, especially as individuals spend approximately 90% of their time indoors and frequently interact with these surfaces. However, the temporal relationships between these constituents remain unclear, as most field studies have relied on single-time snapshots and seldom examined the interplay between chemical and biological dynamics. We conducted a month-long spatiotemporal field study across 20 households in Hong Kong to investigate the factors influencing chemical and biological constituents on common indoor surfaces. Among the 16 household- and occupant-related factors analyzed, routine oil-based cooking was the primary driver of microbial diversity and composition on indoor surfaces. Surfaces in kitchens with frequent cooking exhibited elevated total organic carbon levels, which were linked to an increased bacterial abundance. A focused analysis of six kitchens with well-controlled frequencies of oil-based cooking revealed that cooking-derived organic compounds, particularly alkanes, promoted bacterial abundance while reducing microbial diversity. Network analysis further revealed strong interactions between these organic compounds and bacterial taxa, especially those within the Proteobacteria and Firmicutes phyla. These findings highlight the impact of routine household activities on indoor chemical–biological interactions, enhancing our understanding and informing strategies to improve indoor environments and occupant well-being.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".