Drinking water microbiome assembly induced by water stagnation
Bibliographic record
Abstract
Abstract What happens to tap water when you are away from home? Day-to-day water stagnation in building plumbing can potentially result in water quality deterioration (e.g., lead release or pathogen proliferation), which is a major public health concern. However, little is known about the microbial ecosystem processes in plumbing systems, hindering the development of biological monitoring strategies. Here, we track tap water microbiome assembly in situ, showing that bacterial community composition changes rapidly from the city supply following ~6-day stagnation, along with an increase in cell count from 103 cells/mL to upwards of 7.8 × 105 cells/mL. Remarkably, bacterial community assembly was highly reproducible in this built environment system (median Spearman correlation between temporal replicates = 0.78). Using an island biogeography model, we show that neutral processes arising from the microbial communities in the city water supply (i.e., migration and demographic stochasticity) explained the island community composition in proximal pipes (Goodness-of-fit = 0.48), yet declined as water approached the faucet (Goodness-of-fit = 0.21). We developed a size-effect model to simulate this process, which indicated that pipe diameter drove these changes by mediating the kinetics of hypochlorite decay and cell detachment, affecting selection, migration, and demographic stochasticity. Our study challenges current water quality monitoring practice worldwide which ignore biological growth in plumbing, and suggests the island biogeography model as a useful framework to evaluate building water system quality.
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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) | 0.000 | 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) | 0.001 | 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".