Identification of Factors Affecting Bacterial Abundance and Community Structures in a Full-Scale Chlorinated Drinking Water Distribution System
Bibliographic record
Abstract
Disentangling factors influencing suspended bacterial community structure across distribution system and building plumbing provides insight into microbial control strategies from source to tap. Water quality parameters (residence time, chlorine, and total cells) and bacterial community structure were investigated across a full-scale chlorinated drinking water distribution system. Sampling was conducted in treated water, in different areas of the distribution system and in hospital building plumbing. Bacterial community was evaluated using 16S rRNA gene sequencing. Bacterial community structure clearly differed between treated, distributed, and premise plumbing water samples. While Proteobacteria (60%), Planctomycetes (20%), and Bacteroidetes (10%) were the most abundant phyla in treated water, Proteobacteria largely dominated distribution system sites (98%) and taps (91%). Distributed and tap water differed in their Proteobacteria profile: Alphaproteobacteria was dominant in distributed water (92% vs. 65% in tap waters), whereas Betaproteobacteria was most abundant in tap water (18% vs. 2% in the distribution system). Finally, clustering of bacterial community profiles was largely explained by differences in chlorine residual concentration, total bacterial count, and water residence time. Residual disinfectant and hydraulic residence time were determinant factors of the community structure in main pipes and building plumbing, rather than treated water bacterial communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".