From source to filter: changes in bacterial community composition during potable water treatment
Bibliographic record
Abstract
Rural communities rely on surface water reservoirs for potable water. Effective removal of chemical contaminants and bacterial pathogens from these reservoirs requires an understanding of the bacterial community diversity that is present. In this study, we carried out a 16S rRNA-based profiling approach to describe the bacterial consortia in the raw surface water entering the water treatment plants of 2 rural communities. Our results show that source water is dominated by the Proteobacteria, Bacteroidetes, and Cyanobacteria, with some evidence of seasonal effects altering the predominant groups at each location. A subsequent community analysis of transects of a biological carbon filter in the water treatment plant revealed a significant increase in the proportion of Proteobacteria, Acidobacteria, Planctomycetes, and Nitrospirae relative to raw water. Also, very few enteric coliforms were identified in either the source water or within the filter, although Mycobacterium was of high abundance and was found throughout the filter along with Aeromonas, Legionella, and Pseudomonas. This study provides valuable insight into bacterial community composition within drinking water treatment facilities, and the importance of implementing appropriate disinfection practices to ensure safe potable water for rural communities.
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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 | 0.000 |
| 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".