Influence of water quality on nitrifier regrowth in two full-scale drinking water distribution systems
Bibliographic record
Abstract
The potential for regrowth of nitrifying microorganisms was monitored in 2 full-scale chloraminated drinking water distribution systems in Ontario, Canada, over a 9-month period. Quantitative PCR was used to measure amoA genes from ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA), and these values were compared with water quality parameters that can influence nitrifier survival and growth, including total chlorine, ammonia, temperature, pH, and organic carbon. Although there were no severe nitrification episodes, AOB and AOA were frequently detected at low concentrations in samples collected from both distribution systems. A culture-based presence-absence test confirmed the presence of viable nitrifiers. AOB were usually present in similar or greater numbers than AOA in both systems. As well, AOB showed higher regrowth potential compared with AOA in both systems. Statistically significant correlations were measured between several water quality parameters of relevance to nitrification. Total chlorine was negatively correlated with both nitrifiers and heterotrophic plate count (HPC) bacteria, and ammonia levels were positively correlated with nitrifiers. Of particular importance was the strong correlation between HPC and AOB, which reinforced the usefulness of HPC as an operational parameter to measure general microbiological conditions in distribution systems.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".