Molecular Method for Detection of Total Coliforms in Drinking Water Samples
Bibliographic record
Abstract
This work demonstrates the ability of a bacterial concentration and recovery procedure combined with three different PCR assays targeting the lacZ, wecG, and 16S rRNA genes, respectively, to detect the presence of total coliforms in 100-ml samples of potable water (presence/absence test). PCR assays were first compared to the culture-based Colilert and MI agar methods to determine their ability to detect 147 coliform strains representing 76 species of Enterobacteriaceae encountered in fecal and environmental settings. Results showed that 86 (58.5%) and 109 (74.1%) strains yielded a positive signal with Colilert and MI agar methods, respectively, whereas the lacZ, wecG, and 16S rRNA PCR assays detected 133 (90.5%), 111 (75.5%), and 146 (99.3%) of the 147 total coliform strains tested. These assays were then assessed by testing 122 well water samples collected in the Québec City region of Canada. Results showed that 97 (79.5%) of the samples tested by culture-based methods and 95 (77.9%), 82 (67.2%), and 98 (80.3%) of samples tested using PCR-based methods contained total coliforms, respectively. Consequently, despite the high genetic variability of the total coliform group, this study demonstrated that it is possible to use molecular assays to detect total coliforms in potable water: the 16S rRNA molecular assay was shown to be as efficient as recommended culture-based methods. This assay might be used in combination with an Escherichia coli molecular assay to assess drinking water quality.
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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 | 0.000 | 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".