Comparison of four β-glucuronidase and β-galactosidase-based commercial culture methods used to detect Escherichia coli and total coliforms in water
Bibliographic record
Abstract
The MI agar, Colilert(®), Chromocult coliform(®) agar, and DC with BCIG agar chromogenic culture-based methods used to assess microbiological quality of drinking water were compared in terms of their ubiquity, sensitivity, ease of use, growth of atypical colonies and affordability. For ubiquity, 129 total coliform (representing 76 species) and 19 Escherichia coli strains were tested. Then, 635 1-L well water samples were divided into 100 mL subsamples for testing by all four methods. Test results showed that 70.5, 52.7, 36.4, and 23.3% of the non-E. coli total coliform strains and 94.7, 94.7, 89.5, and 89.5% of the 19 E. coli strains yielded a positive signal with the four methods, respectively. They also yielded a total coliform positive signal for 66.5, 51.7, 64.9, and 55.0% and an E. coli positive signal for 16.1, 14.8, 17.3, and 13.4% of the 635 well water samples tested, respectively. Results showed that Colilert(®) is the most expensive method tested in terms of reactants, yet it is the easiest to use. Large numbers of atypical colonies were also often observed on Chromocult coliform(®) and DC with BCIG, thereby challenging the target microorganism count. Thus, the MI agar method seems to be the best option for the assessment of drinking water 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".