Comparison of MI, Chromocult® coliform, and Compass CC chromogenic culture-based methods to detect Escherichia coli and total coliforms in water using 16S rRNA sequencing for colony identification
Bibliographic record
Abstract
The MI, Chromocult® coliform, and Compass CC chromogenic culture-based methods used to assess water quality by the detection of Escherichia coli and total coliforms were compared in terms of their specificity and sensitivity, using 16S rRNA sequencing for colony identification. A sewage water sample was divided in 2-μL subsamples for testing by all three culture-based methods. All growing colonies were harvested and subjected to 16S rRNA sequencing. Test results showed that all E. coli colonies were correctly identified by all three methods, for a specificity and a sensitivity of 100%. However, for the total coliform detection, the MI agar, Chromocult® coliform agar, and Compass CC agar were specific for only 69.2% (9/13), 47.2% (25/53), and 40.5% (17/42), whereas sensitive for 97.8% (45/46), 97.5% (39/40), and 85.7% (24/28), respectively. Thus, given the low level of specificity of these methods for the detection of total coliforms, confirming the identity of total coliform colonies could help to take public health decisions, in particular for cities connected to a public drinking water distribution system since the growth of few putative total coliform colonies on chromogenic agar is problematic and can lead to unnecessary and costly boiling notices from public health authorities.
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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 | 0.002 | 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".