Multiparametric comparison of chromogenic-based culture methods used to assess the microbiological quality of drinking water and the mFC method combined with a molecular confirmation procedure
Bibliographic record
Abstract
MI agar and Colilert(®), as well as mFC agar combined with an Escherichia coli-specific molecular assay (mFC + E. coli rtPCR), were compared in terms of their sensitivity, ease of use, time to result and affordability. The three methods yielded a positive E. coli signal for 11.5, 10.8, and 11.5% of the 968 well water samples tested, respectively. One hundred and thirty-six (136) samples gave blue colonies on mFC agar and required confirmation. E. coli-specific rtPCR showed false-positive results in 23.5% (32/136) of cases. In terms of ease of use, Colilert was the simplest method to use while the MI method provided ease of use comparable to all membrane filtration methods. However, the mFC + E. coli rtPCR assay required highly trained employees for confirmation purposes. In terms of affordability, and considering contamination rate of well water samples tested, the Colilert method and the mFC + E. coli rtPCR assay were at least five times more costly than the MI agar method. Overall, compared with the other two methods tested, the MI agar method offers the most advantages to assess 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".