Evaluation of the Pathogen Detect<sup>®</sup> System and Anthracene-Based Enzyme Substrates for the Detection and Differentiation of <i>E. coli</i> and Total Coliforms in Water Samples
Bibliographic record
Abstract
Indirect detection of Escherichia coli and total coliforms can be based on the enzymatic activities of β-glucuronidase (β-glu) and β-galactosidase (β-gal). These enzymes utilize the substrates anthracene-β-d-glucuronide and pyrene d-galactopyranoside, respectively. Substrate cleavage by the enzyme releases the soluble fluorescent molecules 2-hydroxyanthracene and 1-hydroxypyrene, which can then be detected by a fluorometer. The Pathogen Detect® system is an automated portable unit that can measure fluorescent enzyme products. In this report, we investigated the utility of the Pathogen Detect® system for potential automation of water quality monitoring. The PDS unit has the ability to detect E. coli, mean 14.7 h at a standard deviation of 1.5, when the sample mean is 9.1 cells in 100 mL with a standard deviation of 12.6. Similarly, total coliforms may be detected at mean 14.7 h with a standard deviation of 1.4 when the sample mean is 59.6 cells in 100 mL, with a standard deviation of 144.5. The PDS unit has the ability to detect single cells of either total coliforms or E. coli in 100 mL water sample within 18 hours. Turbidity and color of water samples have no impact on the detection of E. coli and total coliforms.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".