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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it