False-positive identification of<i>Escherichia coli</i>in treated municipal wastewater and wastewater-irrigated soils
Bibliographic record
Abstract
The increasing use of treated wastewater for irrigation heightens the importance of accurate monitoring of water quality. Chromogenic media, because they are easy to use and provide rapid results, are often used for detection of Escherichia coli in environmental samples, but unique levels of organic and inorganic compounds alter the chemistry of treated wastewater, potentially hindering the accurate performance of chromogenic media. We used MI agar and molecular confirmatory methods to assess false-positive identification of E. coli in treated wastewater samples collected from municipal utilities, an irrigation holding pond, irrigated soils, and in samples collected from storm flows destined for groundwater recharge. False-positive rates in storm flows (4.0%) agreed closely with USEPA technical literature but were higher in samples from the pond, soils, and treatment facilities (33.3%, 38.0%, and 48.8%, respectively). Sequencing of false-positive isolates confirmed that most were, like E. coli, of the family Enterobacteriaceae, and many of the false-positive isolates were reported to produce the β-D-glucuronidase enzyme targeted by MI agar. False-positive identification rates were inversely related to air temperature, suggesting that seasonal variations in water quality influence E. coli identification. Knowledge of factors contributing to failure of chromogenic media will lead to manufacturer enhancements in media quality and performance and will ultimately increase the accuracy of future water quality monitoring programs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".