Fate of<i>Escherichia coli</i>O157:H7 in irrigation water on soils and plants as validated by culture method and real-time PCR
Bibliographic record
Abstract
One of the most common vehicles by which Escherichia coli O157:H7 may be introduced into crops is contaminated irrigation water. Water contamination is becoming more common in rural areas of the United States as a result of large animal operations, and up to 40% of tested drinking-water wells are contaminated with E. coli. In this study, 2 contrasting soil samples were inoculated with E. coli O157:H7 expressing green fluorescent protein through irrigation water. Real-time PCR and culture methods were used to quantify the fate of this pathogen in phyllosphere (leaf surface), rhizosphere (volume of soil tightly held by plant roots), and non-rhizosphere soils. A real-time PCR assay was designed with the eae gene of E. coli O157:H7. The probe was incorporated into real-time PCR containing DNA extracted from the phyllosphere, rhizosphere, and non-rhizosphere soils. The detection limit for E. coli O157:H7 quantification by real-time PCR was 1.2 x 10(3) in the rhizosphere, phyllosphere, and non-rhizosphere samples. E. coli O157:H7 concentrations were higher in the rhizosphere than in the non-rhizosphere soils and leaf surfaces, and persisted longer in clay soil. The persistence of E. coli O157:H7 in phyllosphere, rhizosphere, and non-rhizosphere soils over 45 days may play a significant part in the recontamination cycle of produce in the environment. Therefore, the rapidity of the real-time PCR assay may be a useful tool for quantification and monitoring of E. coli O157:H7 in irrigation water and on contaminated fresh produce.
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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 | 0.000 | 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 | 0.001 | 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 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".