The detection of Yersinia enterocolitica in surface water by quantitative PCR amplification of the ail and yadA genes
Bibliographic record
Abstract
Yersinia enterocolitica has been detected in surface water, and drinking untreated water is a risk factor for infection. PCR-based methods have been used to detect Y. enterocolitica in various sample types, but quantitative studies have not been conducted in water. In this study, quantitative PCR (qPCR)-based methods targeting the Yersinia virulence genes ail and yadA were used to survey the Grand River watershed in southern Ontario, Canada. Initial testing of reference strains showed that ail and yadA PCR assays were specific for pathogenic biotypes of Y. enterocolitica; however the genes were also detected in one clinical Yersinia intermedia isolate. A survey of surface water from the Grand River watershed showed that both genes were detected at five sampling locations, with the ail and yadA genes detected in 38 and 21% of samples, respectively. Both genes were detected more frequently at colder water temperatures. A screening of Yersinia strains isolated from the watershed showed that the ail gene was detected in three Y. enterocolitica 1A/O:5 isolates. Results of this study show that Yersinia virulence genes were commonly detected in a watershed used as a source of drinking water, and that the occurrence of these genes was seasonal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".