Comparative efficacy of five different rep-PCR methods to discriminate Escherichia coli populations in aquatic environments
Bibliographic record
Abstract
Development of efficient techniques to discriminate the sources of E. coli in aquatic environments is essential to improve the surveillance of fecal pollution indicators, to develop strategies to identify the sources of fecal contamination, and to implement appropriate management practices to minimize gastrointestinal disease transmission. In this study the robustness of five different rep-PCR methods, such as REP-PCR, ERIC-PCR, ERIC2-PCR, BOX-PCR and (GTG)(5)-PCR were evaluated to discriminate 271 E. coli strains isolated from two watersheds (Lakelse Lake and Okanagan Lake) located in British Columbia, Canada. Cluster analysis of (GTG)(5)-PCR, BOX-PCR, REP-PCR, ERIC-PCR and ERIC2-PCR profiles of 271 E. coli revealed 43 clusters, 35 clusters, 28 clusters, 23 clusters and 14 clusters, respectively. The discriminant analysis of rep-PCR genomic fingerprints of 271 E. coli isolates yielded an average rate of correct classification (watershed-specific) of 86.8%, 82.3%, 78.4%, 72.6% and 55.8% for (GTG)(5)-PCR, BOX-PCR, REP-PCR, ERIC-PCR and ERIC2-PCR, respectively. Based on the results of cluster analysis and discriminant function analysis, (GTG)(5)-PCR was found to be the most robust molecular tool for differentiation of E. coli populations in aquatic environments.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".