Comparative efficacy of five different rep-PCR methods to discriminate Escherichia coli populations in aquatic environments
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
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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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it