Comparison of five rep-PCR genomic fingerprinting methods for differentiation ofâ fecal<i>Escherichia coli</i>from humans, poultry and wild birds
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
The development of a methodology to identify the origin of fecal pollution is important both for assessing the degree of risk posed to public health and for developing strategies to mitigate the environmental loading of pathogens associated with waterborne disease transmission. Five rep-PCR genomic fingerprinting methods, such as rep-PCR, enterobacterial repetitive intergenic consensus (ERIC)-PCR, ERIC2-PCR, BOX-PCR and (GTG)(5)-PCR, were assessed for their potential in differentiation of 232 fecal Escherichia coli isolates obtained from humans, poultry (chicken, duck and turkey) and wild birds (Canada goose and gull). Based on the results of cluster analysis and discriminant function analysis, (GTG)(5)-PCR was found to be the most suitable method for molecular typing of fecal E. coli, followed by BOX-PCR, REP-PCR, ERIC-PCR and ERIC2-PCR. A discriminant function analysis of (GTG)(5)-PCR fingerprints showed that 94.1%, 79.8%, 80.5%, 74.4%, 86.7% and 88.6% of turkey, chicken, duck, Canada goose, gull and human E. coli isolates were classified into the correct host group, respectively. Subsequently, (GTG)(5)-PCR was tested for its ability to track the origin of 113 environmental E. coli isolated from natural pond water. In conclusion, the (GTG)(5)-PCR genomic fingerprinting method can be considered as a promising genotypic tool for epidemiological surveillance of fecal pollution 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 | 0.000 |
| Scholarly communication | 0.000 | 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 it