A comparison of AFLP and ERIC-PCR analyses for discriminating Escherichia coli from cattle, pig and human sources
Bibliographic record
Abstract
Amplified fragment length polymorphism (AFLP) and enterobacterial repetitive intergenic consensus polymerase chain reaction (ERIC-PCR) genomic fingerprinting assays were compared for their ability to differentiate Escherichia coli isolates obtained from various host sources, and with respect to their pathogenicity. One hundred and ten verotoxigenic, enterotoxigenic and non-pathogenic E. coli isolates obtained from cattle, humans and pigs were used in this study. The AFLP assay was shown to be highly effective in predicting both the host source and pathogenicity of the E. coli isolates. A stepwise discriminant function analysis showed that 91.4, 90.6 and 97.7% of the human, bovine and pig isolates were classified into the correct host types, respectively. The analysis also distinguished the non-pathogenic E. coli from the verocytotoxigenic and enterotoxigenic virulence phenotypes at 100, 100 and 90.9% accuracy, respectively. Sixty-two E. coli strains from the collection were subjected to the ERIC-PCR fingerprinting analysis. Using this method, only 28.6, 0 and 75.0% of the human, bovine and pig isolates were classified into the correct host types, respectively. Overall, the AFLP method was able to ascribe host source with a high level of confidence and readily discriminate pathogenic from non-clinical isolates of E. coli.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".