Optimization and validation of rep-PCR genotypic libraries for microbial source tracking of environmental Escherichia coli isolates
Bibliographic record
Abstract
Escherichia coli can be used to help identify sources of fecal contamination in the environment. Escherichia coli genotypic fecal libraries and pattern-matching algorithms were assessed for their effectiveness in correctly identifying sources. Fecal samples (n = 172) were collected from various sources from three agricultural landscapes in Canada. Escherichia coli isolates were fingerprinted using BOX- and enterobacterial repetitive intergenic consensus (ERIC) - polymerase chain reaction primers, revealing 769 and 1 057 distinct genotypes, respectively, for the 9 047 isolates collected in 2004 in Ontario. The average rate of correct classification (ARCC) was comparable for BOX- (48%) and ERIC-based (62%) libraries and between libraries with clones removed per sample (55%) and clones removed per unit (54%). ARCC increased with fewer classification units (from 44% to 65%). ARCC for k-nearest neighbour (64%) and maximum similarity (60%) algorithms were comparable, but maximum similarity had better sensitivity and specificity than k-nearest neighbour. Geographical and temporal shifts in community composition resulted in loss of accuracy. Several ERIC genotypes (n = 112) were common between sources and were removed from the library, improving ARCC (77%). The latter library proved to be more accurate, but its accuracy with respect to sourcing environmental isolates remains to be tested.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".