Source tracking of<i>Escherichia coli</i>by 16S–23S intergenic spacer region denaturing gradient gel electrophoresis (DGGE) of the<i>rrnB</i>ribosomal operon
Bibliographic record
Abstract
This research validates a novel approach for source tracking based on denaturing gradient gel electrophoresis (DGGE) analysis of DNA extracted from Escherichia coli isolates. Escherichia coli from different animal sources and from river samples upstream from, at, and downstream of a combined sewer overflow were subjected to DGGE to determine sequence variations within the 16S-23S intergenic spacer region (ISR) of the rrnB ribosomal operon. The ISR was analyzed to determine if E. coli isolates from various animal sources could be differentiated from each other. DNA isolated from the E. coli animal sources was PCR amplified to isolate the rrnB operon. To prevent amplification of all 7 E. coli ribosomal operons by PCR amplification using universal primers, sequence-specific primers were utilized for the rrnB operon. Another primer set was then used to prepare samples of the 16S-23S ISR for DGGE. Comparison of PCR-DGGE results between human and animal sources revealed differences in the distribution and frequency of the DGGE bands produced. Human and Canada Goose isolates had the most unique distribution patterns and the highest percent of unique isolates and were grouped separately from all other animal sources. Method validation suggests that there are enough host specificity and genetic differences for use in the field. Field results at and around a combined sewer overflow indicate that this method can be used for microbial source tracking.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".