Development of a Procedure for Discriminating among <i>Escherichia coli</i> Isolates from Animal and Human Sources
Bibliographic record
Abstract
Counts of Escherichia coli cells in water indicate the potential presence of pathogenic microbes of intestinal origin but give no indication of the sources of the microbial pollution. The objective of this research was to evaluate methods for differentiating E. coli isolates of livestock, wildlife, or human origin that might be used to predict the sources of fecal pollution of water. A collection of 319 E. coli isolates from the feces of cattle, poultry, swine, deer, goose, and moose, as well as from human sewage, and clinical samples was used to evaluate three methods. One method was the multiple-antibiotic-resistance (MAR) profile using 14 antibiotics. Discriminant analysis revealed that 46% of the livestock isolates, 95% of the wildlife isolates, and 55% of the human isolates were assigned to the correct source groups by the MAR method. Amplified fragment length polymorphism (AFLP) analysis, the second test, was applied to 105 of the E. coli isolates. The AFLP results showed that 94% of the livestock isolates, 97% of the wildlife isolates, and 97% of the human isolates were correctly classified. The third method was analysis of the sequences of the 16S rRNA genes of the E. coli isolates. Discriminant analysis of 105 E. coli isolates indicated that 78% of the livestock isolates, 74% of the wildlife isolates, and 80% of the human isolates could be correctly classified into their host groups by this method. The results indicate that AFLP analysis was the most effective of the three methods that were evaluated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".