Prevalence of Escherichia coli serogroups and human virulence factors in faeces of urban Canada geese ( Branta canadensis )
Bibliographic record
Abstract
This was the first study to exhaustively characterize the prevalence of Escherichia coli sero-groups in any wildlife species. Faecal samples from Canada geese (Branta canadensis) were collected over a single year in Fort Collins, Colorado, USA. The overall prevalence for E. coli ranged from 2% during the coldest time of the year to 94% during the warmest months of the year. During the time of year when nonmigratory geese dominated the local goose population (March-July) the prevalence of enterotoxogenic (ETEC) forms of E. coli was 13.0%. The prevalence of enterohemorrhagic (EHEC) forms was 6.0%, while prevalence for enteroinvasive (EIEC) and enteroagglomerative (EAEC) forms was 4.6 and 1.3%, respectively, during the same period. We also examined all samples positive for E. coli for genes coding for virulence factors, including: SLT-I, SLT-II, eae, hly-A, K1, LT, STa, STb, CNF1, and CNF2. Three isolates were positive for human virulence factors, representing a 2% prevalence for faeces containing potential human toxins. Genes for STa were isolated from ETEC strains O-8 and O-167, while the gene for K1 was isolated from an O-8 (ETEC) serogroup. These data will prove useful in focusing attention on the risks that increasing populations of urban Canada geese pose to public health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".