Prevalence and diversity of waterborne <i>Arcobacter butzleri</i> in southwestern Alberta, Canada
Bibliographic record
Abstract
Arcobacter butzleri is a potential enteric pathogen to human beings, but its reservoirs and modes of transmission are largely unverified. Microbiological and molecular detection and subtyping techniques can facilitate surveillance of A. butzleri in hosts and environmental reservoirs. We isolated A. butzleri from 173 surface water samples (25.6%) and 81 treated wastewater samples (77.9%) collected in southwestern Alberta over a 1-year period. Arcobacter butzleri isolates (n = 500) were genotyped and compared to determine diversity of A. butzleri in southwestern Alberta. Culture methods affected the frequency of detection and genotype diversity of A. butzleri, and isolation comprehensiveness was different for surface waters and treated wastewaters. Detection of A. butzleri in the Oldman River Watershed corresponded with season, river flow rates, and fecal coliform densities. Arcobacter butzleri was detected most frequently in treated wastewater, in the Oldman River downstream from treated wastewater outfalls, and in tributaries near areas of intensive confined feeding operations. All sample sources possessed high genotype diversity, and A. butzleri isolates from treated wastewaters were genetically similar to isolates from the Oldman River downriver from treated wastewater outfall sites. In southwestern Alberta, municipal and agricultural activities contribute to the density and genotype diversity of A. butzleri in surface waters.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".