Occurrence, Sources and Virulence Potential of <i>Arcobacter butzleri</i> in Urban Municipal Stormwater Systems
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide A. butzleri is an underappreciated emerging global pathogen, despite growing evidence that it is a major contributor of diarrheal illness. Few studies have investigated the occurrence and public health risks that this organism possesses from waterborne exposure routes including through stormwater use. In this study, we assessed the prevalence, virulence potential, and primary sources of stormwater-isolated A. butzleri in fecally contaminated urban stormwater systems. Based on qPCR, A. butzleri was the most common enteric bacterial pathogen [25%] found in stormwater among a panel of pathogens surveyed, including Shiga-toxin producing Escherichia coli (STEC) [6%], Campylobacter spp. [4%], and Salmonella spp. [<1%]. Concentrations of the bacteria, based on qPCR amplification of the single copy gene hsp60, were as high as 6.2 log 10 copies/100 mL, suggesting significant loading of this pathogen in some stormwater systems. Importantly, out of 73 unique stormwater culture isolates, 90% were positive for the putative virulence genes cadF, ciaB, tlyA, cjl349, pldA, and mviN, while 50–75% of isolates also possessed the virulence genes irgA, hecA, and hecB . Occurrence of A . butzleri was most often associated with the human fecal pollution marker HF183 in stormwater samples. These results suggest that A. butzleri may be an important bacterial pathogen in stormwater, warranting further study on the risks it represents to public health during stormwater use.
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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 | 0.000 | 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) | 0.001 | 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".