Zoonotic Fecal Pathogens and Antimicrobial Resistance in Canadian Petting Zoos
Bibliographic record
Abstract
This study aimed to better understand the potential public health risk associated with zoonotic pathogens in agricultural fairs and petting zoos in Canada. Prevalence of Salmonella, Shiga toxin-producing Escherichia coli (STEC) O157:H7, and top six non-O157 STEC serogroups in feces (n = 88), hide/feather (n = 36), and hand rail samples (n = 46) was assessed, as well as distributions of antimicrobial resistant (AMR) broad and extended-spectrum β-lactamase (ESBL)-producing E. coli. Prevalence of methicillin-resistant Staphylococcus aureus (MRSA) in pig nasal swabs (n = 4), and Campylobacter, Cryptosporidium, and Giardia in feces was also assessed. Neither Salmonella nor MRSA were detected. Campylobacter spp. were isolated from 32% of fecal samples. Cryptosporidium and Giardia were detected in 2% and 15% of fecal samples, respectively. Only one fecal sample was positive for STEC O157, whereas 22% were positive for non-O157 STEC. Multi-drug resistance (MDR) to antibiotics classified as critically and highly important in human medicine was proportionally greatest in E. coli from cattle feces. The β-lactamase-producing E. coli from pig, horse/donkey feces, and hand rail samples, as well as the STEC E. coli from handrail swabs were MDR. The diversity and prevalence of zoonotic pathogens and AMR bacteria detected within agricultural fairs and petting zoos emphasize the importance of hygienic practices and sanitization with respect to reducing associated zoonotic risks.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".