Zoonotic Fecal Pathogens and Antimicrobial Resistance in Canadian Petting Zoos
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it