Impact of Season, Demographic and Environmental Factors on Salmonella Occurrence in Raccoons (Procyon lotor) from Swine Farms and Conservation Areas in Southern Ontario
Bibliographic record
Abstract
Salmonella has been detected in the feces of many wildlife species, including raccoons (Procyon lotor), but little is known about the epidemiology of Salmonella in wildlife living in different habitat types. Our objective was to investigate demographic, temporal, and climatic factors associated with the carriage of Salmonella in raccoons and their environment on swine farms and conservation areas. Using a repeated cross-sectional study design, we collected fecal samples from raccoons and environmental samples (soil, manure pits, dumpsters) on 5 swine farms and 5 conservation areas in Ontario, Canada once every five weeks from May to November, 2011-2013. Salmonella was detected in 26% (279/1093; 95% CI 22.9-28.2) of raccoon fecal samples, 6% (88/1609; 95% CI 4.5-6.8) of soil samples, 30% (21/69; 95% CI 20.0-42.7) of manure pit samples, and 23% (7/31; 95% CI 9.6-41.0) of dumpster samples. Of samples testing positive for Salmonella, antimicrobial resistance was detected in 5% (14/279; 95% CI 2.8-8.3) of raccoon fecal, 8% (7/89; 95% CI 3.2-15.5) of soil, 10% (2/21; 95% CI 1.2-30.4) of manure pit, and 0/7 dumpster samples. Using multi-level multivariable logistic regression analyses, we found location type (swine farm or conservation area) was not a significant explanatory variable for Salmonella occurrence in raccoon feces or soil (p > 0.05). However, detection of Salmonella in raccoon feces was associated with rainfall, season, and sex with various interaction effects among these variables. We detected a variety of Salmonella serovars that infect humans and livestock in the feces of raccoons indicating that raccoons living near humans, regardless of location type, may play a role in the epidemiology of salmonellosis in livestock and humans in southwestern Ontario.
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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 | 0.001 |
| 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) | 0.001 | 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".