Antimicrobial Resistance in <i>Escherichia coli</i> Isolates from Swine and Wild Small Mammals in the Proximity of Swine Farms and in Natural Environments in Ontario, Canada
Bibliographic record
Abstract
Wild animals not normally exposed to antimicrobial agents can acquire antimicrobial agent-resistant bacteria through contact with humans and domestic animals and through the environment. In this study we assessed the frequency of antimicrobial resistance in generic Escherichia coli isolates from wild small mammals (mice, voles, and shrews) and the effect of their habitat (farm or natural area) on antimicrobial resistance. Additionally, we compared the types and frequency of antimicrobial resistance in E. coli isolates from swine on the same farms from which wild small mammals were collected. Animals residing in the vicinity of farms were five times more likely to carry E. coli isolates with tetracycline resistance determinants than animals living in natural areas; resistance to tetracycline was also the most frequently observed resistance in isolates recovered from swine (83%). Our results suggest that E. coli isolates from wild small mammals living on farms have higher rates of resistance and are more frequently multiresistant than E. coli isolates from environments, such as natural areas, that are less impacted by human and agricultural activities. No Salmonella isolates were recovered from any of the wild small mammal feces. This study suggests that close proximity to food animal agriculture increases the likelihood that E. coli isolates from wild animals are resistant to some antimicrobials, possibly due to exposure to resistant E. coli isolates from livestock, to the resistance genes of these isolates, or to antimicrobials through contact with animal feed.
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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".