Piglet Gut and in-Barn Manure from Farms on a Raised without Antibiotics Program Display Reduced Antimicrobial Resistance but an Increased Prevalence of Pathogens
Bibliographic record
Abstract
In response to new stringent regulations in Canada regarding the use of antibiotics in animal production, many farms have implemented practices to produce animals that are raised without antibiotics (RWA) from birth to slaughter. This study aims to assess the impact of RWA production practices on reducing the actual total on-farm use of antibiotics, the occurrence of pathogens, and the prevalence of antimicrobial resistance (AMR). A 28-month longitudinal surveillance of farms that adopted the RWA program and conventional farms using antibiotics in accordance with the new regulations (non-RWA) was conducted by collecting fecal samples from 6-week-old pigs and composite manure from the barn over six time points and applying whole-genome sequencing (WGS) to assess the prevalence of AMR genes as well as the abundance of pathogens. Analysis of in-barn drug use records confirmed the decreased consumption of antibiotics in RWA barns compared to non-RWA barns. WGS analyses revealed that RWA barns had reduced the frequency of AMR genes in piglet feces and in-barn manure. However, metagenomic analyses showed that RWA barns had a significant increase in the frequency of pathogenic Firmicutes in fecal samples and pathogenic Proteobacteria in barn manure samples.
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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 | 0.000 |
| 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".