A health metadata-based management approach for comparative analysis of high-throughput genetic sequences for quantifying antimicrobial resistance reduction in Canadian hog barns
Bibliographic record
Abstract
New Canadian regulations have required that all use of antibiotics in livestock animal production should be under veterinary prescription and oversight, while the prophylactic use and inclusion of these agents in animal feed as growth promoters are also banned. In response to this new rule, many Canadian animal producers have voluntarily implemented production practices aimed at producing animals effectively while avoiding the use of antibiotics. In the swine industry, one such program is the 'raised without antibiotics' (RWA) program. In this paper, we describe a comprehensive investigative methodology comparing the effect of the adoption of the RWA approach with non-RWA pig production operations where antibiotics may still be administered on animals as needed. Our experimental approach involves a multi-year longitudinal investigation of pig farming to determine the effects of antibiotic usage on the prevalence of antimicrobial resistance (AMR) and pathogen abundance in the context of the drug exposures recorded in the RWA versus non-RWA scenarios. Surveillance of AMR and pathogens was conducted using whole-genome sequencing (WGS) in conjunction with open source tools and data pipeline analyses, which inform on the resistome, virulome and bacterial diversity in animals and materials associated with the different types of barns. This information was combined and correlated with drug usage (types and amounts) over time, along with animal health metadata (stage of growth, reason for drug use, among others). The overarching goal was to develop a set of interconnected informatic tools and data management procedures wherein specific queries could be made and customized, to reveal statistically valid cause/effect relationships. Results demonstrating possible correlations between RWA and AMR would support the Canadian pig industry, as well as regulatory agencies in new efforts, focused on reducing overall antibiotics use and in curbing the development and spread of AMR related to animal agriculture.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".