Exploring the indoor airborne microbiome and resistome in layer barns across Alberta, Canada
Bibliographic record
Abstract
The air in poultry barns carries a diverse range of microbial communities including potential opportunistic pathogens, which are important for both animal and human health. Bacteria carrying antimicrobial resistance genes (ARGs) can become airborne within poultry barns and be transmitted to animals and poultry workers, presenting a serious One Health concern. This study was aimed at characterizing the microbiome and resistome of bioaerosols sampled from layer chicken barns across Alberta. In total, 15 barns (9 cage housed and 6 floor housed) were sampled in this study using a microbial air sampler to characterize the microbiome and resistome using a shotgun metagenomic sequencing approach. The most abundant bacterial phyla found in the air of both housing systems for pullets were Bacilliota, Actinomycetota, and Bacteroidota. The respiratory pathogens such as Gallibacterium anatis, Ornithobacterium rhinotracheale, and Pasteurella multocida were relatively more abundant in the air of cage-housed barns, whereas Escherichia coli and Avibacterium paragallinarum were more prevalent in floor-housed barns. In total, 113 unique ARGs subtypes from 19 classes of antimicrobials were identified in this study. ARGs were significantly more abundant in the air of cage-housed barns compared to floor-housed barns. In particular, genes associated with resistance to tetracyclines, lincosamides, and macrolides were more frequently detected in cage-housed environments. Overall, both microbial and resistance gene levels were higher in the bioaerosols of cage-housed barns than in those from floor-housed pullet barns. The study results demonstrate the potential for air as a reservoir of ARGs and highlights microbial differences within cage and floor housing.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".