Contribution of Manure-Spreading Operations to Bioaerosols and Antibiotic Resistance Genes’ Emission
Bibliographic record
Abstract
Manure spreading from farm animals can release antibiotic-resistant bacteria (ARB) carrying antimicrobial resistance genes (ARGs) into the air, posing a potential threat to human and animal health due to the intensive use of antibiotics in the livestock industry. This study analyzed the effect of different manure types and spreading methods on airborne bacterial emissions and antibiotic resistance genes in a controlled setting. Cow, poultry manure, and pig slurry were spread in a confined environment using two types of spreaders (splash plate and dribble bar), and the resulting emissions were collected before, during, and after spreading using high-volume air samplers coupled to a particle counter. Total bacteria, fecal indicators, and a total of 38 different subtypes of ARGs were further quantified by qPCR. Spreading poultry manure resulted in the highest emission rates of total bacteria (1011 16S gene copies/kg manure spread), Archaea (106 16S gene copies/kg manure), Enterococcus (105 16S gene copies/kg manure), and E. coli (104 16S gene copies/kg manure), followed by cow manure and pig slurry with splash plates and the dribble bar. Manure spreading was associated with the highest rates of airborne aminoglycoside genes for cow and poultry (106 gene copies/kg manure), followed by pig slurry (104 gene copies/kg manure). This study shows that the type of manure and spreading equipment can affect the emission rates of airborne bacteria, and ARGs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".