Reduction of Bioaerosols Emitted from a Swine Confinement Building by a Percolating Biofilter During a 10-Month Period
Bibliographic record
Abstract
The release of pathogens into the air from swine confinement buildings are mitigated through preventative measures, such as outgoing air filtration, to reduce the risk of spread to nearby barns and communities. The present study aims to characterize the effectiveness of a percolating biofilter developed by the Research and Development Institute for the Agri-environment (IRDA) to capture airborne contaminants, such as bacteria and viruses emitted from a swine finishing room. Over a 10-month period (summer, fall, and winter), air was sampled upwind and downwind of the biofilter using two wet walled cyclonic samplers. Culture-dependent and molecular biology analyses were used to track changes in microbial concentrations and populations both captured and emitted by the percolating biofilter. Results revealed a minor reduction (median reduction efficiency 14.4%) in culturable bacteria. There was a decrease in total bacteria (qPCR) (75.0%) and other qPCR targeted organisms: archaea (42.1%), coliphages (25.6%), Enterococcus (76.1%), and Escherichia coli (40.9%). The community analyses showed similar bacterial diversity in the air upwind and downwind of the biofilter although more Proteobacteria were present downwind of the unit, likely attributable to the Proteobacteria-rich nutritive solution. Evidence is provided for bioaerosols reduction by a percolating biofilter treating air from a swine fattening-finishing room.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".