Culture‐independent approach of the bacterial bioaerosol diversity in the standard swine confinement buildings, and assessment of the seasonal effect
Bibliographic record
Abstract
The bacterial bioaerosol community of eight swine confinement buildings (SCB) was monitored during two visits in the winter, and one during the summer. To our knowledge, culture-independent approaches and molecular biology tools such as biomass quantification and biodiversity analyses have never been applied to swine building bioaerosol analyses. Total DNA of each sample was extracted and analysed by quantitative real-time polymerase chain reaction, denaturing gradient gel electrophoresis (DGGE) and phylogenetic analysis using primers targeting the bacterial 16S rRNA gene. Even though the total bacterial concentration was higher in winter than in summer, the total bacterial concentration for both seasons was 100 to1000 times higher than the total cultural bacteria. The concentration of bioaerosol was influenced by the temperature indoors, which was regulated with an electronic fan system driving warm air and particles outside of the SCB. Comparison of the DGGE profiles showed the same biodiversity in each SCB during both seasons. The phylogenetic analysis revealed a large number of sequences (93.8%) related to Gram-positive anaerobic bacteria, such as Clostridia, and dominated by the Clostridia cluster I (C. disporicum) and the Clostridia cluster XI (C. glycolycum). The bioaerosol diversity also contained also a low proportion of Bacteroidetes and Lactobacillales-Streptococcales sequences. Analyses of the global community and phylotype diversity showed that the main source of bioaerosols could come from the pig manure slurry.
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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 | 0.000 | 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".