Characterization of Bioaerosols from Dairy Barns: Reconstructing the Puzzle of Occupational Respiratory Diseases by Using Molecular Approaches
Bibliographic record
Abstract
To understand the etiology of exposure-related diseases and to establish standards for reducing the risks associated with working in contaminated environments, the exact nature of the bioaerosol components must be defined. Molecular biology tools were used to evaluate airborne bacterial and, for the first time, archaeal content of dairy barns. Three air samplers were tested in each of the 13 barns sampled. Up to 10(6) archaeal and 10(8) bacterial 16S rRNA genes per m(3) of air were detected. Archaeal methanogens, mainly Methanobrevibacter species, were represented. Saccharopolyspora rectivirgula, the causative agent of farmer's lung, was quantified to up to 10(7) 16S rRNA genes per m(3) of air. In addition, a wide variety of bacterial agents were present in our air samples within the high airborne bioaerosol concentration range. Despite recommendations regarding hay preservation and baling conditions, farmers still develop an S. rectivirgula-specific humoral immune response, suggesting intense and continuous exposure. Our results demonstrate the complexity of bioaerosol components in dairy barns which could play a role in occupational respiratory diseases.
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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 | 0.000 |
| 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) | 0.000 | 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".