Identification of mycobacteria in peat moss processing plants: application of molecular biology approaches
Bibliographic record
Abstract
Peat moss processing plant workers are exposed to high concentrations of bioaerosols. Although mycobacteria have been cultured from peat moss, no study has examined the workers' exposure to mycobacterial bioaerosols. We evaluated the presence of mycobacteria in air samples from peat moss processing plants using molecular biology approaches (cloning-sequencing and polymerase chain reaction (PCR)) and the workers exposure using immunoglobulin G (IgG) complexes to mycobacteria. In addition, species detected in air samples and in peat moss were compared. Two peat moss processing plants were chosen among 14 previously studied. A total of 49 clones were sequenced. Real-time PCR was also performed on the same air samples to evaluate the airborne concentration of mycobacteria and estimate exposure levels. Several Mycobacterium species were present in the air samples (M. malmoense, M. smegmatis, M. graceum, M. bohemicum, and M. interjectum). Mycobacterium avium was recovered by culture in peat moss but not in the air using the molecular approach. Total airborne Mycobacterium concentration was estimated at 8.2 x 10(8)/m3. Workers had IgG against the mycobacterial mix and M. avium, suggesting significant exposure. The findings from air samples, supported by IgG measurements, demonstrate that peat moss processing plant workers are exposed to mycobacteria in addition to other biological agents.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".