Sensitization to Airborne Molds and Its Health Effects in Peat Moss Processing Plant Workers
Bibliographic record
Abstract
The goal of this study was to evaluate the incidence of sensitization to the major molds found in peat dust in workers exposed to stored peat moss and the health impact of this sensitization. Air samples from each plant were obtained to measure the levels of airborne molds, bacteria, and dust. There were 189 workers from 14 peat moss processing plants (3 all-year mixing plants and 11 seasonal plants) recruited for the study. The subjects completed a symptoms questionnaire, underwent spirometric measurements and skin-prick tests, and gave venous blood samples. Blood samples from 43 nonexposed control subjects were also taken. A similar percentage of smokers from both plant types was observed. Twenty-eight percent of the workers tested had a positive serum reaction to at least one of the tested molds. The percentage of positive workers varied from plant to plant, going from none in 4 plants to 14 out of 21 for 1 plant. This variability was not correlated with the airborne levels of molds. FEV tended to be lower in the workers with positive antibodies compared with seronegative workers. IgG positive frequency was higher for those workers employed in the all-year plants, and workers from those plants had lower FEV/FVC than seasonal plant workers. Seasonal plants were more contaminated with molds than all-year mixing plants, suggesting that the duration of exposure may trigger more sensitization than the level of exposure. We conclude that there is a high incidence of mold sensitization in peat moss factory workers and that this sensitization may have a negative respiratory health impact.
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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 | 0.001 |
| 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) | 0.001 | 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".