Bioaerosol exposure assessment in the workplace: the past, present and recent advances
Bibliographic record
Abstract
Louis Pasteur described the first measurements of airborne microorganisms in 1861. A century later, the inhalation of spores from thermophilic microorganisms was shown to induce attacks of farmers' lung in patients with this disease, while endotoxins originating from Gram-negative bacteria were identified as causal agents for byssinosis in cotton workers. Further epidemiological and toxicological studies have demonstrated inflammatory, respiratory, and pathogenic effects following exposure to bioaerosols. Exposure assessment is often confounded by the diversity of bioaerosol agents in the environment. Microorganisms represent a highly diverse group that may vary in toxicity. Fungi and bacteria are mainly quantified as broad groups using a variety of viable and nonviable assessment methods. Endotoxins and β(1 → 3)-glucans are mainly measured by their activity in the Limulus amebocyte lysate assay, enzymes by immuno-chemical methods and mycotoxins by liquid chromatography-mass spectrometry. Few health-based occupational exposure limits (OELs) are available for risk assessment. For endotoxins, a health-based OEL of 90 endotoxin units m(-3) has been proposed in the Netherlands. A criteria document for fungal spores recently proposed a lowest observed effect level of 100,000 spores m(-3) for non-pathogenic and non-mycotoxin producing species based on inflammatory respiratory effects. Recent developments in bioaerosol assessment were presented at the Organic Dust Tromsø Symposium including molecular biological methods for infectious agents and organisms that are difficult to cultivate; studies of submicronic and hyphal fragments from fungi; the effect of biodiversity of microorganisms in asthma studies; and new/improved measurement methods for fungal antigens, enzymes and allergens. Although exposure assessment of bioaerosol agents is complex and limited by the availability of methods and criteria, the field is rapidly evolving.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".