Review / SynthèseBioaerosols from municipal and animal wastes: background and contemporary issues
Bibliographic record
Abstract
Global population increases, coupled with intensive animal and livestock production practices, have resulted in the generation, accumulation, and disposal of large amounts of wastes around the world. Aerosolization of microbial pathogens, endotoxins, odors, and dust particles is an inevitable consequence of the generation and handling of waste material. Bioaerosols can be a source of microbial pathogens, endotoxins, and other allergens. Given the close proximity of population centers to concentrated animal-rearing operations and municipal treatment facilities in many parts of the world, there is concern regarding the occupational and public health impacts associated with the exposure to bioaerosols from municipal and animal wastes. Major advances have been made in our understanding of bioaerosol characteristics, identifying the hazards, and identifying possible human and animal health links with aerosolized pathogens and allergens. However, significant knowledge and technology gaps still exist. These include a lack of clear understanding of the fate and transport of bioaerosols, especially within the open environment, an inability to accurately predict the health risks associated with bioaerosolized pathogens, and a lack of standardized bioaerosol sampling protocols, and efficient samplers. This review synthesizes the information related to bioaerosols and addresses the contemporary issues associated with bioaerosols from municipal and animal wastes, with a focus on pathogens.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".