Field sampling of indoor bioaerosols
Bibliographic record
Abstract
Because bioaerosols are related to adverse health effects in exposed humans and indoor environments represent a unique framework of exposure, concerns about indoor bioaerosols have risen over recent years. One of the major issues in indoor bioaerosol research is the lack of standardization in the methodology, from air sampling strategies and sample treatment to the analytical methods applied. The main characteristics to consider in the choice of indoor sampling methods for bioaerosols are the sampler performance, the representativeness of the sampling, and the concordance with the analytical methods to be used. The selection of bioaerosol collection methods is directly dependent on the analytical methods, which are chosen to answer specific questions raised while designing a study for exposure assessment. In this review, the authors present current practices in the analytical methods and the sampling strategies, with specificity for each type of microbe (fungi, bacteria, archaea and viruses). In addition, common problems and errors to be avoided are discussed. Based on this work, recommendations are made for future efforts towards the development of viable bioaerosol samplers, standards for bioaerosol exposure limits, and making association studies to optimize the use of the big data provided by high-throughput sequencing methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".