Factors Affecting Reduction of Infectious Aerosols by Far-UVC and Portable HEPA Air Cleaners
Bibliographic record
Abstract
Technologies that can reduce concentrations of airborne microorganisms through either particle capture or inactivation are important tools for reducing the risk of disease transmission and improving overall indoor air quality. The effectiveness of these technologies is tested in different ways, and as a result, it is challenging to compare results and optimize their use in applied settings. In this study, experiments were conducted in a large bioaerosol chamber to evaluate the efficacy of far-UVC and portable HEPA air cleaners against the bacteriophage MS2 as a surrogate for human viral pathogens. For both technologies, changing the media used to aerosolize the microorganism from deionized water to a simulated saliva doubled effectiveness metrics (both log 10 reductions and clean air delivery rates). Because reductions did not follow first order, log–linear dynamics, using different segments of the test period to calculate efficacy also significantly impacted reported performance. Evidence shown here indicates that both microbiological and particle dynamics likely play a role in impacting test outcomes under current methods, and more research is needed to improve repeatable and reliable standardized approaches for determining technology performance against infectious aerosols.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".