Filtration efficiency of different protective masks against viral aerosols
Bibliographic record
Abstract
During the SARS-CoV-2 pandemic, masks were widely used to reduce the spread of the virus through aerosols and droplets. While these are generally tested in laboratories for their effectiveness against particles and bacteria, their efficiency against viruses is seldom evaluated. Given the absence of standardized rules governing filtration efficiency against viruses, this study sought to examine how various types of masks perform against virus-containing polydisperse aerosols. Additionally, it aimed at assessing the consistency of mask filtration performances under similar test conditions, considering the heterogeneous nature of the standards for particles. Masks’ filtration efficiencies were determined using a wind tunnel specially designed for this kind of testing. Bacteriophages were used as a proxy for human viruses. Overall, the viral filtration efficiency was higher than that of particles. No significant difference was observed between infectious and total viruses. Particulate filtration performance varied among masks compared to their standard requirements. Filtration efficiency testing should report the specific size used whether it was tested with mono- or polydisperse aerosols to gain a clearer understanding of their effectiveness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".