Total outward leakage reduction efficiency of different protective masks using model viruses
Bibliographic record
Abstract
Total outward leakage reduction efficiency against viral particles for masks is not well known. The objective of this study was to evaluate this efficiency for various masks against virus-containing polydisperse aerosols. To achieve that, a test bench previously built for particulate total outward leakage reduction efficiency evaluation was adapted to generate viral aerosols. Total outward leakage reduction efficiency against viral particles was measured for 10 different masks using a wind tunnel and a mannequin head. The impact of washing on the total outward leakage reduction efficiency was assessed for three barrier face coverings against viral particles. Total outward leakage reduction efficiency for viruses was generally higher than for particles (0.52–3.3 µm) since each of these particles could contain more than one virion and have a greater impact on the viral efficiency compared to particles. Washing did not have a major impact on the efficiency measured for the barrier face coverings tested. Total outward leakage reduction efficiency tests could be done using inert particles since total outward leakage reduction efficiency was lower against particles (0.52–3.3 µm) than against infectious viruses. However, using biological particles may be a better way to interpret the risk associated with infectious aerosols.Copyright © 2025 American Association for Aerosol Research
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".