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Record W4410395764 · doi:10.1080/02786826.2025.2498993

Total outward leakage reduction efficiency of different protective masks using model viruses

2025· article· en· W4410395764 on OpenAlexafffund
Vincent Brochu, Sandrine Chazelet, Pauline Loison, Stéphanie Pacault, Nathalie Turgeon, Marc Veillette, Philippe Duquenne, Caroline Duchaine

Bibliographic record

VenueAerosol Science and Technology · 2025
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLeakage (economics)Reduction (mathematics)Coronavirus disease 2019 (COVID-19)ChemistryEnvironmental scienceMedicineMathematicsInfectious disease (medical specialty)Internal medicine

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.300
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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