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Record W3041683026 · doi:10.1080/02786826.2020.1790495

Ozone treatment in a wind tunnel for the reduction of airborne viruses in swine buildings

2020· article· en· W3041683026 on OpenAlexaff
Jonathan M. Vyskocil, Nathalie Turgeon, Jean-Gabriel Turgeon, Caroline Duchaine

Bibliographic record

VenueAerosol Science and Technology · 2020
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsUniversité LavalCentre de Développement du Porc du QuébecInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsOzoneIndoor bioaerosolEnvironmental scienceRelative humidityWind tunnelBioaerosolEnvironmental chemistryAerosolEnvironmental engineeringChemistryMeteorologyGeography

Abstract

fetched live from OpenAlex

Ozone is effective against bacteria and viruses although its influence over bioaerosols is understudied and could be useful particularly in agricultural buildings such as swine confinement buildings. Ozone treatment of air within ventilation plenum could be applied for biosecurity purposes during the quarantine of incoming animals. In this study, a bacteriophage (PhiX174) was used as a surrogate for eukaryotic viruses in nebulization experiments inside a wind tunnel to study the factors that affect ozone’s efficacy to reduce the virus. A tunnel system was installed in the workshop of a swine building with controlled relative humidity (40% and 80%), ozone concentration (0, 0.3, 0.6, 0.9, 1.2, 1.5, and 1.8 ppm), and exposure time (up to approximately 6 min) of PhiX174 nebulized. Although there was no effect of ozone on phage genomes, culturable phages were inactivated. There was a reduction of PhiX174 infectious ratios with increasing ozone concentrations and stronger effects of ozone observed at 80% relative humidity. The data suggests the potential success of a wind tunnel and ozone air treatment to control infectious viruses emitted from swine barns or quarantine buildings.Copyright © 2020 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 score0.108
Threshold uncertainty score0.123

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 studies0.0000.000
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 head0.029
GPT teacher head0.292
Teacher spread0.263 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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