MétaCan
Menu
Back to cohort
Record W3015490153 · doi:10.1371/journal.pone.0231164

Ozone efficacy for the control of airborne viruses: Bacteriophage and norovirus models

2020· article· en· W3015490153 on OpenAlexafffund
Marie-Ève Dubuis, Nathan Dumont-Leblond, Camille Laliberté, Marc Veillette, Nathalie Turgeon, Julie Jean, Caroline Duchaine

Bibliographic record

VenuePLoS ONE · 2020
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsMinistère de l'Agriculture, des Pêcheries et de l'AlimentationUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
KeywordsNorovirusMurine norovirusBacteriophage MS2InfectivityOzoneDisinfectantBacteriophageVirus quantificationVirus inactivationRelative humidityMicrobiologyVirusVirologyBiologyChemistry

Abstract

fetched live from OpenAlex

This study was designed to test the efficacy of an air treatment using ozone and relative humidity (RH) for the inactivation of airborne viruses. Four phages (φX174, PR772, MS2 and φ6) and one eukaryotic virus (murine norovirus MNV-1) were exposed to low ozone concentrations (1.23 ppm for phages and 0.23 ppm for MNV-1) and various levels of RH for 10 to 70 minutes. The inactivation of these viruses was then assessed to determine which of the tested conditions provided the greatest reduction in virus infectivity. An inactivation of at least two orders of magnitude for φX174, MS2 and MNV-1 was achieved with an ozone exposure of 40 minutes at 85% RH. For PR772 and φ6, exposure to the reference condition at 20% RH for 10 minutes yielded the same results. These findings suggest that ozone used at a low concentration is a powerful disinfectant for airborne viruses when combined with a high RH. Air treatment could therefore be implemented inside hospital rooms ventilated naturally.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.142
GPT teacher head0.303
Teacher spread0.161 · 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

Citations139
Published2020
Admission routes2
Has abstractyes

Explore more

Same venuePLoS ONESame topicViral gastroenteritis research and epidemiologyFrench-language works237,207