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Record W3207281178 · doi:10.1128/spectrum.00537-21

Robust Evaluation of Ultraviolet-C Sensitivity for SARS-CoV-2 and Surrogate Coronaviruses

2021· article· en· W3207281178 on OpenAlexafffund
Scott Joseph Boegel, Merheb Gabriel, Michael Sasges, Brian Petri, Michael R. D’Agostino, A Zhang, Jann C. Ang, Matthew S. Miller, Solène M. Meunier, Marc G. Aucoin

Bibliographic record

VenueMicrobiology Spectrum · 2021
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsMcMaster University Medical CentreTrojan Technologies (Canada)University of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaMitacsGovernment of Canada
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakPandemicEnvironmental scienceWastewaterBetacoronavirusUltravioletCoronavirusCoronavirus InfectionsSensitivity (control systems)Environmental healthMedicineVirologyEnvironmental engineeringOutbreakMaterials scienceEngineeringPathology

Abstract

fetched live from OpenAlex

Disinfection of SARS-CoV-2 is of particular importance due to the global COVID-19 pandemic. UV-C irradiation is a compelling disinfection technique because it can be applied to surfaces, air, and water and is commonly used in drinking water and wastewater treatment facilities. UV inactivation depends on the dose received by an organism, regardless of the intensity of the light source or the optical properties of the medium in which it is suspended. The 254 nm irradiation sensitivity was accurately determined using benchmark methodology and a collimated beam apparatus for four coronaviruses (SARS-CoV-2, HCoV-229E, HCoV-OC43, and MHV), a surrogate indicator organism (T1UV), and a resistant recombinant virus (baculovirus vector). Considering the light distribution across the sample surface, the attenuation of light intensity with fluid depth, the optical absorbance of the fluid, and the sample uniformity due to mixing enable accurate measurement of the fundamental inactivation kinetics and UV sensitivity.

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)0.001
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 score0.072
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.075
GPT teacher head0.324
Teacher spread0.249 · 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

Citations30
Published2021
Admission routes2
Has abstractyes

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