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Record W4379054197 · doi:10.1038/s41598-023-36030-6

Electromagnetic deactivation spectroscopy of human coronavirus 229E

2023· article· en· W4379054197 on OpenAlexafffund
Hayden Banting, Ian Goode, Carla E. Gallardo Flores, Che C. Colpitts, Carlos E. Saavedra

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicInfrared Thermography in Medicine
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de la Défense NationaleQueen's UniversityGovernment of Canada
KeywordsCoronavirus disease 2019 (COVID-19)SpectroscopyCoronavirus2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyChemistryPhysicsBiologyMedicineInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

An investigation of the deactivation of pathogens using electromagnetic waves in the microwave region of the spectrum is achieved using custom-built waveguide structures. The waveguides feature sub-wavelength gratings to allow the integration of an air cooling system without disturbing the internal propagating fields. The waveguides are tapered to accommodate an experimental sample internally with sufficient surrounding airflow. The proposed methodology allows for precise control over power densities due to the well-defined fundamental mode excited in each waveguide, in addition to temperature control of the sample due to microwave exposure over time. Human coronavirus (HCoV-229E) is investigated over the 0-40 GHz range, where a peak 3-log viral reduction is observed in the 15.0-19.5 GHz sub-band. We conclude HCoV-229E has an intrinsic resonance in this range, where nonthermal structure damage is optimal through the structure-resonant energy transfer effect.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.025
GPT teacher head0.322
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2023
Admission routes2
Has abstractyes

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