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Record W4256659354 · doi:10.31219/osf.io/mjzyf

Relationships between Influenza viruses A and B and Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) by sequence homologies

2020· preprint· en· W4256659354 on OpenAlexaff
Makiko TAKECHI, Shinya Nagasaki, Kibo Nagasaki

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVirologyCoronavirusVirusBiologyPandemicGeneH5N1 genetic structureInfluenza A virusCoronavirus disease 2019 (COVID-19)MedicineGeneticsInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Background: While the severe acute respiratory syndrome coronavirus (SARS-CoV-2) has been spreading and sweeping the world, the number of patients with influenza was much smaller than that in the past years. It is suspected that the coronavirus is not really a new virus that humans experience for the first time. From the aspect of view of gene comparison of influenza A and B viruses, and coronavirus, they are encoded of identified homologous regions. Methods: The gene sequences of influenza A and B viruses, and coronavirus were identified using data from the National Center for Biotechnology Information (NCBI; Bethesda, MD, USA).Results: Relatively high homology in amino acid sequences between SARS-CoC-2 and influenza viruses, especially influenza B virus was identified. Coronavirus CDS 9 and CDS 11 encodes proteins that are homologous to proteins of influenza A or B viruses. The results suggest the possibility that antivirals in development or already in use for the medicine treatment of influenza can be effective against coronavirus. This may also suggest the possibility that individuals who experienced an influenza A or B infection within the past 1–2 years would have some immunity to coronavirus.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.453
GPT teacher head0.460
Teacher spread0.007 · 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 designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

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