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Record W1988047282 · doi:10.1099/vir.0.045930-0

Genomic reassortment of influenza A virus in North American swine, 1998–2011

2012· article· en· W1988047282 on OpenAlexaff
Martha I. Nelson, Susan E. Detmer, David E. Wentworth, Yi Tan, Aaron Schwartzbard, Rebecca Halpin, Timothy B. Stockwell, Xudong Lin, Amy L. Vincent, Marie Gramer, Edward C. Holmes

Bibliographic record

VenueJournal of General Virology · 2012
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of Saskatchewan
FundersFogarty International CenterNational Institute of Allergy and Infectious DiseasesU.S. Department of AgricultureU.S. Public Health ServiceNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsReassortmentBiologyNeuraminidaseVirologyGenomePhylogenetic treeGeneticsAntigenic shiftViral phylodynamicsInfluenza A virusVirusGeneEvolutionary biologyAntigenic driftCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

Revealing the frequency and determinants of reassortment among RNA genome segments is fundamental to understanding basic aspects of the biology and evolution of the influenza virus. To estimate the extent of genomic reassortment in influenza viruses circulating in North American swine, we performed a phylogenetic analysis of 139 whole-genome viral sequences sampled during 1998-2011 and representing seven antigenically distinct viral lineages. The highest amounts of reassortment were detected between the H3 and the internal gene segments (PB2, PB1, PA, NP, M and NS), while the lowest reassortment frequencies were observed among the H1γ, H1pdm and neuraminidase segments, particularly N1. Less reassortment was observed among specific haemagglutinin-neuraminidase combinations that were more prevalent in swine, suggesting that some genome constellations may be evolutionarily more stable.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.060
GPT teacher head0.364
Teacher spread0.305 · 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 designObservational
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

Citations48
Published2012
Admission routes1
Has abstractyes

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