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Record W2996608527 · doi:10.1099/jgv.0.001365

ICTV Virus Taxonomy Profile: Peribunyaviridae

2019· article· en· W2996608527 on OpenAlexaff
Holly R. Hughes, Scott Adkins, Sergey Alkhovskiy, Martin Beer, Carol D. Blair, Charles H. Calisher, Mike Drebot, Amy J. Lambert, William Marciel de Souza, Marco Marklewitz, Márcio Roberto Teixeira Nunes, Xiǎohóng Shí

Bibliographic record

VenueJournal of General Virology · 2019
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsPublic Health Agency of Canada
FundersWellcome Trust
KeywordsBiologyReassortmentVirologyVirus classificationTaxonomy (biology)Transmission (telecommunications)OrthobunyavirusVector (molecular biology)VirusZoologyGeneticsGeneInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

Peribunyaviruses are enveloped and possess three distinct, single-stranded, negative-sense RNA segments comprising 11.2–12.5 kb in total. The family includes globally distributed viruses in the genera Orthobunyavirus , Herbevirus , Pacuvirus and Shangavirus . Most viruses are maintained in geographically-restricted vertebrate–arthropod transmission cycles that can include transovarial transmission from arthropod dam to offspring. Others are arthropod-specific. Arthropods can be persistently infected. Human infection occurs through blood feeding by an infected vector arthropod. Infections can result in a diversity of human and veterinary clinical outcomes in a strain-specific manner. Segment reassortment is evident between some peribunyaviruses. This is a summary of the International Committee on Taxonomy of Viruses (ICTV) Report on the taxonomy of the family Peribunyaviridae , which is available at ictv.global/report/peribunyaviridae .

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.004

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 head0.021
GPT teacher head0.275
Teacher spread0.254 · 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 designNot applicable
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

Citations114
Published2019
Admission routes1
Has abstractyes

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