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Record W2795622894 · doi:10.1128/jvi.00515-18

Ortervirales: New Virus Order Unifying Five Families of Reverse-Transcribing Viruses

2018· letter· en· W2795622894 on OpenAlexaff
Mart Krupovìč, Jonas Blomberg, John M. Coffin, Indranil Dasgupta, Hung Fan, Andrew D. W. Geering, Robert J. Gifford, Balázs Harrach, Roger Hull, Welkin E. Johnson, Jan Kreuze, Dirk Lindemann, Carlos Lloréns, Ben Lockhart, Jens Mayer, Emmanuelle Muller, Neil E. Olszewski, H. R. Pappu, Mikhail M. Pooggin, Katja R. Richert‐Pöggeler, Sead Sabanadzovic, Hélène Sanfaçon, James E. Schoelz, Susan Seal, Livia Stavolone, Jonathan P. Stoye, Pierre‐Yves Teycheney, Michael Tristem, Eugene V. Koonin, Jens H. Kuhn

Bibliographic record

VenueJournal of Virology · 2018
Typeletter
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesU.S. Department of Health and Human ServicesNational Cancer InstituteAgence Nationale de la RechercheFrancis Crick Institute
KeywordsBiologyRNAVirologyVirusDNAGenomegenomic DNAGeneticsGiant VirusReverse transcriptaseComputational biologyGene

Abstract

fetched live from OpenAlex

International audience

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)0.002
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: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0030.002

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 head0.073
GPT teacher head0.288
Teacher spread0.215 · 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
GenreOther

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

Citations118
Published2018
Admission routes1
Has abstractyes

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