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Record W3163882661 · doi:10.1089/vbz.2020.2736

Sequences of Previously Unknown Rhabdoviruses Detected in Bat Samples from the Republic of the Congo

2021· article· en· W3163882661 on OpenAlexaff
Kenneth Neill Cameron, Fabien Roch Niama, Ben Hayes, Placide Mbala, Sarah H. Olson, Jean Michel Takuo, Alain Ondzie, Joseph Le Doux Diffo, Brett R. Smith, Jasmine Pante, Anne Laudisoit, Matthew LeBreton, Ubald Tamufe, Maria Makuwa, Damien O. Joly, Tracey Goldstein, Jean Jacques Muyembe Tamfum, Romain Bagamboula MPassi, Christian E. Lange

Bibliographic record

VenueVector-Borne and Zoonotic Diseases · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicRabies epidemiology and control
Canadian institutionsMinistry of Environment
Fundersnot available
KeywordsRhabdoviridaeBiologyRabies virusRabiesVirologyPhylogenetic treeLyssavirusVirus classificationMononegaviralesZoologyVirusParamyxoviridaeGeneticsViral diseaseGenomeGene

Abstract

fetched live from OpenAlex

contains diverse viruses, including vector-borne and nonvector-borne viruses, some that are human pathogens, including rabies virus and also nonpathogenic viruses. Bats, which are a known reservoir of viruses with zoonotic potential including coronaviruses, also carry multiple rhabdoviruses such as but not limited to lyssaviruses. We collected samples from 193 insectivorous and frugivorous bats in the Republic of the Congo and tested them for rhabdovirus RNA. Four samples were found positive for viral RNA representing sequences of four different, not previously described rhabdoviruses. Although phylogenetic and taxonomic placement of the novel sequences is uncertain, similarities with previously detected rhabdovirus sequences in bats suggest that these could represent vertebrate viruses. Considering the pathogenic risks some rhabdoviruses pose for humans, these results highlight the need for more research and surveillance regarding rhabdoviruses and bats.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.127
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.018
GPT teacher head0.235
Teacher spread0.217 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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