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Record W4362586263 · doi:10.3390/v15040915

Characterization of a Novel African Swine Fever Virus p72 Genotype II from Nigeria

2023· article· en· W4362586263 on OpenAlexaff
Aruna Ambagala, Kalhari Goonewardene, Lindsey Lamboo, Melissa Goolia, Cassidy N. G. Erdelyan, Mathew Fisher, Katherine Handel, Oliver Lung, Sandra Blome, Jacqueline King, Jan H. Forth, Sten Calvelage, Edward Spinard, Douglas P. Gladue, Charles Masembe, Adeyinka Adedeji, Toyin Olubade, Nanven A. Maurice, Hussaini Ularamu, Pam Dachung Luka

Bibliographic record

VenueViruses · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsUniversity of ManitobaCanadian Food Inspection Agency
Fundersnot available
KeywordsAfrican swine fever virusOutbreakVirologyBiologyGenotypeVirusGenomeGeneGenetics

Abstract

fetched live from OpenAlex

African swine fever (ASF) is a high-consequence transboundary hemorrhagic fever of swine. It continues to spread across the globe causing socio-economic issues and threatening food security and biodiversity. In 2020, Nigeria reported a major ASF outbreak, killing close to half a million pigs. Based on the partial sequences of the genes B646L (p72) and E183L (p54), the virus responsible for the outbreak was identified as an African swine fever virus (ASFV) p72 genotype II. Here, we report further characterization of ASFV RV502, one of the isolates obtained during the outbreak. The whole genome sequence of this virus revealed a deletion of 6535 bp between the nucleotide positions 11,760-18,295 of the genome, and an apparent reverse complement duplication of the 5' end of the genome at the 3' end. Phylogenetically, ASFV RV502 clustered together with ASFV MAL/19/Karonga and ASFV Tanzania/Rukwa/2017/1 suggesting that the virus responsible for the 2020 outbreak in Nigeria has a South-eastern African origin.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.071
GPT teacher head0.261
Teacher spread0.190 · 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 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

Citations34
Published2023
Admission routes1
Has abstractyes

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