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Record W2506858452 · doi:10.1016/j.onehlt.2016.07.003

Evidence of canine parvovirus transmission to a civet cat (Paradoxurus musangus) in Singapore

2016· article· en· W2506858452 on OpenAlexfundno aff
Ian H. Mendenhall, Dolyce H. W. Low, Erica Sena Neves, Ali Anwar, Serena Oh, Yvonne C. F. Su, Gavin J. D. Smith

Bibliographic record

VenueOne Health · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsnot available
FundersMinistry of Health, British ColumbiaDuke-NUS Medical School
KeywordsBiologyCanine parvovirusVirologyCATSParvovirusZoologyTransmission (telecommunications)EnzooticOutbreakCladeWildlifeVirusGenotypePhylogenetic treeEcologyGeneticsMedicine

Abstract

fetched live from OpenAlex

Cross-species transmission can often lead to deleterious effects in incidental hosts. Parvoviruses have a wide host range and primarily infect members of the order Carnivora. Here we describe juvenile common palm civet cats ( Paradoxurus musangus ) that were brought to the Singapore zoo and fell ill while quarantined. The tissues of two individual civets that died tested PCR-positive for parvovirus infection. Phylogenetic analysis revealed this parvovirus strain falls in a basal position to a clade of CPV that have infected dogs in China and Uruguay, suggesting cross-species transmission from domestic to wild animals. Our analysis further identified these viruses as genotype CPV-2a that is enzootic in carnivores. The ubiquity of virus infection in multiple tissues suggests this virus is pathogenic to civet cats. Here we document the cross-species transmission from domestic dogs and cats to wild civet populations, highlighting the vulnerability of wildlife to infectious agents in companion animals.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.070
GPT teacher head0.371
Teacher spread0.301 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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