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Record W7075359460

Special Issue: Emerging Wildlife Viral Diseases

2022· article· en· W7075359460 on OpenAlexaboutno aff

Bibliographic record

VenueResearchOnline at James Cook University (James Cook University) · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeWildlife tradeEbola virusEmerging infectious diseaseWest Nile virusHost (biology)MonkeypoxHabitatDisease reservoirPandemicZoonosis
DOInot available

Abstract

fetched live from OpenAlex

[Extract] The past several decades have seen the emergences of novel viral infectious diseases increase steadily in wildlife populations globally [1,2,3,4,5,6,7,8]. Emerging viral diseases are acknowledged as an apparently growing trend of threats to wildlife and act as the source of a series of high-impact diseases recently emerging as pathogens affecting humans [9]. Most emerging viral pathogens, including Ebola and Marburg virus, human immunodeficiency virus virus-1 and -2, Nipah, Sin Nombre virus, Hendra and Menangle virus, West Nile virus, Middle East respiratory syndrome, and different subtypes of avian influenza, originate in wildlife and spill over into human hosts due to a range of ecological, demographic, and socio-economic changes [10]. Diseases caused by viruses, recently exemplified by the spread of SARS-CoV-2 (COVID-19) to human populations, also threaten wild animals from amphibians to mammals [8]. Habitat destructions, pollution, and international trade are among the factors contributing to a growing opportunity for viruses to spread to new hosts and cause disease. Three independent studies deal with the emergency of novel viruses such as the protoparvovirus [11], a number of mongoose-associated circoviruses and cycloviruses [12], and a poxvirus, cheloniid poxvirus 1 (ChePV-1) [1]. Using various deployed approaches, the detected novel Newlavirus, a protoparvovirus of foxes in Newfoundland and Labrador, was shown to be 38.7–54.1% identical to the members of the genus Protoparvovirus, compared to NS1 proteins [11]. Although some aspects of novel protoparvovirus ecology were examined, future studies will require the assessment of the host specificity and its geographical distribution. In a study on Circoviridae [12], 76 of 83 of apparently healthy small Indian mongooses were investigated using PCR and determined complete genomes of three mongoose-associated circoviruses and six cycloviruses. Although the detected circoviruses in this study show a strong phylogenetic congruence with other animal and human origin circoviruses, it is unknown whether the detected circoviruses/cycloviruses have the ability to replicate in the mongoose host or are derived from dietary origin during prey–predator interaction [12].

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)0.002
metaresearch head score (Gemma)0.005
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.076
Threshold uncertainty score0.253

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0020.002
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0760.038

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 head0.026
GPT teacher head0.210
Teacher spread0.184 · 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
GenreEditorial

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

Citations0
Published2022
Admission routes1
Has abstractyes

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