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Record W2107910947 · doi:10.1186/1743-422x-8-271

Molecular characterization and phylogenetic analysis of small ruminant lentiviruses isolated from Canadian sheep and goats

2011· article· en· W2107910947 on OpenAlexafffundabout
Yvan L’Homme, Mourad Ouardani, Valérie Lévesque, Giuseppe Bertoni, Carole Simard, G. Pisoni

Bibliographic record

VenueVirology Journal · 2011
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversité de MontréalCanadian Food Inspection Agency
FundersCanadian Food Inspection Agency
KeywordsFlockBiologyPhylogenetic treeLentivirusVirologyGenetic diversityPhylogeneticsVirusLivestockGeneticsVeterinary medicineViral diseaseGeneEcology

Abstract

fetched live from OpenAlex

BACKGROUND: Small Ruminant Lentiviruses (SRLV) are widespread in Canadian sheep and goats and represent an important health issue in these animals. There is however no data about the genetic diversity of Caprine Arthritis Encephalitis Virus (CAEV) or Maedi Visna Virus (MVV) in this country. FINDINGS: We performed a molecular and phylogenetic analysis of sheep and goat lentiviruses from a small geographic area in Canada using long sequences from the gag region of 30 infected sheep and 36 infected goats originating from 14 different flocks. Pairwise DNA distance and phylogenetic analyses revealed that all SRLV sequences obtained from sheep clustered tightly with prototypical Maedi visna sequences from America. Similarly, all SRLV strains obtained from goats clustered tightly with prototypical US CAEV-Cork strain. CONCLUSIONS: The data reported in this study suggests that Canadian and US SRLV strains share common origins. In addition, the molecular data failed to bring to light any evidence of past cross species transmission between sheep and goats, which is consistent with the type of farming practiced in this part of the country where single species flocks predominate and where opportunities of cross species transmissions are proportionately low.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.651
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.016
GPT teacher head0.215
Teacher spread0.199 · 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

Citations38
Published2011
Admission routes3
Has abstractyes

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