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Record W3047491149 · doi:10.1016/j.meegid.2020.104491

Molecular phylogeny of sturgeon mimiviruses and Bayesian hierarchical modeling of their effect on wild Lake Sturgeon (Acipenser fulvescens) in Central Canada

2020· article· en· W3047491149 on OpenAlexafffundabout
Sharon C. Clouthier, Amanda Caskenette, Elissa Van Walleghem, Tamara Schroeder, Don MacDonald, Eric Anderson

Bibliographic record

VenueInfection Genetics and Evolution · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsGovernment of ManitobaFisheries and Oceans Canada
FundersFisheries and Oceans CanadaDairy Farmers of OntarioManitoba Hydro
KeywordsLake sturgeonSturgeonAcipenserBiologyPopulationFisheryEcologyZoologyDemographyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Sturgeon mimiviruses can cause a lethal disease of the integumentary systems of sturgeon (Acipenseridae). Here we provide phylogeographic evidence that sturgeon mimivirus is endemic in endangered populations of wild Lake Sturgeon within Canada's Hudson Bay drainage basin. Namao virus (NV) variants were diagnosed in 24% of Lake Sturgeon samples (n = 1329) collected between 2010–2015. Lake Sturgeon populations with the highest virus prevalence were from the Nelson River (58%) in 2015, Saskatchewan River (41%) in 2010 and South Saskatchewan River (36%) in 2011. Bayesian phylogenetic reconstructions suggested that four NV variants, designated HBDB I-IV, co-circulate temporally and spatially within and between the genetically and biogeographically distinct Lake Sturgeon populations. Evidence from recapture studies suggested that Lake Sturgeon across the basin are persistently infected with NV at prevalence and titer (103.6 equivalent plasmid copies per μg DNA) levels consistent with the hypothesis that wild Lake Sturgeon populations serve as a maintenance population and reservoir for sturgeon mimiviruses. Bayesian hierarchical modeling of NV in the Landing River population of Lake Sturgeon suggested that host weight and age were the best predictors of sturgeon mimivirus presence and titer, respectively, whereas water flow rate, level and temperature, and number of previous captures did not significantly improve model fit. A negative relationship was estimated between sturgeon mimivirus presence and Lake Sturgeon weight and between virus titer and Lake Sturgeon age.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.977

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)0.0000.000

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 head0.011
GPT teacher head0.226
Teacher spread0.216 · 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 designBench or experimental
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

Citations12
Published2020
Admission routes3
Has abstractyes

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