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Record W1997018778 · doi:10.1099/vir.0.83038-0

Bayesian coalescent inference of hepatitis A virus populations: evolutionary rates and patterns

2007· article· en· W1997018778 on OpenAlexaff
Gonzalo Moratorio, Mauro Costa‐Mattioli, Rosina Piovani, Héctor Romero, Héctor Musto, Juan Cristina

Bibliographic record

VenueJournal of General Virology · 2007
Typearticle
Languageen
FieldMedicine
TopicHepatitis Viruses Studies and Epidemiology
Canadian institutionsMcGill University
FundersInternational Atomic Energy AgencyPennsylvania State University
KeywordsCoalescent theoryBiologyMarkov chain Monte CarloVirologyBayesian probabilityHepatitis a virusBayesian inferencePicornaviridaeInferenceGeneticsEvolutionary biologyVirusStatisticsPhylogeneticsPoliovirusGeneMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Hepatitis A virus (HAV) is a hepatotropic member of the family Picornaviridae. Previous studies suggested that HAV may evolve more slowly than other members of the family. To estimate HAV substitution rates precisely, we used a Bayesian Markov chain Monte Carlo (MCMC) approach on temporally sampled HAV VP1 full-length sequences from strains isolated in France. A mean rate of evolutionary change of 9.76 x 10(-4) nucleotide substitution per site per year was found. The results also revealed that the synonymous rate found for HAV is lower than that of other members of the family. Bayesian skyline plots revealed a sharp decline in the effective number of infections in 1996, coinciding with the introduction of HAV vaccine.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.048
GPT teacher head0.356
Teacher spread0.309 · 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

Citations34
Published2007
Admission routes1
Has abstractyes

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