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Record W2884750589 · doi:10.1128/jvi.00913-18

Genetic Evidence Supports Sporadic and Independent Introductions of Subtype H5 Low-Pathogenic Avian Influenza A Viruses from Wild Birds to Domestic Poultry in North America

2018· article· en· W2884750589 on OpenAlexaboutno aff
Lei Li, Andrew S. Bowman, Thomas J. DeLiberto, Mary Lea Killian, Scott Krauss, Jacqueline M. Nolting, Mia Kim Torchetti, Andrew M. Ramey, Andrew B. Reeves, David E. Stallknecht, Richard J. Webby, Xiu‐Feng Wan

Bibliographic record

VenueJournal of Virology · 2018
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Institutes of Health
KeywordsReassortmentBiologyBiosecurityInfluenza A virus subtype H5N1Highly pathogenicVirusInfluenza A virusVirologyGenomeGeneticsEcologyGeneCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Integration of viral genome sequencing into influenza surveillance for wild birds and domestic poultry can elucidate evolutionary pathways of economically costly poultry pathogens. Evolutionary analyses of H5 LPAIVs detected in domestic poultry in the United States and Canada during 2001 to 2017 suggest that these viruses originated from repeated introductions of IAVs from wild birds, followed by various degrees of reassortment. Reassortment was observed where biosecurity was low and where opportunities for more than one virus to circulate existed (e.g., congregations of birds from different premises, such as live-bird markets). None of the H5 lineages identified were maintained for the long term in domestic poultry, suggesting that management strategies have been effective in minimizing the impacts of virus introductions on U.S. poultry production.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.957
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.046
GPT teacher head0.368
Teacher spread0.322 · 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

Citations43
Published2018
Admission routes1
Has abstractyes

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