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Record W2022964359 · doi:10.1637/8841-040309-reg.1

Studying Possible Cross-Protection of Canada Geese Preexposed to North American Low Pathogenicity Avian Influenza Virus Strains (H3N8, H4N6, and H5N2) Against an H5N1 Highly Pathogenic Avian Influenza Challenge

2010· article· en· W2022964359 on OpenAlexafffundabout
Yohannes Berhane, Marsha K. Leith, Carissa Embury‐Hyatt, James Neufeld, Shawn Babiuk, Tamiko Hisanaga, Helen Kehler, Kathleen Hooper‐McGrevy, John Pasick

Bibliographic record

VenueAvian Diseases · 2010
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsCanadian Food Inspection Agency
FundersCanadian Food Inspection Agency
KeywordsInfluenza A virus subtype H5N1BiologyVirologyVirusH5N1 genetic structurePathogenicityMicrobiologyCoronavirus disease 2019 (COVID-19)MedicinePathology

Abstract

fetched live from OpenAlex

Highly pathogenic avian influenza (HPAI) H5N1 virus infections have caused unprecedented morbidity and mortality in different species of domestic and wild birds in Asia, Europe, and Africa. In our previous study, we demonstrated the susceptibility and potential epidemiologic importance of H5N1 HPAI virus infections in Canada geese. In this study, we investigated the potential of preexposure with North American lineage H3N8, H4N6, and H5N2 low pathogenicity avian influenza (LPAI) viruses to cross-protect Canada geese against a lethal H5N1 HPAI virus challenge. Based on our results, birds that were primed and boosted with an H5N2 LPAI virus survived a lethal H5N1 challenge. In contrast, only two of five birds from the H3N8 group and none of the birds preexposed to H4N6 survived a lethal H5N1 challenge. In vitro cell proliferation assays demonstrated that peripheral blood mononuclear cells collected from each group were no better stimulated by homologous vs. heterologous antigens.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

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 head0.044
GPT teacher head0.329
Teacher spread0.285 · 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 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

Citations42
Published2010
Admission routes3
Has abstractyes

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