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Record W2024538399 · doi:10.7589/0090-3558-48.3.768

Antibodies to Influenza A Virus in Wild Birds across Mongolia, 2006–2009

2012· article· en· W2024538399 on OpenAlexaff
Farah Ishtiaq, Martin Gilbert, Justin D. Brown, Priscilla H. Joyner, Ruuragchaa Sodnomdarjaa, M. Page Luttrell, David E. Stallknecht, Damien O. Joly

Bibliographic record

VenueJournal of Wildlife Diseases · 2012
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsWildlife Conservation Society Canada
FundersNational Institute of Allergy and Infectious Diseases
KeywordsAnseriformesCharadriiformesBiologyWaterfowlAntibodyVeterinary medicineZoologyVirusInfluenza A virus subtype H5N1Influenza A virusVirologyEcologyImmunology

Abstract

fetched live from OpenAlex

Wild waterbirds sampled July 2006-September 2009 in Mongolia were tested for antibodies to avian influenza (AI) virus with the use of a commercially available blocking enzyme-linked immunosorbent assay. Antibodies were detected in 25% (572/2,282) of tested birds representing 26 species, and all antibody-positive samples were from 12 species in the orders Anseriformes and Charadriiformes. The highest antibody prevalence was in Ruddy Shelducks (Tadorna ferruginea; 61.7%; n=261; 95% confidence interval [CI] 55.8-67.6%), Whooper Swans (Cygnus cygnus; 38.4%; n=242; 95% CI 32.3-44.5%), Swan Geese (Anser cygnoides; 15%; n=127; 95% CI 8.6-21.4%), Bar-headed Geese (Anser indicus; 13%; n=738; 95% CI 10.3-15.1%), and Mongolian Gulls (Larus mongolicus; 3.9%; n=255; 95% CI 1.3-6.5%). There was no significant temporal or spatial variation in the presence of antibodies in the sampled species. However, Bar-headed Geese and Mongolian Gulls showed spatial variation in antibody prevalence in 2007 and 2008, respectively. Our study provides insights into the hatch year waterbirds' exposure to AI virus at their natal and molting sites in Mongolia.

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 score0.011
Threshold uncertainty score0.511

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 head0.028
GPT teacher head0.358
Teacher spread0.329 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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