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Record W1970023765 · doi:10.7589/0090-3558-48.2.444

Bait Trapping Linked to Higher Avian Influenza Virus Detection in Wild Ducks

2012· article· en· W1970023765 on OpenAlexaffabout
Catherine Soos, E. Jane Parmley, Keith McAloney, Bruce Pollard, Emily Jenkins, Fred Kibenge, Frederick A. Leighton

Bibliographic record

VenueJournal of Wildlife Diseases · 2012
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of Prince Edward IslandUniversity of GuelphUniversity of SaskatchewanEnvironment and Climate Change Canada
Fundersnot available
KeywordsBiologyWaterfowlInfluenza A virus subtype H5N1NettingVirologyCloacaTransmission (telecommunications)Avian influenza virusVeterinary medicineTrappingVirusZoologyEcologyHabitat

Abstract

fetched live from OpenAlex

In 2007, we assessed whether trapping method influenced apparent prevalence of low pathogenic avian influenza viruses (AIV) in wild ducks sampled during Canada's Inter-agency Wild Bird Influenza Survey. Combined cloacal and oropharyngeal swabs were collected from 514 ducks captured by bait trapping (356) and netting from airboats (158), and tested by real-time reverse transcriptase polymerase chain reaction for influenza type A viruses. When controlling for species and capture site, ducks caught in bait traps were 2.6 times more likely to test positive for AIV compared with those netted from airboats (95% CI=1.2-6.0). If bait trapping increases AIV transmission among artificially aggregated ducks, this could have important implications for interpretation of disease surveillance results and waterfowl management programs.

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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.071
GPT teacher head0.373
Teacher spread0.301 · 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

Citations13
Published2012
Admission routes2
Has abstractyes

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