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Record W2146969759 · doi:10.1080/15321810701603443

Prototype Single Step Lateral Flow Technology for Detection of Avian Influenza Virus and Chicken Antibody to Avian Influenza Virus

2007· article· en· W2146969759 on OpenAlexafffund
Kent Jacob Nielsen, Yu Wang, Mei-Jun Lin, Sheree Davis, C. Elmgren, Ross MacKenzie, Jamshid Tanha, Shangjie Li, G. Dubuc, E. G. Brown, Liya Keleta, John Pasick

Bibliographic record

VenueJournal of Immunoassay and Immunochemistry · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsUniversity of OttawaInstitute for Biological SciencesCanadian Food Inspection Agency
FundersCanadian Institutes of Health ResearchCanadian Food Inspection Agency
KeywordsAvian influenza virusInfluenza A virus subtype H5N1VirologyVirusInfluenza A virusAntibodyBiologyImmunology

Abstract

fetched live from OpenAlex

A rapid and effective lateral flow assay (LFA) for detection of avian influenza virus (AIV) was developed. For antigen capture, the assay used monoclonal antibody specific for a conserved nuclear protein (NP) epitope, immobilized on a cellulose acetate matrix, in conjunction with a second NP monoclonal antibody chemically linked to either coloured latex beads or colloidal gold particles contained in a sample pad for detection. Virus sample added to the sample pad flowed into the trapping antibody to form a visible band as well as a second, control band further along the acetate strip. The control band consisted of recombinant protein A/G, also immobilized on the matrix. A second LFA for detection of chicken antibody to AIV was developed where NP antigen was immobilized on the matrix with recombinant protein A/G immobilized as a control band. Latex beads or colloidal gold particles to which monoclonal anti-chicken antibody was attached, were used as the indicator system.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.275
Teacher spread0.257 · 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 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

Citations17
Published2007
Admission routes2
Has abstractyes

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