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Record W2101147061 · doi:10.1177/104063870101300416

Application of Competitive Enzyme-Linked Immunosorbent Assay for the Serologic Diagnosis of Classical Swine Fever Virus Infection

2001· article· en· W2101147061 on OpenAlexaffabout
Alfonso Clavijo, Min Lin, J. Riva, En‐Min Zhou

Bibliographic record

VenueJournal of Veterinary Diagnostic Investigation · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsCanadian Science Centre for Human and Animal Health
Fundersnot available
KeywordsClassical swine feverSerologyVirologyAntibodyPestivirusVirusOutbreakRecombinant DNABiologyMedicineImmunologyViral diseaseFlaviviridaeGene

Abstract

fetched live from OpenAlex

A competitive enzyme-linked immunosorbent assay (C-ELISA), based on a truncated E2 recombinant protein of the Alfort/187 strain of classical swine fever virus (CSFV) and a specific monoclonal antibody M1669, was evaluated using 2,000 sera from clinically healthy pigs in Canada (a CSFV-free country) and sera from experimentally infected pigs. The relative specificity and sensitivity of the C-ELISA were 100% and 86%, respectively, at a cutoff of 25% inhibition using negative and positive pig sera, as defined by the neutralizing peroxidase-linked assay (NPLA). A kappa value of 0.91 was obtained, indicating an excellent level of agreement between the NPLA and the C-ELISA. When sera from 120 infected pigs were used in the test at > or = 21 days postinfection, the sensitivity of the C-ELISA and the kappa value increased to 97% and 0.98, respectively. This C-ELISA will be useful when a large number of samples must be tested, as could occur during a disease outbreak or for surveillance or prevalence studies.

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)0.002
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 score0.404
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.056
GPT teacher head0.282
Teacher spread0.226 · 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

Citations19
Published2001
Admission routes2
Has abstractyes

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