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Record W2065598518 · doi:10.2478/acve-2014-0005

Performance of assays for testing antibodies against porcine reproductive and respiratory syndrome virus in sera collected from swine farms in a region with an extreme virus heterogeneity

2014· article· en· W2065598518 on OpenAlexaff
Uladzimir Karniychuk, Hans Nauwynck

Bibliographic record

VenueActa veterinaria · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsMcMaster University
FundersEuropean Commission
KeywordsPorcine reproductive and respiratory syndrome virusSerologyVirologyBiologyVirusGenotypeAntibodySerotypeClassical swine feverImmunologyGeneGenetics

Abstract

fetched live from OpenAlex

Abstract Porcine reproductive and respiratory syndrome is the most economically important viral disease in the swine industry worldwide. Porcine reproductive and respiratory syndrome virus (PRRSV) strains are classified into two distinct genotypes, the European genotype and the North American genotype. The European PRRSV genotype has been divided into three subtypes: a pan-European subtype 1 and East European subtypes 2 and 3. The aim of this study was to evaluate the performance of commercial and homemade serological assays to test field sera from a geographical region with an extreme PRRSV heterogeneity. Belarus became the country of choice for sample collection because heterologous PRRSV strains of all known European subtypes circulate in this country. Sera from Belarusian swine farms were tested in immunoperoxidase monolayer assays based on pan-European subtype 1, East European subtype 3 and North American strains as antigens and commercial enzyme-linked immunosorbent assays (IDEXX and INGEZIM). The obtained results suggest that none of the serological tools for PRRSV diagnosis can guarantee a flawless detection of antibodies at the individual animal level. Considering heterogeneity of recently isolated European PRRSV strains the problem can be relevant in many countries.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score1.000

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 head0.090
GPT teacher head0.257
Teacher spread0.167 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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