MétaCan
Menu
Back to cohort
Record W2469086269 · doi:10.1590/0103-8478cr20151098

A cross-sectional study to estimate the frequency of anti-bovine viral diarrhea virus-1 antibodies in domestic pigs of Mossoró region in the state of Rio Grande do Norte, Brazil

2016· article· en· W2469086269 on OpenAlexaff
Igor Renan Honorato Gatto, Andréia G. Arruda, Henrique Meiroz de Souza Almeida, Glaucenyra Cecília Pinheiro da Silva, Alexandro Íris Leite, Samir Issa Samara, Iveraldo S. Dutra, Renato Akio Ogata, Luís Guilherme de Oliveira

Bibliographic record

VenueCiência Rural · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsUniversity of Guelph
FundersUniversidade Estadual PaulistaConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsHerdLogistic regressionTiterVeterinary medicineAntibodyVirusDiarrheaAntibody titerBiologyAnimal scienceVirologyMedicineImmunologyInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT: This study investigated the occurrence of antibodies for BVDV-1 in swine herds located in the region of Mossoró city of the state of Rio Grande do Norte, Brazil. A sample size of 412 animals was estimated assuming unknown prevalence (set at 50%). Virus neutralization assay was used to the detect the presence of antibodies for BVDV-1 and the results found were analysed using multivariable logistic regression model. The obtained prevalence was 4% at animal level and 45% at the animal and herd level. The titers were highly variable between animals and within farms. The multivariable logistic regression analysis showed an association between being housed outside and exposure to BVDV-1 (OR=0.24, 95% CI:0.06, 0.96, P=0.04). Highly correlated data and low prevalence of antibodies at the animal level resulted in insufficient power to detect significant differences with other selected risk factors. In conclusion, the prevalence is within the range reported for other 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.481

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 head0.023
GPT teacher head0.314
Teacher spread0.290 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCiência RuralSame topicAnimal Disease Management and EpidemiologyFrench-language works237,207