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Record W4417178459 · doi:10.31949/agrivet.v13i2.16390

Prevalensi brucellosis pada ternak di jawa tengah: evaluasi metode rose bengal test dan complement fixation test di balai besar veteriner wates, yogyakarta (2024–2025)

2025· article· W4417178459 on OpenAlexaff
Ahmad Alifudin, Faruq Iskandar, Roisu Eny Mudawaroch

Bibliographic record

VenueAgrivet Jurnal Ilmu-Ilmu Pertanian dan Peternakan (Journal of Agricultural Sciences and Veteriner) · 2025
Typearticle
Language
FieldVeterinary
TopicBrucella: diagnosis, epidemiology, treatment
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsComplement fixation testBrucellosisLivestockAnimal healthBovine brucellosisDiagnostic test

Abstract

fetched live from OpenAlex

Brucellosis is a strategic zoonotic disease that significantly affects animal health, human health, and the livestock economy. This study aimed to evaluate the effectiveness of the Rose Bengal Test (RBT) as a screening method and the Complement Fixation Test (CFT) as a confirmatory diagnostic tool using serum samples collected from various districts and cities in Central Java. The research was conducted at the Regional Veterinary Laboratory (Balai Besar Veteriner) in Wates, Yogyakarta, from April to June 2025. A total of 3,393 serum samples were examined. The RBT results showed a higher number of reactive cases compared to the CFT outcomes; however, several reactive samples were not confirmed as positive upon retesting with the CFT. Boyolali recorded the highest number of confirmed cases (12 cases), followed by Kudus (6 cases), Batang (2 cases), and Banyumas (1 case). The findings demonstrate that RBT serves as a rapid and practical screening tool, yet confirmation through CFT remains essential to ensure diagnostic accuracy. These results emphasize the need for combining laboratory diagnostic methods with strict biosecurity implementation and livestock monitoring, particularly in densely populated farming areas.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0010.002
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 head0.047
GPT teacher head0.324
Teacher spread0.277 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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