Prevalensi brucellosis pada ternak di jawa tengah: evaluasi metode rose bengal test dan complement fixation test di balai besar veteriner wates, yogyakarta (2024–2025)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".