Deteksi Larva Cacing Pada Sapi dengan Pola Pemeliharaan yang Berbeda di Wilayah Kabupaten Sumedang
Bibliographic record
Abstract
The aim of this research is to analyze differences in levels of digestive tract worm infections in cattle with different rearing patterns in the Sumedang Regency area. The research was carried out in January 2024 in the Paseh sub-district, Sumedang Regency. Worm larvae detection examinations were carried out at the Subang Veterinary Laboratory (B-VET), Jln. Garuda Canal, Werasari Block, Dangdeur, Subang District, Kab. Subang. The method in this research is descriptive observational carried out in the field and laboratory. The technique for collecting feces samples is carried out rectally, approximately 5 grams per cow, selecting samples based on simple random sampling. Fresh feces were put into 50 ml jars along with formalin to prevent eggs from hatching during transportation and storage. Each sample is given a label that includes the sample code and age information. After that, the samples are carried using a coolbox from the sampling location until they are examined in the laboratory. The research results showed that the prevalence pattern of worm infections in semi- intensive rearing was higher than in intensive rearing. The types of worms identified are Nematoda, Trematoda and Protozoa. Different maintenance patterns (Semi-intensive and Intensive) in Paseh District are classified as mild infections (1 – 156 epg).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.001 |
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".