Sebaran rumpun, pola warna bulu, dan jenis tanduk domba lokal betina di Kabupaten Bandung
Bibliographic record
Abstract
The variety of environmental and cultural conditions in each region in Indonesia causes differences in sheep rearing patterns, resulting in a variety of breeds, coat color patterns and types of horns of sheep being cultivated. The Bandung Regency area has a climate and environment that is suitable for raising livestock, so raising sheep has become a part of the culture in the area. Choosing flocks, coat color patterns and types of sheep horns that suit consumer needs can increase the selling value of sheep. The aim of this research was to determine the distribution of clump types, coat color patterns and horn types of local ewes in several animal markets in Bandung Regency. The research was conducted in June 2023 in three animal markets, namely Majalaya, Pacet, and Banjaran Animal Markets. The research method used is descriptive analytic and data collection uses the census method. Based on the research results, it can be concluded that the distribution of local ewes includes Garut sheep 86.78% and Priangan sheep 13.22%, while the distribution of local ewes's coat color patterns is dominated by white 63.64%, a combination of 25.62%, and black 10.74%, as well as muser horn types 54.55%, hornless 33.88%, and horned 11.57%. The total number of research objects was 121 animals, dominated by Garut sheep, the dominant white coat color pattern, and muser horn type
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".