Milk Production and Quality of Murrah Buffalo Supplemented by Turmeric Powder and Casava Leaf in Agam Regency, Indonesia
Bibliographic record
Abstract
Buffalo farming in Indonesia is still managed traditionally due to low milk production and quality. Like in other developing countries, buffalo farming has becomea side business. Murrah Buffalo milk has better fat and protein content compared to dairy milk. Production and quality of buffalo milk affect farming management, such as keeping systems and feed management. The study aimed to reveal the effect of turmeric powder supplement and cassava leaf as forage on Murrah Buffalo in terms of milk production and quality (protein, fat, and lactose content). The study was conducted experimentally for four female Murrah Buffalo fed several formula feeds. The formula feed treatments are A (basal feed 100%), B = A + cassava leaf (1kg) + Turmeric Powder (0.015% Body weight), C = A + Cassava Leaf (1.5kg) + Turmeric Powder (0.030% Body Weight), D = A + Cassava Leaf (2kg) + Turmeric Powder (0.045% Body Weight). The result shows Milk production, protein, fat, and lactose is 5.40-7.91kg, 2.93-3.41%, 4.81-10.69%, and 4.39-5.11%, respectively. In summary, the best turmeric powder supplementation and Cassava leaf supply belong to treatment D, which significantly increases Murrah Buffalo milk production and quality.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".