Nilai Pemanfaatan Limbah Kelapa Sawit untuk Pakan Ternak (Studi Kasus Pakan Ruminasia)
Bibliographic record
Abstract
The issue of feed has a large proportion of the sustainability of livestock businesses. On the other hand, palm fronds as post-harvest agricultural waste and bran as post-processing waste have not been utilized optimally. Even though the nutritional content contained in it is quite high. Agricultural and plantation waste by-products have good potential for use as animal feed. In order to guarantee safety and suitability, further processing and analysis is needed. Processing of raw materials is carried out in a combination (physical, chemical and biological). Preparation of feed formulations using Pearson's Quadrilateral Method. Testing is carried out in laboratories that consistently apply ISO/IEC 17025: 2017 from the National Accreditation Committee (KAN), namely the East Java Province Animal Husbandry Service. The standard test results refer to SNI 3148-2:2017 concerning Concentrate Feed for Fattening Beef Cattle. Ruminant animal feed that meets SNI 3148-2:2017 includes levels of ash, crude fat, phosphorus, TDN, aflatoxin and crude fiber. Meanwhile, the nutritional content that is close to SNI is crude protein and aNDF. The test results also showed that the content did not meet SNI, including water and calcium content. Meanwhile, fish feed (pellets) that meet SNI 01-4087-2006 contain 3 types of content including ash, crude fat and aflatoxin. The types of content that do not meet SNI are crude protein, crude fiber, phosphorus, buoyancy and pellet diameter. The estimated cost of making ruminant feed is IDR 5,986 per kilogram. Cost analysis is relevant to feed quality and daily requirements without reducing nutritional value. Further research is needed to ensure that artificial feed is more cost efficient than conventional feed.
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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) | 0.015 | 0.003 |
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".