KADAR KOLESTEROL DAGING KERBAU \nDENGAN METODE PEMASAKAN YANG BERBEDA
Bibliographic record
Abstract
Tujuan penelitian ini adalah untuk mengetahui kadar kolesterol daging kerbau dengan \nmetode pemasakan yang berbeda. Penelitian ini akan dilaksanakan dengan dengan \nmenggunakan Rancangan Acak Lengkap (RAL) dengan taraf 4 perlakuan dan 5 ulangan. \nPerlakuan tersebut adalah kontrol (daging segar), pembakaran, perebusan dan pengukusan. \nPenelitian ini dilaksanakan di Laboratorium Teknologi Pasca Panen, Laboratorium Nutrisi \ndan Kimia, dan Laboratorium Patologi, Entomologi, dan Mikrobiologi. Jika pada analisis \nsidik ragam menunjukkan pengaruh nyata atau sangat nyata dilakukan uji lanjut Duncan’s \nMultiple Range Test (DMRT). Berdasarkan hasil penelitian kadar kolesterol daging kerbau \ndengan metode pemasakan yang berbeda berkisar antara 55,5273 mg/100g – 106,4841 \nmg/100g. Kadar koleseterol tertinggi terdapat pada daging segar sebesar 106,4841 mg/100g, \nmetode pembakaran 80,3703 mg/100g, perebusan 71,3305 mg/100g dan pengukusan adalah \npaling rendah yaitu 55,5273 mg/100g. Metode pemasakan yang berbeda pada daging kerbau \nmenunjukan adanya perbedaan nyata (P<0,05) pada daging segar, metode pembakaran, \nmetode perebusan dan metode pengukusan. Dari hasil penelitian ini dapat disimpulkan bahwa \nmetode pemasakan yang bebeda (kontrol, pembakaran, perebusan dan pengukusan) dapat \nmenurunkan kadar kolesterol yang terdapat pada daging kerbau. \nKata kunci : Daging kerbau, kolesterol, metode pemasakan
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".