Level Ekstrak Buah Nanas (Ananas Comosus L. Merr) dan Lama Perendaman Terhadap Kualitas Daging Itik Afkir
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui level ekstrak buah nanas (Ananas comosus L. Merr) dan lama perendaman terhadap kualitas daging itik afkir. Parameter yang diamati adalah pengaruh penambahan ekstrak buah nanas yang mengandung bromelin, lama perendaman dan interaksi antara kedua faktor terhadap susut masak, pH, kadar protein, kadar lemak dan kadar air daging itik afkir. Itik yang digunakan adalah 18 ekor itik Tegal umur 84 minggu. Sampel yang digunakan untuk uji susut masak, pH, kadar air,kadar lemak, dan kadar air adalah daging paha. Rancangan penelitian menggunakan Rancangan Acak Lengkap (RAL) Pola Faktorial 3 × 3, dengan faktor pertama penambahan ekstrak buah nanas (0%, 10%, 20%) dan faktor kedua lama perendaman (15, 30, 45 menit). Terdapat interaksi antara penambahan ekstrak buah nanas dan lama perendaman (P<0,01) pada kadar lemak dan (P<0,05) pada kadar protein. Penambahan ekstrak buah nanas dan lama perendaman dapat meningkatkan kadar protein daging itik afkir dan menurunkan kadar lemak daging itik afkir. Penambahan ekstrak buah nanas 10% dan waktu perendaman 30 menit menghasilkan kualitas daging itik yang terbaik karena mampu menurunkan kadar lemak dan meningkatkan kadar protein.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".