Penggunaan Feses Kerbau Dan Sapi Sebagai Inokulum Pengganti Cairan Rumen Dalam Mendegradasi NDF,ADF Dan Hemiselulosa Pakan Ternak Secara Metoda In Vitro
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengevaluasi penggunaan feses kerbau dan feses sapi sebagai in-okulum dalam penggantian cairan rumen dalam mendegradasi Neutral detergent fibre (NDF), acid detergent fibre (ADF) dan hemiselulosa secara In vitro. Penelitian ini dilaksanakan selama 1 bulan di Laboratorium Nutrisi dan Makanan Ternak Fakultas Peternakan Universitas Jambi. Rancangan penelitian yang digunakan adalah Rancangan Acak Lengkap (RAL) dengan 3 perla-kuan dan 6 ulangan. Perlakuan adalah P0 = Cairan rumen (kontrol) , P1 = Cairan feses kerbau + Molases 5%, dan P2 = Cairan feses sapi + Molases 5%. Peubah yang diamati adalah degradasi Neutral Detergent Fiber (NDF), Acid Detergent Fiber (ADF) dan Hemiselulosa. Data diolah secara statistik dengan analisis ragam ANOVA (Analisi of Variance) dan jika terdapat pengaruh perla-kuan yang nyata dilanjutkan uji Duncan pada taraf 5%. Hasil penelitian menunjukkan bahwa per-lakuan berpengaruh nyata (P<0.05) terhadap degradasi NDF, ADF dan Hemiselulosa. Kesimpulan dari penelitian ini yaitu Penggunaan inokulum feses kerbau belum mampu menyamai cairan ru-men sebagai inokulum namun inokulum feses sapi dapat digunakan sebagai pengganti cairan rumen dalam mendegradasi NDF, ADF dan hemiselulosa karena terlihat bahwa perlakuan inoku-lum feses kerbau dan sapi lebih tinggi dibandingkan cairan rumen dalam mendegradasi NDF, ADF dan Hemiselulosa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".