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Record W4396676180 · doi:10.30997/jpn.v10i1.12351

PENGARUH PENGGUNAAN SILASE LIMBAH SAWI PUTIH (BRASSICA PIKENENSIA L.) TERHADAP KONSUMSI DAN KECERNAAN ENERGI DAN PROTEIN PADA TERNAK BABI GROWER

2024· article· id· W4396676180 on OpenAlexaff
David A. Nguru, Dedi Jems Ndolu, Sabarta Sembiring, Ni Nengah Suryani

Bibliographic record

VenueJurnal Peternakan Nusantara · 2024
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicAgricultural and Biological Research
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsBrassicaBiologyHorticulture

Abstract

fetched live from OpenAlex

Tujuan dari penelitian ini adalah untuk mengetahui pengaruh penggunaan silase limbah sawi putih (Brassica pekinensia L) dalam ransum terhadap konsumsi dan kecernaan energi dan protein pada babi grower. Ternak yang digunakan adalah babi peranakan landrace fase grower sebanyak 12 ekor yang berumur 3-4 bulan dengan bobot badan awal berkisar 29-52 kg dan rataan 36 kg (KV = 17,72%). Penelitian ini menggunakan metode percobaan dengan Rancangan Acak Kelompok (RAK) yang terdiri dari empat perlakuan dan tiga ulangan sehingga terdapat 12 unit percobaan. Perlakuan yang digunakan adalah R0: 100% ransum basal, R1: 90% ransum basal + 10% silase limbah sawi putih, R2: 85% ransum basal + 15% silase limbah sawi putih dan R3: 80% ransum basal + 20% silase limbah sawi putih. Variabel yang diteliti adalah konsumsi ransum, konsumsi energi, konsumsi protein, kecernaan energi dan kecernaan protein. Hasil penelitian menunjukkan bahwa perlakuan berpengaruh tidak nyata (P>0.05) terhadap semua variable. Berdasarkan hasil penelitian ini, dapat disimpulkan bahwa penggunaan silase limbah sawi putih (Brassica pekinensia L) dalam ransum pada level 10%, 15% dan 20% memberikan pengaruh yang sama terhadap konsumsi dan kecernaan energi dan protein. Disarankan limbah sawi putih dapat diolah menjadi silase dan dapat digunakan 20% mengganti ransum babi grower. Kata kunci: babi grower, energi, protein, silase limbah sawi putih.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0030.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.027
GPT teacher head0.255
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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