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Record W4315782293 · doi:10.22437/jiiip.v25i1.23246

Penggunaan Feses Kerbau Dan Sapi Sebagai Inokulum Pengganti Cairan Rumen Dalam Mendegradasi NDF,ADF Dan Hemiselulosa Pakan Ternak Secara Metoda In Vitro

2023· article· id· W4315782293 on OpenAlexaff
Ayu Silaban, M. Afdal, Darlis Darlis

Bibliographic record

VenueJurnal Ilmiah Ilmu-Ilmu Peternakan · 2023
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicFood and Agricultural Sciences
Canadian institutionsNutrasource
Fundersnot available
KeywordsAnimal scienceRumenChemistryBiologyFood scienceFermentation

Abstract

fetched live from OpenAlex

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.005
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0040.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.024
GPT teacher head0.243
Teacher spread0.219 · 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
Published2023
Admission routes1
Has abstractyes

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