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Record W4408409338 · doi:10.18280/ijdne.200205

Impact of Silages Made of Lignocellulosic Biomass on Rumen Fermentation In-Vitro and Potential Mitigation of Acid Accumulation by Bentonite Supplementation

2025· article· en· W4408409338 on OpenAlexvenueno aff
Darwin Darwin, Ramayanty Bulan, Salwa Rana Fitria, Andriy Anta Kacaribu

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
FundersUniversitas Syiah KualaLembaga Pengelola Dana PendidikanLembaga Penelitian dan Pengabdian Kepada MasyarakatBadan Riset dan Inovasi Nasional
KeywordsRumenBiomass (ecology)FermentationBentoniteLignocellulosic biomassPulp and paper industryFood scienceChemistryBiotechnologyAgronomyBiologyChemical engineeringEngineering

Abstract

fetched live from OpenAlex

The main purpose of this experimental study was to assess the accumulation of organic acids and their mitigation in rumen cultures fermenting silages.The silages used in the rumen fermentation were composed of corn stalks, bagasse, and rice straw.The rumen fermentation was conducted in vitro using batch systems at 38.5 ± 0.5℃ for 48 hours.The results showed that within 4 hours of incubation, the rumen culture fermenting bagasse silage produced 40% more VFA than other silages.Lactic acid build-up occurred exclusively in the rumen culture fermenting bagasse silage.Lactic acid production peaked after 8 hours of incubation, reaching a pH of 5.5.The addition of bentonite to the rumen culture fermenting bagasse silage reduced lactic acid concentration by 65%.Supplementing the feed with 10% bentonite can significantly increase pH from 5.5 to 6.2, potentially mitigating ruminal acidosis.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.291
Teacher spread0.280 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Design & Nature and Ecodynamics→Same topicRuminant Nutrition and Digestive Physiology→French-language works237,207→