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Record W2974000387 · doi:10.1186/s40104-019-0382-1

Feed nutritional value of brewers’ spent grain residue resulting from protease aided protein removal

2019· article· en· W2974000387 on OpenAlexafffund
Yizhao Shen, Ranithri Abeynayake, Xin Sun, Jianguo Li, Lingyun Chen, Wenzhu Yang

Bibliographic record

VenueJournal of Animal Science and Biotechnology/Journal of animal science and biotechnology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of AlbertaUniversity of Lethbridge
FundersAgriculture and Agri-Food Canada
KeywordsResidue (chemistry)ChemistryFermentationProteaseFood scienceDry matterRuminantNutrientHydrolysisEnzymatic hydrolysisFerulic acidEnzymeChromatographyBiochemistryAgronomyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: This study was conducted to evaluate the feed nutritional value of brewers' spent grain (BSG) residue resulting from protease aided protein removal. The nutritional value was measured as nutrient content, gas production, nutrient digestibility and fermentation characteristics in batch culture. RESULTS: Protein extraction process decreased content of crude protein but concentrated the neutral detergent fibre (NDF) and ferulic acid in BSG residue. The changes in the chemical composition of BSG residue varied with enzyme and enzyme dosage. Digestibility of dry matter (DMD) and NDF of residue differed among proteases. Increasing alcalase dosage linearly decreased DMD, whereas, the DMD linearly increased as everlase or flavourzyme dosage increased. Compared with BSG, the DMD, gas production and fermentation acid concentration of BSG residues were lower, whereas NDF digestibility was higher. CONCLUSIONS: NDF digestibility due to protease hydrolysis suggest that BSG residue can be potentially exploited as a viable fibre source for ruminant feeding.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.124
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.006
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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 teacher head, not a consensus.

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

Citations45
Published2019
Admission routes2
Has abstractyes

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