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Record W3013136595 · doi:10.1139/cjas-2018-0167

Effects of <i>Broussonetia papyrifera</i>-fermented feed on the growth performance and muscle quality of Hu sheep

2020· article· en· W3013136595 on OpenAlexvenueno aff
Yingyu Su, Guoshun Chen, Yu Cai, Bolan Gao, Xijun Zhi, Fengjiang Chang

Bibliographic record

VenueCanadian Journal of Animal Science · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNatural product bioactivities and synthesis
Canadian institutionsnot available
FundersGansu Agricultural University
KeywordsBroussonetiaFermentationFood scienceChemistryFlavorFatty acidGas chromatography–mass spectrometryAnimal scienceBiologyChromatographyMass spectrometryBiochemistryBotany

Abstract

fetched live from OpenAlex

This study aims to determine the effect of adding different proportions of Broussonetia papyrifera (BP)-fermented feed on Hu sheep. A total of 40 male Hu sheep (weighting 20.6 ± 2.20 kg) were collected and then divided into group I, II, III, and IV, with 0%, 5%, 10%, and 15% of BP-fermented feed to based diet, respectively. After the trial period of 10 and 50 d, the sheep were slaughtered by conventional methods for the chemical analyses. It showed that adding 10% fermented feed could significantly increase the growth performance of the Hu sheep. Adding the fermented feed can improve the protein level, main flavor amino acid content, and fatty acid in the muscle. Based on the headspace solid-phase microextraction and gas chromatography – mass spectrometry methods, a total of 125, 120, 119, and 117 kinds of volatile compounds were identified in group I, II, III, and IV, among which the relative content of the acid compound, ester compound, ketone compound, and aldehydes in group II, III, and IV were higher than that in control group, respectively. Addition of BP-fermented feed could significantly improve growth performance and meat quality of Hu sheep.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.255
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.218
Teacher spread0.205 · 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.

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

Citations19
Published2020
Admission routes1
Has abstractyes

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