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Record W2180572607 · doi:10.1111/jam.13005

Live yeasts enhance fibre degradation in the cow rumen through an increase in plant substrate colonization by fibrolytic bacteria and fungi

2015· article· en· W2180572607 on OpenAlexaff
F. Chaucheyras-Durand, Aurélie Ameilbonne, A. Bichat, Pascale Mosoni, Faisury Ossa, Évelyne Forano

Bibliographic record

VenueJournal of Applied Microbiology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsLallemand (Canada)Biotechnology Research Institute
Fundersnot available
KeywordsRumenFood scienceBiologyBranYeastFibrobacter succinogenesBacteriaSilageFermentationBiochemistry

Abstract

fetched live from OpenAlex

AIMS: To monitor the effect of a live yeast additive on feedstuff colonization by targeted fibrolytic micro-organisms and fibre degradation in the cow rumen. METHODS AND RESULTS: Abundance of adhering fibrolytic bacteria and fungi on feedstuffs incubated in sacco in the cow rumen was quantified by qPCR and neutral detergent fibre (NDF) degradation was measured. Saccharomyces cerevisiae I-1077 (SC) increased the abundance of fibre-associated Fibrobacter succinogenes on wheat bran (WB) and that of Ruminococcus flavefaciens on alfalfa hay (AH) and wheat silage (WS). The greatest effect was observed on the abundance of Butyrivibrio fibrisolvens on AH and soya hulls (SH) (P < 0·001). Fungal biomass increased on AH, SH, WS and WB in the presence of SC. NDF degradation of AH and SH was improved (P < 0·05) with SC supplementation. CONCLUSIONS: Live yeasts enhanced microbial colonization of fibrous materials, the degree of enhancement depended on their nature and composition. As an effect on rumen pH was not likely to be solely involved, the underlying mechanisms could involve nutrient supply or oxygen scavenging by the live yeast cells. SIGNIFICANCE AND IMPACT OF THE STUDY: Distribution of this microbial additive could be an interesting tool to increase fibre digestion in the rumen and thereby improve cow feed efficiency.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.148

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.226
Teacher spread0.209 · 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

Citations55
Published2015
Admission routes1
Has abstractyes

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