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Record W3109323476 · doi:10.3390/microorganisms8121854

The Duration of Increased Grain Feeding Affects the Microbiota throughout the Digestive Tract of Yearling Holstein Steers

2020· article· en· W3109323476 on OpenAlexafffund
J.C. Plaizier, Paula Azevedo, B. L. Schurmann, P. Górka, G.B. Penner, Ehsan Khafipour

Bibliographic record

VenueMicroorganisms · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of SaskatchewanUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCecumIleumBiologyJejunumHayAnimal scienceGastrointestinal tractRumenDry matterFirmicutesFood science16S ribosomal RNABacteriaEcologyBiochemistryFermentationGenetics

Abstract

fetched live from OpenAlex

Effects of the duration of moderate grain feeding on the taxonomic composition of gastrointestinal microbiota were determined in 15 Holstein yearling steers. Treatments included feeding a diet of 92% dry matter (DM) hay (D0), and feeding a 41.5% barley grain diet for 7 (D7) or 21 d (D21) before slaughter. At slaughter, digesta samples were collected from six regions, i.e., the rumen, jejunum, ileum, cecum, colon, and rectum. Extracted DNA from these samples was analyzed using MiSeq Illumina sequencing of the V4 region of the 16S rRNA gene. Three distinct PCoA clusters existed, i.e., the rumen, the jejunum/ileum, and the cecum/colon/rectum. Feeding the grain diet for 7 d reduced microbial diversity in all regions, except the ileum. Extending the duration of grain feeding from 7 to 21 d did not affect this diversity further. Across regions, treatment changed the relative abundances of 89 genera. Most of the changes between D0 and D7 and between D7 and D21 were opposite, demonstrating the resilience of gastrointestinal microbiota to a moderate increase in grain feeding. Results show that the duration of a moderate increase in grain feeding affects how gastrointestinal microbiota respond to this increase.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.317

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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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

Citations8
Published2020
Admission routes2
Has abstractyes

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