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Record W3215390346 · doi:10.1016/j.crmicr.2021.100085

Distinctive roles between rumen epimural and content bacterial communities on beef cattle feed efficiency: A combined analysis

2021· article· en· W3215390346 on OpenAlexafffund
Mi Zhou, Bibaswan Ghoshal, Paul Stothard, Le Luo Guan

Bibliographic record

VenueCurrent Research in Microbial Sciences · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Livestock and Meat Agency
KeywordsBacteroidetesFirmicutesBiologyPrevotellaProteobacteriaRumen16S ribosomal RNAResidual feed intakeBeef cattleAnimal scienceBacteriaFood scienceFeed conversion ratioFermentationBody weightGenetics

Abstract

fetched live from OpenAlex

Rumen content-associated (RC) and epithelial tissue-attached (RT) bacterial communities are composed of different phylotypes and play distinctive roles. This study aimed to compare the composition of the RT and RC bacterial communities of steers differing in feed efficiency. The microbiota of RT and RC samples collected from sixteen beef steers with high or low residual feed intake (RFI) were analyzed through sequencing of partial 16S rRNA gene amplicons. Bacteroidetes, Proteobacteria and Firmicutes were the predominant phyla and Prevotella was the most abundant genus in both RC and RT bacterial communities. In total, 19 OTUs of the RC samples and 19 OTUs of the RT samples were differentially abundant (DA) between H-RFI and l-RFI steers. Among them, a common DA OTU belonged to Prevotella genus was identified in both RC and RT samples, making it the potential key microbial marker for indicating feed efficiency of steers. The co-occurrence of the DA OTUs among RT and RC samples suggest the importance of these two communities function as a complete system in influencing host 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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
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 head0.188
GPT teacher head0.364
Teacher spread0.176 · 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 designObservational
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

Citations14
Published2021
Admission routes2
Has abstractyes

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