MétaCan
Menu
Back to cohort
Record W3159464679 · doi:10.3389/fmicb.2021.652031

Correlation of Breed, Growth Performance, and Rumen Microbiota in Two Rustic Cattle Breeds Reared Under Different Conditions

2021· article· en· W3159464679 on OpenAlexaff
Matteo Daghio, Francesca Ciucci, Arianna Buccioni, Alice Cappucci, Laura Casarosa, Andrea Serra, Giuseppe Conte, Carlo Viti, Breanne M. McAmmond, Jonathan D. Van Hamme, Marcello Mele

Bibliographic record

VenueFrontiers in Microbiology · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsThompson Rivers University
FundersRegione Toscana
KeywordsRumenBiologyBreedFeedlotAnimal scienceBeef cattleFibrobacter succinogenesComposition (language)Food scienceGrazingEcologyFermentation

Abstract

fetched live from OpenAlex

The use of rustic cattle is desirable to face challenges brought on by climate change. Maremmana (MA) and Aubrac (AU) are rustic cattle breeds that can be successfully used for sustainable production. In this study, correlations between two rearing systems (feedlot and grazing) and the rumen microbiota, the lipid composition of rumen liquor (RL), and the growth performance of MA and AU steers were investigated. Bacterial community composition was characterized by high-throughput sequencing of 16S rRNA gene amplicons, and the RL lipid composition was determined by measuring fatty acid (FA) and the dimethyl acetal profiles. The main factor influencing bacterial community composition was the cattle breed. Some bacterial groups were positively correlated to average daily weight gain for the two breeds (i.e., Rikenellaceae RC9 gut group, Fibrobacter and Succiniclasticum in the rumen of MA steers, and Succinivibrionaceae UCG-002 in the rumen of AU steers); despite this, animal performance appeared to be influenced by short chain FAs production pathways and by the presence of H 2 sinks that divert the H 2 to processes alternative to the methanogenesis.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.258

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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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

Citations51
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in MicrobiologySame topicRuminant Nutrition and Digestive PhysiologyFrench-language works237,207