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Record W4396859054 · doi:10.3390/ani14101447

Fecal Microbial Communities of Nellore and Crossbred Beef Calves Raised at Pasture

2024· article· en· W4396859054 on OpenAlexaff
José Antônio Bessegatto, Júlio Augusto Naylor Lisbôa, Bruna Parapinski dos Santos, Juliana Massitel Curti, Carlos Henrique Montemór, Amauri Alcindo Alfieri, Núria Mach, Márcio Costa

Bibliographic record

VenueAnimals · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversité de Montréal
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsCrossbreedBiologyWeaningPurebredAnimal scienceBreedFecesPastureVeterinary medicineBeef cattleEcology

Abstract

fetched live from OpenAlex

This study aimed to investigate the effect of age and genetics on the fecal microbiota of beef calves. Ten purebred Nellore (Bos taurus indicus) and ten crossbreed 50% Nellore-50% European breed (Bos taurus taurus) calves co-habiting on the same pasture paddock had fecal samples collected on days five (5 d), 14 d, 28 d, 60 d, 90 d, 180 d, 245 d (weaning) and 260 d after birth. All calves were kept with their mothers, and six Nellore dams were also sampled at weaning. Microbiota analysis was carried out by amplification of the V4 region of the 16S rRNA gene following high-throughput sequencing with a MiSeq Illumina platform. Results revealed that bacterial richness increased with age and became more similar to adults near weaning. Differences in microbiota membership between breeds were found at 60 d and 90 d and for structure at 60 d, 90 d, 245 d, and 260 d (p < 0.05). In addition, crossbreed calves presented less variability in their microbiota. In conclusion, the genetic composition significantly impacted the distal gut microbiota of calves co-habiting in the same environment, and further studies investigating food intake can reveal possible associations between microbiota composition and performance.

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 score0.273
Threshold uncertainty score0.402

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 head0.013
GPT teacher head0.274
Teacher spread0.261 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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