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Record W4388529073 · doi:10.1093/jas/skad281.511

PSIX-19 Association of the Rumen Microbiota with Carcass Merit and Beef Quality

2023· article· en· W4388529073 on OpenAlexaff
Devin B. Holman, Katherine E. Gzyl, Haley Scott, N. Prieto, Ó. López-Campos

Bibliographic record

VenueJournal of Animal Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsMarbled meatRumenIntramuscular fatBiologyBeef cattleMicrobiomeAnimal scienceRuminantLatin squareCarcass weightFood scienceBiotechnologyBody weightFermentationAgronomyPastureGenetics

Abstract

fetched live from OpenAlex

Abstract The microbial community in the rumen has an important role in the health and production of beef cattle providing up to 70% of the daily energy requirements from the metabolism of otherwise non-digestible dietary carbohydrates. Diet is often the largest determinant of rumen microbiome composition, but it is also affected by host age, genetics, and sex. Performance attributes such as average daily gain and feed efficiency have all been linked to the rumen microbiome and recent studies with relatively small numbers of cattle have also suggested there may be an association with certain beef quality traits such as marbling, which largely determines the carcass value. Thus, the objective of this study was to evaluate the association of the rumen microbiota with carcass merit and beef quality traits. Two hundred steers were slaughtered, and rumen samples were collected immediately post-slaughter. Subsequently, carcass merit and meat quality attributes were evaluated. The rumen microbiota of these animals was then characterized using 16S rRNA gene sequencing. Although we did not identify a significant association between marbling score and the structure of the rumen microbiota (PERMANOVA; P > 0.05), there were significant associations between rumen microbial community structure and commercial weight, dressed weight percentage, drip loss, and intramuscular fat content (P < 0.05). There were also individual bacterial taxa that were correlated with various carcass merit and beef quality traits. Members of the Acidaminococcus genus were negatively associated with both intramuscular fat content and marbling score (P < 0.10) while Coprococcus, Christensenellaceae R-7 group, Lachnospiraceae NK3A20 group, Moryella, and Prevotella were among those genera positively correlated with intramuscular fat content (P < 0.10). Christensenellaceae R-7 group, Lachnospiraceae NK3A20 group, Moryella, and Prevotella were also significantly associated with dressed weight percentage. Commercial weight was positively correlated with the genera Catonella, Prevotellaceae NK3B31 group, Pseudoramibacter, Selenomonas, and Sutterella and negatively associated with Oribacterium and Ruminococcaceae UCG-001 (P < 0.10). Therefore, bacteria within these genera may be ideal targets for future microbiome-based strategies to improve carcass merit and beef quality.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.274
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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