Associations between the rumen microbiota and carcass merit and meat quality in beef cattle
Bibliographic record
Abstract
The rumen microbiota is important for energy and nutrient acquisition in cattle, and therefore its composition may also affect carcass merit and meat quality attributes. In this study, we examined the associations between archaeal and bacterial taxa in the rumen microbiota of beef cattle and 12 different attributes, including hot carcass weight (HCW), dressing percentage, ribeye area (REA), intramuscular fat content, marbling score, fat thickness, yield grade, moisture content, purge loss, and shear force. There were significant correlations between the relative abundance of certain archaeal and bacterial genera and these attributes. Notably, Selenomonas spp. were positively correlated with live weight and HCW, while also being negatively correlated with purge loss. Members of the Christensenellaceae R-7, Moryella, and Prevotella genera exhibited positive and significant correlations with various attributes, such as dressing percentage and intramuscular fat content. Ruminococcaceae UCG-001 was negatively correlated with live weight, HCW, and dressing percentage, while Acidaminococcus and Succinivibrionaceae UCG-001 were negatively correlated with intramuscular fat content, moisture content, and marbling score. Overall, our findings suggest that specific changes in the rumen microbiota could be a valuable tool to improve beef carcass merit and meat quality attributes. Additional research is required to better understand the relationship between the rumen microbiota and these attributes, with the potential to develop microbiome-targeted strategies for enhancing beef production. KEY POINTS: • Certain rumen bacteria were associated with carcass merit and meat quality • Moryella was positively correlated with intramuscular fat in beef carcasses • Acidaminococcus spp. was negatively correlated with marbling and intramuscular fat.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".