Bibliographic record
Abstract
Abstract Cattle rumen microbiome converts fibrous feed to nutrients that directly influence cattle’s growth and performance. Recent research has revealed the composition and function of rumen microbiome can affect many economically important traits in cattle such as feed efficiency, milk/meat yield and quality, methane emission, metabolic health and so on. In this presentation, the research on the assessment of to what extent the rumen microbiome and its metabolites, as well as host metabolites contribute to milk production and quality traits will be focused and discussed. It has been evident that some dairy cows can produce large amounts of milk under the same nutritional and management conditions. Recent research has revealed that in addition to host genetics and diet, parity and days in milk of dairy cows can affect the rumen microbiota, and both core and pan rumen bacteriome can potentially contribute to variations of milk production traits. Additionally, the analysis of rumen metagenomics and metabolomics of dairy cows with varied milk production yield showed that some key bacterial taxa (for example, Prevotella species), and their functions (such as branched-chain amino acids biosynthesis, methanogenesis), some rumen microbial metabolites (mainly amino acids, carboxylic acids, and fatty acids) significantly differed between high and low production cows. Further integrated analysis with serum metabolomics of the cos revealed the varied contributions of rumen microbial composition, functions, metabolites, and the serum metabolites to the milk protein yield of a dairy cow, a potential new trait that can be used for high-quality milk production, respectively. The fundamental understanding of the role of rumen microbiome and its metabolome contributing to milk yield and protein yield traits can provide novel insights into future manipulation of rumen microbiome to enhance milk production and quality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".