Effects of the dietary grain content on rumen and fecal microbiota of dairy cows
Bibliographic record
Abstract
Six non-lactating Holstein dairy cows received diets with forage to grain mixture ratios of 100:0 (G0), 75:25 (G25), and 50:50 (G50) that contained 0.5%, 10.0%, and 19.5% dry matter (DM) of starch, respectively. Rumen fluid and feces were sampled, and methane emissions were determined during the last week of 5 wk experimental periods. Taxonomic compositions of microbiota were determined using Illumina 16S rRNA sequencing. Increased grain feeding increased the acidity and volatile fatty acid concentrations of rumen fluid and feces, and decreased methane emissions expressed as L·kg −1 DM. Microbial diversities were highest for G25. The numbers of identified genera in rumen fluid were 185, 182, and 171 for G0, G25, and G50, respectively. In feces, these numbers were 197, 182, and 171 for these diets, respectively. In rumen digesta, seven genera were correlated positively to G0 and negatively to G50, and six genera were correlated negatively to G0 and positively to G50. In feces, 16 genera were correlated positively to G0 and negatively to G50, and 13 genera were correlated negatively to G0 and positively to G50. Increasing grain feeding affected a limited number of genera in rumen digesta and feces. This could not explain treatment effects on the functionalities of microbiota.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".