Rumen Microbiome Composition in Cattle during Grain-Induced Subacute Ruminal Acidosis (SARA)
Bibliographic record
Abstract
Six lactating, rumen cannulated, Danish Holstein cows were used in a blocked design study including two blocks. In the first block, two cows received control diet and two cows received SARA-challenge diet. In the second block, former control cows received SARAdiet while two new cows received control diet. All cows received a total mixed ration (TMR; 24% concentrate) for four weeks before the trial. SARA was induced by gradual substitution of 40% of TMR with grain pellets (50:50 wheat:barley) over 3 days. Full SARAdiet was fed for four days. Rumen samples were taken at 9 am and 9 pm on last day of the control period, and first and last day of full SARA-feeding. DNA was extracted and V4 region of bacterial 16S rRNA amplified and subjected to Illumina sequencing. Data was analyzed using QIIME pipelines and resultant operational taxonomic units (OTUs) were aligned to Greengenes database. Differences in bacterial communities between groups were tested based on weighted Unifrac distance using PERMANOVA and partial least square discriminant analysis (PLS-DA). 556,536 high quality sequences were collected, resulting in identification of 18,326 OTUs from 18 phyla of which 81 were classified at the genus level. The rumen bacterial communities were altered in response to SARA (P=0.01). The proportion of several taxa was significantly higher in SARA samples, including S24-7, Erysipelotrichales. Lactobacillus, C lostridia, Moryella, Butyrivibrio, Olsenella, and C oprococcus. Microbiome profiling of rumen during SARA could provide new knowledge of the pathogenesis and might be used as a biological marker of the disease.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".