236 Effects of Saccharomyces Cerevisiae Fermentation Products (SCFP) and Subacute Ruminal Acidosis (SARA) on Co-occurrence Patterns and Hub Taxa of Rumen Liquid Microbiota in Lactating Dairy Cows
Bibliographic record
Abstract
Abstract Thirty-two dairy cows receiving a basal diet containing 34.9% NDF, and 18.6 % starch, were randomly assigned to four treatments: supplementation either with 140 g/d of ground corn (Control), 126 g/d of ground corn and 14 g/d of XPC (SCFPa, Diamond V Original XPC Cedar Rapids, IA), 121 g/d of ground corn and 19 g/d of NutriTek (SCFPb-1x, NutriTek, Diamond V), or 102 g/d of ground corn and 38 g/d of NutriTek (SCFPb-2x) from 4 wk before until 12 wk after calving. SARA was induced during wk 5 and wk 8 after calving by replacing 20% of the basal diet with pellets containing 50% barley and 50% wheat. Ruminal fluid samples were collected at 6 h after feeding on the second day of wk 4 to wk 10 after calving. Weeks 4, 7 and 10 were considered to be non-SARA. Microbial DNA was extracted, llumina sequenced at the V1–V2 regions of the 16S rRNA gene, and operational taxonomic units (OUT) were assigned using QIIME2. Correlation network analysis (CoNet) determined connections between the relative abundances of OTU, including co-occurrences, and to identify hub OTUs with over 15 connections with other OTUs. The degree of connectedness of phyla was normalized as the total number of positive and negative correlations observed for each phylum divided by their relative abundance in the community. In the Control treatment, SARA reduced positive degree of connectedness (19.33 vs 11.95). In all SCFP treatments, SARA increased the positive degree of connectedness (SCFPb-2x: 25.73 vs 23.50, SCFPb-1x: 87.47 vs 116.32, SCFPa: 34.01 vs 51.56). The relative abundances of the hub taxa Bacteroidales RF16 group and Lachnospiraceae, Christensenellaceae R-7 group and Ricenellaceae RC9 gut group were stabilized by SCFPb-2x. Hence, SCFP attenuated the negative effect of SARA on the co-occurrence patterns in the rumen fluid 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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".