Comparison of fecal microbiota composition of blue sheep fed <i>Lolium perenne</i> versus <i>Sorghum sudanense</i>
Bibliographic record
Abstract
Lolium perenne L. and Sorghum sudanense (Piper) Stapf. are 2 common forages fed to blue sheep (Pseudois nayaur Hodgson, 1833) in captivity. However, the effect of these 2 forages on the gastrointestinal microbiota is largely unknown. We analyzed the diversity of the microbiota in the feces of captive blue sheep fed with L. perenne (group F1) and S. sudanense (group F2) by 16S rRNA sequencing. A total of 20 major phyla and 29 genera fecal bacterial communities were detected in the 2 groups. The F1 and F2 groups shared common microbiota at the phylum level, which mainly consisted of Firmicutes and Bacteroidetes. Ruminococcaceae_UCG-005, Eubacterium_coprostanoligenes_group, Ruminococcaceae_UCG-013, and Ruminococcaceae_UCG-010 were the top 4 dominant taxa at the genus level. The percentage of Ruminococcaceae_UCG-010 was significantly higher in the F2 group (∼2.75-fold) than in F1 group. The diversity and abundance of the microbial community in F2 was higher than that in F1. Although both of L. perenne and S. sudanense affect the metabolism of the gastrointestinal microbiota of blue sheep, the S. sudanense improves more aspects of metabolism and biogenesis. In summary, our results demonstrated that L. perenne and S. sudanense affect blue sheep gastrointestinal microbiota in different ways. But S. sudanense efficiently improved the gastrointestinal microbiota of blue sheep.
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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".