Bacterial community composition and fermentation in the rumen of Xinjiang brown cattle ( <i>Bos taurus</i> ), Tarim red deer ( <i>Cervus elaphus yarkandensis</i> ), and Karakul sheep ( <i>Ovis aries</i> )
Bibliographic record
Abstract
The rumen microbiota plays a major role in the metabolism and absorption of indigestible food sources. Xinjiang brown cattle (Bos taurus), Tarim red deer (Cervus elaphus yarkandensis), and Karakul sheep (Ovis aries) are important ruminant species for animal husbandry in the Tarim Basin. However, the microbiota and rumen fermentation of these animals are poorly understood. Here, we apply high-throughput sequencing to examine the bacterial community in the rumen of cattle, red deer, and sheep and measured rumen fermentation products. Overall, 548 218 high-quality sequences were obtained and then classified into 6034 operational taxonomic units. Prevotella spp., Succiniclasticum spp., and unclassified bacteria within the families Succinivibrionaceae, Lachnospiraceae, and Veillonellaceae were the dominant bacteria in the rumen across the 3 hosts. Principal coordinate analysis identified significant differences in the bacterial communities across the 3 hosts. Pseudobutyrivibrio spp., Oscillospira spp., and Prevotella spp. were more prevalent in the rumen of the cattle, red deer, and sheep, respectively. Among the 3 hosts, the red deer rumen had the greatest amounts of acetate and butyrate and the lowest pH value. These results showed that Prevotella spp. are the dominant bacteria in the rumen of the cattle, red deer, and sheep, providing new insight into the rumen fermentation of ruminants distributed in the Tarim Basin.
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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 | 0.000 |
| 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) | 0.000 | 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".