Survey of the fecal microbiota of indigenous small ruminants living in different areas of Guizhou
Bibliographic record
Abstract
Introduction: Gut microbiota are associated with the health and performance of ruminant species, and they are affected by altitude, host genetics, and sex. However, there has been little research on comparing the fecal microbiota of indigenous small ruminants such as sheep and goats in Guizhou province, China. In the present study, we revealed the effect of altitude, genetics, and sex on fecal microbiota profiles and enterotypes in indigenous small ruminants of Guizhou province, China. Methods: Fecal samples were collected from Hei and Qianbei Ma goats and Weining sheep in the Chinese province of Guizhou. 16S rRNA gene sequencing targeting the V3-V4 region was performed using the Illumina MiSeq platform. Sequences were processed using QIIME2, and the qualified sequences were processed using the plugin DADA2 to generate amplicon sequence variants (ASVs). The statistical analysis was performed using R studio. Results: , which may be highly driven by the host's genetics (breed). The predicted functional profiles of the fecal microbiota were also assigned to two clusters that corresponded exactly to the enterotypes. Cluster 1 of the functional profiling was characterized by biosynthesis pathways, and cluster 2 was characterized by energy metabolism pathways. Discussion: Our findings may provide new insights into the fecal microbial community and enterotypes in small ruminants by herds, offering clues for understanding the mechanisms by which the fecal microbiota contribute to divergent host phenotypes in indigenous small ruminants in Guizhou.
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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 | 0.001 |
| 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".