Comparative analyses of fecal microbiota in Tibetan and Chinese Han living at low or high altitude by barcoded 454 pyrosequencing
Bibliographic record
Abstract
Knowledge about the impact of altitude and ethnicity on human gut microbiota is currently limited. In this study, fecal microbiota from 12 Tibetans (T group), 11 Chinese Han living in Tibet (HH group) and 12 Chinese Han living in Shaanxi province (LH group) were profiled by 454 pyrosequencing. Analysis of UniFrac principal coordinates showed significant structural changes in fecal microbiota among the three groups. There were significant differences in the composition of fecal microbiota among the three groups at phylum and genus levels. At the phylum level, the fecal samples of HH and T groups had higher relative abundances of Firmicutes, whereas the LH group had a higher relative abundance of Bacteroidetes. These changes at the phylum level reflected different dominant genus compositions. Compared with the LH group, changes of Firmicutes and Bacteroidetes were mainly due to a significant decrease of Prevotella in the HH group and were primarily attributable to significant decreases of Bacteroides and Prevotella as well as a significant increase of Catenibacterium in the T group. In conclusion, our results suggest that high altitude may contribute to shaping human gut microbiota. Genetic and dietary factors may also explain the different microbiota compositions between Tibetan and Chinese Han.
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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".