Comparative Analysis of the Gut Microbial Communities in Forest and Alpine Musk Deer Using High-Throughput Sequencing
Bibliographic record
Abstract
The gut ecosystem is characterized by dynamic and reciprocal interactions between the host and bacteria. Although characterizing microbiota for herbivores has become recognized as important tool for gauging species health, no study to date has investigated the bacterial communities and evaluated the age-related bacterial dynamics of musk deer. Moreover, gastrointestinal diseases have been hypothesized to be a limiting factor of population growth in captive musk deer. Here, high-throughput sequencing of the bacterial 16S rRNA gene was used to profile the fecal bacterial communities in juvenile and adult alpine and forest musk deer. The two musk deer species harbored similar bacterial communities at the phylum level, whereas the key genera for the two species were distinct. The bacterial communities were dominated by Firmicutes and Bacteroidetes, with the bacterial diversity being higher in forest musk deer. The Firmicutes to Bacteroidetes ratio also increased from juvenile to adult, while the bacterial diversity, within-group and between-group similarity, all increased with age. This work serves as the first sequence-based analysis of variation in bacterial communities within and between musk deer species, and demonstrates how the gut microbial community dynamics vary among closely related species and shift with age. As gastrointestinal diseases have been observed in captive populations, this study provides valuable data that might benefit captive management and future reintroduction programs.
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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 | 0.001 | 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 | 0.001 | 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".