Lower dietary concentrate level increases bacterial diversity in the rumen of <i>Cervus elaphus</i> <i>yarkandensis</i>
Bibliographic record
Abstract
The ruminal microbiota plays major roles in feed digestion. The composition and fermentation of the bacterial communities in 3 important ruminant species have been studied previously. Here, we extended this research to the effect of concentrate-to-forage ratios on ruminal bacterial communities in Tarim red deer (Cervus elaphus yarkandensis). Different concentrate-to-forage ratios (2:8, 3:7, 4:6, and 5:5) were fed to Tarim red deer for 20 days. Ruminal bacterial communities were elucidated by 16S ribosomal RNA gene sequencing on an Illumina HiSeq 2500 platform. The microbial composition and biodiversity at the different concentrate-to-forage ratio levels were analyzed using clustering of operational taxonomic units based on 97% sequence identity, taxonomic classification at the phylum and genus levels, α diversity, and β diversity. Rumen microorganisms of deer fed a diet with a concentrate-to-forage ratio of 2:8 had the highest species diversity, followed by ratios of 3:7, 4:6, and 5:5. The community structure of the A1 and A2 samples and the A3 and A4 samples was similar. The bacterial composition appeared to be affected by diet, with a lower dietary concentrate level tending to increase the richness and diversity of ruminal bacteria in the rumen of Tarim red deer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".