Impact of dietary fiber/starch ratio in shaping caecal microbiota in rabbits
Bibliographic record
Abstract
The objective of this experiment was to determine whether changing the dietary neutral detergent fiber (NDF)/starch ratio affected caecal microbiota when 4 different diets (diet A: 2.3 NDF/starch, diet B: 1.9, diet C: 1.4, diet D: 1.0) were formulated. A total of 200 weaned rabbits (35 days old, 50 per group) were used for the experiment, which started after an adaptation period of 7 days (i.e., day 42). Caecal contents were obtained from rabbits fed different NDF/starch diets at 52, 62, 72, and 82 days of life. The bacterial community structure was characterized by high-throughput 16S rRNA sequencing. Firmicutes, Actinobacteria, Synergistetes, and Tenericutes did not significantly change with diet or age. However, Bacteroidetes (P < 0.05), Proteobacteria (P < 0.01), and Verrucomicrobia (P < 0.05) reads were significantly affected by diet, and Proteobacteria (P < 0.01) and Verrucomicrobia (P < 0.05) reads were significantly influenced by age. At the genus level, Escherichia/Shigella (P < 0.01) was overrepresented in diet A (high fiber) relative to diet D (high starch) in 52- and 62-day-old rabbits. Venn diagrams and heat map plot analyses revealed that the number of gut species shared between animals with different diet treatments increased with age. These results suggest that dietary fiber per starch ratios and age significantly alter the composition of caecal microbiota in growing rabbits.
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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 | 0.000 | 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) | 0.000 | 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".