Effects of dietary fiber on fecal microbiota of grower–finisher pig offspring from parents with divergent estimated breeding value for feed conversion ratio
Bibliographic record
Abstract
The study investigated how dietary fiber influences fecal microbiota in grower–finisher pigs, particularly in those genetically selected for feed efficiency based on estimated breeding value for feed conversion ratio (EBV_FCR). Pigs were fed either a high-fiber (HF, 5.62% crude fiber, dry matter basis, DM) or low-fiber (2.46% crude fiber, DM basis) diet throughout three feeding phases (25–50, 50–75, and 75–100 kg) over 70 days. Fresh fecal samples were manually collected at various stages of pig growth and analyzed using 16S rRNA gene sequencing. The results showed that Firmicutes and Bacteroidetes were the dominant phyla in pigs, irrespective of genetic background or diet. However, bacterial families and genera were differentially influenced by genetic factors (feed efficiency selection) and dietary fiber content. High-efficiency pigs had higher abundances of Desulfovibrionaceae, while low-efficiency pigs exhibited greater levels of Campylobacter. Overall, increased dietary fiber enhanced microbial richness and evenness, underscoring its role in shaping the pig gut microbiota. Butyrate-producing bacteria, including those from Ruminococcus, Lachnospira_1, and Fibrobacter_1, were significantly higher in pigs fed HF diets. The study highlighted the complex interactions between genetics, diet, and age in modulating microbiota composition.
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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 | 0.000 | 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".