Milk yield, rumen fermentation, and microbiota of Shami goats fed diets supplemented with spirulina and yeast
Bibliographic record
Abstract
Microalgae and live yeast have gained interest in improving animal performance. This study evaluated the effect of supplementation with Spirulina platensis, Saccharomyces cerevisiae, or their combination on the in vitro and in vivo rumen fermentation, rumen microbiota, and milk yield and composition of lactating Shami goats. The in vitro experiment included four diets: non-supplemented basal diet consisted of Alfalfa hay and a concentrate feed mixture (C); basal diet supplemented with 1% Saccharomyces (Y) based on dry matter; basal diet supplemented with 1% Spirulina (A); and basal diet supplemented with 1% of a mixture of Saccharomyces and Spirulina (AY). In the in vivo experiment, twenty-one lactating goats were divided into three groups (n = 7) to receive one of three diets: C, A, and AY. Group AY had higher in vitro gas production, dry matter digestion (DMD), and volatile fatty acids (VFA) (p < 0.05). Milk yield and feed efficiency were higher in groups A and AY compared to group C. Group AY goats exhibited higher rumen total VFA, acetic, and propionic, while group A showed higher butyric acid. Lower predicted methane was observed in group AY. Groups A and AY showed distinctive microbial communities. The bacterial community was dominated by phylum Bacteroidota, and genera Prevotella and Rikenellaceae RC9 gut group, which were higher in the AY group. The archaeal community was dominated by the genus Methanobrevibacter, which had a lower prevalence in group AY. The combination of live yeast and Spirulina improved rumen fermentation and the milk yield; therefore, it could be used as a feed additive for lactating goats.
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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) | 0.000 | 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".