Silage of Marandu Grass with Levels of Stylo Legume Treated or Not with Microbial Inoculant
Bibliographic record
Abstract
The chemical composition, fermentation profile, microbial population and dry matter recovery were evaluated in marandu grass silages containing different levels of Stylo legume cv. Campo Grande treated or not with microbial inoculant. A 4 × 2 factorial arrangement (four levels of Stylo legume, with and without microbial inoculant) was used in a completely randomized design with four replications. The levels of Stylo legume used were 0, 10, 20 and 30% of the weight in the natural matter. The marandu grass was harvested at 70 days of regrowth and the Stylo legume at the pre-flowering stage. It was observed the effect of interaction between levels of Stylo legume and microbial inoculant on dry matter content, effluent losses, dry matter recovery and yeast and molds populations. The dry matter content of the silages with and without inoculant increased (P < 0.05) linearly with the addition of Stylo legume. A linear decreasing effect was observed for neutral detergent fiber contents and a linear crescent effect for the crude protein contents of the silages with addition of Stylo legume. The pH values had a quadratic effect with addition of Stylo legume, with a maximum value of 4.16 in the inclusion of 12.25% of Stylo legume. The LAB population increased linearly with an increase in the levels of Stylo legume. The dry matter recovery increased linearly with the addition of Stylo legume, in the silages without inoculant. It is concluded that the consortium of marandu grass with Stylo legume improves the chemical composition, fermentation profile, and decrease the dry matter losses of the silages, regardless of the use of microbial inoculant.
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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) | 0.001 | 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".