Effects of different additives on fermentation characteristics, nutrient composition and microbial communities of Leymus chinensis silage
Bibliographic record
Abstract
BACKGROUND: Leymus chinensis (Trin.) Tzvel is a perennial high-quality indigenous grass in China; characterized by high yield, elevated crude protein content, excellent palatability, substantial leaf volume, adaptability, and longevity. This study aimed to examine the impact of Lactiplantibacillus plantarum (LP), Lactobacillus buhneri (LB), their combination (LPLB), and complex enzyme preparation (CE) on the quality and microbial community of Leymus chinensis silage. RESULTS: Throughout silage fermentation, pH levels decreased in all treatment groups relative to the control group. The LPLB group exhibited elevated levels of lactic acid (LA) and water-soluble carbohydrates (WSC) content, alongside reduced concentrations of acidic detergent fibre (ADF), and neutral detergent fibre (NDF) content. It exhibited superior silage outcomes compared to the other groups. Lactobacillus predominated in the treatment groups, but Enterobacter was predominant in the control group. The microbial diversity was decreased in LP and LPLB within the treatment groups due to the suppression of unwanted bacteria. Functional predictions indicated that glyoxylate and dicarboxylate metabolism, starch and sucrose metabolism, glycolysis/gluconeogenesis, amino sugar metabolism, and nucleotide sugar metabolism were the most significant metabolic pathways, with LP being particularly important in each. CONCLUSION: The experimental results demonstrated that the incorporation of various additives influenced the bacterial community structure, fermentation quality, and nutrient composition of Leymus chinensis silage differently. The LP and LPLB groups decreased pH and ADF levels and amassed a significant quantity of LA during fermentation, while preserving CP and WSC content. The microbial composition exhibited greater stability, which markedly enhanced the quality of Leymus chinensis silage, hence preserving its nutrient composition. CLINICAL TRIAL NUMBER: Not applicable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".