Ensiled Mixed Vegetables Enriched Carbohydrate Metabolism in Heterofermentative Lactic Acid Bacteria
Bibliographic record
Abstract
This study evaluated the fermentation quality, nutritive profile, in vitro fermentation, and microbial communities colonising sorghum ensiled with an unsalable vegetable mixture (chopped beans, carrot, and onion (1:1:1) ) including: (1)−100% sorghum; (2)−80% sorghum + 20% vegetable mix or (3)−60% sorghum + 40% vegetable mix, on a dry matter (DM) basis, with or without a probiotic inoculant. Samples were obtained across 0, 1, 3, 5,7, and 101 days ensiling and after 14 d aerobic exposure. The V4 region of the 16S rRNA gene and the ITS1 region were sequenced to profile bacterial, archaeal, and fungal communities. Compared to the 0% DM, ethanol increased (p < 0.01) from 8.42 to 20.4 ± 1.32 mM with 40% DM vegetable mix inclusion, while lactate decreased from 5.93 to 2.24 ± 0.26 mM. Linear discriminant analysis revealed that relative abundances of 12 bacterial taxa were influenced by silage treatments (log LDA score ≥ 4.02; p ≤ 0.03), while predicted functional pathways of alternative carbohydrate metabolism (hexitol, sulfoquinovose and glycerol degradation; N-acetyl glucosamine biosynthesis; log LDA score ≥ 2.04; p ≤ 0.02) were similarly enriched. This study indicated that carbohydrate metabolism by heterofermentative lactic acid bacteria can increase the feed value of sorghum when ensiled with an unsalable vegetable mixture at 40%DM, without requiring a high quantity of lactate.
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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".