Unsalable Vegetables Ensiled With Sorghum Promote Heterofermentative Lactic Acid Bacteria and Improve in vitro Rumen Fermentation
Bibliographic record
Abstract
This study characterized the nutritive and microbial profiles and the fermentation characteristics of silage with the following compositions on a dry matter (DM) basis: (1) 100% sorghum, (2) 70% sorghum + 30% carrot or pumpkin, and (3) 40% sorghum + 60% carrot or pumpkin. The treatments were further divided based on the addition or no addition of a probiotic inoculant. After 70 days of ensiling, the silage was incubated for 48 h using the in vitro batch culture technique. Crude protein and non-fiber carbohydrates in the silage increased (P ≤ 0.01) by 5.7 percent point (pp) and 9.6 pp, respectively, with pumpkin at 60% DM. The V4 region of the 16S rRNA gene was sequenced to profile pre-ensiled and ensiled archeal and bacterial communities. Silages containing carrot or pumpkin strongly influenced the microbial structure (PERMANOVA: R2 = 0.75; P < 0.001), despite the ensiled treatments being dominated by Lactobacillus spp., except for the control, which was dominated by Weissella and Pediococcus spp. (P < 0.01). Linear discriminant analysis indicated that carrot and pumpkin silages were responsible for the increased relative abundance of Lactobacillus and Acinetobacter spp. (log LDA score ≥ 2), respectively. After 48 h of incubation, carrot and pumpkin inclusion increased (P < 0.01) the in vitro DM digestibility by 22.5 and 31.3%, increased the total volatile fatty acids (VFAs) by 16 and 20.6% (P < 0.01), respectively, and showed a tendency (P = 0.07) to increase the gas production. Therefore, this study supports the use of carrot or pumpkin in sorghum silages to maximize feed digestibility and total VFA concentrations.
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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".