Effect of Inoculation of Lactic Acid Bacteria and Fibrolytic Enzymes on Microbiota in the Terminal and Aerobically Exposed Short-Growing Season Whole-Plant Corn Silage
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Bibliographic record
Abstract
An experiment was conducted to evaluate the effects of mixed lactic acid bacteria (LAB) plus fibrolytic enzymes (xylanase + β-glucanase) on bacterial and fungal communities in terminal and aerobically exposed whole-plant corn silage ensiled in a temperate zone. Short-season corn forage was either uninoculated (C) or inoculated (I) with a mixture of LAB containing 1.5 × 105 colony-forming units (cfu)/g Lentilactobacillus hilgardii, 1.5 × 105 cfu/g of Lentilactobacillus buchneri, and 1.0 × 105 cfu/g Pediococcus pentosaceus plus a combination of xylanase + β-glucanase. Silage samples were taken after ensiling in bag silos for 418 days (terminal silage; TS), with subsamples of TS subsequently exposed to air for 14 days (aerobically exposed silage; AS). Regardless of treatment, Firmicutes, Proteobacteria, Cyanobacteria, and Actinobacteria were the predominant phyla in the bacterial microbiome, whilst Ascomycota and Basidiomycota were the predominant phyla in the fungal microbiome in both TS and AS. Lactobacillus, Acetobacter, and Bacillus were the most abundant bacterial genera, whilst Candida, Aspergillus, Vishniacozyma, Pichia, and Issatchenkia were the most abundant fungal genera. Use of silage additive did not change bacterial or fungal alpha or beta diversity during ensiling or aerobic exposure, but decreased (p < 0.01) the relative abundance (RA) of Proteobacteria in both TS and AS, increased (p < 0.01) RA of Firmicutes in AS, but did not affect the RA of fungal phyla in either TS or AS. At the genus level, the additive significantly decreased (p < 0.01) RA of Acetobacter in both TS and AS. The silage additive used in this study significantly affected the composition of multiple microbial genera during ensiling and aerobic exposure by shifting bacterial communities towards enhanced aerobic stability.
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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 it