Dynamic Succession of Microbiota during Ensiling of Whole Plant Corn Following Inoculation with Lactobacillus buchneri and Lactobacillus hilgardii Alone or in Combination
Bibliographic record
Abstract
Lactic acid bacteria (LAB) used as silage additives have been shown to improve several fermentation parameters, including aerobic stability. Inoculation with a combination of Lactobacillus buchneri NCIMB40788 and Lactobacillus hilgardii CNCM-I-4785, contributes to an increase in aerobic stability, compared to each strain inoculated independently. To understand the mode of action of the combination on the LAB community, a fermentation-kinetic study was performed on corn. Four treatments, Control, Lb. buchneri, Lb. hilgardii, and a combination of the two strains, were fermented 1, 2, 4, 8, 16, 32, and 64 days. Corn silage inoculated by both strains had a lactate:acetate ratio of 0.59 after 64 days and a higher concentration of lactate than Lb. buchneri. Analysis of the microbiota by 16S and ITS amplicon metasequencing demonstrated that inoculation led to lower bacterial diversity after 1 day, from 129.4 down to 40.7 observed operational taxonomic units (OTUs). Leuconostocaceae represented the dominant population by day 1, with 48.1%. Lactobacillaceae dominated the succession by day 4, with 21.9%. After 32 days, inoculation by both strains had the lowest bacterial alpha diversity level, with 29.0 observed OTUs, compared to 61.3 for the Control. These results confirm the increased fermentation efficiency when the two Lactobacillus strains are co-inoculated, which also led to a specific yeast OTUs diversity profile, with Hannaella as the main OTU.
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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".