Using molecular microbial ecology to define differential responses to the inoculation of barley silage
Bibliographic record
Abstract
Previously, we investigated the impact of a mixed Lactobacillus buchneri, Lactobacillus plantarum, and Lactobacillus casei inoculant on fermentation and aerobic stability of barley silage over two years in 2009 and 2010. In 2009, a classical response to inoculation was obtained with an increase in acetic acid concentration of silage ensiled in both mini- and bag silos. In 2010, this classical response was not observed in mini-silos but was observed in bag silos. The objective of this study was to determine if molecular microbial ecology could explain the differential responses to the inoculation of barley silage between the two years. The Illumina MiSeq sequencing results showed that inoculation increased Lactobacillus and lowered Pediococcus, Weissella, and Leuconostoc in both types of silos in 2009. However, a similar trend was not observed in mini-silos, but was instead observed in bag silos in 2010. Inoculation did not alter the core fungal community in either silo type in either year. Cladosporium, Leptosphaeria, and Cryptococcus were abundant in fresh forage, but were superseded by Pichia and Kazachstania after ensiling. Our results suggest that changes in silage chemistry corresponded to differences observed in microbial ecology. Inoculation may have less impact when using more mature crops with shorter ensiling times.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".