Microbiome dynamics during malting of barley grains infested by <i>Fusarium graminearum</i> strains
Bibliographic record
Abstract
Abstract Barley grain harvested for malting poses an interesting case study for the impacts of plant‐associated microbiomes because the conditions created during malting favour continued vigorous microbial activity after grain harvest. Suppression of pathogens is among the key functions performed by microbiomes, and it would be beneficial if we could harness this function. Over a 3‐year field trial, we micro‐malted barley that had been experimentally infested with each of seven different strains of the fungal pathogen Fusarium graminearum , and profiled bacterial microbiomes at key stages in the malting process (grain, post‐steeping, post‐germination and final dried malt). The greatest impacts on bacterial microbiomes were observed to be from the year and stage of malting, with comparatively fewer impacts of barley cultivar or pathogen strain. Relatively limited impacts of the presence and strain identity of fungal pathogen on the bacterial microbiome of malting barley probably reflects the colonization by other fungi, as our inoculation took place under field conditions with supplemental irrigation. Both community level shifts and significant responses by individual taxa were evident. Microbiome characteristics or the relative abundances of specific bacterial taxa helped to explain pathogen biomass, mycotoxin production and beer gushing. Manipulative experiments will be required to test hypotheses suggested by these microbiome profiles.
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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 | 0.001 | 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".