Together is better. Experience of simultaneous fermentation of yeast and bacteria as a possible strategy to prevent stuck fermentation in difficult wines
Bibliographic record
Abstract
In the field of winemaking, malolactic fermentation is a key aspect in obtaining high quality wines. Unfortunately, in some oenological contexts effective evolution does not take place because of the occurrence of certain limiting factors for malolactic bacteria in wine. Simultaneous alcoholic and malolactic fermentation in grape must is a promising alternative that promotes the survival of bacteria, due to the absence of certain limiting factors such as ethanol or other toxic substances produced by yeasts in the native grape must. The risk of wine depreciation due to the spoilage activity of malolactic bacteria can be reduced by using selected strains of Oenococcus oeni, with proven behaviour in terms of malolactic fermentation occurring in the presence of sugars. In this work we compared the activity of a strain of Oenococcus oeni in malolactic fermentation of a Chardonnay grape must, using different winemaking protocols characterised by sequential or simultaneous inoculums of microbial starters. The results are discussed both in terms of fermentative behaviour and the quality of the wines obtained, with careful analysis of the main chemical parameters of the wines and of 47 different volatile compounds, giving an exhaustive overview of the opportunities and the risks related to different wine fermentation strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".