The Effect of Non-Saccharomyces and Saccharomyces Non-Cerevisiae Yeasts on Ethanol and Glycerol Levels in Wine
Bibliographic record
Abstract
Non-Saccharomyces and Saccharomyces non-cerevisiae studies have increased in recent years due to an interest in uninoculated fermentations, consumer preferences, wine technology, and the effect of climate change on the chemical composition of grapes, juice, and wine. The use of these yeasts to reduce alcohol levels in wines has garnered the attention of researchers and winemakers alike. This review critically analyses recent studies concerning the impact of non-Saccharomyces and Saccharomyces non-cerevisiae on two important parameters in wine: ethanol and glycerol. The influence they have in sequential, co-fermentations, and solo fermentations on ethanol and glycerol content is examined. This review highlights the need for further studies concerning inoculum rates, aeration techniques (amount and flow rate), and the length of time before Saccharomyces cerevisiae sequential inoculation occurs. Challenges include the application of such sequential inoculations in commercial wineries during harvest time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".