Evaluation of Zygosaccharomyces bailii to Metabolize Residual Sugar Present in Partially-Fermented Red Wines
Bibliographic record
Abstract
An alternative approach to remove residual sugar from red wines using strains of Zygosaccharomyces bailli was studied. Fructose (40 or 60 g/L) and alcohol (13%, 15%, or 17% v/v) were added to a Cabernet Sauvignon wine before inoculation of Z. bailii B2, B6, or W3, or Saccharomyces cerevisiae EC1118. Most yeasts maintained populations ≥106 cfu/mL up to 100 days—the exceptions being W3 and EC1118, which declined to ≤30 cfu/mL in 17% alcohol wines beyond day 75. Wines containing 40 g/L fructose and 13% alcohol achieved dryness (<2 g/L), except those inoculated with B6. At 15% alcohol, B6, W3, and EC1118 consumed large levels of fructose (>80% of the 40 g/L; >50% of the 60 g/L) but limited amounts from wines containing 17% alcohol. Volatile acidities were higher in wines inoculated with strains of Z. bailli compared to S. cerevisiae (0.88 and 0.75 g/L, respectively). Fructose utilization in a partially-fermented Syrah wine varied, with dryness achieved by EC1118 or a mixed culture of B2 and B6. While Z. bailii metabolized residual fructose in wines of varying alcohol content, the use of S. cerevisiae EC1118 was generally as effective and did not produce as much volatile acidity.
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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) | 0.001 | 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".