Effect of sulfite addition and<i>pied de cuve</i>inoculation on the microbial communities and sensory profiles of Chardonnay wines: dominance of indigenous<i>Saccharomyces uvarum</i>at a commercial winery
Bibliographic record
Abstract
The microbial consortium of wine fermentations is highly dependent upon winemaking decisions made at crush, including the decision to inoculate and the decision to add sulfur dioxide (SO2) to the must. To investigate this, Chardonnay grape juice was subjected to two inoculation treatments (uninoculated and pied de cuve inoculation) as well as two SO2 addition concentrations (0 and 40 mg/L). The bacterial communities, fungal communities and Saccharomyces populations were monitored throughout fermentation using culture-dependent and culture-independent techniques. After fermentation, the wines were evaluated by a panel of experts. When no SO2 was added, the wines underwent alcoholic fermentation and malolactic fermentation simultaneously. Tatumella bacteria were present in significant numbers, but only in the fermentations to which no SO2 was added, and were likely responsible for the malolactic fermentation observed in these treatments. All fermentations were dominated by a genetically diverse indigenous population of Saccharomyces uvarum, the highest diversity of S. uvarum strains to be identified to date; 150 unique strains were identified, with differences in strain composition as a result of SO2 addition. This is the first report of indigenous S. uvarum strains dominating and completing fermentations at a commercial winery in North America.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".