Basidiomycete Yeasts of Wine Grapes and Their Potential Applications in Winemaking
Bibliographic record
Abstract
Vineyards support highly diverse communities of native yeasts, but only a small proportion are fermentative ascomycetes capable of alcoholic fermentation. Many non-fermentative species are also present, including a range of metabolically active basidiomycete yeasts that can influence wine aromatic profiles, especially in the early stages of fermentation. In some cases, basidiomycete yeasts, such as Filobasidium, Rhodotorula, Sporobolomyces, and Vishniacozyma, are more abundant and diverse than ascomycete yeasts in grape musts, with some persisting throughout fermentation. As the existing information on the role of basidiomycete yeasts in winemaking is fragmented, we synthesize the records of these yeasts in association with wine grapes and musts, as well as the research on their potential applications in winemaking. Basidiomycete yeasts are gaining attention for their unique biochemical contributions to wine flavor, influencing sensory attributes through the production of metabolites such as acids, higher alcohols, aldehydes, ketones, esters, and glycerol, as well as by their enzymatic activities and by the production or utilization of resources used by fermentative yeasts. Basidiomycete yeasts play especially important roles in shaping spontaneously fermented wines (which rely solely on the yeasts present on the grapes) and have the potential to help produce wines with increased aromatic complexity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".