Evaluation of Yeast Strains during Fermentation of Riesling and Chenin blanc Musts
Bibliographic record
Abstract
Ten strains of Saccharomyces cerevisiae (V1116, D254, UCD 522, Bourgoblanc, EC1118, UCD 595, S6U, Wadenswil 27, 71B, T73) were evaluated in terms of their impact on the chemical composition of fermenting Riesling and Chenin blanc musts and on the sensory characteristics of their resultant wines. All yeasts completely utilized glucose present. Fructose concentrations in finished wines ranged from 1.5 to 7.0 g/L and were highest in UCD 595. Glycerol concentration was highest in Bourgoblanc and lowest in T73. Ethanol production was similar among the various strains, although UCD 522 produced higher ethanol than D254. Some differences existed between the yeasts in terms of production and/or use of six organic acids (citric, tartaric, malic, succinic, lactic, and acetic). Lowest tartaric and malic acids were found in 71B fermentations, while highest acetic acid concentrations were measured in both 71B and Wadenswil 27. Wadenswil 27 fermentations also contained highest tartaric, succinic (along with Bourgoblanc), and lactic acids. Highest citric acid was found in S6U fermentations, while both S6U and V1116 contained lowest acetic acid concentrations. T73 contained highest malate and lowest citrate. Sensory evaluation of Riesling wines showed that most yeasts produced more intense aromas and flavors than those fermented with EC1118. Wadenswil 27 produced Chenin blanc wines with more aroma and flavor intensity than EC1118.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".