Ethanol Difference Thresholds in Wine and the Influence of Mode of Evaluation and Wine Style
Bibliographic record
Abstract
This study sought to determine the minimum change in ethanol concentration (ETOH) before consumers could perceive a difference in wine (ethanol difference threshold, EDT). Ethanol difference thresholds were determined orthonasally and retronasally in four base wines in order to investigate the effect of wine style, evaluation mode, and initial ETOH on EDTs. Wines were Chardonnay with 11.6% v/v ETOH (CL), Chardonnay with 13.4% v/v ETOH (CH), Zinfandel with 11.5% v/v ETOH (ZL), and Zinfandel with 13.4% v/v ETOH (ZH) Individual best-estimate thresholds were determined for 13 Asian and 13 Caucasian subjects using the ASTM method E679-04. Group ethanol difference thresholds (% ethanol, v/v; orthonasally and retronasally, respectively) were CL: 0.50 and 1.20; CH: 0.58 and 1.03; ZL: 1.08 and 1.32; and ZH: 1.14 and 1.31. Significant effects on ethanol difference thresholds were found for wine style, evaluation mode, and their interaction, but not initial ETOH. Differences in best-estimate thresholds were observed for ethnicity, wine consumption level, sensory panel experience and experience*gender, but not for gender. While ethanol difference thresholds in wine were found to be lower than previously reported, the results raise questions about the rationale underlying some alcohol adjustment decisions and practices in industry.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".