Self-Rated Aversion to Taste Qualities and the PROP Taster Phenotype Associate with Alcoholic Beverage Intake and Preference
Bibliographic record
Abstract
Consumers often identify “taste” as an important factor when selecting alcoholic beverages. Although it is assumed that reduced alcohol consumption in PROP super-tasters is due to a greater dislike of the nominally aversive sensations that they experience more intensely (e.g., bitterness) when compared to PROP non-tasters, this question has not been specifically asked to them. Therefore, we examined consumers’ self-reported aversion towards specific sensory attributes (bitter, hot/burn, dry, sour, sweet, carbonation) for four alcoholic beverage types (white wine, red wine, beer, spirits) using a convenience sample of U.S. wine consumers (n = 925). Participants rated 18 statements describing different combinations of sensory attributes and alcoholic beverages on a 5-point Likert scale (e.g., Beer tastes too bitter for me). Individuals who tended to agree more strongly with the statements (i.e., they were more averse; p(F) < 0.05) tended to (i) consume less of all beverage types, (ii) consume a higher proportion of white wine (p(r) < 0.05), and (iii) were more likely to be female or PROP super-tasters. The results suggest that self-reported aversion to specific sensory attributes is associated with not only lower overall intake of alcoholic beverages, but also a shift in the relative proportions of beverage type consumed; a key finding for studies investigating how taste perception impacts alcohol consumption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".