Alcoholic Beverage Strength Discrimination by Taste May Have an Upper Threshold
Bibliographic record
Abstract
BACKGROUND: Given the association between alcohol consumption and negative health consequences, there is a need for individuals to be aware of their consumption of ethanol, which requires knowledge of serving sizes and alcoholic strength. This study is one of the first to systematically investigate the ability to discriminate alcoholic strength by taste. METHODS: Nine discrimination tests (total n = 413) according to International Standardization Organization (ISO) 4120 sensory analysis methodology "triangle test" were performed. RESULTS: A perceptible difference was found for vodka in orange juice (0.0 vs. 0.5% vol; 0 vs. 1% vol), pilsner and wheat beer (0.5 vs. 5% vol), and vodka in orange juice (5 vs. 10% vol, 20 vs. 30% vol, and 30 vs. 40% vol). The percentage of the population perceiving a difference between the beverages varied between 36 and 73%. Alcoholic strength (higher vs. lower) was correctly assigned in only 4 of the 7 trials at a significant level, with 30 to 66% of the trial groups assigning the correct strength. For the trials that included beverages above 40% vol (vodka unmixed, 40 vs. 50% vol and vodka in orange juice, 40 vs. 50% vol), testers could neither perceive a difference between the samples nor assign correct alcoholic strength. CONCLUSIONS: Discrimination of alcoholic strength by taste was possible to a limited degree in a window of intermediate alcoholic strengths, but not at higher concentrations. This result is especially relevant for drinkers of unlabeled, over-proof unrecorded alcoholic beverages who would potentially ingest more alcohol than if they were to ingest commercial alcohol. Our study provides strong evidence for the strict implementation and enforcement of labeling requirements for all alcoholic beverages to allow informed decision making by consumers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".