Sensory Discrimination Tests for Low- and High-Strength Alcohol
Bibliographic record
Abstract
Research is limited on consumers’ ability to detect perceptible sensory differences between low- and high-strength alcoholic beverages. This study, therefore, conducted three pilot experiments using ISO sensory analysis methods to assess accuracy for evaluating beverages of different strengths. Participants were food production professionals trained in sensory analysis. Experiment 1 used a wide-range discrimination test to estimate low- to high-strength beverages (0–60% alcohol by volume (ABV) in 10% intervals). Experiment 2 included a narrower range of intermediate to high strengths (25–45% ABV in 5% intervals). Experiment 3 used 3-alternative forced choice tests (ISO 13301) to discriminate between beverages of varying strengths. Experiment 1 (n = 16) indicated that estimation ability was dependent upon the beverages’ ABV; as ABV increased, estimation significantly decreased (p < 0.005). These findings were not replicated in Experiment 2 (n = 13). In Experiment 3 (n = 17), a significant perceptible difference between high- and low-strength samples was observed in two of nine conditions (35% vs. 31% ABV (p = 0.009); 41% vs. 37% ABV (p = 0.037)). While people can detect large differences in beverage ABVs, they may have a moderate to poor ability to discriminate between beverages of similar strengths. These findings provide support for public health interventions that promote lower-strength alcoholic beverages.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".