Using check‐all‐that‐apply to evaluate wine and food pairings: An investigation with white wines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Growing consumer interest in food and wine pairing leads to a need for more studies to be conducted evaluating consumers' perception of food pairings. Many studies have used trained panelists to evaluate food and wine pairings; however, this study sought to determine how consumers evaluate food and wine pairings using the check‐all‐that‐apply (CATA) method. The participants ( n = 112) were asked to evaluate five white wines for their liking of the wine, their sensory perception of the wines and identify which food items they would pair with the wine using a CATA question. The participants separated the wines based on their sweetness and dryness, as well as their acidity. The participants liked sweet, citrus, fruity and floral white wines, and disliked earthy and sour attributes. When the participants paired the wine with hard cheeses and chocolate their liking increased; however, when the wines were paired with French fries, steak, and lemon pie, it detracted from their liking. Future research should ask participants to explain their pairing choices using open‐ended comment questions. Practical Applications Very few studies have explored consumers' food and wine pairing preferences. This study identified which food consumers pair with different white wines and how food items can positively or negatively impact consumers' liking of the wine. The results of this study are important to those working in the food and wine service industry. The findings of this study contribute to the gap in knowledge of consumers' preferred food pairings and investigate the use of a check‐all‐that‐apply question to evaluate food and wine pairings. Future research should ask consumers to explain their food pairings, as well as evaluate how wine knowledge and familiarity with wine impact their pairing decisions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it