EVALUATION OF IDEAL WINE AND CHEESE PAIRS USING A DEVIATION‐FROM‐IDEAL SCALE WITH FOOD AND WINE EXPERTS
Bibliographic record
Abstract
ABSTRACT Most information regarding the suitability of wine and cheese pairs is anecdotal information. The objective of this research was to provide recommendations based on scientific research for the most desirable “wine & cheese pairs” using nine award‐winning Canadian cheeses and 18 BC wines (six white, six red and six specialty wines). Twenty‐seven wine and food professionals rated the wine and cheese pairs using a bipolar structured line scale (12 cm). The “ideal pair,” scored at the midpoint of the scale, was defined as a wine and cheese combination where neither the wine nor the cheese dominated. For each cheese, mean deviation‐from‐ideal scores were determined and evaluated by analysis of variance. Scores closest to six were considered “ideal,” while higher or lower scores represented pairs where the “wine” or the “cheese” dominated, respectively. In general, white wines had mean scores closer to six (“ideal”) than either the red or specialty wines. The late harvest, ice and port‐type wines were more difficult to pair . Judges varied considerably in their individual assessments reflecting a high degree of personal expectation and preference.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".