EVALUATION OF IDEAL WINE AND CHEESE PAIRS USING A DEVIATION‐FROM‐IDEAL SCALE WITH FOOD AND WINE EXPERTS
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| 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