EVALUATION OF WINE COMPETITION JUDGE PERFORMANCE USING PRINCIPAL COMPONENT SIMILARITY ANALYSIS
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Principal component similarity (PCS) analysis was used to evaluate judge performance from a wine competition. Data were analyzed for five international judges and seven wine makers, for 42 white, 30 red and 25 specialty wines, using a 20‐point quality scoring system. Principal similarity plots were used to group judges according to judging 'style’ and to identify outliers, for each wine category. Judge groupings were consistent when three different references were used; however, the most interpretable PCS plot was obtained when the overall mean‐judge‐score was used as the reference. Results from PCS were compared to principal component analysis (PCA). PCS analysis allowed the information from all significant principal components to be graphically represented in two dimensions and was more successful in classifying judges than plots based on the first three principal components. The technique of PCS is an important complement to existing methodologies, and can provide wine competition coordinators with an objective technique for judge evaluation and selection.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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