A COMPARISON OF METHODS FOR EVALUATING THE PERFORMANCE OF A TRAINED SENSORY PANEL<sup>1</sup>
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 Cluster analysis, consonance analysis, principal component analysis (PCA) and the GRAPES program (Schlich 1994) were compared for the evaluation of panel performance. Ten judges evaluated 25 Merlot wines for 24 color, aroma and flavor attributes. Cluster analysis grouped similar judges. PCA identified judges according to their attribute use. Consonance analysis determined a numerical index for attribute agreement and the GRAPES program compared judges in their use of the scale, reliability, discrimination and disagreement. Three of the four techniques provided a graphical representation of similarities and differences between judges. Methodologies were best used in conjunction with one another. Ultimately the application of these tools will serve to improve the quality of sensory evaluations.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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