New multivariate strategy for panel evaluation using principal component similarity
Bibliographic record
Abstract
Abstract Advantages of principal component similarity (PCS) as an unsupervised classification techniques compared to supervised methods are the detectability of outliers or anomalies, a capacity to classify continuum, easy identification of causes for grouping, and a potential of discovering new groups. The ability of detecting outliers or anomalies was utilized to eliminate panel members who were not appropriate for the classification purposes by using PCS scattergrams. After eliminating the unqualified panelists, the evaluation score tables were realigned from the one based on panelists to that based on samples. Accumulated principal component score were computed for the samples in a form of SPCi × Si, where PCi was the principal component score of sample i and Si was its proportion within the total variation. The conventional averaging algorithms of evaluated scores are useful, as this techniques absorbs the effect of unjustifiable negative scores reported by outliers. However, more reasonable summary scores for the samples could be obtained by eliminating unreasonable evaluation scores made by the outliers as well as by using the above new summary values. Differences in deviations of evaluation among attributes, such as those in likenesses of color and taste of meat products, were normalized. Specific training or selecting qualified panelists prior to panel evaluation is unnecessary when the information obtained from panel evaluation is required to reflect the variable broad patterns of consumer preference. Information obtained from consumer preference test based on cluster analysis, which was applied to a beverage, might be more readily recovered by using this new strategy.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".