Product selection for liking studies: The sensory informed design
Bibliographic record
Abstract
Liking studies are designed to ascertain consumers likes and dislikes on a variety of products. However, it can be undesirable to construct liking studies where each panelist evaluates every target product. In such cases, an incomplete-block design, where each panelist evaluates only a subset of the target products, can be used. These incomplete blocks are often balanced, so that all pairs occur the same number of times. While desirable in many situations, balanced incomplete blocks have the disadvantage that, by their nature, they cannot favor placing dissimilar products next to one another. A novel incomplete-block design is introduced that utilizes the target product’s sensory profile to allocate products to each panelist so that they are, in general, as dissimilar as possible while also ensuring position balance. The resulting design is called a sensory informed design (SID). Herein, details on the formulation of SIDs are given. Data arising from these SIDs are analyzed using a simultaneous clustering and imputation approach, and the results are discussed.
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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 | 0.050 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".