Complex choices: Pole selection for polarized projective mapping applied to a complex product set
Bibliographic record
Abstract
Abstract Polarized projective mapping (PPM) employs reference samples called poles, positioned in the sensory space, to which samples are compared. This structure enables comparisons across sessions. Selecting the right poles is therefore critical for accurate and reliable comparisons. The current study examined whether simple poles (single fruit juices) or complex poles (fruit juice blends) better represented the sensory space in PPM using a trained panel ( n = 12). Thirteen commercial fruit juices, including single juices and blends, were assessed using projective mapping to understand product diversity and select three simple and three complex poles. PPM was then conducted using simple poles in one session and complex poles in another. Six of the thirteen juices used in PPM and three new juices were evaluated to validate the results. Findings indicated that while both simple and complex poles described the product space, simple poles were more able to capture the diversity of the sensory space. Practical Applications The study's insights into appropriate pole selection in PPM for complex product sets contribute to the technique's usability and robustness. The guidance on pole selection ensures selected poles anchor the product space, enabling meaningful comparisons across sessions. The finding that simple poles are able to describe complex product sets may assist in minimizing fatigue, a common challenge in evaluating complex products like wine, spirits, and cider. By addressing the unique challenges of complex products, this research enhances the useability of PPM as a valuable tool for comprehensively evaluating sensory characteristics in product categories prone to fatigue and limited consumption.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".