Use of experienced panelists and the projective mapping task in comparison to trained panelists and naïve consumers
Bibliographic record
Abstract
Abstract Projective mapping (PM) is rapid sensory method that is becoming more popular among sensory scientists to obtain general product descriptions. This study compares the results of a descriptive analysis panel completed by trained panelists to the results of a PM task completed by experienced panelists (n = 22) and naïve consumers (n = 79), using cookies made with alternative grains as a model. The experienced panelists in this trial were considered as those who have experience with the PM task; however, they do not experience with the products being tested. There was no correlation between the naïve consumers and experienced panelists (RV = 0.297). The RV coefficient between the experienced panelists and the trained panelists was 0.665, indicating a high similarity. These results indicate that experience with the sensory task has drastic effects on the panelists' evaluations. Future work needs to explore when experienced panelists are the most suitable group of assessors to be used. Practical applications Experienced panelists are panelists who have extensive knowledge and experience with a particular sensory method. However, in this trial, they do not possess knowledge or training about the products being assessed. This study investigates how experienced panelists performing the PM task compare to naïve consumers and trained panelists. There was no correlation between the naïve consumers and the experienced panelists; however, there was a correlation between the trained and experienced panelists. Further examination is necessary; however, research may indicate that when time, resources, or product is limited, experienced panelists may be a good surrogate population for trained panelists.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".