Projective mapping and ultra‐flash profile studies should include a list of descriptors and definitions: An investigation into descriptors used by untrained panelists
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 Projective mapping (PM) and ultra‐flash profile (UFP) have been used to evaluate various products, including wine. However, the meaning or definition of the descriptor participants use during a UFP task can be unknown. This study's objective was to have participants ( n = 81) evaluate white wine (seven different wines and one duplicate sample) using PM and UFP. After completing the task, the 10 most commonly used descriptors were included in a survey, and the participants were asked to define the descriptors using open‐ended questions. The survey was sent to all participants, but only 62 participants completed it. The results of the survey indicated the participants used different definitions to define the descriptors. In future research using PM and UFP, sensory researchers could provide a list of descriptors and their definitions for the participants, especially when they are untrained, or ask the participants to concentrate on only one sensory modality (appearance, taste, aroma, mouthfeel). Practical Applications Projective mapping and ultra‐flash profile trials can generate much information about a large number of products rapidly. However, these methods also have some limitations, including understanding the definition of the descriptors provided by the participants during the ultra‐flash profile task. This study hoped to overcome this limitation by asking the participants to complete a follow‐up survey to define the most frequently used descriptors from the ultra‐flash profile task. Unfortunately, the participants supplied contrasting definitions for the attributes. Future studies may want to supply the participants with a list of descriptors and definitions to lead to more consensus in the results.
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 | 0.001 | 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 | 0.001 |
| 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