Applying temporal check‐all‐that‐apply (TCATA) to mouthfeel and texture properties of red wines
Bibliographic record
Abstract
Abstract Temporal check‐all‐that‐apply (TCATA) has been used to characterize wines on a nonspecific basis using a range of attributes to investigate sensory differences between wines. The aim of this study was to ascertain whether TCATA, when focused on specific modalities, could distinguish red wines made from the same grape variety, according to mouthfeel and texture descriptors only. Two trained panels evaluated three wines, made from three grape varieties. A combined training approach that used tactile touch standards together with wine sensory evaluation was used to identify mouthfeel and texture sensations. Panelists identified four sensations relevant to all wines: grippy, fine, coarse, and astringent. Differences between wines produced from the same varieties were found for Pinot noir and Cabernet franc but not Cabernet sauvignon. Our results indicate that TCATA is a reliable technique to discriminate red wines according to their mouthfeel and texture profiles during consumption. Practical applications This study investigated the ability of the temporal check‐all‐that‐apply (TCATA) sensory method to distinguish between red wines made from the same grape variety based on mouthfeel and texture properties only. Results from the present work show that TCATA could be used to identify differences in monovarietal wines made from different winemaking techniques.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".