Temporal Check‐All‐That‐Apply Characterization of Syrah Wine
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
Temporal Check-All-That-Apply (TCATA) is a new dynamic sensory method for which analysis techniques are still being developed and optimized. In this study, TCATA methodology was applied for the evaluation of wine finish by trained panelists (n = 13) on Syrah wines with different ethanol concentrations (10.5% v/v and 15.5% v/v). Raw data were time standardized to create a percentage of finish duration, subsequently segmented into thirds (beginning, middle, and end) to capture panel perception. Results indicated the finish of the high ethanol treatments lasted longer (approximately 12 s longer) than the low ethanol treatment (P ≤ 0.05). Within each finish segment, Cochran's Q was conducted on each attribute and differences were detected amongst treatments (P ≤ 0.05). Pairwise tests showed the high ethanol treatments were more described by astringency, heat/ethanol burn, bitterness, dark fruit, and spices, whereas the low ethanol treatment was more characterized by sourness, red fruit, and green flavors (P ≤ 0.05). This study demonstrated techniques for dealing with the data generated by TCATA. Furthermore, this study further characterized the influence of ethanol on wine finish, and by extension wine quality, with implications to winemakers responsible for wine processing decisions involving alcohol management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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