How do Syrah winemakers from two different French regions conceptualise peppery wines?
Bibliographic record
Abstract
This work investigated Syrah producers' conceptualisation of peppery wines from two different wine regions. For the study, a long-term memory approach was used; in addition, the effects of the region of origin, as well as the sensitivity of the participants to detect rotundone, were evaluated. A total of 101 winemakers from the Northern Rhone Valley (NRV) and Languedoc-Roussillon (LR) were interviewed face-to-face after they had participated in two 3-alternative forced tests to assess their ability to detect rotundone. As part of the interview, participants were asked to remember the last peppery red wine they had tasted, to provide technical information about that wine and, more generally, about practices enhancing this character in wine, and to give their overall appreciation of such peppery notes. Only minor differences were observed between participants with either low or high sensitivity to rotundone; in contrast, an important regional effect on the conceptualisation of peppery notes was observed. Experts from the NRV recognised this character as a marker of wines made from under-ripe grapes. Overall, they perceived this flavour as a positive attribute, notably at a moderate level, but some experts also perceived it negatively. For LR participants, peppery notes were associated with powerful, full-bodied wines from very ripe grapes produced in the South of France and were notably perceived as a positive character. Our results are particularly relevant for the wine community as they show that the conceptualisation of a given wine aroma characteristic by winemakers can strongly differ according to their region of origin.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".