An exploratory study about the wine tasting terminology to non-expert wine drinkers
Bibliographic record
Abstract
Wine is a product known and produced in various regions of the world for many centuries and is part of the human heritage/culture. However, despite being such a common product, studies indicate that the wine terminology used by experts (bottle label; tasting notes) several consumers do not understand it. To understand if this applies to European Portuguese, we applied a survey to European Portuguese informants. Firstly, we select terms related to color (retinto, carregado, limpo), aroma (equilibrado, frutado, leve), and flavor (frutado, austero, mineral) among others. After we created the question structure "Define, in your own words, what you understand by (insert term) wine?" The informant had no space limit to write. With this question, we wanted to see how informants defined by their words, without resorting to existing sources (such as glossaries, dictionaries) what they understood by that particular word in the specific context related to wine. Secondly, we searched the exact terms in specialized reference works (wine dictionaries/glossaries/ vocabularies) and compiled the lexicographic definitions. In a third moment, we organized the definitions of the surveys and the lexicographic definitions and confronted the results. The results of this sample allowed us to identify the following aspects: (i) Whether informants/consumers are, on the one hand, aware of what definition is and what essential characteristics should be present when defining something; (ii) What is the degree of proximity (or not) of the informant definitions to the expert definitions; (iii) Which terms have definitions closest to lexicographic definitions and which terms have definitions that are more distant. The findings may also provide clues to future methodologies on how to make lexicographical definitions about wine tasting, so the non-specialist-consumer is able to better understand the wine.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".