Understanding consumers’ perceptions of smoke-affected wines
Bibliographic record
Abstract
Smoke taint in grapes and wine is complicated. If a vineyard is exposed to smoke, there are a whole range of factors that determine whether or not the wine eventually made from those grapes will be smoke-affected and to what degree. While many of those factors are now quite well understood (Coulter, 2022; Parker et al., 2023), questions still remain about what consumers think. Do they notice smoke characters in wine? Do they like or dislike them? How strong do smoke characters need to be to cause a reaction in consumers? And are all consumers the same when it comes to smoky wines?Three recent consumer sensory studies at the AWRI aimed to learn more about the answers to these questions. This article presents a summary of the results and conclusions of this work. Full details were recently published by Bilogrevic et al. (2023) as an open access article in the peer-reviewed journal, OENO One (https://doi.org/10.20870/oeno-one.2023.57.2.7261).
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.
How this classification was reachedexpand
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 | 0.000 | 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) | 0.001 | 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 teacher head, 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".