Consumer response to wine made from smoke-affected grapes
Bibliographic record
Abstract
When vineyards and grapes are exposed to smoke from wildfires or controlled burns, this can result in wines with smoky, burnt or ashy attributes that have been linked to the presence of elevated concentrations of volatile phenols and phenolic glycosides. These smoky flavours are considered undesirable by winemakers, but there is little information about how consumers respond to smoke-affected wines. To investigate whether consumers respond negatively to smoky attributes when wine is tasted blind, three studies assessing sets of Pinot noir rosé, Chardonnay and unoaked Shiraz wines with varied smoke flavour were conducted. Overall, wines rated high in smoke flavour were less liked compared to non-smoke-affected wines. Independent of wine type, there was a strong negative correlation between smoky flavour and overall consumer liking. Detailed data analysis revealed that consumers who are wine drinkers fell into one of three categories: highly responsive to smoke, moderately responsive, or a smaller group of non-responders. This consumer-based information is essential for guiding the assessment of risk from smoke exposure of grapes and potential for quality defects in wine, as well as identifying and benchmarking management options for wine producers, not only in Australia, but globally.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".