Quantitating Volatile Phenols in Cabernet Franc Berries and Wine after On-Vine Exposure to Smoke from a Simulated Forest Fire
Bibliographic record
Abstract
Smoke-taint is a wine defect linked to organoleptic volatile phenols (VPs) in Vitis vinifera L. berries that have been exposed to smoke from wildland fires. Herein, the levels of smoke-taint-associated VPs are reported in Cabernet Franc berries from veraison to commercial maturity and in wine after primary fermentation following on-vine exposure to simulated wildland fire smoke. VPs increased after smoke exposure were rapidly stored as acid-labile conjugates, and the levels of both free VPs and conjugated forms remained constant through ripening to commercial maturity. An increase in total VPs after primary fermentation suggested the existence of VP-conjugates other than the acid-labile VP-glycosides already reported. This conclusion was supported with base hydrolysis on the same samples. Relative to published results, the data suggested a multifactorial regional identity for smoke-taint and they inform efforts to produce a predictive model for perceptible smoke-taint in wine based on the chemical composition of smoke-exposed berries.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".