Large-Scale Reassessment of In-Vineyard Smoke-Taint Grapevine Protection Strategies and the Development of Predictive Off-Vine Models
Bibliographic record
Abstract
Smoke taint in wine is thought to be caused by smoke-derived volatile phenols (VPs) that are absorbed into grape tissues, trapped as conjugates that are imperceptible by smell, and subsequently released into wines as their free odor-active forms via metabolism by yeasts during fermentation. Blocking VP uptake into grapes would, therefore, be an effective way for vineyards to protect ripening grape crops exposed to smoke. Here, we re-evaluated a biofilm that had previously shown promise in pilot studies in reducing levels of smoke-derived VPs. A suite of nine free and acid-labile VPs were quantitated in Pinot Noir grapes that had been exposed to smoke after being coated with the biofilm one, seven or 14 days earlier. In contrast with earlier studies, our results demonstrated that in all cases, the biofilm treatments led to increased concentrations of both free and total VPs in smoke-exposed grapes, with earlier applications elevating concentrations of some VPs more than the later time points. Tracking VP concentrations through the grape ripening process demonstrated that some (phenol, p/m-cresol, and guaiacol) were not entirely sequestered in grapes as acid-labile conjugates, suggesting the presence of VP storage forms beyond simple glycosides. Free VPs in grapes, though a minor portion of the total, most clearly correlated with concentrations present in the resulting wines. Finally, red table grapes, available year round, were observed to replicate the effects of the biofilm treatments and were capable of transforming most VPs into acid-labile conjugates in under 24 h, indicating that they might be an effective model for rapidly assessing smoke-taint prophylactic products in the laboratory.
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 | 0.000 |
| 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 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".