Development and Evaluation of a Vineyard-Based Strategy To Mitigate Smoke-Taint in Wine Grapes
Bibliographic record
Abstract
Smoke-taint is a wine defect that may occur when ripening grape crops absorb volatile phenols (VPs), compounds associated with the negative sensory attributes of smoke-taint, due to exposure of grapes to wildfire smoke. This study examined potential methods to reduce the impact that smoke-exposure has on wine grapes. Specifically, agricultural sprays normally used to protect grapes from fungal pathogens and a spray used to prevent cracking in soft-fleshed fruits were assessed for their capacity to inhibit increases in VP concentrations in wine grapes following on-vine smoke-exposure. The results indicated that an artificial grape cuticle applied 1 week before exposure to simulated forest fire smoke (at 1-2 weeks after veraison) can significantly hinder an increase in VP concentrations in smoke-exposed grapes at commercial maturity. This reduction in VP concentrations may mitigate crop losses experienced globally by the wine industry due to exposure of grapes on-vine (at key phenological stages) to wildfire smoke.
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".