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.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".