Prevalence of Wildfire Smoke Exposure Markers in Oaked Commercial Wine
Bibliographic record
Abstract
<h3>Abstract</h3> <h3>Background and goals</h3> Grapes exposed to wildfire smoke and wine produced from contaminated grapes can be robustly identified through quantitative analysis of smoke exposure markers, volatile phenols, and phenolic glycosides (PGs). This assessment is based on comparison of data from suspect samples to concentrations of phenolic compounds typically found in non-smoke-exposed grapes and unoaked wines. Oak products for winemaking are typically heat treated and represent a major source of guaiacol and other volatile phenols in wine. Although contact with oak products is thought to contribute negligible concentrations of PGs, the lack of data from oaked wines confounds the identification of a potential risk of smoke taint development in wine when assessing commercially produced, oaked wine. Therefore, this study aimed to determine the typical concentrations of smoke exposure markers in commercially produced, oaked wine. <h3>Methods and key findings</h3> Commercially produced wines (20 to 30 each) of Cabernet Sauvignon, Chardonnay, Pinot noir, and Shiraz cultivars were sourced from Australian regions and vintages free from known wildfire smoke exposure. Gas chromatography-mass spectrometry and high-performance liquid chromatography-mass spectrometry demonstrated that syringol and guaiacol were relatively abundant in oaked wine, reaching concentrations of 200 μg/L. In contrast, most PGs were <10 μg/L, and trace concentrations of cresols were infrequently found. <h3>Conclusions and significance</h3> The concentrations of established wildfire smoke marker compounds (guaiacol, 4-methylguaiacol, syringol, 4-methylsyringol, <i>o</i>-cresol, <i>m</i>-cresol, <i>p</i>-cresol, syringol gentiobioside, 4-methylsyringol gentiobioside, cresol rutinoside, phenol rutinoside, guaiacol rutinoside, and 4-methylguaiacol rutinoside) were determined in oaked Australian Cabernet Sauvignon, Chardonnay, Pinot noir, and Shiraz wines. The data enable confident identification of smoke-affected wine that has been in contact with oak.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".