Metabolomicsto Understand the Impacts of Wildfireson Wines
Bibliographic record
Abstract
The Okanagan Valley is one of Canada’s premier wine producing regions. Wines produced in the Okanagan can be damaged by exposure to wildfire smoke. We hypothesized that exposure to wildfire smoke creates a terroir signature that can be detected by metabolomics. Our study developed a high-resolution mass spectrometry-based metabolomics approach to characterize the nonvolatile metabolites that are accumulated in grapes exposed to wildfires and retained throughout the wine-making process. We developed a method for wine metabolomics by reversed-phase chromatography with compound detection by high-resolution mass spectrometry (UHPLC-OrbiTrap MS). Data was extracted, aligned, and filtered by MZMine version 3.9.0 and analyzed with MetaboAnalyst, SIRIUS, and ZODIAC for ranking of formula candidates, CSI/FingerID to identify the structures of compounds, COSMIC to assign a confidence score to CSI/FingerID structure identifications, and CANOPUS to predict compound classes. More than 90 significant features were identified as being characteristic of the smoky wine, and two novel hypotheses were generated regarding nitrogen cycling physiology and flavonoid metabolism in wine grapes. This research contributes valuable insights into the broader impact of climate-induced challenges on wine composition and quality.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.185 | 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".