Table_2_Characterization of Sub-Regional Variation in Saccharomyces Populations and Grape Phenolic Composition in Pinot Noir Vineyards of a Canadian Wine Region.DOCX
Bibliographic record
Abstract
<p>Wine is a product of grape juice fermentation by yeast. Terroir is a term that encompasses all environmental factors and interactions at a specific geographical site, resulting in the development of regional-specific microbial strains and grape metabolites. In this study we determine the distribution of vineyard-associated wine yeast strains and characterize the flavonoid profile of Pinot Noir grapes among 3 sub-regions in the Okanagan Valley (OV), a major wine region in British Columbia, Canada. Pinot Noir grape samples were collected from 13 vineyards among 3 sub-regions of the OV, namely Kelowna (KE), Naramata-Penticton (NP) and Oliver-Osoyoos (OO), within a week prior to the winery harvesting date in 2016 and 2017. A total of 156 spontaneous Pinot Noir fermentations were conducted and vineyard-associated Saccharomyces strains were isolated from fermentations that reached two-thirds sugar depletion. Using microsatellite genotyping, we identified 103 Saccharomyces cerevisiae strains and 9 Saccharomyces uvarum strains. We also identified Saccharomyces paradoxus in one vineyard using ITS sequencing. We developed a microsatellite database of 160 commercial S. cerevisiae strains to determine the identity of the isolated strains and we include the database herein. Commercial strains were widely distributed across the three sub-regions. Forty-two of our 103 S. cerevisiae strains were equivalent or highly similar to commercial strains whereas the remaining 61 were considered as ‘unknown’ strains. Two S. uvarum strains were previously isolated in other OV studies and none matched the S. uvarum commercial strain BMV58. S. cerevisiae population structure was driven by sub-region, although S. cerevisiae populations did not differ significantly across vintages. S. uvarum and S. paradoxus were only identified in the 2017 vintage, demonstrating dynamic wine yeast populations between vintages. We found that the flavonoid profile of Pinot Noir grapes from the same 13 vineyards was also affected by sub-regional terroir. The anthocyanin content was lower and the proportion of methoxylated anthocyanins and flavonols was higher in Pinot Noir grapes from OO, the warmer sub-region as compared to KE, the cooler sub-region. Our study demonstrates that both yeast populations and metabolites associated with the Pinot Noir variety have sub-regional variation within a viticultural area.</p>
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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 | 0.000 |
| 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".