Grape-associated fungal community patterns persist from berry to wine on a fine geographical scale
Bibliographic record
Abstract
Wine grape fungal community composition is influenced by abiotic factors including geography and vintage. Compositional differences may correlate with different wine metabolite composition and sensory profiles, suggesting a microbial role in the shaping of a wine's terroir, or regional character. While grape and wine-associated fungal community composition has been studied extensively at a regional and sub-regional scale, it has not been explored in detail on fine geographical scales over multiple harvests. Over two years, we examined the fungal communities on Vitis Vinifera cv. Pinot noir grape berry surfaces, in crushed grapes, and in lab spontaneous fermentations from three vineyards within a < 1 km radius in Canada's Okanagan Valley wine region. We also evaluated the effect of winery environment exposure on fungal community composition by sampling grapes crushed and fermented in the winery at commercial scale. Spatiotemporal community structure was evident among grape berry surface, crushed grape and fermentation samples, with each vineyard exhibiting a distinct fungal community signature. Crushed grape fungal populations were richer in fermentative yeast species compared to grape berry surface fungal populations. Our study suggests that, as on a regional level, fungal populations may contribute to fine-scale -terroir,' with significant implications for single-vineyard wines.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".