Quantifying the grapevine xylem embolism resistance spectrum to identify varieties and regions at risk in a future dry climate
Bibliographic record
Abstract
Maintaining wine production under global warming partly relies on optimizing the choice of plant material for a given viticultural region and developing drought-resistant cultivars. However, progress in these directions is hampered by the lack of understanding of differences in drought resistance among Vitis genotypes. We investigated patterns of xylem embolism vulnerability within and among 30 Vitis species and sub-species (varieties) from different locations and climates, and assessed the risk of drought vulnerability in 329 viticultural regions worldwide. Within a variety, vulnerability to embolism decreased during summer. Among varieties, we have found wide variations in drought resistance of the vascular system in grapevines. This is particularly the case within Vitis vinifera, with varieties distributed across four clusters of embolism vulnerability. Ugni blanc and Chardonnay featured among the most vulnerable, while Pinot noir, Merlot and Cabernet Sauvignon ranked among the most resistant. Regions possibly at greater risk of being vulnerable to drought, such as Poitou-Charentes, France and Marlborough, New Zealand, do not necessarily have arid climates, but rather bear a significant proportion of vulnerable varieties. We demonstrate that grapevine varieties may not respond equally to warmer and drier conditions, and highlight that hydraulic traits are key to improve viticulture suitability under climate change.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".