A Modern Ampelography: A Genetic Basis for Leaf Shape and Venation Patterning in Grape
Bibliographic record
Abstract
Terroir, the unique interaction between genotype, environment, and culture, is highly refined in domesticated grape (Vitis vinifera). Toward cultivating terroir, the science of ampelography tried to distinguish thousands of grape cultivars without the aid of genetics. This led to sophisticated phenotypic analyses of natural variation in grape leaves, which within a palmate-lobed framework exhibit diverse patterns of blade outgrowth, hirsuteness, and venation patterning. Here, we provide a morphometric analysis of more than 1,200 grape accessions. Elliptical Fourier descriptors provide a global analysis of leaf outlines and lobe positioning, while a Procrustes analysis quantitatively describes venation patterning. Correlation with previous ampelography suggests an important genetic component, which we confirm with estimates of heritability. We further use RNA-Seq of mutant varieties and perform a genome-wide association study to explore the genetic basis of leaf shape. Meta-analysis reveals a relationship between leaf morphology and hirsuteness, traits known to correlate with climate in the fossil record and extant species. Together, our data demonstrate a genetic basis for the intricate diversity present in grape leaves. We discuss the possibility of using grape leaves as a breeding target to preserve terroir in the face of anticipated climate change, a major problem facing viticulture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".