Grape ripening speed slowed down using natural variation
Bibliographic record
Abstract
ABSTRACT Understanding ripening patterns and governing ripening speed are central aspects of grapevine ( Vitis vinifera ) berry biology owing to the importance of grape ripeness in winemaking. Despite this, the genetic control of ripening is largely unknown. Here, we report a major quantitative trait locus that controls ripening speed, expressed as speed of sugar accumulation. A haplotype originating from the species Vitis riparia halves maximum speed regardless of crop levels and berry sizes. The sequence of events that are normally completed at the onset of ripening in a two-week period known in viticulture as veraison are taking place at a slower speed, thereby attaining ripeness under milder weather conditions in late summer. V. vinifera cultivars show limited phenotypic variation for ripening speed and no selective sweep in the causal genomic region that could derive from domestication or improvement. Closely related species make up for the lack of standing variation, supplying major effect alleles for adapting grape cultivars to climate change. HIGHLIGHT / SIGNIFICANCE STATEMENT Reducing the speed of fruit ripening genetically is a means for adapting the grape berry developmental program to the changing needs of the wine industry and in response to global warming. We identified a haplotype in a wild grape species that slows down the speed of ripening in progenies of Vitis vinifera by limiting the speed of sugar accumulation throughout the duration of ripening, a condition of great importance for winemakers to harvest their grapes at the desired level of technological ripeness.
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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 | 0.001 |
| 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) | 0.001 | 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".