Effects of Leaf Removal and Applied Water on Flavonoid Accumulation in Grapevine (<i>Vitis vinifera</i> L. cv. Merlot) Berry in a Hot Climate
Bibliographic record
Abstract
The relationships between variations in grapevine (Vitis vinifera L. cv. Merlot) fruit zone light exposure and water deficits and the resulting berry flavonoid composition were investigated in a hot climate. The experimental design involved application of mechanical leaf removal (control, pre-bloom, post-fruit set) and differing water deficits (sustained deficit irrigation and regulated deficit irrigation). Flavonol and anthocyanin concentrations were measured by C18 reversed-phased HPLC and increased with pre-bloom leaf removal in 2013, but with post-fruit set leaf removal in 2014. Proanthocyanidin isolates were characterized by acid catalysis in the presence of excess phloroglucinol followed by reversed-phase HPLC. Post-fruit set leaf removal increased total proanthocyanidin concentration in both years, whereas no effect was observed with applied water amounts. Mean degree of polymerization of skin proanthocyanidins increased with post-fruit set leaf removal compared to pre-bloom, whereas water deficit had no effect. Conversion yield was greater with post-fruit set leaf removal. Seed proanthocyanidin concentration was rarely affected by applied treatments. The application of post-fruit set leaf removal, regardless of water deficit. increased the proportion of proanthocyanidins derived from the skin, whereas no leaf removal or pre-bloom leaf removal regardless of water deficit increased the proportion of seed-derived proanthocyanidins. The study provides fundamental information to viticulturists and winemakers on how to manage red wine grape low molecular weight phenolics and polymeric proanthocyanidin composition in a hot climate.
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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 | 0.000 | 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".