Impact of Leaf Removal, Applied Before and After Flowering, on Anthocyanin, Tannin, and Methoxypyrazine Concentrations in ‘Merlot’ (<i>Vitis vinifera</i> L.) Grapes and Wines
Bibliographic record
Abstract
The development and accumulation of secondary metabolites in grapes determine wine color, taste, and aroma. This study aimed to investigate the effect of leaf removal before flowering, a practice recently introduced to reduce cluster compactness and Botrytis rot, on anthocyanin, tannin, and methoxypyrazine concentrations in 'Merlot' grapes and wines. Leaf removal before flowering was compared with leaf removal after flowering and an untreated control. No effects on tannin and anthocyanin concentrations in grapes were observed. Both treatments reduced levels of 3-isobutyl-2-methoxypyrazine (IBMP) in the grapes and the derived wines, although the after-flowering treatment did so to a greater degree in the fruit specifically. Leaf removal before flowering can be used to reduce cluster compactness, Botrytis rot, and grape and wine IBMP concentration and to improve wine color intensity but at the expense of cluster weight and vine yield. Leaf removal after flowering accomplishes essentially the same results without loss of yield.
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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 | 0.001 |
| 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".