Applied <scp>GA<sub>5</sub></scp>, <scp>GA<sub>4</sub>,</scp> and <scp>GA<sub>4/7</sub></scp> increase berry number per bunch, yield, and grape quality for winemaking in <scp><i>Vitis vinifera</i> L. cv. Malbec</scp>
Bibliographic record
Abstract
Abstract BACKGROUND The gibberellins (GAs) GA5 (inhibitor of GA3‐oxidase), GA4 (biologically active), GA4/7 (commercially available mixture of Ga4 and GA7) prohexadione‐calcium (ProCa, inhibitor of dioxygenases that render GAs bioactive, negative control), and GA3 (positive control) were applied to bunches of Vitis vinifera cv. Malbec. Different techniques, doses, and timings were used in a 3‐year field experiment. In year 1, GA5, ProCa, and GA3 were applied at 35, 20, and 0 days before veraison (DBV) by dipping bunches three times. In year 2, single applications of GA5 and GA3, also by immersion, were tested at 60, 45, and 30 DBV. In year 3, applications at 60 and 30 DBV of GA5, GA4, and a mixture of GA4/7 were evaluated by dipping or spraying the bunches. RESULTS Vegetative growth, berry weight, and sugar content were unaffected by treatments. ProCa did not affect the yield with respect to water control, although it reduced the levels of phenolics in berry skins, an undesirable effect for winemaking. GA5, in the dose range 5–50 mg L−1, raised berry numbers, thereby augmenting bunch weight and skin phenolics at harvest, so increasing berry quality for winemaking. GA4 and GA4/7 produced similar benefits to GA5, with similar doses. CONCLUSION The applications of GA5, GA4, and GA4/7 to developing grape berry bunches, in a range of concentrations and by dipping or spraying, increased berry numbers per bunch at harvest. The method can be used as a viticultural practice to improve the production and quality of wine grapes. © 2021 Society of Chemical Industry.
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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) | 0.002 | 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".