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) GA 5 (inhibitor of GA 3 ‐oxidase), GA 4 (biologically active), GA 4/7 (commercially available mixture of Ga 4 and GA 7 ) prohexadione‐calcium (ProCa, inhibitor of dioxygenases that render GAs bioactive, negative control), and GA 3 (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, GA 5 , ProCa, and GA 3 were applied at 35, 20, and 0 days before veraison (DBV) by dipping bunches three times. In year 2, single applications of GA 5 and GA 3 , also by immersion, were tested at 60, 45, and 30 DBV. In year 3, applications at 60 and 30 DBV of GA 5 , GA 4 , and a mixture of GA 4/7 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. GA 5 , 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. GA 4 and GA 4/7 produced similar benefits to GA 5 , with similar doses. CONCLUSION The applications of GA 5 , GA 4 , and GA 4/7 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".