Pyroligneous Acid Affects Grapevine Growth, Yield, and Chemical Composition of Leaf, Pomace, and Juice
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In the past decade, many studies have investigated the effects of biostimulants on viticulture. However, the impact of pyroligneous acid (PA) on grape ( Vitis vinifera ) production has not yet been reported. In this study, PA at varying concentrations (0, 4, 8, and 12% PA) and application frequencies (14-, 21-, and 28-day intervals) were applied to enhance the growth, yield, and quality of grapes (cv. KWAD7-1). The results showed that the treated grapes responded differently to PA application. The 4 and 8% PA showed a nonsignificant ( p > 0.05) increase in yield of about 0.37- and 0.18-fold, respectively, compared to the 0% PA. The 12% PA, on the other hand, reduced the yield by approximately 0.03-fold compared to the 0% PA. Carotenoid, total phenolics, flavonoid, and sugar were altered by the PA. Interestingly, the 4% PA significantly ( p < 0.05) improved total carotenoids (0.34-fold), total phenolics (0.26-fold), and flavonoids (0.26-fold) compared to the 0% PA. The 4% PA applied at 21-day and 28-day intervals remarkably improved vine and leaf growth, respectively. In conclusion, the 21-day interval of PA application significantly ( p < 0.05) improved fruit fresh weight, juice weight, juice volume, press weight, °Brix, pH, salinity, total dissolved solids, electrical conductivity, and titratable acidity. Further study is necessary to assess how PA can influence the metabolites present in grape wine.
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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".