Amino Acid Composition of Grape (<i>Vitis vinifera</i>L.) Juice in Response to Applications of Urea to the Soil or Foliage
Bibliographic record
Abstract
Applications of nitrogen to vineyard foliage or soil at veraison can improve grape juice yeast assimilable nitrogen concentrations and may prevent the excessive vine growth, delayed maturity, and adverse changes in fruit properties sometimes associated with high applications of N earlier in the growing season. However, the consequences of late-season foliar- and soil-applied nitrogen for grape juice yeast assimilable nitrogen (YAN) and, specifically, grape juice amino acid profiles have rarely been directly compared. Over two years in drip-irrigated Merlot and Pinot gris vineyards, grape juice amino acid concentrations were measured from vines to which urea had been applied three times around veraison at 3.8 g N/vine to either the foliage or the soil surface. Foliar-applied urea (applied as a 2% w/v solution) was usually more effective at boosting grape juice ammonium and amino acid concentrations, although soil-applied urea improved some grape juice amino acids at the Pinot gris site. Changes in the amino acid profiles of grape juice, observed in response to foliar N applications but not soil N applications, may have implications for wine quality. Applications of <sup>15</sup>N-labeled urea at the Pinot gris site demonstrated that a greater percentage of fertilizer N was incorporated into grape juice amino acids when urea was applied to the foliage than when it was applied to the soil surface. Late-season foliar applications of urea are a reliable, efficient, and effective method of improving grape juice YAN. Further work is required to examine how treatment effects vary among sites and cultivars under different management practices and to understand the implications of altered grape juice amino acid profiles for wine quality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".