The concentration of yeast assimilable nitrogen in Merlot grape juice is increased by N fertilization and reduced irrigation
Bibliographic record
Abstract
Hannam, K. D., Neilsen, G. H., Forge, T. and Neilsen, D. 2013. The concentration of yeast assimilable nitrogen in Merlot grape juice is increased by N fertilization and reduced irrigation. Can. J. Plant Sci. 93: 37–45. Vineyard management practices that can be used to elevate yeast-assimilable nitrogen (YAN) above the 140 mg N L −1 required for efficient fermentation are of critical interest. The effects of N fertilization and reduced irrigation frequency on grape juice YAN, fruit composition and yield were examined in a 5-yr study on Merlot (Vitis vinifera L.) vines. Fertilization with N increased the concentration of YAN in grape juice by improving grapevine N status as indicated by petiole N concentrations. Reduced irrigation frequency appeared to have no effect on grape juice YAN status but short-term reductions in the quantity of applied water during the early stages of berry development in 2 of the study years did increase YAN. Juice pH was sometimes increased by reduced irrigation and N application treatments, but levels remained acceptable for wine production. Other measures of fruit composition were less sensitive to irrigation and N fertilization treatments. Inter-annual variability played an important role in determining grape juice YAN, fruit composition and yield. Future work should focus on refining management practices, e.g., the timing of N application, to minimize the effects of annual variability on grape juice YAN concentrations.
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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 | 0.001 | 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 | 0.001 |
| 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".