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−1required 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 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".