Nutritional status differentially affect yield and must composition of hybrids and<i>V. vinifera</i>varieties established under cold climate conditions
Bibliographic record
Abstract
Nova Scotia is characterized by cold climate and acidic soils and high organic matter for viticultural development. There is little scientific information available about the nutritional management of grapevine varieties cultivated under cold climate conditions even in Nova Scotia. This study carried out in three seasons aimed to find correlations between tissue nutrients with yield and must composition in order to provide better nutritional management for a given variety. The yield of “L’Acadie” variety was correlated with B, K and Mg. In this variety, Ca to Mg ratio could be an interesting indicator of yield. The yield of “New York Muscat” variety was correlated with Zn, while N, P and K was related to bunch weight and weight of berries. Interspecific hybrids presented lower accumulation of B in their tissues than V. vinifera varieties. Zn and Fe were found as determinant micronutrients in “Chardonnay” variety. “Riesling” variety accumulated high levels of K in their tissues even over the optimal values recommended for grapevines. K to Ca ratio could be an important indicator of yield in “Pinot Noir” variety. A strong correlation between the petiole and blade analysis was found in macro and micro-nutrients. These results may contribute to improve the nutritional management of grapevines grown under cold climate conditions, mostly in Nova Scotia.
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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".