Impact on agronomic parameters in Vines and wine quality of foliar treatments with specific fractions of yeast derivatives
Bibliographic record
Abstract
In hot Spanish climate, Toledo, Syrah and Sauvignon blanc Vineyard were treated in pre veraison with yeast derivatives RD-LM and RD- LA to stimulate phenolic and aromatic maturity respectively (application of yeast derivatives specifically designed to be used with the patent foliar application technology WO/2014/024039, Lallemand Inc. Canada). For studied effects in berry and wine composition three harvest time had been done. Experimented yeast derivatives had no significant effects on yield components and vegetative growth in both varieties. The Syrah RD-LM variety presented higher total and extractable anthocyanins and also more amount of tannins, although this last ones are not evident in the sensory analysis. The sensory analysis of wine has given very similar results in both varieties but with significant results in favored by phenols and tannins derived RD- LM and RD-LA respectively.
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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 | 0.000 |
| 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) | 0.000 | 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".