Impact of Training System on Fruit Composition and Sensory Attributes of Syrah in California's Dunnigan Hills
Bibliographic record
Abstract
‘Syrah’ vines in the Dunnigan Hills, California were trained to one of three canopy systems [Smart-Dyson (SD); southern sprawl (SS); full sprawl (FS)] over 3 years. The canopy system had little impact on yield components. The SS had the smallest berries in 2005, and in 2006, both SS and FS had the smallest berries. Soluble solids were highest in SS in 2005 and 2006 and lowest in FS in 2006. The SS had the highest berry titratable acidity (TA) in 2005 and 2006, FS had the highest TA in 2004 and the lowest in 2006, and SD had the lowest TA in 2004 and 2005. Color intensity was highest in SS in all 3 years and lowest in SD and FS in 2 of 3 vintages. In 2005, berry anthocyanins were highest in SS and lowest in FS. SD berries had the lowest phenols in all 3 years, with FS fruit having similar values in 2005 and 2006. SS berries had the highest phenols in 2005 and 2006. In 2005, SS wines had the highest blueberry and lavender aromas and lowest pepper and herbal aromas, along with the highest anise and lowest cherry flavors and lowest astringency. Wines from SD and FS were lowest for blueberry aromas and highest for herbal; SD was also lowest for lavender aroma. The FS wines had the highest cherry and lowest anise flavors. In 2006, SS wines were highest for lavender, black pepper, and blackberry aromas and weight. SD wines were equal to SS in blackberry, and highest for blueberry aromas. FS wines were the lowest for black pepper, blueberry, blackberry, and lavender aromas; blueberry, blackberry, and lavender flavors; weight and length; and most astringent. The SD system provided the lowest average maximum cluster temperature throughout the growing season, while the FS system experienced highest maximum cluster temperatures, which occasionally were >45°C. Data suggest that canopy systems that provide some degree of cluster shading with an accompanied reduction in cluster temperatures during the growing season might be beneficial in warm winegrowing regions in terms of improving wine sensory quality.
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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 | 0.001 | 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) | 0.002 | 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".