Vineyard floor management improves wine quality in highly vigorous <i>Vitis vinifera</i> ’Cabernet Sauvignon’ in New Zealand
Bibliographic record
Abstract
Abstract Five inter‐row soil management techniques were applied to a vigorous Vitis vinifera ’Cabernet Sauvignon’ vineyard in Hawke's Bay, New Zealand—a permanent chicory ( Chicorium intybus var. sativum ’Puna') cover crop; chicory sprayed with herbicide before veraison; incorporated pine ( Pinus radiata ) sawdust and bare soil maintained using cultivation or non‐selective herbicide. Both chicory treatments significantly reduced the soil water content and shoot growth late in the season compared to the other treatments. Petiole nitrate concentration and leaf size were lowest in the sawdust and both chicory treatments. The pruning weights in the two chicory treatments were reduced to about half those found in the other treatments. No significant differences among treatments were found in yield or other viticultural characteristics examined. Both chicory treatments resulted in advanced ripening (increased soluble solids and decreased titratable acids), increased anthocyanins, and reduced ammonia content of berries compared to other treatments. Sensory evaluation of wines produced from the cultivation (bare soil) and the permanent chicory cover crop treatment were conducted after 4 years of bottle age, and showed riper fruit aroma and flavour and a higher overall quality score in the chicory treatment. Competition imposed for two seasons using a permanent chicory cover crop has resulted in improved viticultural and oenological characteristics of a highly vigorous ‘Cabernet Sauvignon’ vineyard in a marginal site in New Zealand.
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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".