Impact of Cluster Thinning and Basal Leaf Removal on Fruit Quality of Cabernet Franc (Vitis vinifera L.) Grapevines Grown in Cool Climate Conditions
Why this work is in the frame
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Bibliographic record
Abstract
Achieving desired fruit quality at harvest in cool climate conditions is a challenge, especially for red varieties, and the typical inability of fruit to reach technological maturity is a critical contributing factor requiring examination. To probe this issue, this research investigated the impact of two levels of crop thinning and of basal leaf removal at three phenological stages in the 2011 and 2012 growing seasons in Michigan. Experiments were conducted at the Southwest Michigan Research and Extension Center (SWMREC) in Benton Harbor. Using ‘Cabernet franc’ ( Vitis vinifera L.) vines, yield components (yield per vine, pruning weight, and cluster and berry weight) and basic fruit composition traits [total soluble solids (TSS), pH, titratable acidity, anthocyanins, and phenolics) were studied to investigate the effect of cluster thinning and basal leaf removal on vine performance and fruit quality at harvest. Neither of the treatments significantly impacted TSS in either of the two seasons. Cluster thinning treatment successfully altered cropload ratio, indexed as Ravaz Index (RI), independently of the time of application. Basal leaf removal increased exposed berry temperature, cluster light exposure, and subsequent anthocyanin and phenolic content of the berry in both seasons, again independent of application date, whereas cluster thinning was effective only in 2012. Crop thinning coupled with basal leaf removal resulted in an increased efficiency in anthocyanin accumulation in relation to TSS accumulation, expressed as anthocyanin:sugar, in both years. This is significant because it offers potential for vineyard management practices aiming to improve fruit quality in cool climates where the onset of anthocyanin accumulation could be reduced and decoupled from sugar accumulation.
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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 | 0.001 | 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 it