Role of Cluster Thinning and Viral Load on the Effects of Grapevine Leafroll Disease in Merlot and Cabernet Sauvignon in British Columbia, Canada
Bibliographic record
Abstract
Abstract Background and goals Grapevine leafroll-associated virus 3 (GLRaV-3) is an economically important virus that negatively affects grapevine health and fruit composition. This study investigated the use of crop thinning to mitigate the effects of GLRaV-3 in Merlot and Cabernet Sauvignon vines in the Okanagan Valley, and to understand if GLRaV-3 viral load correlates with decreased vine health and fruit composition. Methods and key findings A commercial vineyard containing Merlot and Cabernet Sauvignon vines was studied over two years. GLRaV-3-positive (GLRaV-3(+)) and GLRaV-3-negative (GLRaV-3(−)) vines were paired and designated a cropping treatment, either 1.5 clusters per shoot (1.5 c/s) or one cluster per shoot (1.0 c/s). Vine health and fruit composition were measured during the growing season and at harvest, respectively. Viral load was measured at four stages during the growing season using Droplet Digital PCR (ddPCR). GLRaV-3(+) vines had increased crop load and titratable acidity (TA), and reduced total soluble solids. 1.0 c/s vines had lower TA and anthocyanins, and increased pH. Thinning GLRaV-3(+) vines significantly increased pH. GLRaV-3 viral load was negatively correlated with photosynthesis, stomatal conductance, SPAD values, cluster weight, berry weight, and yeast assimilable nitrogen, and positively correlated with skin and seed phenolics. Conclusions and significance GLRaV-3 effects were mild and differed by cultivar and year. No consistent correlation was found between GLRaV-3 viral load and adverse vine health or fruit composition. Crop thinning did not improve vine health or fruit composition of GLRaV-3(+) or GLRaV-3(−) vines. This study shows no benefit to thinning vines lower than 1.5 c/s in the Okanagan Valley and thus, growers should keep their fruit to avoid diminishing returns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".