Cluster thinning as a tool to hasten ripening of wine grapes in the Okanagan Valley, British Columbia
Bibliographic record
Abstract
Hannam, K. D., Neilsen, G. H., Neilsen, D. and Bowen, P. 2015. Cluster thinning as a tool to hasten ripening of wine grapes in the Okanagan Valley, British Columbia. Can. J. Plant Sci. 95: 103-113. Achieving fruit maturity can be a challenge on some Okanagan vineyards in some years. Cluster thinning is widely used to hasten ripening, but may not be effective on sites with balanced crop loads. In a Merlot vineyard in Summerland, BC, the effects of cluster thinning on juice soluble solids (an indicator of fruit maturity), yield and vine growth were examined between 2008 and 2011 across a range of treatments that manipulated the frequency and quantity of applied irrigation water. Cluster thinning increased juice soluble solids in 2 out of 3 study years and consistently increased cluster weights, but had few effects on juice pH, titratable acidity or yield. In 2 of 3 yr, correlation analyses showed that cluster thinning was most effective at improving the maturity of fruit with low soluble solids. Irrigation treatments did not have a consistent effect on juice composition but year-to-year variability was significant. Response ratios calculated from values reported in the literature show that cluster thinning in this region causes small but inconsistent improvements in juice soluble solids across a range of crop loads.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".