The influence of bird netting on yield and fruit, juice, and wine composition of <em>Vitis vinifera</em> L.
Bibliographic record
Abstract
Aims: To investigate the impact of semi-permanent bird netting and timing of its application on Cabernet franc grapevine yield components and fruit, juice, and wine composition. Methods and results: Semi-permanent bird netting was installed over Cabernet franc grapevines at various times – post-bloom, bunch closure, and veraison – of the 2004 growing season in the Niagara Peninsula of Canada. At harvest, vine yield components were measured followed by berry and must compositional analysis of soluble solids, pH, titratable acidity (TA), color, and polyphenols. Wines made from these grapes were also analyzed (pH, TA, color, and polyphenols). It was found that installation of bird netting over grapevines had minimal effect on yield components and berry composition regardless of when the nets were installed. Must composition revealed significant decreases in soluble solids, pH, and color as a result of the netting, the least impact being when the nets were applied at post-bloom. Wine composition was similar to the must data with the netted treatments resulting in lower pH, higher TA, and decreased color. Total anthocyanins and polyphenols were slightly reduced as a result of the netting. Conclusions: Minimal impact of bird netting on yield, fruit, must and wine quality is a positive finding since netting is becoming more prevalent in vineyards worldwide due to changing migratory patterns of birds. It is recommended that netting be applied around post-bloom for the ease of application, to minimize shading effects, which could lead to decreased fruit quality, and to maintain yield. Significance and impact of the study: Use of bird netting is becoming more prevalent by grape growers worldwide due to changing migratory patterns of birds that feed on grapes. This study shows that bird netting is not detrimental to yield and fruit and wine quality especially when applied early in the growing season.
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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".