Best Management Practices for Reducing Winter Injury in Grapevines – An Overview
Bibliographic record
Abstract
Growing grapes in cool climates is a challenging task. Vineyard health and productivity is a function of the local site and climatic conditions during the growing season AND the dormant period. Successful vineyards are those that are productive and sustainable over multiple years (where sustainable is defined as producing an annual crop at economically viable levels for the grape grower each year). The potential for vine injury due to low temperatures during the dormant period adds another level of vine management to be undertaken. Not only must a vineyard operator manage the vines to produce a high quality crop based upon the weather conditions of the current growing season, practices must be employed to ensure that vines achieve optimum health to withstand cold winter temperatures during the dormant period. This best management practices manual for reducing winter injury in grapevines has been developed as a result of 5 years of research and practices in commercial vineyards across Ontario. The information is for existing vineyards and will provide guidance for preventing winter injury and suggested practices for responding to winter injury. The manual does not provide guidelines for assessing new vineyard locations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".