DEVELOPMENTS IN HIGH DENSITY SWEET CHERRY PRUNING AND TRAINING SYSTEMS AROUND THE WORLD
Bibliographic record
Abstract
The success of high density plantings with apple over the last 40 years has stimulated sweet cherry growers to plant higher and higher tree densities. In contrast to the apple story, the development of high density cherry management systems was done initially with vigorous, non-precocious rootstocks. More recently, the development of dwarfing and semi-dwarfing precocious cherry rootstocks has greatly stimulated high density sweet cherry production. This has been accompanied by the development of numerous systems of planting, pruning and training cherry trees. The significant advances in the last 10 years prompted us to organize a workshop on high density cherry systems at the International Horticulture Congress in August 2002 in Toronto, Canada. This workshop focused on the practical aspects of the leading cherry planting systems from around the world, with the goal of understanding the common fundamental elements of all successful planting systems. The papers from this workshop show how growers around the world are integrating the factors of variety, rootstock, spacing and training system with their climate, soil type, and management ability to be successful with many different systems. It is clear from the different approaches used around the world that successful integration of the puzzle pieces into high density orchards can lead to high early yields, high sustained yields and excellent fruit quality with any one of several orchard planting systems.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".