Varying Density with Constant Rectangularity: II. Effects on Apple Tree Yield, Fruit Size, and Fruit Color Development in Three Training Systems over Ten Years
Bibliographic record
Abstract
The effect of increasing planting density at constant rectangularity on the fruit yield, fruit size, and fruit color of apple [ Malus × sylvestris (L) var. domestica (Borkh.) Mansf.] in three training systems (slender spindle, tall spindle, and Geneva Y trellis) was assessed for 10 years. Five tree densities (from 1125 to 3226 trees/ha) and two cultivars (Royal Gala and Summerland McIntosh) were tested in a fully guarded split-split plot design. Density was the most influential factor. As tree density increased, per-tree yield decreased, but yield per unit area increased. The relation between cumulative yield per ha and tree density was linear at the outset of the trial, but soon became curvilinear, as incremental yield diminished with increasing tree density. The chief advantage of high density planting was a large increase in early fruit yield. In later years, reductions in cumulative yield efficiency, and in fruit color for `Summerland McIntosh', began to appear at the highest density. Training system had no influence on productivity for the first 5 years. During the second half of the trial, fruit yield per tree was greater for the Y trellis than for either spindle form at lower densities but not at higher densities. The slender and tall spindles were similar in nearly all aspects of performance, including yield. `Summerland McIntosh' yielded almost 40% less than `Royal Gala' and seemed more sensitive to the adverse effects of high tree density on fruit color.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".