Varying Density with Constant Rectangularity: I. Effects on Apple Tree Growth and Light Interception in Three Training Systems over Ten Years
Bibliographic record
Abstract
The effect of increasing planting density at constant rectangularity on the vegetative growth and light interception of apple [ Malus × sylvestris (L) var. domestica (Borkh.) Mansf.] trees 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. Planting density was the most influential factor. As tree density increased, tree size decreased, and leaf area index and light interception increased. A planting density between 1800 and 2200 trees/ha (depending on training system) was needed to achieve at least 50% light interception under the conditions of this trial. Training system altered tree height and canopy diameter, but not total scion weight. Training system began to influence light interception in the sixth leaf, when the Y trellis system intercepted more light than either spindle form. Trees trained to the Y trellis tended to have more spurs and a lower proportion of total leaf area in shoot leaves than the other two systems. The slender and tall spindles were similar in most aspects of performance. Tall spindles did not intercept more light than slender spindles. `Royal Gala' and `Summerland McIntosh' trees intercepted about the same amount of light. `Royal Gala' had greater spur leaf area per tree than `Summerland McIntosh', but the cultivars were similar in shoot leaf area per tree and spur density.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".