Effects of the Contribution Rate of Leaves on the Fractal Dimension Number of Young Apple Trees Trained to A Slender Spindle Configuration
Bibliographic record
Abstract
Three cultivars were used as materials,their were 'Changfu 2','Fuhongzaoga','Yanfu 6'.All trees were trained to a slender spindle configuration.The fractal dimension of 2-D images with and without leaves employing a box-counting dimension number combined with Photoshop image processing technology were investigated.The influence of leaves weight and shoots length on fractal dimension number were analyzed by investigated the relationship among three kind of shoots length(trunk,main shoots,lateral shoots),leaves weight,and linear regression of 2-D fractal dimension number.The results showed that the fractal dimension,the shoot length and leaf weight were significantly different among the three cultivars;The contribution rate of leaves(CRL) to tree architecture varied between 14.72% to 16.70% for the three cultivars;As the shoot length and leaf weight increased,the fractal dimension and CRL significantly increased.The fractal dimension can be used as an index to evaluate the architecture of apple trees,and guidelines to direct train,thin activities.
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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".