Algorithm development for pattern generation inspired from tree growth
Bibliographic record
Abstract
The nature was the source of inspiration in many designs and products. Learning the algorithms from nature and incorporating them in the product design can be another level of inspiration. Algorithms for generating the pattern of tree-growth and venation in a leaf are nature-based algorithms for various uses in design and modeling. Development of the plant-pattern generators was initially intolerant of target shape until introduction of the space colonization algorithm (SCA). The SCA had a target area filled with points. The nodes, which create the final shape, start growing the pattern from an initial point to cover the target area. The points have an attraction field, which determines the direction of pattern. This project consists of two phases. The first phase improved some the features in the SCA including: i) the capability of starting the branching pattern from outside the target area, ii) tolerating symmetric distribution of points in target area, and iii) not canceling the effect of points from each other. The second phase used a branching equation assigned thickness to the members. The parameter in the equation was optimized to achieve the minimum variance of stress/capacity ratio among members.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".