Bibliographic record
Abstract
Venation structures in leaves fulfil both circulatory as well as structural functions within the organism they belong to. A possible digital simulation algorithm for the growth of venation patterns based on the leaf surface has been described by the Department of Computer Science at the University of Calgary.Cell-based growth algorithms to generate surface meshes have been developed by biological and medical scientists as well as artists, in order to gain an understanding of developmental biology or to generate artistic form. This paper suggests the combination of the two algorithms in order to generate the morphologies of leaves and other structures while at the same time generating the corresponding venation system.The resulting algorithm develops large non-manifold mesh structures based on local rules of division of the individual cells. The venation system develops in parallel based on the flow of the plant hormone auxin from those cells towards the start point or petiole of the leaf. Different local behaviours of the cells towards their adjacent neighbours, towards their rules of division and towards the rules of developing veins have been investigated. The eventual aim of the algorithms is their application as tools to develop architectural and structural morphologies.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".