Modeling lobed leaves
Bibliographic record
Abstract
In contrast to the extensively researched modeling of plant architecture, the modeling of plant organs largely remains an open problem. We propose a method for modeling lobed leaves. This method extends the concept of sweeps to branched skeletons. The input of the model is a 2D leaf silhouette, which can be defined interactively or derived from a scanned leaf image. The algorithm computes the skeleton (medial axis) of the leaf and approximates it using spline curves interconnected into a branching structure (sticky splines). The leaf surface is then constructed by sweeping a generating curve along these splines. The orientation of the generating curve is adjusted to properly capture the shape of the leaf blade near the extremities and branching points of the skeleton, and to avoid self-intersections of the surface. The leaf model can be interactively modified by editing the shape of the silhouette and the skeleton. It can be further manipulated in 3D using functions that control turning, bending, and twisting of each lobe.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".