Modeling dense inflorescences
Bibliographic record
Abstract
Showy inflorescences - clusters of flowers - are a common feature of many plants, greatly contributing to their beauty. The large numbers of individual flowers (florets), arranged in space in a systematic manner, make inflorescences a natural target for procedural modeling. We present a suite of biologically motivated algorithms for modeling and animating the development of inflorescences with closely packed florets. These inflorescences share the following characteristics: (i) in their ensemble, the florets form a relatively smooth, often approximately planar surface; (ii) there are numerous collisions between petals of the same or adjacent florets; and (iii) the developmental stage and type of a floret may depend on its position within the inflorescence, with drastic or gradual differences. To model flat-topped branched inflorescences (corymbs and umbels), we propose a florets-first algorithm, in which the branching structure self-organizes to support florets in predetermined positions. This is an alternative to previous branching-first models, in which floret positions were determined by branch arrangement. To obtain realistic visualizations, we complement the algorithms that generate the inflorescence structure with an interactive method for modeling floret corollas (petal sets). The method supports corollas with both separate and fused petals. We illustrate our techniques with models from several plant families.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 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".