ART AND SCIENCE OF LIFE: DESIGNING AND GROWING VIRTUAL PLANTS WITH L-SYSTEMS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Virtual plants are computer models that recreate the structure and simulate the development of plants. Virtual plant modeling is an interdisciplinary area combining mathematical formalisms, biological knowledge, and computer graphics techniques. An important modeling method is based on the theory of Lindenmayer systems (L-systems). At present, L-system models make it possible to: (a) accurately reproduce the structure and development of plants; (b) show how architectural parameters (e.g., branching angles, elongation rates, vigor of branches) affect the appearance of plants; (c) simulate plant physiology and investigate the effects of manipulations (e.g., pruning, bonsai techniques) or different external conditions (e.g., local light microclimate, water availability, crowding) on plant development; and (d) simulate plants not only in isolation, but also in their ecological contexts. Currently constructed research models also address the genetic basis of plant form. In horticulture, the models are potentially useful as an exploration tool, indicating desirable directions of breeding and manipulating ornamental plants for maximum visual impact, and fruit plants for maximum yield. Other applications include fundamental research and teaching of biology, and landscape design. This paper describes the current state of the L-system-based modeling methodology as supported by L-studio and Virtual Laboratory, plant modeling software developed at the University of Calgary.
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.
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 it