Application of Semitotalistic 2D Cellular Automata on a Triangulated 3D Surface
Bibliographic record
Abstract
This paper presents the application of semi-totalistic, also called outer totalistic cellular automata on any three-dimensional (3D) surface. Cellular automata (CA) can be applied in controlling the state of a freeform building envelope. Such an intelligent 'skin of a building' can have any shape and a certain 'organic' appearance which can be dynamically controlled in response to the changes of the external conditions or users' requirements. Any 3D surface can be triangulated. This means that it can become a grid of topologically identical elements. With the exception of boundary conditions, applicable to the elements positioned at the edge of the surface or around holes, every triangle in the grid has exactly three neighboring triangles. As with a CA, every element of a triangulated surface can be individually assigned with characteristics such as color or transparency level, which is analogous to a CA 'state'. Therefore, it is possible to control to some degree the state of the whole surface taking advantage of the emergent behavior of the CA. The concept of CA on a triangular tessellation is discussed followed by discussion about the entities of irregularity of a grid, called 'holes' and 'edges'. The concept of an 'organic' pattern in the context of CA is also briefly discussed. Two-dimensional (2D) CA in the triangular tessellation is discussed and implemented. A brief study covers the entities of neighborhood, type of rules (semi-totalistic and general), rule encoding and search for rules that meet given criteria. An implementation is performed on a regular triangular grid, irregular triangular grid and an imported triangulated 3D mesh which is irregular and has holes. A selected 2D triangular CA is applied on an imported triangulated 3D model.
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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".