Reinterpreting Rasmi Domes with Geometric Constraints: A Case of Goal-Seeking in Parametric Systems
Bibliographic record
Abstract
Geometry has long been a generator of architecture. In traditional Persian architecture, Rasmi domes project a drawing onto a predefined 3D geometry. In fact, the word ‘rasmi’ and the verb for drawing in Persian have the same linguistic root. Projection is readily done in manual drawings or conventional CAD programs. From a constraint perspective, the dome is constrained by the drawing and the 3D geometry. If the latter constraint is replaced by invariance of distance on the original drawing, a class of domes results, but members of this class cannot be computed conventionally. Class members are developable from a planar layout of triangles, which is, in turn, generated by a simple drawing rule. This yields a parametric structure of four parameters. Three determine the initial planar diagram. One determines configuration. Further, domes in the class are mechanisms: they are not fully specified by the constraints and parameters. We develop the geometric constraints representing the location of the defining points of a dome and present a goal-seeking algorithm to solve the constraints within a propagation-based parametric modeling system.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".