Compositional modeling as a method of study the architectural heritage in educational design
Bibliographic record
Abstract
Abstract The article presents the methodology of the authors’ work with first-year students of architectural specialties INRTU on the topic of the course project “Compositional Analysis of an Architectural Object”. This methodology is based on the search for analogies as a system of regularities that makes it possible to recreate plastic models and architectural drawings of lost temples. The result of this work was the creation of models for the museum of the Irkutsk customs. It was recreated as separate buildings and structures, and 19th century customs quarter with Chudotvorskaya church - the lost monument of architecture 18th – 19th centuries. Further research was related to the study of the complex Siberian Baroque temples in Irkutsk and various types of church architecture in the mid-18th - early 20th centuries. The article is illustrated with graphic and model research of the architectural composition of architectural monuments various types and historical periods. The main stages of the course project are presented from the construction of functional planning and volumetric models to the design of the figurative and structural characteristics of the object in the presentation of the tablet. There is also shown the comparison of models for the evolution of an individual temple and analogs.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".