Explorative study for the structural elements of Mimar Sinan mosques: an evaluation with k-means clustering algorithm
Bibliographic record
Abstract
Mimar Sinan, who served as master architect for nearly fifty years in the 16th century, when the Ottoman Empire was at its strongest, designed landmark buildings that left their mark on the city identities within the empire's borders. The subject of this study is to evaluate the mosques designed by Mimar Sinan, the most well-known architect of the 16th-century Islamic Region, in the capital, Istanbul, and other cities. The structural components and features of 44 mosques designed/built by Mimar Sinan (dome diameter, height of the dome from the ground, width/height dimensions, number of minarets and minaret balconies, location, top covering elements (domes, half domes, small domes, quarter domes), number of load-bearing elements, transition elements to the dome and their numbers) were analyzed in order to identify and discuss possible relationships and patterns between them. Since the number of studies evaluating and exploring structural system properties of Mimar Sinan mosques is very few, this study is very important in terms of the contribution to the existing literature. The data from the literature review are searched with the K-means clustering algorithm, a machine learning method, and the relationships and patterns between them are revealed. The results are converted into definitions of variables for discussion and evaluation.
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 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".