The Importance of Classifying the Traditional Mosulian Ornaments in Enhancing the Conservation Process
Bibliographic record
Abstract
Ornaments are effective interior design elements, which enhance the identity and heritage value of heritage buildings. The Mosulian ornaments represent the mixture of the culture and architectural, which are produced by Mosulian marble ‘Farish’. After the war in 2017, there was a dramatic disappearance of the original types of Mosulian ornament because of the non-oriented restoration and conservation process. UNESCO and the government sector suffered from the availability of information to find a guideline to be used in the restoration and conservation process. However, the shortage of documentations led to the disappearance of valuable ornaments, especially after investing the new technology in producing the ornaments in the conservation processes. The study aims to classify the Mosulian ornaments in terms of shape, material, and position, to construct a platform and data set of the Mosulian ornaments. The study applied a qualitative approach using observation and visual analysis methods to observe the results of the analysis with a checklist form. The results indicated that Mosulian architecture is rich in ornamental elements, floral ornaments are used in the greatest ratio than geometric, while animal shapes are rarely used. The principle of repetition in two and four steps is the main principle used in generating process. The classification of ornament can enhance the heritage value of the buildings, designers and developers can rely on this classification to design and reuse the ornament.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".