Retrofitting Mechanisms of Valuable Heritage Buildings: Al-Kifl Shrine as a Case Study
Bibliographic record
Abstract
Historic buildings that represent the cultural heritage of countries face many problems due to natural and unnatural deterioration factors, and their conservation requires multi-level interventions.Scientific and technological progress has enhanced the sustainability of these buildings by providing modern techniques and materials used in all stages of conservation, starting from documentation and architectural survey, determining the causes of damage, and ending with choosing the appropriate technique and material for implementation by international conservation principles.The research problem is the lack of local studies that address retrofitting mechanisms and the role of modern techniques in preserving historical buildings in Iraq.The research aims to identify appropriate techniques, considering specialized technical conditions such as safety, structural compatibility and efficiency to preserve buildings from deterioration.The research focuses on studying the shrine of Al-Kifl in Babylon Governorate as a model.The study introduces the application of a proposed retrofitting mechanisms using modern materials and techniques to strengthen and support the shrine within the comprehensive conservation processes.The study concluded that the mechanisms of retrofitting to preserve heritage buildings used in the study are effective, do not damage details, preserve the authenticity of the building, and predict future damage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".