RESTORATION TECHNIQUES FOR THE HERITAGE BUILDING / KIRKUK QISHLA AS A CASE STUDY
Bibliographic record
Abstract
National heritage buildings are considered as the memory of the cities and its civilization’s legacy. Such buildings are receiving more attention these days especially from architectures who are trying to restore them by using the proper techniques to bring them back to their former appearance to serve the community in the present time. From that point, the general goal of this research is to concentrate on the used techniques of restoring national heritage buildings by using the proper materials for the damaged or the collapsed parts as the restoration process differs from a building to another. All these points should be done according to the laws and protocols of UNSCO and ICOMOS to keep the authentic features of the building and pass it on to the next generations. This research deals specifically and practically with restoring the Qishla building in Kirkuk as a restoration sample. Some parts of that building collapsed recently and the research aims to save that building by studying the techniques of restoring it using the materials and building techniques of the era the building was built in. This research rises an important point, which is finding a general prospective of scientific and technical ways in restoring cultural heritage buildings which are made from stone specifically. From all that above the main issue that this research deals with is “The unclear techniques of restoring cultural heritage buildings in general and the restoration process of Qishla building with stone in specific”. Accomplishing the aim of this research lies in two points: First: Studying the rules which govern the restoration process of cultural heritage buildings. Second: Studying the restoration process of Qishla building and that includes; the restoration process with stone should be done by examining the chemical and physical features of the stone, durability of the used stone, conducting chemical tests in international labs as (ACMAE ANALYSES/ Canada) and the last step is to analyze and discuss the results to take final decisions.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".