Prioritization of Heritage Buildings in Historic Cairo for Restoration Funding
Bibliographic record
Abstract
Egypt is one of the richest countries in its historic and tourism attractions which are among the main contributors to the country’s gross domestic product (GDP). Historic Cairo which has hundreds of mesmerizing historic Coptic and Islamic structures (mosques, churches, mausoleums, etc.) has been identified by UNESCO as a world heritage site since 1979. However, it has been noticed that its share in the tourism revenues is quite low compared to its value. One of the reasons is that many of the historic structures are closed because they are severely deteriorated due to urban expansion, pollution, environmental hazards, and aging. To revive the tourism in Historic Cairo, the government has been directing its efforts towards the conservation of those structures and reopening them to the public, and thus increase tourism-based revenues. However, the funding needed to restore all structures is very limited. There are hundreds of historic structures in need for restoration with a budget of more than one billion EGP. Accordingly, this research proposes a decision support system inspired by infrastructure asset management system (IAMS) to guide the fund allocation process. It follows the sequential steps of IAMS from asset inventory, condition assessment, up to prioritization and fund-allocation, yet considering the unique value of each heritage building and the expected socioeconomic benefits of restoring the structures and upgrading their surrounding areas. Therefore, this new structured decision support system will help policy makers develop the best rational restoration plan that will help rejuvenate Historic Cairo, and subsequently Egypt’s tourism revenues.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".