EMPLOYING SUSTAINABLE DEVELOPMENT CONCEPTS TO REVITALIZE THE HISTORIC URBAN QUARTER OF AL-MUIZZ STREET, CAIRO, EGYPT
Bibliographic record
Abstract
Historical preservation helps keep communities beautiful, vibrant and livable, and gives people a stake in their surroundings, for the fact that such historical and valuable places provide a sense of stability and a tangible link with the past (historichawaii.org Feb.2020). Historical places constitute a valuable front for cities with a well-known identity that lasted for centuries. Focusing on developing these places to ensure their sustainability and preserving their history constitutes a mission that has impact not only on the urban environment but also on people using it. Fundamentally, cities bring creative and productive people together helping them to do what they do best: exchange, create and innovate. Culture lies at the heart of urban renewal and innovation.(Culture: urban future: global report on culture for sustainable urban development, Unesco 2016). Al-Muizz Street, an urban space in Cairo, Egypt is the study of this research that aims, first, to assess the current situation of the case study and its physical conditions. Second, to employ new concepts of sustainability in order to revitalize and preserve the cultural heritage of Cairo city, and to propose sustainable design based on the field survey, in order to achieve the best development. Al-Muizz Street constitutes a history that is worth preserving and developing because of its importance in the city life cycle. By being the lifeline of Cairo, restoring life to it, helps ensure an active living and a healthy environment for people.
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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 | 0.000 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".