Natural infrastructure concept in arid regions: two case studies in egyptian context
Bibliographic record
Abstract
The term 'green infrastructure' in arid areas may not be 'green' as a colour. It is used as a metaphor to improve the cultural, historic and social values in these areas. Thus, natural infrastructure (NI) as a whole term and green infrastructure as a specific and common concept are classified as valuable infrastructure in Middle Eastern countries, particularly Egypt, possibly because in arid areas there is relatively little landscape that is naturally green with a small amount of rainfall. Additionally, the social and cultural resources are considered as the basic factors of green and blue infrastructure in arid zones because the nature of people who live in arid areas is different from others. This paper outlines the concept of NI in the Egyptian context via two different case studies, each of which has its own aspects, in terms of spatial planning strategies. There have been specific features that highlight and draw the main theme of each case. These features are related to specific criteria, such as location, scale, urban features and associated activities. The first case is about the natural connectivity along the River Nile that naturally links the three biggest islands and connects different urban communities. The second case is the ecological connection of a green network within the urban district of Maadi and the natural, environmental and historic protected area of Wadi Degla. These main two areas represent the natural, cultural activities and bio diversity. This article, therefore, analyses these cases by using IKONOS GIS maps of Greater Cairo. These maps have been based on the official documentations, from site visits and through conducting interviews with specialists who have responsibilities for spatial planning either in academic or professional sectors in Egypt.
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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 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".