Problematic Soil Risk Assessment Approach for Sustainable Spatial Suitability of Urban Land Use in New Cities by Using GIS and Remote Sensing: A Case Study of New East Port Said City
Bibliographic record
Abstract
The geoenvironmental risk assessment approach is one of the important approaches that provides new city planners with an important tool to cope with emerging environmental problems, such as pollution, epidemics, climate change, and flood.However, few scholars have explored the assessment of soil risk at the local level, which is common in new cities in Egypt.Ignoring the problematic soil in urban planning would present future risks for residents, undermine the effect of proposed land uses, and waste resources in plan implementation.To solve these problems, this paper carries out a risk assessment of problematic soil for the urban planning of New East Port Said City, aiming to ensure the efficiency of future land uses and the sustainability of similar new cities.The types of soil in the new city were summarized, which include soft clay soil, sabkha soil and sand dunes.With the aid of geographic information system (GIS) and remote sensing, the factors leading to the geological risk of each type of soil were detected separately.Besides, the vulnerability of proposed land uses was evaluated, and the geo-hazards of problematic soil were mapped comprehensively.On this basis, the land use plan was optimized for the new city according to the soil risks, and the land uses affected by soil risks.The research results shed new light on the application of environmental risk assessment in urban planning, and promote the risk mitigation, and sustainability of new cities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".