Resource-based zoning map for sustainable industrial development in north sinai using remote sensing and multicriteria evaluation
Bibliographic record
Abstract
Due to rapid urbanization in Egypt, the need for job creation and redistribution of population became a top priority for the Egyptian government.Creating infrastructure and new industrial zones in Sinai Peninsula can participate in solving the problem.Geographic Information System (GIS) and Spatial Multicriteria Evaluation (SMCE) have been widely used to analyze the land utilization based on the land's potentials and constraints.Using Shuttle Radar Topography Mission (SRTM) digital elevation model, meteorological data and various land use information, a holistic approach involving generation of thematic maps for two themes, natural resources theme and a least-cost theme, was adopted.Data such as accessibility, soil type, land cover, utilities and other ancillary information was employed to arrive at a locale-specifi c prescription for an industrial land use strategy.Analytical hierarchy process was conducted to investigate the resource-based suitability while minimizing cost of development using various spatial data.Expert knowledge was used to weigh factors within the natural resources theme based on three development objectives (scenarios).Running the weighted overlay model for each of the three objectives, three suitability index maps were produced.Potential sites for developing new industrial zones were identifi ed based on the high suitability values for each scenario.Results highlight a good opportunity for developing the middle zones of Sinai (El Hassana and Nekhel divisions) in addition to the coastal belt.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".