Environmental Planning and Spatial Modeling for Wind Energy Farm Sites (Case Study: Najaf Secondary Region)
Bibliographic record
Abstract
The transition to renewable energy is not merely a technical option; it is a fundamental element in making and shaping policies for achieving sustainable development in the energy sector.This achieves a degree of integration among economic, environmental, and social urban policies in the secondary Najaf region.The use of renewable energy for wind farms, contributes to providing energy for human activities and various processes, by harnessing kinetic energy from wind movement to generate electrical power through the operation of wind turbines, wind energy undergoes a transformation from mechanical energy to electrical energy, meeting the energy demands of urban centers and rural areas.Therefore, this study aims to introduce a renewable energy source from wind energy, as a solution to the current and anticipated electricity deficit in the study area.In addition to contributing to urban development, agriculture, environmental conservation and related fields.This research adopts for multi-criteria decision-making (MCDM) methodology within the geographic information system (GIS) to identify the most suitable spatial location.As a result, the study has identified appropriate and efficient sites for wind farms, through establishing planning and design criteria and standards for wind farm development.The proposed design includes a well-planned wind farm covering 40.3 Km 2 , equipped with 99 turbines, capable of producing 198 Mw of electrical energy to alleviate shortages and meet future energy demands.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".