Assessing Potential Landfill Sites Using GIS and Remote Sensing Techniques: A Case Study in Kirkuk, Iraq
Bibliographic record
Abstract
Solid waste management poses a significant challenge in rapidly growing urban centers in developing countries, including Iraq.Landfilling is the most prevalent method for solid waste disposal, and identifying suitable landfill locations that minimize environmental and societal impacts is crucial.The proliferation of random waste disposal sites in Kirkuk city underscores the need for the application of international standards in selecting optimal landfill sites.In this study, Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) were integrated to determine the most appropriate landfill site in Kirkuk city.A model was developed to identify the most suitable location for a proposed landfill, taking into account various factors.Four potential sites were proposed and compared to the existing location, with the selection based on multiple criteria.Key criteria included proximity to villages, wells, rivers, surface water, hospitals, schools, oil pipelines, airports, and parks; environmental factors such as agricultural land, hydrology, groundwater, and land use/land cover (LULC); engineering aspects including soil, roads, slopes, railways, and valleys; and socio-economic factors like cost and public acceptance.The results indicated that the current landfill site exhibited the least negative impact on environmental, economic, and social aspects.The proposed method demonstrated efficiency in application, reducing the time and cost with remarkable accuracy.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".