Assessing Potential Landfill Sites Using GIS and Remote Sensing Techniques: A Case Study in Kirkuk, Iraq
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it