Process for Sustainable Solid Waste Management in Tuti Island, Sudan, during Nile Flood Season; Comparison with International Standards Khogali Hind1
Bibliographic record
Abstract
Tuti Island is at the confluence of the White and Blue Niles and lies at the center of Greater Khartoum. The solid waste volume from houses is estimated at 20 tons, which has a significant impact on human health, the environment, and climate change. This study aims to investigate the process of solid waste management on Tuti Island. Solid waste includes food remains, construction debris, wood, scrap iron, discarded furniture, and collecting and reusing flood and rainwater International standards state that the solid waste disposal process passes through specific stages. This study adopts a scientific methodology to analyze the current scenario of measuring the types and quantities of solid waste and find appropriate methods for its collection, transportation, classification, treatment, recycling, reduction, and reuse. The study additionally uses questionnaires for the identification of issues, the types of solid waste, and proposed solutions. The results demonstrated that 37% of the respondents agreed that solid waste management is addressed it means there’s a process, 45% agreed that they should pay for this service, and 75% agreed that one solution for waste management could be to increase people's awareness. It was proposed to continue this project by supporting people, demanding legislation from the government represented by the Ministry of Environment and policies that support the recycling process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".