Towards sustainability in municipal solid waste management in South Africa: a survey of challenges and prospects
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
In most developing countries, the huge amount of unmanaged municipal solid wastes and the inefficiency of the current waste management system have resulted in an unprecedented detrimental effect on human health and the quality of the environment. The drive towards sustainability in solid waste management in South Africa has led to the promulgation of several legislations and policies directed towards increased efficiency of solid waste management strategies. However, despite the progress in South Africa’s waste management systems over the years, it still faces several challenges and shortcomings. To achieve sustainable development through the transition from a linear economic model to a circular economy, there is a need to revamp the waste management sector. This study presents a survey of the key physical elements of integrated waste management in South Africa. The study further discusses the challenges, with a major emphasis on the future directions of integrated waste management. Waste management decisions are data-driven decisions. This study identifies the lack of accurate and reliable waste-related data as one of the major factors that impede the fast-track growth towards sustainable waste management in South Africa. A data-mining approach that emphasises intelligent modeling of waste management systems is recommended to support the national waste database, which will aid waste management decisions and optimise waste management facilities and investments. Multi-sector intervention and involvement are required to stimulate sustainable development in waste management in South Africa.
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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 | 0.001 |
| 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