Global capacity, potentials and trends of solid waste research and management
Bibliographic record
Abstract
In this study, United States, China, India, United Kingdom, Nigeria, Egypt, Brazil, Italy, Germany, Taiwan, Australia, Canada and Mexico were selected to represent the global community. This enabled an overview of solid waste management worldwide and between developed and developing countries. These are countries that feature most in the International Conference on Solid Waste Technology and Management (ICSW) over the past 20 years. A total of 1452 articles directly on solid waste management and technology were reviewed and credited to their original country of research. Results show significant solid waste research potentials globally, with the United States leading by 373 articles, followed by India with 230 articles. The rest of the countries are ranked in the order of: UK > Taiwan > Brazil > Nigeria > Italy > Japan > China > Canada > Germany >Mexico > Egypt > Australia. Global capacity in solid waste management options is in the order of: Waste characterisation-management > waste biotech/composting > waste to landfill > waste recovery/reduction > waste in construction > waste recycling > waste treatment-reuse-storage > waste to energy > waste dumping > waste education/public participation/policy. It is observed that the solid waste research potential is not a measure of solid waste management capacity. The results show more significant research impacts on solid waste management in developed countries than in developing countries where economy, technology and society factors are not strong. This article is targeted to motivate similar study in each country, using solid waste research articles from other streamed databases to measure research impacts on solid waste management.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.020 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".