A Review and Bibliometric Analysis of Sorting and Recycling of Plastic Wastes
Bibliographic record
Abstract
Global plastic pollution is a negative impact on the environment as the production and use of plastic are increasing rapidly. Plastic recycling is a significant step towards a circular economy. Over the decades, much plastic has been in circulation for various applications. Recycling plastic wastes (PW) entails waste sorting using some physical properties including plastic types, colors, and shapes, to produce high-quality recycled plastics. Classification of PWs includes common plastic types: Polyethylene terephthalate (PET), High-density polyethylene (HDPE), Polyvinyl chloride (PVC), Low-density polyethylene (LDPE), Polypropylene (PP), Polystyrene (PS), and others. The traditional method of sorting PW achieves good accuracy but low throughput at an excessive cost. Automated processes in plastic sorting are developed to overcome this. This study analyzes automated sorting techniques and examines bibliometric data on plastic waste research over the past four decades. The Scopus database was used to retrieve statistics on the subject, which were then examined using the bibliometric program in the VOSviewer software. The data visualization was also carried out with VOSviewer. The results of this study can guide future research and provide crucial details to improve plastic 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.054 | 0.079 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".