A Bibliometric Analysis of Research Trends in Biodegradation of Plastics
Bibliographic record
Abstract
The rapid growth in the production and application of plastic globally has resulted in plastic pollution with a negative impact on the environment, especially the marine ecosystem. One main disadvantage in the majority of polymers is disposal after a useful life span. Non-degradable polymers create severe difficulty in plastic waste management that might end up in landfills or wash into the ocean. The biodegradation of plastic waste is one solution to this critical problem of pollution. Hence, there is a need to consider the advancement of research in this subject area, in pursuit of a way out of plastic pollution. Thus, this study was designed to map the biodegradation of plastic-related research from 2000 to 2021. Statistical information on the topic was recovered from the Web of Science Core Collection and analysed using the bibliometrix package in RStudio statistical software, while data visualisation was conducted via VOSviewer. Our evaluation indicated that the amount of research on the biodegradation of plastic increased over the last decade, and the annual growth rate of publication trends was 11.84%. The study revealed that 1131 authors wrote the 290 analysed documents, with a collaboration index of 4.04. Cooper DG (n = 11) was the most relevant author, McGill University (n = 21) was the most active university, and the Journal of Polymers and the Environment (n = 19) the leading journal. The outcome of this study can guide prospective research and offer vital information for improving the management of plastic waste.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.143 | 0.187 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".