Comparative bibliometric trends of microplastics and perfluoroalkyl and polyfluoroalkyl substances: how these hot environmental remediation research topics developed over time
Bibliographic record
Abstract
Microplastics (MPs) and per- and polyfluoroalkyl substances (PFAS) are ubiquitous in the environment due to consumer and industrial use. These compounds are very persistent in the environment and human body, which has made them hot environmental topics in recent years; but how this did come about? What factors have been driving the trends of their publication records, the public concern over environmental and public health issues caused by these pollutants, and the collaboration between scientists, regulators, and policymakers to solve these problems? In this paper, to understand these factors and contrast them between the two hot topics ("PFAS" and "MPs"), the changes in the bibliometric and scientometric trends of their publication records (extracted from the Web of Science from 1990 to 2020) have been visualized over time based on different classification perspectives, such as year, country, source, and organization. According to the analyses performed on these records, utilizing publication ratios and principal component and cluster analyses, in recent years (beginning in 2018) research topics related to MPs have surpassed PFAS topics. In addition, the economic, social, and geographical conditions of the top 20 countries with the highest number of publications in MPs and PFAS were explored to identify which countries are most concerned about each of these topics and why. For instance, PFAS research topics were more prevalant in countries with larger water areas compared to land area; while MPs topic were more prevelant in countries that produced more plastic wastes, and had higher landfilling and recycling rates and greater proportion of treated wastewater.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.070 | 0.139 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".