Riverine Plastic Pollution in Asia: Results from a Bibliometric Assessment
Bibliographic record
Abstract
Rivers are important ecosystems, vital to the livelihoods of hundreds of millions of humans and other species. Despite their environmental, social, and economic importance, current use of rivers is unsustainable, due to a combination of solid waste and high levels of pollutants. Plastic materials are among the most predominant of such pollutants. Based on the need for additional research in this area, this study examines pressures put to rivers and explores trends related to riverine plastic pollution, with a focus on Asia. Apart from the bibliometric analysis, and relying on the collected information, examples describing the drivers of riverine plastic pollution in a sample of Asian countries are described, outlining the specific problem and its scope. Among some of the results obtained from it, mention can be made to the fact that much of the literature focuses on plastic pollution as a whole and less on one of its most significant ramifications, namely microplastics. Additionally, there is a need related to data availability on riverine plastic data and improving the understanding of transport mechanisms in relation to riverine plastic emission into the ocean. The results from this study illustrate the significance of the problems posed by plastic waste to Asian rivers and point out the fact that there are still significant gaps in respect of regulations and standards, which prevent improvements that are highlighted in this study. Based on the results of this bibliometric assessment, specific measures via which levels of riverine plastic pollution may be reduced are presented, bringing relevant new insights on this topic beyond the existing reviews.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.102 | 0.185 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".