Constructed wetlands as treatment systems: An overview and bibliometric analysis
Bibliographic record
Abstract
Abstract Constructed wetlands have gained a major role in treating various forms of wastewaters. According to their cost-effectiveness and myriads of side benefits, a substantial body of research has grown around this topic in recent years. Being situated at the center of studies from diverse fields, there is a demand for a study to show different themes inside this field of research. This paper aims to explore research connected to the application of constructed wetlands for water and wastewater treatment using bibliometric analysis of data retrieved from Scopus database from the first appearance of this topic to 2021. The results suggest that more than three thousand papers have been published by 503 journals. About 8000 scholars have contributed to this topic, who are mostly from China, United States, United Kingdom, Spain, and India. The analysis on author keywords interaction network found 4 major clusters, each indicating to various parts of research on constructed wetlands. Finally, most recent research trends were detected via overlay network, indicating the focus on micropollutants and emerging contaminants (such as antibiotics) and microbial fuel cells as trends of future study in this field.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.184 | 0.257 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".