Past, Present, and Future of Groundwater Remediation Research: A Scientometric Analysis
Bibliographic record
Abstract
In this study, we characterize the body of knowledge of groundwater remediation from 1950 to 2018 by employing scientometric techniques and CiteSpace software, based on the Science Citation Index Expanded (SCI-E) databases. The results indicate that the United States and China contributed 56.4% of the total publications and were the major powers in groundwater remediation research. In addition, the United States, Canada, and China have considerable capabilities and expertise in groundwater remediation research. Groundwater remediation research is a multidisciplinary field, covering water resources, environmental sciences and ecology, environmental sciences, and engineering, among other fields. Journals such as Environmental Science and Technology, Journal of Contaminant Hydrology, and Water Research were the major sources of cited works. The research fronts of groundwater remediation were transitioning from the pump-and-treat method to permeable reactive barriers and nanoscale zero‑valent iron particles. The combination of new persulfate ion‑activation technology and nanotechnology is receiving much attention. Based on the visualized networks, the intelligence base was verified using a variety of metrics. Through landscape portrayal and developmental trajectory identification of groundwater remediation research, this study provides insight into the characteristics of, and global trends in, groundwater remediation, which will facilitate the identification of future research directions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".