Research status and emerging trends in remediation of contaminated sites: a bibliometric network analysis
Bibliographic record
Abstract
Site contamination poses a grave danger to the environmental quality and human health, and its remediation has been a focus of worldwide concern over the last few decades. Based on 5068 bibliographic data (2001–2022) acquired from the Web of Science Core Collection, this study employed a scientometric analysis approach to analyze the present state and investigate the trends of contaminated site remediation studies. The results of this study provide an in-depth response to the following: (1) publication characteristics of polluted site restoration studies; (2) basic information on countries, institutions, journals, and disciplines engaged in remediation research in contaminated areas; and (3) a summary of development trends and hotspots in poisoned field cleanup investigations. In summary, this study assessed the results of research on contaminated site remediation. Those unfamiliar with contaminated site remediation could utilize the information in this study to rapidly merge into the field and grasp the forefront of research on this subject. This article can be regarded as a reference for scholars who desire to conduct further research on relevant subjects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.047 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".