Research Trends of Soil Mercury Pollution Based on the Bibliometric Analysis of 2014–2024 SCI Papers
Bibliographic record
Abstract
In recent years, soil mercury pollution has become a global environmental problem, especially in countries with rapid development of industry like China. To analyze the current status of published papers and research hotspots in the field of soil mercury pollution, this review statistically analyzed and visualized the SCI papers in this field from 2014 to 2024 using bibliometric and CiteSpace knowledge graph analysis. The results show that the number of SCI papers in the field of global soil mercury pollution is on the rise, covering 76 disciplines, of which ecological and environmental science accounts for more than half. The main countries of publication are China, the United States, Canada, Spain and India, and the cooperation network has gradually changed from scattered to close. China has become a core research country, and the cooperation with other countries has been continuously enhanced. Based on keyword analysis, global research on soil mercury pollution initially focused on the accumulation and transport mechanisms of mercury, then shifted to the forms, pollution sources and ecological impacts of mercury in the medium term and has recently concentrated on the behaviors and ecosystem interactions of mercury in water bodies. Moreover, significantly influenced by geography and environment, research of China in this field has initially concentrated on mercury release and contamination from mining activities, subsequently shifted to aquatic bioaccumulation and impacts on water bodies, and has recently emphasized ecological risk assessment and remediation.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.120 | 0.173 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".