An analysis and visualization of global wetlands based on scientmetric from 2002 to 2022
Bibliographic record
Abstract
Abstract Recent studies have focused on wetlands as they can benefit humans in terms of spiritual satisfaction and mental health. This paper evaluated the scientometric analysis of 2,388 studies published on the Web of Science between 2002 and 2022. It identified hotspots and trends in wetland research using VOS viewer, Origin, and Citespace software. Analyzing global wetland research publications shows a clear upward trend. Moreover, researchers in the United States, the People's Republic of China, Australia, Canada, and India devote considerable attention to wetland research. Network keyword co-occurrence analysis showed that wetland research covers constructed wetlands, climate change, wastewater treatment, phytoremediation, restoration, and hydrology. Furthermore, the United States is the world's main hotspot for wetland research; China, Canada, Australia, and others are behind. Due to the growing appreciation of wetlands' importance, wetland research will receive more attention from researchers around the globe. Additionally, wetland tourism articles should have improved quality since most publications are rarely cited. This paper presents an overview of the scientometric methodology for global wetlands research. Additionally, scholars working on wetlands can use scientometric analysis meaningfully and effectively for their future research.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.073 | 0.078 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".