A Critical Review and Bibliometric Analysis on Applications of Ground Penetrating Radar in Science Based on Web of Science Database
Bibliographic record
Abstract
Ground-penetrating radar (GPR) is an established technology with a wide range of applications for civil engineering, geological research, archaeological studies, and hydrological practices. In this regard, this study applies bibliometric and scientometric assessment to provide a systematic review of the literature on GPR-related research. This study reports the publication trends, sources of publications and subject categories, cooperation of countries, productivity of authors, citations of publications, and clusters of keywords in GPR-related research. The Science Citation Index Expanded (SCI-EXPANDED) and the Social Sciences Citation Index (SSCI), which can be accessed through the Web of Science Core Collection, are used as references. The findings report that the number of publications is 6880 between 2001 and 2021. The number of annual publications has increased significantly, from 139 in 2001 to 576 in 2021. The studies are published in 894 journals, and the annual number of active journals increased from 68 in 2001 to 215 in 2021. Throughout the study, the number of subject categories involved in GPR-related research fluctuated, ranging from 38 in 2001 to 68 in 2021. The research studies originated from 118 countries on 6 continents, where the United States and the People’s Republic of China led the research articles. The top five most common keywords are ground-penetrating radar, non-destructive testing, geophysics, electrical resistivity tomography, and radar. After investigating the clusters of keywords, it is determined that civil engineering, geological research, archaeological studies, and hydrological practices are the four main research fields incorporating GPR utilization. This study offers academics and practitioners an in-depth review of the latest research in GPR research as well as a multidisciplinary reference for future studies.
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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 | 0.026 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.196 | 0.181 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".