A visualized bibliometric analysis on remote sensing monitoring of methane emissions in coal mines
Bibliographic record
Abstract
Remote sensing monitoring of methane emissions in coal mines has become a major field of research in the global efforts to monitor potent greenhouse gas emissions. While existing studies predominantly concentrated on the technological dimensions, this study proposes to review previous stu-dies, analyze major trends and pinpoint the current research priorities for this field of study. We retrieved domestic and international literature on remote sensing monitoring of methane emissions in coal mines since 2000 from Web of Science and China National Knowledge Infrastructure (CNKI) databases via keyword searches. A visualized bibliometric analysis was then conducted using the CiteSpace tool. Results show that research on remote sensing monitoring of methane emissions in coal mines exhibited significant growth, with an average annual increase of 20 %. We identified the evolving trends of leading countries, research institutions, and research domains with top research contributions and proposed intermediary centrality metrics that reflect patterns of international collaboration: The United States (181 articles) and China (98 articles) are the 2 leading countries with the highest contributions of publications, followed by Germany and Canada; The National Aeronautics and Space Administration (NASA) ranks the first among research institutions with 66 publications, while the Chinese Academy of Sciences ranks the fifth globally; Satellite remote sensing, methane concentration retrieval, methane quantification, and intelligent data processing are the current research priorities in this field; International studies focus more on global methane emission monitoring and policy-driven technological innovations while domestic studies excel in monitoring and intelligent management of methane emissions in coal mines; Co-citation analysis revealed that advancements in high-resolution remote sensing and retrieval technologies have significantly enhanced the spatial and temporal precision of methane emission monitoring. This study therefore suggests areas for future research, such as the precision and efficiency of remote sensing monitoring, intelligent data processing and algorithms, multi-platform integration and fusion, and international collaboration and data sharing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.054 | 0.083 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".