Visual analysis of coal fire detection research based on bibliometrics
Bibliographic record
Abstract
Accurate detection of coal fires is crucial for effective fire control planning. To enhance our comprehension of the global landscape of coal fire technology research and to propel theoretical advancements in coal fire prevention and control, this study conducted a comprehensive analysis of 1036 papers from the Web of Science core database and Scopus. Employing bibliometric methods and knowledge mapping tools like Vosviewer and Citespace, the analysis encompassed various aspects including annual distribution, geographical distribution, institutional distribution, main journals, core literature, core authors, and research hot topics. The results reveal that coal fire detection technology research has experienced a significant increase in the past decade. The most active countries in this field are China, the United States, India, Germany, Spain, Australia, United Kingdom, Poland, Canada, and Brazil, with the US having the greatest influence. Most of the leading research institutions are based in China. The main journals publishing research in this area are Fuel, Energy & Fuels, International Journal of Coal Geology, International Journal of Remote Sensing, and Remote Sensing. At present, the underlying theories and research frameworks in this field are well-established, but there is a lack of forward-looking studies. The main objectives of coal fire detection research are the development of fine data processing techniques and high-precision detection equipment. The research frontier in this field includes remote sensing technology, satellite sensors, coal fire physical models, and algorithmic programming.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.238 | 0.191 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".