Bibliometric analysis and review of mine ventilation literature published between 2010 and 2023
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To provide scholars with a quick understanding of the current status, research hotspots, and future trends in the field of mine ventilation, this paper conducted a visualized bibliometric analysis and a comprehensive review of mine ventilation-related literature from 2010 to 2023 using CiteSpace. A thorough analysis of the publication time, co-authorship, co-citation, keywords, and research topics of the literature was carried out. Based on this, through systematic literature reading and summarization, research topics in the field of mine ventilation were organized, analyzed, and classified. The results indicate that mine ventilation research from 2010 to 2023 went through three stages: stable development, slow growth, and rapid ascent. Nie Wen and China Univ Min & Technol were the most prolific authors and institutions in the field of mine ventilation. China had the highest number of publications during 2010-2023, while Canada and Poland exhibited the highest centrality, signifying their key roles in the mine ventilation domain. Deep mine ventilation and intelligent mine ventilation emerged as research hotspots and mainstream trends in the future. The analysis of multiple hazard coupling studies represents a research direction that mine ventilation needs to develop. Numerical simulation techniques should not be limited to static analysis, as dynamic simulation is a focal area of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.053 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it