Bibliometric analysis and review of mine ventilation literature published between 2010 and 2023
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.
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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.225 | 0.273 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".