A bibliometric review of the most cited literature related to mining injuries
Bibliographic record
Abstract
AbstractThis review analysed the top 56 annual and lifetime cited articles related to mining injuries. The number of annual and lifetime citations, year of publication, design, topic of focus, number of authors, and country of publication, were collected and reported. Cross-sectional studies were the most commonly used design type and the majority of the corresponding authors were from the United States. Lost-time injury as an outcome measure (73% of articles) was the most frequently discussed topic. The findings of this review article are designed to facilitate future research by revealing existing patterns and trends within the science.Keywords: Mining injurycitation analysisepidemiologyreview AcknowledgementAll authors substantially contributed to the conception of the work and to the interpretation of the data. Furthermore, all authors substantially contributed to the drafting and revising of the work. All authors are in agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy and integrity of any part of the work are appropriately investigated and resolved. All authors approve of the final version to be published.Disclosure statementNo conflict of interest was reported by the authors.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one teacher head, 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".