Advances in mining safety theory, technology, and equipment
Bibliographic record
Abstract
Mineral resources are an important pillar of human social development. With the gradual depletion of shallow resources, deep mining is an inevitable choice for ensuring energy security in various countries in the future. The complex and harsh environment in deep mining areas has led to frequent occurrences of hazardous events such as rockburst, coal and gas outburst, and mine fires. Mining safety science and engineering issues are receiving increasing attention. In August 2023, the 6th International Symposium on Mine Safety Science and Engineering was held in Harbin to promote innovative development of mining safety, and foster international collaborations among scholars in the field of mining safety. It served as a platform for the exchange of the most recent advancements in mining safety scientific theories, technologies, and equipment by bringing together global talent. Over 400 delegates representing 9 countries, including Australia, Russia, United State, Kazakhstan, and Canada, engaged in academic discussions and knowledge sharing on new theories, technologies, equipment, and methods in mining safety science and engineering. The latest research results are of great significance in enhancing the practices of preventing mine disaster and ensuring the safety of mining operations. Document Type: Editorial Cited as: He, S., He, X., Mitri, H., Meng, S., Wu, Q., Ren, T., Liu, S. Advances in mining safety theory, technology, and equipment. Advances in Geo-Energy Research, 2023, 10(2): 71-76. https://doi.org/10.46690/ager.2023.11.01
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".