The use of virtual simulators for emergency response training in the mining industry
Bibliographic record
Abstract
By its very nature, underground mining can be a hazardous activity. The history of all countries where mining has taken place unfortunately often contains major disasters. The successful initial control of such incidents is crucially dependent on the effectiveness of the mine’s immediate emergency response and the mine’s emergency preparedness arrangements, which underpin this response.Emergency response is sometimes given a low priority in training planning because catastrophic events occur infrequently. The majority of mine emergency rescue training is traditionally focused on training the rescue teams. A number of computer augmented training systems have recently been developed to perform or assist all levels of mine personnel in the process of mine rescue training.Modern simulation systems range from tactile systems that physically represent the real world to purely computer generated visualizations. In a mining context, a primary aim of developing virtual environments is to allow mine personnel to practice and experience mine processes that will be encountered in the day-to-day operations at a mine site.This article provides a review of the use of such simulators in the mining industry and details current work being undertaken in Australia and Canada to develop the next generation of this technology in the mining field. This work is based on an extensive literature review and the insight and the experience of the two authors who have each worked in this field for more than 20 years.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".