Understanding Secondary Fragmentation Characteristics in Cave Mining: A Simulation-Based Analysis of Impact and Compression-Induced Breakage
Bibliographic record
Abstract
This study investigates the characteristics of secondary fragmentation and fines generation in cave mining through DEM simulations. The objective is not to develop a tool for accurately estimating fragmentation observed at drawpoints. Instead, the research focuses on an improved understanding of the impact of critical parameters (tensile strength, damping coefficients, and micro-defects) on secondary fragmentation characteristics. Attempting to predict outcomes without first comprehending the underlying mechanisms risks oversimplifying complex mine-scale conditions. The analysis shows that tensile failure is the dominant mechanism governing fragmentation. Size-distribution curves of fragmented blocks under impact breakage demonstrate a concave-up exponential relationship between percentage mass passing at 1/10th of the original size (t10) and kinetic energy. Furthermore, the analysis of compression-induced breakage highlights the significant role of tensile strength and micro-defects in determining the extent of fragmentation under different conditions. By better understanding these underlying mechanisms, the research establishes a solid foundation for predicting fines generation and ultimately enhancing decision making and operational strategies in mining.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".