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 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".