Application of the network connectivity index on fragmentation assessment in cave mine design
Bibliographic record
Abstract
Cave mine design relies on reasonable fragmentation assessment to optimise production efficiency and minimise operational costs. In the past decade the volumetric fracture intensity (P32) obtained from discrete fracture network (DFN) models has been widely used for fragmentation assessment in cave mine design. This paper examines the relationship between P32 and block sizes. The results show that P32 does not correlate well with key block size parameters D20, D50, and D80, which are sizes at 20, 50 and 80% mass passing, respectively. As an alternative, the network connectivity index 3D (NCI3D) is proposed as a geometric parameter to evaluate its correlation with block sizes. Results indicate that NCI3D exhibits stronger associations with D20, D50, and D80 compared to P32. Furthermore, NCI3D can be a computationally efficient alternative to the traditional DFNbased block formation approach for evaluating fragmentation characteristics in cave mine design. This parameter could be applied to mine-scale DFN models for assessing localised fragmentation within various locations of the orebody.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".