Tracking Grade Heterogeneity in a Panel Cave Mine: A Reconciliation Study Investigating the Impact of Mixing from an Ore Sorting Perspective
Bibliographic record
Abstract
Bulk ore sorting is a preconcentration method applied to bulk streams or batches of material. The effectiveness of bulk ore sorting relies on the degree of the naturally occurring in situ grade heterogeneity of an ore deposit. The blending of ore during mining and material handling degrades the in situ grade heterogeneity initially present in an orebody and reduces the potential to apply bulk ore sorting. Block and panel cave mines experience mixing from the establishment of caves to the delivery of the caved ore to concentrators. This study focused on tracking the grade heterogeneity and quantifying the impact of mixing on the sorting potential of the Cadia East panel cave mine via the reconciliation of the grade measurements performed at different locations. The results showed that the copper and gold grades were almost fully homogenized via various mixing events identified for the mine. The tracked grade heterogeneity values revealed that the mixing during material handling, which included ore blending according to mine planning, reduced the grade variability more drastically than the mixing within the caves. As the ore travelled from its original in situ location, the grade distributions gradually transformed, and the grades eventually normalized around the mean values. Only insignificant amounts of low-grade material that could have potentially been discarded were reported to the on-belt sensor located at the surface. The results of this study are significant for cave mines exploring sorting systems for preconcentration as they highlight how mixing can impact the variability in ore grades.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".