Simulating Bulk Ore Sorting Performance of a Panel Cave Mine: A Comparison between Two Approaches
Bibliographic record
Abstract
Conceptual bulk ore sorting studies are essential for determining a potential improvement in mine economics before undertaking on-site sensor trials. Two approaches, block modelling and drill core compositing, are applied to simulate the bulk ore sorting performance of mining operations. While one employs the grade data of a block model, the other approach utilizes composited drill core grades. This study aimed at comparing these two approaches by estimating in-situ grade heterogeneities and simulating the bulk ore sorting performances of the currently active caves of the Cadia East panel cave mine. The results show that block modelling tends to smooth the grade variability that initially exists in the drill core grade data. Particularly in the portions of the deposit where drilling is sparse or widely spaced compared to the selected block size, block modelling leads to lower grade heterogeneity and bulk ore sorting performance estimates. However, when the drill hole data is nonrepresentative of the area of interest, block modelling can predict more realistic bulk ore sorting performances compared to drill core grades. The assessments performed with the blocks and drill core composites of various sizes showed that grade heterogeneity was adversely affected by an increased sorting scale due to averaged metal 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 | 0.000 | 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".