Cave to mill: improving value of caving operations
Bibliographic record
Abstract
Forecasts indicate that there will be an increase in copper production from underground operations over the coming decades as available resources trend deeper. It is anticipated that a significant portion of this production will be from caving operations. Caving has a very different risk profile to open pit mining and other underground methods, with one of the critical areas being run-of-mine (ROM) grades that are delivered to the mill. Though caving can match pits in terms of operating cost per tonne, it suffers from limited grade selectivity with ROM grades trending toward the orebody average due to the consequences of mixing in cave columns. Research and subsequent operational trials have demonstrated that bulk sorting can manage this lack of selectivity by ‘pre-concentrating’ the ROM ore, providing the mill with a more consistent and higher grade feed. The paper describes the ‘cave to mill’ concept where the objective is to provide consistent feed (tonnes and grade) to the flotation circuit. This is done in three stages. The first stage is better characterisation of the material reporting to drawpoints. Secondly, measurement of the variation in metal content that is delivered from drawpoints through the use of scanners and sensors at various points in the ore flow system. Finally, bulk sorting systems offer flexibility and control by allowing classification of waste, low-grade and mill-grade streams in real time. The potential for bulk sorting is dependent on the heterogeneity of the ore and the ability of sensors to detect the heterogeneity. The broad technical requirements for each stage are discussed together with the associated business case.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".