Increasing the value of heterogeneous ore deposits by high-resolution deposit-modelling and flexible extraction techniques
Bibliographic record
Abstract
Continued profitability in mineral resource extraction is challenged by depressed prices and decreasing grades, combined with increased extraction and processing costs, as well as the increasing depth and complexity of available deposits. A standard industry response to these challenges has been to adopt economies of scale; however, this approach is proven to have limitations in the current cost/price environment. Improved precision and accuracy in ore routing can overcome these challenges to a larger extent, but in order to achieve this, a new tool set consisting of high resolution data capture and modelling, coupled with flexible, real-time, online in-pit mineral classification appears to be required. This paper examines preliminary developments in measuring and modelling deposit heterogeneity at two copper mines in Chile. This examination is then followed by the development and use of a data model to evaluate the opportunity to introduce selective partitioning of ores through in-pit sensing and decision support tools prior to conventional processing in leach or flotation circuits. The results of this study suggest that the value proposition for the use of in-mine sensors for classifying and segregating valuable ore from waste, and to improve the accuracy of dispatch to the leaching, milling, or waste disposal stages, is significant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".