Data driven block discretisation method for predicting the bulk ore sorting benefits applied at distinctly heterogeneous open pit mines
Bibliographic record
Abstract
Recent mining technology innovations such as robust shovel bucket mounted sensors allow ore grades to be measured at the mine face for every scoop and truck during extraction. There are currently limited established methods for estimating the ore control benefits of enhancing mining selectivity at a given deposit. A discretisation method is presented using blasthole grades where a selective mining unit block is first discretised to truck or shovel bucket sized subblocks. Subblock grades are assumed to follow a distribution with the same average grade as the host block and the variance scaled from locally surrounding blastholes. The ore control benefits are quantified by comparing the ore and waste grade tonnage of the coarser original blocks to the more selective subblocks. The discretisation methodology is applied reducing the selective mining unit on 2 months of production data from three distinctly heterogeneous open pit mines. Discretising to truck sized blocks resulted in a 10–30% decrease in dilution and ore loss depending on the coefficient of variation, strip ratio, average grades above and below cut-off, and spatial continuity. Operational considerations for bulk ore sorting are provided as productivity could be impacted as a tradeoff for the enhanced selectivity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".