Automatic creation of mining polygons using hierarchical clustering techniques
Bibliographic record
Abstract
There are multiple stages in a mining operation in which a mining engineer must draw polygons to be used as operation guidelines. These polygons are drawn by hand and based on the engineer’s experience and knowledge of the deposit. However, automatic procedures for forming the shapes can increase the quality and decrease the efforts required. Long-term planning requires large polygons that can be used as mining cuts. On the other hand, short-term planning requires mineable shapes to be used as mining units. These shapes need to be homogenous in grades and rock types so that the quality and dilution of material sent to the plant can be estimated with good approximation. In addition, the direction of mining can affect the desired shapes of the polygons. To address these problems, a clustering algorithm with shape control is introduced, which can provide reasonable guidelines for all the aforementioned shapes by calibrating its parameters. The implementations of the algorithm on two small datasets with 874 and 2794 blocks are illustrated. Performance of the algorithm on a real gold deposit with different mining strategies is also presented and evaluated based on homogeneity of grade, rock types, determined destinations, and run times.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".