Effect of stope construction parameters on ore dilution in narrow vein mining
Bibliographic record
Abstract
Ore dilution has an important influence on mining costs and hence on the viability of a mining operation. Costs associated with ore dilution with barren waste or material below the cut-off grade are linked to all mining and milling stages. Several research efforts were made in the past to help better understand the causes of ore dilution, and some of these led to the development of empirical formulae for the estimation of ore dilution in different mining environments. Stope geometry was identified as one of the principal factors influencing ore dilution attributed to stope wall overbreak. This thesis examines the effects of stope geometric parameters on ore dilution – namely the stope width and strike length, as well as stope undercutting and the orebody dip angle. The study uses nonlinear finite element modelling as the analysis tool. Numerical model predictions are compared, whenever possible, with surveyed stope profile obtained from field measurements. The scope of the thesis is a case study of the Lapa Mine of Agnico Eagle Mines Limited, a gold mining operation situated in the Abitibi region of Quebec. The Lapa orebody is a narrow vein, steeply dipping deposit with varying width, and is extracted with longitudinal retreat mining method.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".