Integrated geotechnical feasibility analysis for an open pit mine in the canadian arctic
Bibliographic record
Abstract
This paper presents the geotechnical results of a comprehensive feasibility case study for the Tiriganiaq pit of the Meliadine project in the Canadian arctic. A geomechanical model was constructed based on the ore reserve block model, and field and laboratory data collection campaigns. The results of oriented core logging were used to identify and zone the mine. Working in parallel with the pit optimization process a series of comprehensive slope stability analyses were undertaken on a block by block basis on selected optimised pits. The innovation of the undertaken approach resides in the smooth integration of the geological and geotechnical models with the optimised ultimate pit geometry to provide input for the slope stability analysis. Rather than working with “representative” slope geometries the actual planned slope geometries were used to compute the slope orientation at bench and inter-ramps levels using Geographical Information Systems (GIS) algorithms. This has allowed the stability analysis of the complete 3D numerical mine model and facilitates the identification of potentially unstable zones. The stability analyses were based on deterministic and probabilistic limit equilibrium techniques. It was possible to investigate the stability of all benches and inter-ramps for the ultimate pit defined by the block model. The factor of safety (FS) and probability of failure (PF) were assessed for every block of the optimised pit. In order to quantify the impact of the prevailing geotechnical conditions on the proposed pit shells a series of multi-criteria stability analyses were employed to assess the potential for localised instability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".