Structural complexity and slope design at the ArcelorMittal Mont-Wright mine
Bibliographic record
Abstract
Large-scale geological structures and rock mass fabric exert significant kinematic controls for slopes excavated in hard rock masses. At the ArcelorMittal Mining Canada (AMMC) Mont-Wright mine, slopes are developed in a stratified, highly foliated orebody that has undergone tight folding due to at least two major orogenic events. The resulting shearing and faulting has led to significant variations in foliation orientation, both vertically and laterally, and often over short distances. Additionally, complexly folded lithological contacts, locally altered and overprinted by shears, result in variable rock mass conditions at these contacts. This structural complexity poses challenges in collecting representative structural data necessary to achieve an industry-standard geotechnical confidence level (GCL) for slope design. As some design sectors of this mine are currently approaching final pit wall phases, AMMC has implemented a long-term geotechnical investigation program to increase the GCL. This includes regional geological interpretation, wall mapping, drone surveys, drilling with core orientation and televiewer surveys to refine geological and development of structural models. These models define structural domains, assess data reliability and delineate the extent of data extrapolation. Additionally, the mine has developed a laboratory testing program to characterise materials at contact zones, allowing for classification based on shear strength. This information will be used to enhance the slope design of the final walls and implement robust risk assessment tools, such as stability analyses using discrete fracture network models to complement kinematic assessments. This paper discusses the challenges of defining structural domains along kilometre-scale pit walls. A case study is presented of a multi-bench instability event, where an unexpected fold altered the dip of a weak contact zone despite a high confidence level, highlighting the complexities of structural interpretation in slope design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".