The Use of FLAC Software for Assessing Deformation Rates during Ice Excavation for Open Pit Mining in Glaciers
Bibliographic record
Abstract
Resource developments in glacial environments are becoming increasingly attractive as currently glacier-covered mineral deposits become exposed, easier to access and more economical to develop. Some mining projects are proposing to excavate large volumes of glacier ice to develop an open pit. Mining into an ice mass is challenging and needs to address the complex interaction of the glacier with pit excavation. Estimations of ice creep movements towards the pit are required to assess operational efforts and mine economics. In this paper the application of the finite difference computer software FLAC in estimating glacier ice deformation rates adjacent to a proposed open pit mine is presented. It is shown that the movement of glacier ice can be adequately modelled with a creep power law constitutive model (Glen's flow law) for ice. However, measured glacier movements are required to properly calibrate the constitutive model parameters. The calibrated model is then used to project glacier movements during ice pit expansion and to assess the stability of the excavated glacier ice face considering different excavation scenarios such as excavation slope angles and stages. The assessment shows that 2D numerical modelling is capable of providing useful insight into potential glacier movement mechanisms and changes in ice deformation rates. It can be a useful tool for planning safe excavation of the glacier ice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".