Numerical modelling and scientific visualisation – integration of geomechanics into modern mine designs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As mines progress to depths for which the induced stress levels exceed the intact strength of the host rock, significant challenges related to rock mass instability must be met. However, given complexity and the scale of orebodies in deep mines, it is increasingly more challenging to predict/pinpoint where and when stress levels will become problematic. Prediction of where and when large scale instabilities will occur continues to be the ‘holy grail’ of rock mechanics in deep mining. There is no perfect solution; however, there have been a number of technological advancements that greatly helped to develop our understanding of rock mass behaviour and the risks pertaining to deep hard rock mines. It is recognised that at the mine scale, geology and material properties are not fully known, however, using past experience and sound engineering judgment, it is possible to use innovative tools and methodologies to arrive at a reasonable approximation of how a rock mass will behave at depth. The main goal of this paper is to provide an overview of how some of these tools and methodologies have evolved and are actively being applied to the planning of deep mines. Vale Canada Ltd.'s Creighton Mine will be used as a case study to demonstrate how these new techniques have contributed to a better understanding, and hence a better mine planning approach for hard rock mines at depth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it