Characterisation of foliated rock masses using implicit modelling to guide geotechnical domaining and slope design
Bibliographic record
Abstract
The stability of rock slopes designed and excavated within anisotropic rock masses are influenced by several factors, some of which include spacing intensity, continuity, roughness, dip and dip direction, and waviness. Foliation-parallel instabilities are often expressed at the multi-bench or inter-ramp scale due to the high persistence, larger-scale dilation and breaking of intact rock bridges. Functional pit slope designs need to account for the variability in foliation character with consideration to the mining geometries being developed. The accepted approach is to partition the rock mass into three-dimensional (3D) geotechnical domains to reduce complexity for slope design guidelines. Inputs guiding the development of the geotechnical domains can either be from highly manual interpretations, which are tedious and coarse in resolution, or implicit 3D foliation modelling methods. To demonstrate this, implicit models, such as form interpolants and/or block models, were first used at Jwaneng mine and have subsequently been generated and used to inform domaining and design at two operating mines (Rainy River and Rosebel Gold mines) and one other project site. The models were developed from different sources of geotechnical/geological data (i.e. oriented drillhole logging, televiewer, grade cutoff, pit face and photogrammetry mapping). The validation of the models has been further investigated with techniques presented in this paper.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".