Time for a Change? the Role of Rockmass Characterization and Discrete Fracture Networks on the Assessment of Caving Rockmasses
Bibliographic record
Abstract
ABSTRACT: With the trend for block and panel caving projects being in typically deeper, stronger and more heterogenous rock mass conditions, the need for better rock mass characterization has never been greater so that a reliable design can be developed. However, with many of the worldwide caving operations been carried out in more massive rock masses with relatively few joints being present, there is a requirement to improve rock mass characterization which has historically been focused on joints which are not often present in porphyry deposits at depth, the typical caving host rock masses. Failure to step outside historic characterization approaches has resulted in mis-characterized resources and a significant difference between forecasted and actual caving performance. Increasingly improvements in rock mass characterization methods have benefited from more detailed investigations of veining, their role in rock failure mechanisms, and their integration within Discrete Fracture Network (DFN) models. DFN modelling allows for a more statistical approach to rock fabric description by explicitly building 3D synthetic rock mass descriptions for improved visualization and analysis. The objective of rock mass characterization is to be able to accurately define the nature of the rock mass being excavated in terms of whether it is massive, moderately jointed/veined or highly blocky. This understanding is the basis for better analysis with both empirical design methods and advanced numerical modelling. Collectively, the assessments based on a more robust, statistically significant geomechanical representation of these rock masses provides for improved forecasts of extraction level stability, production ramp-up rates, hangup management requirements need for preconditioning, and ground support.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".