A digital face mapping case study in an underground hard rock mine
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a case study of a digital discontinuity mapping system used as a rock mass characterization tool in an underground hard rock mine. This mapping system allows for a fast acquisition of information that can best characterize the geological structural regime without exposing workers to potentially unsafe conditions. This method can be used to overcome some of the shortcomings of traditional mapping methods, such as limited access to rock exposures. Photographic images of the exposed rock mass are introduced into a software package that has been developed to extract potential discontinuity traces using detection algorithms. Detected features that do not describe discontinuity traces are removed from the images using artificial neural networks. Operator intervention can improve the reliability of the system by linking incomplete discontinuity segments. This developed process results in the construction of a discontinuity trace map that can be used for rock mass characterization purposes. The system was employed to construct discontinuity trace maps of twenty 1.8 m by 1.8 m mapping windows from two locations in an underground hard rock mine. The ability of the system to quantify the geomechanical characteristics of the rock mass was evaluated by comparing the results with those of manually drawn discontinuity trace maps. The results of this study have helped to evaluate the digital face mapping system and identify its limitations.Key words: rock mass characterization, image processing, discontinuity networks, neural networks.
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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 | 0.001 |
| 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