Machine learning framework application for modelling geomechanical instabilities: a caving case study
Bibliographic record
Abstract
The field of machine learning (ML) has had a significant impact on, and adoption in, many fields of science and engineering, yet in mining is still not very well developed, with many publications exploring scopes of application and potential areas of integration. As mining reaches deeper environments where most traditional methods of stability analysis have yet to be calibrated, there are good opportunities to apply ML methods to diverse types of, for example, failure phenomena. Still there is a necessity to properly account for adequate data inclusion and problem definition to apply these kinds of analysis, which is why data representation and availability with regards to a particular problem are crucial In this paper a case study of the application of data extraction and the ML modelling process applied to rock mass failure phenomena taking place in an underground cave mine is presented as an illustrative example of the practical application of ML methods of analysis in mining. The results show that ML methods have high potential in mining applications when coupled with careful consideration of input variables and the correct choice of ML approaches. The fundamentals and practical aspects are outlined such that the methodology of the case study is generalisable to different kinds of geomechanical problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".