Bibliographic record
Abstract
Geomechanical risk in mining is universally understood to depend on many apparently disparate factors acting together such as stress, stiffness, mine geometry, rock mass character, rock type, structure, excavation rate and volume, blasting, and seismicity. We have worked on many case studies over the years in both underground and open pit mines with the objective of discovering and documenting the correlation of such factors with the experience of geomechanical failure. Whether that failure is slope failure, strainbursting, fault slip-induced rockbursting, roof fall, or any other of many possible failure types, statistical correlations among the different classes of data can be found, and predictive rules for understanding geohazard based on their quantitative combination can be established and deployed in day-to-day operations. This data-driven approach requires application of methods and avoidance of pitfalls that can be standardised into a universally applicable workflow. We discuss the workflow and the pitfalls in analysis to be avoided through case study examples.
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.
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 | 0.116 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.013 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".