Stability of mine development intersections — a probabilistic analysis approach
Bibliographic record
Abstract
Mine developments, such as haulage drifts, cross-cuts, and intersections, are the only way to access valuable ore from mining zones; they link mine developments with the nearest ore access points. Thus, they must remain stable throughout their service life or production plan. Mine development instability can cause production delay, loss of reserves, as well as damage to equipment and injury to miners. This paper presents a stepwise methodology to assess the stability of mine development intersections with respect to the mine production plan. A case study, the #1 Shear East orebody at Vale Garson Mine in Sudbury, Ontario, is presented. A three-dimensional, elastoplastic, finite difference model (FLAC 3D) is created to simulate the development of an intersection situated 1.5 km below ground surface. The unsatisfactory performance of the intersection is evaluated in terms of strength-to-stress ratio with respect to mining sequence. A failure criterion is defined by a minimum strength-to-stress ratio of 1.4, and is used for mine developments (temporary openings). The intersection stability is evaluated at various mining stages and the modified “point-estimate method” (PEM) of (2n2 + 1) is then invoked to study the probability of drift instability at the intersection. The results are presented and categorized with respect to probability, instability, and mining stage.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".