Three-dimensional modelling of development intersections at the Eleonore mine
Bibliographic record
Abstract
Several ground control challenges are encountered during the narrow-vein mining of parallel orebodies in a complex geologic environment at the Eleonore mine. Mining-induced seismicity and related rock mechanics instability events often take place at vulnerable locations such as development intersections. Both static and dynamic ground support systems are used to control the effects of these events. The mining sequence followed is usually pyramidal and moves from bottom to top with regional pillars separating the different blocks. Numerical modelling is used to assess the stress redistributions based on the stope sequences being planned. In this paper a geometrically simplified 3D linear elastic model of the Eleonore mine is constructed for several levels, along with the drift and crosscut systems on L 860, L 830 and L 800. Rock mass properties used as model inputs are obtained from recent laboratory tests and core logs, as well as older studies conducted for the Eleonore mine. Calibration is conducted with boundary stresses being applied to obtain pre-mining magnitudes comparable to those measured in the field. A typical pyramidal sequence is implemented and locations within the development network where potential instability could take place are identified based on 1 and brittle shear ratio (BSR) thresholds. Based on these initial results a detailed 3D model is then constructed of potential vulnerable intersections. The outputs from the two models are compared and the impact of mining is assessed with respect to the mode of instability observed at these intersections.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".