Estimating the probability of unsatisfactory performance associated with the instability of mine developments
Bibliographic record
Abstract
Mine developments are the main access to extract tabular ore deposits in deep underground mines. Therefore, their stability is considered the principal priority during the mine production plan. The success of ore extraction mainly depends on the stability and servicibility of mine developments. Mine development instability is expensive and is a risk to personnel and equipment and in turn, it will raise the operational cost (e.g, repair costs: slashing, rehabilitation costs, costs of adding secondary support, miners wages and delay of production) (Ellefmo, and Eidsvik, 2009; Abdellah et al. 2014a, 2014b and 2014c). This paper aims to develop a hybrid approach in which deterministic numerical modelling is integrated with probabilistic methods to estimate the probability of unsatisfactory performance (e.g. rating and ranking) associated with the instability of mine developments with respect to mining sequences adopting Rosenblueth’s Point-Estimate Method (RPEM). A three-dimensional, elastoplastic, finite difference model (FLAC3D) is created (Itasca, 2009). The results are presented and categorized with respect to probability of 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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".