Laboratory Testing and Stress Measurements in Anisotropic Rock at an Underground Mine
Bibliographic record
Abstract
Rock mechanics is considered a data-limited science that may be applied to the design of excavations in rock. Design and forecasting of excavation performance requires a number of input parameters, namely the far-field in-situ stresses and material properties. Measurements of in-situ stresses in underground mines are difficult and expensive. As a result, a new underground mine may consider a far-field in-situ stress that is based on literature, and an understanding of the tectonic history of the deposit. Material properties can be measured from laboratory test data, however this often fails to reliably capture the effect of anisotropy on rock strength and elastic properties. The work completed provides some measurements of in-situ stress in anisotropic rock, at an underground mine. The process used to measure in-situ stress, is one that can be applied by other practitioners and does not require extensive use of third-party contractors. The laboratory testing completed does also capture the effect of anisotropy on intact rock strength, and elastic properties. The availability of this information enables more accurate forecasting of excavation performance, which facilitates improved decision making. In addition, the characterization of anisotropy may be applicable to other mining operations located in greenstone belts, which host a number of the prolific gold mining districts in Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".