In-situ stress measurements at depth using the overcoring method at Vale's Creighton and Garson Mines
Bibliographic record
Abstract
ABSTRACT: As mines are going deeper, the proper evaluation and understanding of the anticipated induced stress conditions is critical for optimal design of excavations in underground mines. Accordingly, numerical modelling programs play a key role in estimating the anticipated induced stress conditions due to various mining activities like development and stopping operations. As such, the in-situ stress data are key input parameters for carrying out numerical modelling studies to assess the impact of the induced stress state on the stability of various underground excavations. This paper will review the factors that were considered to successfully carry out in-situ stress measurements using the overcoring method at depths ranging from 1000 m to 2600 m at the Creighton and Garson Mines. Specific factors that will be discussed include but are not limited to; selection of the test site location, known geological structures and the possible influence of these on the measurements, borehole diameter, temperature control, length of test holes relative to the excavation size, and the required number of tests undertaken at each location to determine a representative result. This paper will also review some of the challenges encountered during the in-situ stress measurement program carried out at the Creighton and Garson Mines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".