Geostatistical interpolation of estimated RQD values and its use in geomechanics design considerations – a case study
Why this work is in the frame
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Bibliographic record
Abstract
Geostatistical interpolation techniques have proved its utility in all disciplines of geosciences and have potential to be used in geomechanics design applications. The principals of geostatistics are developed based on the fact that almost all geosciences data has some relationship with its location in space. Geostatistical tools decipher this relationship and exploit it to estimate the rock properties at an unsampled location. This work is an attempt to apply geostatistics in geomechanical design using nearest neighbour, inverse power and kriging interpolation of estimated Rock Quality Designation index (RQD) values from drill cores from part of one and four shear orebodies of Vale’s Garson Mine in Sudbury, Ontario, Canada. A comparative study of different methods and their correlation with mapped geology provided the most effective method for RQD interpolation. The part of orebody selected for this application has undergone intensive drilling with sufficient data volume to create an RQD block model with high degree of confidence. The RQD block model provided a basis for rock mass classification and creation of RQD maps along planned mining layouts, which served as an important tool for initial geomechanical assessment. This methodology has a potential to expand its application to other geomechanics parameters, such as RMR and Q values.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 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 it