Integrated geophysical methods for boulder delineation to improve mining
Bibliographic record
Abstract
Abstract Near‐surface boulders can pose serious challenges to opencast mining. They often introduce complexities, delays in drilling, blasting and excavation programmes, which subsequently decrease mining efficiency, increase mining risks and costs. The location of subsurface boulders and the identification of other geological features that may impact mining activities (e.g. fractures, the presence of iron‐rich ultramafic pegmatites and the variation in weathering across a mining region) are necessary to reduce the challenges posed by these geological features, therefore optimizing mining efficiency. In this study, magnetics, electrical resistivity tomography, seismic refraction tomography, ground penetrating radar and borehole data are integrated for boulder delineation and mapping of other geological features that may impact mining using an unmined section at Tharisa Mine, Bushveld Complex (South Africa), as a test site. The results obtained from the different geophysical techniques are found to complement each other and successfully delineate boulders, fractures, iron‐rich ultramafic pegmatites and the variation in weathering and layering across the area. The incorporation of geophysical results can thus improve mining efficiency, while reducing mining risks and costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".