In‐mine (tunnel‐to‐tunnel) electrical resistance tomography in South African platinum mines
Bibliographic record
Abstract
ABSTRACT The applicability of tunnel‐to‐tunnel electrical resistance tomography (ERT) for imaging disruptive geological structures ahead of mining, in an igneous platinum mining environment is assessed. The geophysical targets of interest are slump structures or ‘potholes’ that disrupt the lateral continuity of the thin, tabular platinum orebodies of the Bushveld Complex, South Africa. The study involves a combination of model studies, laboratory property measurements and trial surveys. The property studies indicate that the problem reduces to the challenging scenario of a high‐resistivity background (orebody horizon) in which an even more resistive target (pothole) is embedded. The model studies show that ERT can potentially image disruptive potholes ahead of mining. It is further demonstrated that the 2D approach can generally be used as a reconnaissance tool but that a variety of 3D effects need to be considered and, in some instances, appropriate corrections should be applied. 3D scenarios that are considered include targets with limited extent perpendicular to the image plane, targets with a relatively small volume and targets that are asymmetrical about the image plane. Other 2D model assumption violations considered include the effect of tunnels and multi‐layered backgrounds. Finally, results from an experimental in‐mine survey are included to illustrate that ERT can be used to detect and delineate potholes ahead of mining.
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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 | 0.000 |
| 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) | 0.001 | 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".