Metalliferous mining geophysics — State of the art after a decade in the new millennium
Bibliographic record
Abstract
Abstract Mining exploration was very active during the first decade of the twenty-first century because there were numerous advances in the science and technology that geophysicists were using for mineral exploration. Development came from different sources: instrumentation improvements, new numerical algorithms, and cross-fertilization with the seismic industry. In gravity, gradiometry kept its promise and is on the cusp of becoming a key technology for mining exploration. In potential-field methods in general, numerous techniques have been developed for automatic interpretation, and 3D inversion schemes came into frequent use. These inversions will have even greater use when geologic constraints can be applied easily. In airborne electromagnetic (EM) methods, the development of time-domain helicopter EM systems changed the industry. In parallel, improvements in EM modeling and interpretation occurred; in particular, the strengths and weaknesses of the various algorithms became better understood. Simpler imaging schemes came into standard use, whereas layered inversion seldom is used in the mining industry today. Improvements in ground EM methods were associated with the development of SQUID technology and distributed-acquisition systems; the latter also impacted ground induced-polarization (IP) methods. Developments in borehole geophysics for mining and exploration were numerous. Borehole logging to measure physical properties received significant interest. Perhaps one reason for that interest was the desire to develop links between geophysical and geologic results, which also is a topic of great importance to mining geologists and geophysicists.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".