Introduction to this special section: Geophysics for mineral exploration
Bibliographic record
Abstract
Mineral exploration, the forefront phase of the mineral supply chain, aims to discover and define economically viable mineral deposits. High global demand for mineral resources, driven by modern industry development and the energy transition, has led to a significant increase in applying and developing geophysical technologies to evaluate new and existing mineral resources. Seismic, gravity, magnetic, electromagnetic (EM), electrical, magnetotelluric, induced polarization, self-potential, radiometric, and ground-penetrating radar are commonly used geophysical technologies. They are implemented through spaceborne, airborne, UAV-borne, ground-based, marine-based, and underwater platforms. Different from petroleum exploration, geologic and geophysical information about the earth’s surface is particularly important in mineral exploration. Remote sensing provides this information by measuring the reflected, scattered, or emitted EM radiation or acoustic signals from the surface. Integration of remote sensing (which studies the earth’s surface) and geophysics (which studies the earth’s subsurface) improves data quality and completeness (Li et al., 2019). On other fronts, new geophysical technologies improve regional mapping and prospect evaluation; multiphysics overcomes the shortcomings of technologies; multidisciplinary integration results in a more accurate and comprehensive understanding of mineral deposits; and the use of modern technologies like artificial intelligence (AI), big data, automation, and advanced sensors leads to more efficient mineral exploration.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.095 | 0.082 |
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".