Exploring for deeply buried ore deposits
Bibliographic record
Abstract
The achievement of many of the sustainable development goals depends on new discoveries of minerals and metals. For example, earth materials are essential to generate, store and transmit energy, whether the sources are traditional fossil fuels, or renewable sources, such as solar, geothermal and wind. However, most easy-to-find near-surface deposits have been exhausted. Thus, it is necessary to explore regions where the ore bodies are buried at depths of hundreds or even thousands of metres, or concealed by electrically conductive or magnetic overburden that masks the geophysical signatures of ore bodies in the rocks below. Geophysical methods (such as gravity, magnetics, magnetotellurics, reflection and broadband seismology) are very useful for mapping deep-seated signatures of mineral systems, such as sutures between continental blocks and continental-scale faults, and locating deep ore bodies. We first describe the physical principles and capabilities of the most important technologies. We then review programmes that have been launched in Africa, Australia, Canada and Europe in the last decade to improve technology and to map the Earth’s crust and upper mantle.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.014 |
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".