Mineral Exploration: Discovering and Defining Ore Deposits
Bibliographic record
Abstract
ABSTRACT For economic geologists, mineral exploration has a specific objective: the discovery of mineral concentrations that can be recovered economically to provide resources essential for society. This was achieved consistently until the first decade of the current century, but exploration since then has been wealth destructive. This outcome is a major issue for the mining industry unless reversed. We believe the technologies presently used to discover ore deposits will be as useful in making future discoveries as they were previously. However, we argue that a new approach is required in how exploration is conducted and in how these and emerging technologies are applied. The required changes in approach include improved business models for conducting exploration and acceptance that fewer deposits are likely to be discovered near the surface. We argue that discovery of deeper deposits will be facilitated if exploration teams (1) seek to identify subtle evidence of mineralized rock recognizable within 500 m of the surface, (2) conduct follow-up investigations with a clear understanding of the volumetric dimensions of the discovery target, and (3) drill boldly as a critical exploration tool. We propose that improving the way geoscientists think when exploring—being more predictive—is the immediate key to increasing the number of discoveries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".