Mineral Exploration: Discovering and Defining Ore Deposits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Editor’s note: The Geology and Mining series, edited by Dan Wood and Jeffrey Hedenquist, is designed to introduce early-career professionals and students to a variety of topics in mineral exploration, development, and mining, in order to provide insight into the many ways in which geoscientists contribute to the mineral industry. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it