AI Hunts for Hidden Minerals: Machine Learning is Uncovering Hoards of Vital EV Battery Metals
Bibliographic record
Abstract
In June 2022, six Boeing 737s—fully loaded with tents, food, satellite Internet equipment, drones, geophysical survey gear, drilling equipment, and a team of experienced geologists—flew to a remote airstrip in northern Quebec. The geologists were hunting for major deposits of the minerals needed to power a clean-energy future. Given the mix of cutting-edge scientific computing and old-school bravado, it was as though they were channeling Alan Turing and Indiana Jones simultaneously. • Our startup, KoBold Metals, acquired an 800-square-kilometer mineral claim in this region of Canada based in part on predictions from our artificial intelligence systems. According to the AI, there was good reason to believe we'd find valuable deposits of nickel and cobalt buried below the surface. Summer snowmelts in this near-arctic area created a brief window to bring in a small village's worth of equipment and personnel to test our predictions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".