AI Hunts for Hidden Minerals: Machine Learning is Uncovering Hoards of Vital EV Battery Metals
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
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.
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 | 0.000 |
| 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