Transforming LCT Pegmatite Targeting Models into AI-Powered Predictive Maps of Lithium Potential for Western Australia and Ontario: Approach, Results and Implications
Bibliographic record
Abstract
Lithium-cesium-tantalum (LCT) pegmatites account for circa one-third of global lithium resources and two-thirds of global lithium production. Western Australia, the world's largest supplier of hardrock lithium ores, and Ontario, an emerging lithium mining jurisdiction, have significant endowments that will be critical to the ‘green revolution’ given the predicted transition to lithium-based electromobility. In addition, both jurisdictions show excellent potential for future lithium discoveries given they cover large areas of favorable geology that, by and large, have recorded only limited lithium exploration. Here, we developed holistic LCT pegmatite targeting models for these important jurisdictions, informed by a detailed review of this deposit type and framed in the context of a mineral systems approach. Artificial intelligence (AI)-powered mineral potential modelling (MPM), using multiple, complimentary techniques and guided by the mappable elements of the LCT pegmatite genetic model, not only delivered the first regional scale views of lithium potential across the Archean to Proterozoic terrains of Western Australia and Ontario but also delivered compelling targets for future exploration and though-provoking insights, such as the statistically verifiable proximity relationship between lithium, gold and nickel occurrences. Overall, this study also served to demonstrate the power of precompetitive, high-quality geoscience data, not only for regional scale targeting but also the development of camp-scale targets that are concise enough to be investigated using conventional prospecting techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".