Mineral Prospectivity Modeling of Graphite Deposits and Occurrences in Canada
Bibliographic record
Abstract
Abstract Exploration for graphite in Canada is of economic, strategic and governance priority. In this study, we aimed to develop a reliable prospectivity map for graphite in Canada. Our approach mitigated multiple sources of workflow-induced uncertainty by propagating uncertainty due to the selection of negative labels, machine learning algorithms, feature space dimensionality, and hyperparameter tuning metrics. By averaging an ensemble of de-correlated models, we produced a single-merged model that clearly represents propagated uncertainty through a consensus map and an uncertainty map. These maps adhere to the metrological convention of "result plus/minus associated uncertainty" and are intuitive to use. Our ensemble demonstrated robustness, quickly converging to the consensus model, suggesting that new mineral prospectivity mapping (MPM) products using the same data would unlikely perturb our consensus model’s coverage. We conducted a maximally double-blind study, avoiding geoscientific knowledge during model generation to ensure impartial post-hoc analysis and interpretation. Therefore, our MPM products complement geoscientific knowledge-based exploration, because the targeting information provided in our MPM products constitute a maximally independent source. Our MPM products showed excellent spatial variability, aligning with existing knowledge of graphite deposits in Canada, indicating that combining data-driven rigor with independent interpretation enhances the robustness of our MPM products. Consequently, we believe our MPM products could effectively guide regional exploration of natural graphite in Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".