Mineral Prospectivity Mapping and Differential Metal Endowment Between Two Greenstone Belts in the Canadian Superior Craton
Bibliographic record
Abstract
Abstract Mineral prospectivity maps were produced for gold in two greenstone belts in the Superior geological province in Ontario, Canada, as part of the Metal Earth Project in the Laurentian University, Sudbury, Ontario. These maps, created using the random forest machine learning algorithm, cover the well-endowed Matheson area, which is in the Abitibi sub-province, and the less fertile Dryden area, which is in the Wabigoon sub-province. Newly identified areas for follow-up gold exploration are associated with major faults and 3D geophysical data comprising resistivity, density and susceptibility data. In addition, observations not used in mineral prospectivity mapping based on magnetotelluric, seismic and isotopic data may in part describe why the Matheson greenstone belt is more fertile with respect to gold mineralization than the Dryden greenstone belt. These observations suggest that the Matheson area has major transcurrent faults associated with conductive zones that reach the surface, many of which are associated with deeply penetrating, vertical faults. The isotopic signature of the Matheson crust also suggests it is juvenile, whereas the Dryden area is older.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".