Mineral prospectivity mapping of orogenic gold mineralization in the Malartic-Val-d’Or Transect area, Metal Earth project, Canada
Bibliographic record
Abstract
• Orogenic gold prospectivity mapping reveals new targets in Malartic-Val-d’Or area. • Random forest outperformed logistic regression, showing a higher success rate. • Crustal density, shear zones, and fault density are key for target generation. • Magnetotelluric anomalies highlight shear zones and hydrothermal footprints. Mineral Prospectivity Mapping has been applied to define exploration targets for orogenic gold mineralization in the world-class Malartic-Val-d’Or area (Quebec) of the Abitibi greenstone belt, a region that contributes significantly to Canada’s annual gold production. This research utilizes lithological, geophysical, and structural data from the Malartic transect, collected as part of the Metal Earth project, to investigate factors controlling gold distribution in the region. Stepwise logistic regression and random forest algorithms were employed to map mineral prospectivity for gold potential. The overall accuracy indicates that the random forest method has outperformed logistic regression, although the latter produced an acceptable model. This conclusion is supported by the classification accuracy validated using an independent Au occurrence database and the performance metrics generated by the random forest and logistic regression models. Crustal density, shear zones, and faults are strong predictors for distinguishing mineralized and non-mineralized locations, as identified by both Random Forest and Logistic Regression models. In the Malartic-Val-d’Or region, subvertical conductive anomalies revealed by magnetotelluric data align closely with major shear zones and crustal-scale structures, such as the Cadillac-Larder Lake deformation zone, which hosts significant gold deposits. These anomalies suggest a paleo-hydrothermal footprint of mineralizing fluids and highlight their critical role in the formation of orogenic gold deposits, a pattern also observed in other mining camps across the Superior craton. These results and associated mineral prospectivity maps are integral for greenfields exploration in the Malartic region and may offer valuable insights for mineral exploration in other greenstone belts of the Superior craton.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".