Gold Potential of a Hidden Archean Fault Zone: The Case of the Cadillac-Larder Lake Fault
Bibliographic record
Abstract
By compiling geological, structural, geophysical, and geochemical information into a 3D geological model, we evaluated the orogenic gold potential in the vicinity of a hidden segment of an important Archean fault zone, the Cadillac–Larder Lake fault (CLLF) in the region of Rouyn-Noranda. The segment of CLLF in the present study is partly covered by Proterozoic sedimentary rocks. Because more than 2000 t Au have been extracted along the CLLF to date, our objective is to evaluate the gold potential at depth along a poorly known segment of this fault. A 3D geological model (50 km × 9 km × 1.5 km) including the covered segment was built through the compilation and homogenization of available geological data and the construction of 23 cross sections. The geology under the Proterozoic cover was evaluated using geophysical inversions, drill holes (42 in total), and surrounding geology. All available assays were filtered and upscaled to a 250 m × 250 m × 250 m regular cell grid to determine and quantify spatial relationships between geological features and mineralized occurrences using the weights of evidence method. Structural features, such as E–W-trending faults and fault intersections, and certain lithologies with a high primary porosity such as volcanoclastic rocks of the Blake River Group and Timiskaming sedimentary rocks, proved to be very prospective, yielding favourable factors with a weight of evidence index W + > 0.24. These salient features were then assigned a combination index for ultimately evaluating the orogenic gold potential under the sedimentary cover. The zones resulting in an optimization of exploration targeting were attributed the highest probability, representing ~1% of the initial volume.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".