A fluid-flow modeling approach for predictive mapping of orogenic gold mineralization in the Malartic camp, Canada
Bibliographic record
Abstract
Orogenic gold deposits are structurally controlled and commonly formed in the transition zone between brittle and ductile crustal domains. Formation of disseminated or localized gold mineralization involves structural features (e.g., fault zones, fold hinges), contrasts in physical properties (e.g., rock competency and permeability, lithostatic and hydrostatic pressure, temperature) or chemical variability (e.g., rock chemistry, fluid composition). Orogenic gold deposits form in convergent tectonic settings, at crustal depths of between 3 and 18 km, from the Paleoarchean to the present. However, the goal of this study is just to investigate the hydrothermal properties of a model and predict the influence of deformation zones, rock types and the associated physical parameters on fluid-flow associated with orogenic gold systems, and subsequently develop new feature-engineered layers for mineral exploration purpose. Open-source numerical modeling software OpenGeoSys, has been used to reconstruct the major fault network in the Malartic mining district, in an area 19.7 km long and 7.3 km wide. This model can compare thermal convection fluid flow with deformation induced fluid flow. Results of numerical simulations conducted in OpenGeoSys and relative calculations from different physical parameters along faults or intrusive contacts explain the existence of a spatial association with the distribution of orogenic gold prospects and mines in the Malartic camp. Results of Weight of Evidence demonstrate that the incorporation of faults in 3D finite element models for coupled fluid and heat transport simulations has the potential of indicating favorable areas for gold mineralization in a 3D space, which can ultimately lead to new mineral discoveries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".