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 \nbetween brittle and ductile crustal domains. Formation of disseminated or localized gold \nmineralization involves structural features (e.g., fault zones, fold hinges), contrasts in physical \nproperties (e.g., rock competency and permeability, lithostatic and hydrostatic pressure, \ntemperature) or chemical variability (e.g., rock chemistry, fluid composition). Orogenic gold \ndeposits form in convergent tectonic settings, at crustal depths of between 3 and 18 km, from \nthe Paleoarchean to the present. However, the goal of this study is just to investigate the \nhydrothermal properties of a model and predict the influence of deformation zones, rock types \nand the associated physical parameters on fluid-flow associated with orogenic gold systems, \nand subsequently develop new feature-engineered layers for mineral exploration purpose. \nOpen-source numerical modeling software OpenGeoSys, has been used to reconstruct the major \nfault network in the Malartic mining district, in an area 19.7 km long and 7.3 km wide. This \nmodel can compare thermal convection fluid flow with deformation induced fluid flow. Results \nof numerical simulations conducted in OpenGeoSys and relative calculations from different \nphysical parameters along faults or intrusive contacts explain the existence of a spatial \nassociation with the distribution of orogenic gold prospects and mines in the Malartic camp. \nResults of Weight of Evidence demonstrate that the incorporation of faults in 3D finite element \nmodels for coupled fluid and heat transport simulations has the potential of indicating favorable \nareas for gold mineralization in a 3D space, which can ultimately lead to new mineral \ndiscoveries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".