Geochemical exploration for Au using groundwater in the deep Canadian Shield
Bibliographic record
Abstract
Hydrogeochemical exploration is a promising technique that uses groundwater as a medium to explore for mineral deposits. To evaluate the potential of hydrogeochemistry for Au exploration in the Canadian Shield, we conducted a case study near the Windfall Au deposit, a deep (>1 km) intrusion-related deposit of world-class scale and grade. This unmined deposit, located in a remote area of the Abitibi subprovince, provides an ideal setting to investigate the hydrogeochemical signature of Au mineralization in a cold and humid climate. This study proposes a combined knowledge-based and data-driven multivariate approach to identify dissolved elements that compose the multielement footprint of the Windfall mineralization. Four distinct hydrogeochemical poles (clusters) are defined from hierarchical cluster and principal component analyses: (1) recharge groundwater; (2) recharge gold groundwater; (3) saline groundwater; and (4) saline gold groundwater. Multielement enrichment (Ag, Tl, Th, Sn, Bi, U, La, and Ce) associated with ore minerals or alteration minerals is observed in samples of the Au-bearing recharge and saline clusters, and is particularly striking for Ag, Tl, and Th. Gold-type cluster samples, representing the mineralization footprint, are found in an approximately 1 km wide ENE–WSW corridor centered around the Mazères fault, a major ENE-trending regional-scale structure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".