Gold prospectivity maps of the Red Lake greenstone belt: application of GIS technology
Bibliographic record
Abstract
Recent advances in the use of Geographic Information Systems (GIS) software and analysis can be used in conjunction with traditional geoscience data sets to determine effective predictors for gold mineralization, from which mineral prospectivity maps can be generated that highlight potential exploration targets on a regional scale. In this paper, key components of the Archean lode gold deposit model for the Red Lake belt are selected and modeled using weights of evidence (WofE) analysis and logistic regression, leading to the creation of gold prospectivity maps. The best predictors for past and present gold producers in the Red Lake camp, according to WofE analysis include (1) elevated trace elements, Au, As, and Sb; (2) a number of alteration indices calculated from oxide geochemical data; (3) alteration characterized by pervasive and vein-style ferroan carbonate and elevated Au, As, Sb, and S anomalies; (4) proximity to the Mackenzie Island stock and diorite phases of the Dome stock; and, (5) tholeiitic basaltic flows and associated gabbroic rocks of the Balmer assemblage. Gold prospectivity maps produced by logistic regression using binary evidence maps highlight anomalous localities within known and highly prospective areas in the district (Madsen – Red Lake corridor, Balmertown – Cochenour – East Bay). In addition, a number of localities not known to contain significant deposits were also identified as prospective.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".