Applying Mineral System Criteria to Develop a Predictive Modelling for Epithermal Gold Mineralization in Northern New Brunswick: Using Knowledge-Driven and Data-Driven Methods
Bibliographic record
Abstract
Using mineral prospectivity mapping (MPM), the mineral systems approach enables the identification of geological indicators linked to ore formation. This approach streamlines exploration by minimizing the time and cost required to identify areas with the highest mineral potential. With its extensive till cover and dense forests limiting bedrock exposure, New Brunswick provides an ideal environment to test this approach. The New Brunswick portion of the Canadian Appalachians hosts a diverse range of gold deposits and occurrences that formed during various stages of the Appalachian orogeny. In northern New Brunswick and the adjacent Gaspé Peninsula, the Tobique–Chaleur Zone contains several orogenic and epithermal gold systems that are closely associated with a large-scale crustal fault and its offshoots, i.e., the long-lived trans-crustal Rocky Brook–Millstream Fault system. To identify favorable zones for epithermal gold mineralization in northwestern New Brunswick, this study employed MPM by translating key mineral system components—such as ore metal sources, fluid pathways, traps, and geological controls—into mappable criteria for regional-scale analysis. The data were modeled through the integration of knowledge-based and data-driven methods, including fuzzy logic, geometric average, and logistic regression approaches. The concentration–area (C–A) fractal model was applied to reclassify the final maps based on prospectivity values obtained from these three approaches, dividing the mineral prospectivity maps into six classes, with threshold values emphasizing high-favorability zones. The fuzzy overlay model had the highest predictive accuracy (AUC 0.97), followed by the geometric average model (AUC 0.93), whereas the logistic regression identified more tightly constrained high-potential zones. In the prospectivity models, known epithermal gold mineralization consistently overlaps with regions of high favorability. This suggests a positive result from the use of MPM, indicating that this approach could be applicable to other regions and types of ore deposits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".