Figure 3A ML target architecture results for the entire Ag district, highlighting newly identified areas of interest and successful drilling validations. 3B Details of one of the district targets.in 3D. The ML architecture generated in the second stage, with 100% of the data, was able to identify several zones with high mineralization potential. To minimize the presence of false positives, the focus was on maximizing precision. A total of 34 areas of interest generated through ML have been drilled so far, of which 20 have yielded positive results (with Ag concentrations exceeding 50 ppm, in some cases over 200 ppm). This equates to a drilling success rate of 59%. Additionally, the false positive zones drilled (the ML model indicated no mineralization, but there was anyway) are valuable inputs to keep tuning the ML model and improve its results. We employed explainability tools to understand how ML architecture learned. Geophysical variables contribute the most to the model, followed by lithological variables, and then structural variables. The resulting metrics for the second stage are: 70.86% precision, 28.29% recall, 40.43% F1-score, 94.74% accuracy, and 99.22% specificity. In practical exploration terms, the 70.86% precision indicates that 7 out of 10 targets predicted by the model are likely to be mineralized, which significantly optimizes the allocation of drilling budgets by reducing 'dry' holes. The project's results supported the exploration of new veins, and the global understanding of the variables provided value for understanding the emplacement of the deposit's mineralization. From an exploration perspective, prioritizing precision over recall means that while the model may not find every vein in the district, the targets it does identify have a high probability of success (59%), significantly reducing the financial risk of 'dry' drill holes. 4. CONCLUSIONS Despite being an area with a long history of work, the epithermal system still contains zones with vein-type structures that have not been sufficiently explored and could extend the life of mine. The occurrence of these structures has been identified both along strike and at depth. The study is limited to the extent of the areas covered by the data layers used. An opportunity for improvement involves extending existing studies to areas without coverage, such as the structural information layer, which yielded very good results during the work process. With this new information, the model can be re-run to extend the results to new areas of interest and adjust the outcomes based on the new data provided to the model. 3A 3B
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