resource model derived from the full set of conventionally targeted drill holes. This condition was met after the AI-guided program had targeted drilling meters equivalent to approximately 45% of the total footage drilled in the conventional campaign. Figure 2 compares the resource models generated from the original legacy dataset, the AI-guided drilling campaign, and the conventional drilling campaign. The resulting P10, P50, and P90 ore shells exhibit a high degree of consistency across all three models, with differences in total lowgrade volume of approximately 6%. Figure 3 presents the corresponding resource distributions, indicating that the AI-derived model reproduces the central tendencies and uncertainty bounds of the conventional model within reasonable tolerances. Taken together, these results suggest that AI-guided drilling optimization can achieve comparable levels of resource characterization while substantially reducing drilling requirements. At the project scale, this reduction translates into meaningful decreases in capital expenditure, development timelines, and exposure to exploration risk, with potential implications for both project economics and portfolio-level capital efficiency. Figure 2 - Low and high grade resource ore-shells for the P30, P50, and P70 cases of the predrilling model, AI-targeted drilling model, and conventionally targeted drilling model.
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