the correlation with grade is not guaranteed to be spatially uniform across the deposit. Economic mineralization tends to occur in geologically anomalous zones, which by definition are underrepresented in the bulk of any logging database. This paper addresses two interconnected questions: (i) how to systematically determine which geological data streams merit inclusion in a DL resource estimation model; and (ii) how to quantify whether the resulting drill targets are sufficiently reliable to support capital expenditure decisions. 2. OBJECTIVES AND SCOPE The first objective was to develop and empirically test a framework for quantifying prediction confidence across DL resource models. The intent is to move beyond single-model point estimates toward a multi-model framework capable of identifying which geological data channels function as effective pathfinders and of assigning calibrated confidence levels to block-level predictions. The second objective was to compare alternative strategies for integrating geological logging data into DL models. This comparison includes evaluation of how data are encoded—as input features, as target labels, or as both—along with quantitative measurement of relative accuracy relative to site Kriging models, and assessment of model performance through prospective blind reconciliation rather than retrospective in-sample metrics. The third objective was to validate the methodology across three geologically distinct deposits. The intent was not merely to demonstrate applicability at a single site, but to assess methodological transferability across contrasting geological settings. The three cases examined are: an orogenic lode gold deposit in Western Australia; a manto-type IOCG copper deposit in the Candelaria–Punta del Cobre district of northern Chile; and an orogenic lode gold deposit in western Kazakhstan. These deposits differ in commodity, geological setting, operational maturity, and data quality, providing a basis for evaluating the robustness and generalizability of the methodology. The methodological emphasis throughout is on techniques that use data already available at operating mine sites and that produce outcomes directly connected to drilling decisions and mine plan economics. 3. METHODOLOGY 3.1 Data Characteristics The data environment is the appropriate starting point for this methodological description, as it is the principal determinant of ML model performance in mining applications. At all three study sites, geological logging was extensive. Every meter of core had been logged for lithology, alteration, and associated visual parameters. However, logging had
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