465 Based solely on the data-generating rate, drilling data were grouped into successive campaign periods, enabling objective comparison of data quality and survey reliability over time. This grouping reflected a close association with the Lassonde Curve and the mine’s phases (Figure 5). It is important to clarify that, in this section, the term data quality refers to the overall consistency, accuracy, and completeness of the drilling database, including logging, assay, and validation procedures. In contrast, the term survey reliability refers specifically to the reliability of drillhole positioning, including collar coordinates and downhole survey type and accuracy. The temporal evaluation showed that data quality and survey reliability varied systematically with drilling intensity and project phase. Early exploration campaigns, characterized by lower drilling rates, exhibited higher survey reliability and consistent data quality, reflecting controlled operational conditions. In contrast, periods of intensified drilling, particularly during resource delineation, were associated with a decline in survey reliability, likely due to accelerated execution and increased operational complexity. However, during these high-intensity phases, geological logging quality improved, driven by the need to better define mineralized zones. These results demonstrate that data quality and survey reliability are dynamic properties influenced by data generation rate and the mining project's developmental stage. This behavior underscores the importance of situating database assessments within the mine operational lifecycle, rather than interpreting quality scores in isolation. Governance must adapt to operational scale. Figure 5 – Annual drilling and GDQM scores across a project development stage from a case study. Figure notes: Each bar represents one year of drilling activity, expressed as the number of drill holes (left axis) and drilled meters (right axis). Project stages reflect variations in data-generating rates throughout the mine lifecycle. Data Quality corresponds to an index derived from database integrity parameters, including completeness, uniqueness, interval consistency, depth validation, and standardized geological coding. Survey Reliability reflects drillhole positional accuracy, including collar coordinates and downhole survey quality. The Final Score represents a weighted composite of these parameters, adjusted to specific priorities while maintaining consistent evaluation criteria. Values are reported as mean ± standard deviation by stage. All scores range from 0 (bad) to 100 (good). The Reserve Definition phase had the lowest overall performance, as reflected in the lowest final score (Figure 5). Although data quality remained relatively high during this period, the pronounced decline in survey reliability significantly affected the composite result, underscoring that positional accuracy is more technically relevant than descriptive consistency alone. In resource estimation, a well-documented sample has limited value if its spatial location is uncertain, because positional errors directly affect spatial continuity, variography, and resource classification, as well as domain boundary accuracy, thereby introducing errors into tonnage estimates. This stage also coincided with a marked increase in both drillhole count and total drilled meters, including deeper holes, which increases operational complexity and amplifies the need for
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