AI Grade Control Validation Report Template (JORC/NI 43-101 Compliant)

AI Grade Control Validation Reports serve as critical technical evidence supporting the use of machine learning and deep learning models—such as convolutional neural networks (CNNs), random forests, or Gaussian process regressors—in resource estimation and short-term mine planning. Unlike traditional geostatistical methods, AI models introduce non-linear, data-driven interpolation and classification capabilities but require rigorous validation against independent drill-hole assays, geological domain boundaries, and historical production data. The report must demonstrate model fidelity through statistical metrics (e.g., RMSE, R², spatial bias analysis), uncertainty quantification (e.g., prediction intervals, ensemble variance), and domain-specific geological plausibility checks—including structural consistency, lithological coherence, and grade-tonnage reconciliation. Compliance with JORC and NI 43-101 mandates that all assumptions, data provenance, model architecture, hyperparameter tuning procedures, and validation protocols be fully disclosed and independently verifiable by a Competent Person (JORC) or Qualified Person (NI 43-101). Furthermore, the template incorporates traceability controls—versioned datasets, model checkpoints, and audit logs—to satisfy regulatory expectations around model governance, data lineage, and change management throughout the mine life cycle.