# Uncertainty-Normalized Empirical Calibration

Machine learning screens far more candidates than can be experimentally validated, so selection must control risk before validation. UNEC normalizes predictive margins by local uncertainty and calibrates the selection cutoff directly on held-out losses, without building conformal p- or e-values, and supports general bounded losses and observation-specific thresholds. On solubility, toxicity and lipophilicity screening it holds risk-power trade-offs competitive with conformal selection, the NP method and SCoRE variants; in simulation the full grid took about 1.3 seconds per repetition versus 35 to 37 seconds for SCoRE.

Machine learning screens far more candidates than can be experimentally validated, so selection must control risk before validation. UNEC normalizes predictive margins by local uncertainty and calibrates the selection cutoff directly on held-out losses, without building conformal p- or e-values, and supports general bounded losses and observation-specific thresholds. On solubility, toxicity and lipophilicity screening it holds risk-power trade-offs competitive with conformal selection, the NP method and SCoRE variants; in simulation the full grid took about 1.3 seconds per repetition versus 35 to 37 seconds for SCoRE.