When Identical Rows Disagree: From Benchmark Identifiability to Replication-Robust Anomaly Detection

A released table is often treated as an i.i.d. sample, although its repeated rows may encode business frequency, repeated entities, joins, resampling, or extraction errors. We show that this ambiguity creates a hidden measurement layer with three consequences: feature-identical rows impose an attain