Concerning colloidal aggregation, roughly 30% of the false positives identified via MVS-A were predicted to follow this interference mechanism across all datasets. This confirms our method's ability to detect not just random labeling noise, which is generally how data valuation approaches are benchmarked in the literature, but also structurally similar false positives which are typically found in biochemical assays.