To assess the performance of the CNL model for database fragments, ROC curves were constructed for different mass tolerances (Figure and S4). The ROC curves do not go up to 100% TP and FP rate because the lowest score CN L threshold evaluated is -1, which does not correspond to the highest possible TPr. Overall, the 0.005 Da mass tolerance performed the best at a score CN L threshold of -0.95, which had a TP, FP, and FD rate of 95.0%, 18.7% and 25.6%, respectively. Using the optimized parameters, the model was able to identify 95.0% of the correct cases and only 18.7% of the wrong cases as fragments. Moreover, of all CNLs that were identified as fragments 25.6% were wrongly classified as a fragment. A total of 4,855,332 cases were evaluated, of which 47.1% were true database fragments (i.e., almost a 1:1 ratio of TP and TN cases). Overall, 1,936,697 cases were removed by the CNL model, resulting in a reduction rate of 39.9% of the total number of potential fragments.