58 Forensic Sci Med Pathol (2011) 7:53–62 Fig. 16 Reciprocating blade kerf Fig. 18 Circular saw blade kerf Fig. 17 Circular saw blade Tables 5 and 6 contain the simulation results of the repeated stratiﬁed 10-fold cross-validation, where Table 5 is generated from Model #1 and Table 6 is generated from Model #2. The simulation in R was run with a random seed number 68 to ensure that the results were reproducible. The replication time was 100. Comparisons were made between the two statistical logit models. Model #1 was used to calculate possible saw blade eliminations and likelihood similarities to other saws based on kerf width measurements. This data is summa- rized in Table 5. Based on this model, there is a 48% chance that the hand saw kerf originated from the hand saw, a 22% chance that the hand saw kerf was from the jig saw and a 30% chance that it was from the reciprocating saw. The least accurate predictions for Model #1 were with 123 Fig. 19 Cut-off saw blade the hack saw and molding cut-off saw. There was only a 35% chance that the kerf from the hack saw originated from the hack saw and a 39% chance that kerf was from the portable band saw. For the molding cut-off saw, there was a 25% chance that the molding cut-off saw kerf originated from the molding cut-off saw. There was a 52% chance that the molding cut-off saw kerf was from the bow saw. Model #2 was used to calculate possible saw blade eliminations and likelihood similarities to other saws using kerf widths as well as saw teeth widths. This data is summarized in Table 6. Using this model, all saws were eliminated from the meat saw, circular saw, cut-off saw and jig saw. However, the bow saw kerf was predicted to have a 34% chance of originating from the bow saw and a 47% chance of originating from the molding cut-off saw.