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                            Library & Multimedia
 Microscopic Saw Mark Analysis: An Empirical Approach
NCJ Number
              248808
Journal

*Journal of Forensic Sciences* Dated: 2014
Author(s)
          Jennifer C. Love;
                        Sharon M. Derrick;
                        Jason M. Wiersema
Date Published

January 2015
Length
              6 pages
Annotation
              This study proposes the use of classification trees and random forest classifiers as an optimal, statistically sound approach to mitigate the potential for error of variability and outcome error in microscopic saw-mark analysis.
Abstract
              Microscopic saw-mark analysis is a well-published and generally accepted qualitative analytical method; however, little research has focused on identifying and mitigating potential sources of error associated with the method.  The statistical model was applied to 58 experimental saw marks created with four types of saws. The saw marks were made in fresh human femurs obtained through anatomical gift and were analyzed using a Keyence digital microscope. The statistical approach weighed the variables based on discriminatory value and produced decision trees with an associated outcome error rate of 8.62-17.82 percent.  (Publisher abstract modified)
      
  
          Additional Details
Grant Number(s)
        2010-DN-BX-K235


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)
                                Report (Grant Sponsored)
                                Report (Annual/Periodic)


  
  
        
  Award
        Independent Validation Test of Microscopic Analysis of Saw Marks in Bone
    
  
  
Date Published: January 1, 2015
    
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