Evaluating Likelihood Ratio (LR) for firearm evidence identifications in forensic science based on the Congruent Matching Cells (CMC) method.

Firearm evidence identification has been challenged by the 2008 and 2009 National Research Council (NRC) reports and by legal proceedings on its fundamental assumptions, its procedure involving subjective interpretations, and the lack of a statistical foundation for evaluation of error rates or othe...

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Publicado en:Forensic Science International Vol. 317
Autores principales: Song, John, Chen, Zhe, Vorburger, Theodore V., Soons, Johannes A.
Formato: Journal Article
Publicado: Elsevier B.V. Dec2020
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Dec2020
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      pub: Elsevier B.V.
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        atl: Evaluating Likelihood Ratio (LR) for firearm evidence identifications in forensic science based on the Congruent Matching Cells (CMC) method.
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          Song, John
          Chen, Zhe
          Vorburger, Theodore V.
          Soons, Johannes A.
        affil: Physical Measurement Laboratory (PML), National Institute of Standards and Technology (NIST), Gaithersburg, MD, 20899, USA
      sug:
      ab: Firearm evidence identification has been challenged by the 2008 and 2009 National Research Council (NRC) reports and by legal proceedings on its fundamental assumptions, its procedure involving subjective interpretations, and the lack of a statistical foundation for evaluation of error rates or other measures for the weight of evidence. To address these challenges, researchers of the National Institute of Standards and Technology (NIST) recently developed a Congruent Matching Cells (CMC) method for automatic and objective firearm evidence identification and quantitative error rate evaluation. Based on the CMC method, a likelihood ratio (LR) procedure is proposed in this paper aiming to provide a scientific basis for firearm evidence identification and a method for evaluation of the weight of evidence. The initial LR evaluations using two sets of 9mm cartridge cases' breech face impression images with different sample sizes, imaging methods and ammunition showed that for all the declared identifications of the tested 2D and 3D image pairs, the evaluated LRs for the least favorable scenario were well above an order of 106, which provides Extremely Strong Support for a prosecution proposition (e.g. a same-source proposition) in a Bayesian frame. The LR evaluations also showed that for all the declared exclusions of the tested 3D image pairs, the evaluated LRs for the least favorable scenario were above an order of 102, which provides Moderately Strong Support for a defense proposition (e.g. a different-source proposition) in a Bayesian frame.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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