基于一致匹配单元（CMC）方法评估法医学中枪支证据鉴定的似然比（LR）。
Evaluating Likelihood Ratio (LR) for firearm evidence identifications in forensic science based on the Congruent Matching Cells (CMC) method.
机构信息
Physical Measurement Laboratory (PML), National Institute of Standards and Technology (NIST), Gaithersburg, MD, 20899, USA.
Physical Measurement Laboratory (PML), National Institute of Standards and Technology (NIST), Gaithersburg, MD, 20899, USA; School of Mechatronics Engineering, Harbin Institute of Technology (HIT), Harbin, 150001, China.
出版信息
Forensic Sci Int. 2020 Dec;317:110502. doi: 10.1016/j.forsciint.2020.110502. Epub 2020 Sep 15.
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 10, 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 10, which provides Moderately Strong Support for a defense proposition (e.g. a different-source proposition) in a Bayesian frame.
枪支证据鉴定受到了 2008 年和 2009 年美国国家研究委员会（NRC）报告以及法律程序的挑战，这些报告和程序对其基本假设、涉及主观解释的程序以及缺乏评估错误率或其他证据权重的统计基础提出了质疑。为了应对这些挑战，美国国家标准与技术研究院（NIST）的研究人员最近开发了一种一致匹配单元（CMC）方法，用于自动和客观地鉴定枪支证据，并对定量错误率进行评估。基于 CMC 方法，本文提出了一种似然比（LR）程序，旨在为枪支证据鉴定提供科学依据，并为评估证据权重提供方法。使用两组具有不同样本大小、成像方法和弹药的 9 毫米弹药筒底火印痕图像进行的初始 LR 评估表明，对于测试的二维和三维图像对中所有宣布的鉴定，评估的最不利情况下的 LR 远高于 10 的数量级，这为贝叶斯框架中的检方主张（例如同源主张）提供了极强的支持。LR 评估还表明，对于测试的三维图像对中所有宣布的排除，评估的最不利情况下的 LR 高于 10 的数量级，这为贝叶斯框架中的辩方主张（例如不同源主张）提供了中度支持。
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