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Title: Transfers of gunshot residue (GSR) to hands : an experimental study of mechanisms of transfer and deposition carried out using SEM-EDX, with explorations of the implications for forensic protocol and the application of Bayesian Networks to interpretation
Author: French, J.
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2013
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Abstract:
Gunshot residue (GSR) is produced during a firearm discharge and its recovery from the hands of a suspect may be used to support an inference that the suspect discharged a firearm. Various mechanisms of GSR transfer and deposition involving the hands of subjects were studied through a series of experimental scenarios that were intended to mimic real-world forensic situations. Samples were analysed using SEM-EDX with an automated search and detection package (INCAGSR, Oxford Instruments, U.K.). The results demonstrate the possibility of recovering considerable quantities of GSR from the hands of subjects as a result of a secondary transfer via a handshake with a shooter, or through handling a recently discharged firearm. As many as 129 particles were recovered from a handshake recipient. Additionally, GSR particles were found to undergo tertiary transfer following successive handshakes, while the possibility of GSR deposition on the hands of a bystander was confirmed. Particle size analysis revealed that very large (>50µm and >100µm) particles may undergo secondary transfer. The implications of these findings for forensic investigations are considered, particularly for interpreting the presence of GSR under competing activity level propositions about its deposition and the actions of the suspect. Bayesian Networks are inferential tools that are increasingly being employed in the interpretation of forensic evidence. Using the empirical data derived during the experimentation, the utility of Bayesian Networks for reasoning about mechanisms of GSR deposition is demonstrated. Further research aimed at unlocking the interpretative potential of GSR through empirical research and establishing the use of Bayesian Networks in forensic applications is recommended. It is anticipated that this emphasis on empirical support and probabilistic interpretation, in combination with the findings of this study, will strengthen the scientific basis of inferences made about GSR evidence and contribute to the accurate interpretation of evidence in legal settings.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.626481  DOI: Not available
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