DOI | Resolve DOI: https://doi.org/10.1007/s10967-018-5912-3 |
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Author | Search for: El haddad, Josette1; Search for: Harhira, Aissa1; Search for: Blouin, Alain1; Search for: Sabsabi, Mohamad1; Search for: Jovanovic, Slobodan; Search for: Kell, Tara; Search for: El-jaby, Ali |
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Affiliation | - National Research Council of Canada. Energy, Mining and Environment
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Format | Text, Article |
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Subject | uranium ore concentrate (UOC); nuclear forensics; provenance assessment; chemometrics; support vector machine; discrimination |
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Abstract | This work describes a method for the discrimination of uranium ore concentrates (UOCs) to support provenance assessment for nuclear forensics applications using samples representing twenty producers from around the world. The concentrations were measured using inductively coupled plasma mass spectrometry. UOCs were classified using the support vector machine method relying on 61 down to only 18 element concentrations without affecting the accuracy. New features are calculated from combination of elements from the selected 18 elements, and added to the selected elements improve the classification results. Reducing the number leads to the optimization of laboratory measurements of element signatures in support of nuclear safeguards and forensics applications. |
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Publication date | 2018-05-19 |
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Publisher | Springer |
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In | |
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Language | English |
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Peer reviewed | Yes |
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NPARC number | 23003591 |
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Export citation | Export as RIS |
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Report a correction | Report a correction (opens in a new tab) |
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Record identifier | db9bf660-ecb5-4ddb-91b1-f707244bf866 |
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Record created | 2018-07-20 |
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Record modified | 2020-03-16 |
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