Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise

Abstract The Nuclear Forensics International Technical Working Group (NF-ITWG) conducted an exercise, Galaxy Serpent 3 (GS3), by the National Nuclear Forensics Libraries Task Group, during 2017. The main goal of the GS3 exercise was to illustrate the usefulness of a National Nuclear Forensic Library...
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Autor*in:	Sedgi, Itzhak [verfasserIn] Assulin, Maor Moyal, Amiram Astarchan, Boaz Rubinshtein, Arnon Elish, Eyal

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Nuclear forensics National Nuclear Forensics Libraries Principle component analysis Ratio correlation matrix

Anmerkung:	© Springer Nature Switzerland AG 2019

Übergeordnetes Werk:	Enthalten in: SN applied sciences - [Cham] : Springer International Publishing, 2019, 1(2019), 8 vom: 01. Juli
Übergeordnetes Werk:	volume:1 ; year:2019 ; number:8 ; day:01 ; month:07

Links:	Volltext

DOI / URN:	10.1007/s42452-019-0831-7

Katalog-ID:	SPR038577143

Internformat


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520			\|a Abstract The Nuclear Forensics International Technical Working Group (NF-ITWG) conducted an exercise, Galaxy Serpent 3 (GS3), by the National Nuclear Forensics Libraries Task Group, during 2017. The main goal of the GS3 exercise was to illustrate the usefulness of a National Nuclear Forensic Library for assessing the potential consistency of unknown material with national holdings. Moreover, it offers the possibility to produce a realistic database resembling uranium ore concentrate (UOC) that can be used for algorithm development activities. A database was built based on basalt compositions from different tectonic settings: MORB, IAB, OIB and CRFB. Classes (Affinities) with subtly different chemical compositions. All data are open source and sample names can be traced to original data for error checking. Participants (29 teams) had to use existing techniques, or develop their own algorithms, to allow for dealing with the sparse database and managing incomplete data points. The exercise’s scenario introduced 3 simulated origin-unknown UOC samples, interdicted by authorities. Each team was asked to answer two main questions—“Are the 3 unknown samples consistent with each other?” and “are the 3 unknown samples consistent with the Mines in the database given?”. For the first time, the Israeli National Nuclear Forensic Laboratory (INNFL) participated in the GS exercise. A dedicated team was assigned the task of building capabilities needed and applying them to answer the raised questions. Methodology and tools developed and applied by the Israeli team are described. The results are presented and discussed, including new signatures allowing differentiation between unknown samples and mines.
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matchkey_str	article:25233971:2019----::nlssfeceiabslsaaaessmlnfrrnuoeocnrtsa
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allfields	10.1007/s42452-019-0831-7 doi (DE-627)SPR038577143 (SPR)s42452-019-0831-7-e DE-627 ger DE-627 rakwb eng Sedgi, Itzhak verfasserin aut Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract The Nuclear Forensics International Technical Working Group (NF-ITWG) conducted an exercise, Galaxy Serpent 3 (GS3), by the National Nuclear Forensics Libraries Task Group, during 2017. The main goal of the GS3 exercise was to illustrate the usefulness of a National Nuclear Forensic Library for assessing the potential consistency of unknown material with national holdings. Moreover, it offers the possibility to produce a realistic database resembling uranium ore concentrate (UOC) that can be used for algorithm development activities. A database was built based on basalt compositions from different tectonic settings: MORB, IAB, OIB and CRFB. Classes (Affinities) with subtly different chemical compositions. All data are open source and sample names can be traced to original data for error checking. Participants (29 teams) had to use existing techniques, or develop their own algorithms, to allow for dealing with the sparse database and managing incomplete data points. The exercise’s scenario introduced 3 simulated origin-unknown UOC samples, interdicted by authorities. Each team was asked to answer two main questions—“Are the 3 unknown samples consistent with each other?” and “are the 3 unknown samples consistent with the Mines in the database given?”. For the first time, the Israeli National Nuclear Forensic Laboratory (INNFL) participated in the GS exercise. A dedicated team was assigned the task of building capabilities needed and applying them to answer the raised questions. Methodology and tools developed and applied by the Israeli team are described. The results are presented and discussed, including new signatures allowing differentiation between unknown samples and mines. Nuclear forensics (dpeaa)DE-He213 National Nuclear Forensics Libraries (dpeaa)DE-He213 Principle component analysis (dpeaa)DE-He213 Ratio correlation matrix (dpeaa)DE-He213 Assulin, Maor aut Moyal, Amiram aut Astarchan, Boaz aut Rubinshtein, Arnon aut Elish, Eyal (orcid)0000-0001-8138-0233 aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 1(2019), 8 vom: 01. Juli (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:1 year:2019 number:8 day:01 month:07 https://dx.doi.org/10.1007/s42452-019-0831-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 8 01 07
spelling	10.1007/s42452-019-0831-7 doi (DE-627)SPR038577143 (SPR)s42452-019-0831-7-e DE-627 ger DE-627 rakwb eng Sedgi, Itzhak verfasserin aut Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract The Nuclear Forensics International Technical Working Group (NF-ITWG) conducted an exercise, Galaxy Serpent 3 (GS3), by the National Nuclear Forensics Libraries Task Group, during 2017. The main goal of the GS3 exercise was to illustrate the usefulness of a National Nuclear Forensic Library for assessing the potential consistency of unknown material with national holdings. Moreover, it offers the possibility to produce a realistic database resembling uranium ore concentrate (UOC) that can be used for algorithm development activities. A database was built based on basalt compositions from different tectonic settings: MORB, IAB, OIB and CRFB. Classes (Affinities) with subtly different chemical compositions. All data are open source and sample names can be traced to original data for error checking. Participants (29 teams) had to use existing techniques, or develop their own algorithms, to allow for dealing with the sparse database and managing incomplete data points. The exercise’s scenario introduced 3 simulated origin-unknown UOC samples, interdicted by authorities. Each team was asked to answer two main questions—“Are the 3 unknown samples consistent with each other?” and “are the 3 unknown samples consistent with the Mines in the database given?”. For the first time, the Israeli National Nuclear Forensic Laboratory (INNFL) participated in the GS exercise. A dedicated team was assigned the task of building capabilities needed and applying them to answer the raised questions. Methodology and tools developed and applied by the Israeli team are described. The results are presented and discussed, including new signatures allowing differentiation between unknown samples and mines. Nuclear forensics (dpeaa)DE-He213 National Nuclear Forensics Libraries (dpeaa)DE-He213 Principle component analysis (dpeaa)DE-He213 Ratio correlation matrix (dpeaa)DE-He213 Assulin, Maor aut Moyal, Amiram aut Astarchan, Boaz aut Rubinshtein, Arnon aut Elish, Eyal (orcid)0000-0001-8138-0233 aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 1(2019), 8 vom: 01. Juli (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:1 year:2019 number:8 day:01 month:07 https://dx.doi.org/10.1007/s42452-019-0831-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 8 01 07
allfields_unstemmed	10.1007/s42452-019-0831-7 doi (DE-627)SPR038577143 (SPR)s42452-019-0831-7-e DE-627 ger DE-627 rakwb eng Sedgi, Itzhak verfasserin aut Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract The Nuclear Forensics International Technical Working Group (NF-ITWG) conducted an exercise, Galaxy Serpent 3 (GS3), by the National Nuclear Forensics Libraries Task Group, during 2017. The main goal of the GS3 exercise was to illustrate the usefulness of a National Nuclear Forensic Library for assessing the potential consistency of unknown material with national holdings. Moreover, it offers the possibility to produce a realistic database resembling uranium ore concentrate (UOC) that can be used for algorithm development activities. A database was built based on basalt compositions from different tectonic settings: MORB, IAB, OIB and CRFB. Classes (Affinities) with subtly different chemical compositions. All data are open source and sample names can be traced to original data for error checking. Participants (29 teams) had to use existing techniques, or develop their own algorithms, to allow for dealing with the sparse database and managing incomplete data points. The exercise’s scenario introduced 3 simulated origin-unknown UOC samples, interdicted by authorities. Each team was asked to answer two main questions—“Are the 3 unknown samples consistent with each other?” and “are the 3 unknown samples consistent with the Mines in the database given?”. For the first time, the Israeli National Nuclear Forensic Laboratory (INNFL) participated in the GS exercise. A dedicated team was assigned the task of building capabilities needed and applying them to answer the raised questions. Methodology and tools developed and applied by the Israeli team are described. The results are presented and discussed, including new signatures allowing differentiation between unknown samples and mines. Nuclear forensics (dpeaa)DE-He213 National Nuclear Forensics Libraries (dpeaa)DE-He213 Principle component analysis (dpeaa)DE-He213 Ratio correlation matrix (dpeaa)DE-He213 Assulin, Maor aut Moyal, Amiram aut Astarchan, Boaz aut Rubinshtein, Arnon aut Elish, Eyal (orcid)0000-0001-8138-0233 aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 1(2019), 8 vom: 01. Juli (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:1 year:2019 number:8 day:01 month:07 https://dx.doi.org/10.1007/s42452-019-0831-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 8 01 07
allfieldsGer	10.1007/s42452-019-0831-7 doi (DE-627)SPR038577143 (SPR)s42452-019-0831-7-e DE-627 ger DE-627 rakwb eng Sedgi, Itzhak verfasserin aut Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract The Nuclear Forensics International Technical Working Group (NF-ITWG) conducted an exercise, Galaxy Serpent 3 (GS3), by the National Nuclear Forensics Libraries Task Group, during 2017. The main goal of the GS3 exercise was to illustrate the usefulness of a National Nuclear Forensic Library for assessing the potential consistency of unknown material with national holdings. Moreover, it offers the possibility to produce a realistic database resembling uranium ore concentrate (UOC) that can be used for algorithm development activities. A database was built based on basalt compositions from different tectonic settings: MORB, IAB, OIB and CRFB. Classes (Affinities) with subtly different chemical compositions. All data are open source and sample names can be traced to original data for error checking. Participants (29 teams) had to use existing techniques, or develop their own algorithms, to allow for dealing with the sparse database and managing incomplete data points. The exercise’s scenario introduced 3 simulated origin-unknown UOC samples, interdicted by authorities. Each team was asked to answer two main questions—“Are the 3 unknown samples consistent with each other?” and “are the 3 unknown samples consistent with the Mines in the database given?”. For the first time, the Israeli National Nuclear Forensic Laboratory (INNFL) participated in the GS exercise. A dedicated team was assigned the task of building capabilities needed and applying them to answer the raised questions. Methodology and tools developed and applied by the Israeli team are described. The results are presented and discussed, including new signatures allowing differentiation between unknown samples and mines. Nuclear forensics (dpeaa)DE-He213 National Nuclear Forensics Libraries (dpeaa)DE-He213 Principle component analysis (dpeaa)DE-He213 Ratio correlation matrix (dpeaa)DE-He213 Assulin, Maor aut Moyal, Amiram aut Astarchan, Boaz aut Rubinshtein, Arnon aut Elish, Eyal (orcid)0000-0001-8138-0233 aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 1(2019), 8 vom: 01. Juli (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:1 year:2019 number:8 day:01 month:07 https://dx.doi.org/10.1007/s42452-019-0831-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 8 01 07
allfieldsSound	10.1007/s42452-019-0831-7 doi (DE-627)SPR038577143 (SPR)s42452-019-0831-7-e DE-627 ger DE-627 rakwb eng Sedgi, Itzhak verfasserin aut Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract The Nuclear Forensics International Technical Working Group (NF-ITWG) conducted an exercise, Galaxy Serpent 3 (GS3), by the National Nuclear Forensics Libraries Task Group, during 2017. The main goal of the GS3 exercise was to illustrate the usefulness of a National Nuclear Forensic Library for assessing the potential consistency of unknown material with national holdings. Moreover, it offers the possibility to produce a realistic database resembling uranium ore concentrate (UOC) that can be used for algorithm development activities. A database was built based on basalt compositions from different tectonic settings: MORB, IAB, OIB and CRFB. Classes (Affinities) with subtly different chemical compositions. All data are open source and sample names can be traced to original data for error checking. Participants (29 teams) had to use existing techniques, or develop their own algorithms, to allow for dealing with the sparse database and managing incomplete data points. The exercise’s scenario introduced 3 simulated origin-unknown UOC samples, interdicted by authorities. Each team was asked to answer two main questions—“Are the 3 unknown samples consistent with each other?” and “are the 3 unknown samples consistent with the Mines in the database given?”. For the first time, the Israeli National Nuclear Forensic Laboratory (INNFL) participated in the GS exercise. A dedicated team was assigned the task of building capabilities needed and applying them to answer the raised questions. Methodology and tools developed and applied by the Israeli team are described. The results are presented and discussed, including new signatures allowing differentiation between unknown samples and mines. Nuclear forensics (dpeaa)DE-He213 National Nuclear Forensics Libraries (dpeaa)DE-He213 Principle component analysis (dpeaa)DE-He213 Ratio correlation matrix (dpeaa)DE-He213 Assulin, Maor aut Moyal, Amiram aut Astarchan, Boaz aut Rubinshtein, Arnon aut Elish, Eyal (orcid)0000-0001-8138-0233 aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 1(2019), 8 vom: 01. Juli (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:1 year:2019 number:8 day:01 month:07 https://dx.doi.org/10.1007/s42452-019-0831-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 1 2019 8 01 07
language	English
source	Enthalten in SN applied sciences 1(2019), 8 vom: 01. Juli volume:1 year:2019 number:8 day:01 month:07
sourceStr	Enthalten in SN applied sciences 1(2019), 8 vom: 01. Juli volume:1 year:2019 number:8 day:01 month:07
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Nuclear forensics National Nuclear Forensics Libraries Principle component analysis Ratio correlation matrix
isfreeaccess_bool	false
container_title	SN applied sciences
authorswithroles_txt_mv	Sedgi, Itzhak @@aut@@ Assulin, Maor @@aut@@ Moyal, Amiram @@aut@@ Astarchan, Boaz @@aut@@ Rubinshtein, Arnon @@aut@@ Elish, Eyal @@aut@@
publishDateDaySort_date	2019-07-01T00:00:00Z
hierarchy_top_id	103761139X
id	SPR038577143
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR038577143</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230328215017.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s42452-019-0831-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR038577143</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s42452-019-0831-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sedgi, Itzhak</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Nature Switzerland AG 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The Nuclear Forensics International Technical Working Group (NF-ITWG) conducted an exercise, Galaxy Serpent 3 (GS3), by the National Nuclear Forensics Libraries Task Group, during 2017. 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author	Sedgi, Itzhak
spellingShingle	Sedgi, Itzhak misc Nuclear forensics misc National Nuclear Forensics Libraries misc Principle component analysis misc Ratio correlation matrix Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise
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topic_title	Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise Nuclear forensics (dpeaa)DE-He213 National Nuclear Forensics Libraries (dpeaa)DE-He213 Principle component analysis (dpeaa)DE-He213 Ratio correlation matrix (dpeaa)DE-He213
topic	misc Nuclear forensics misc National Nuclear Forensics Libraries misc Principle component analysis misc Ratio correlation matrix
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title	Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise
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title_full	Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise
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author_browse	Sedgi, Itzhak Assulin, Maor Moyal, Amiram Astarchan, Boaz Rubinshtein, Arnon Elish, Eyal
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title_sort	analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the galaxy serpent 3 exercise
title_auth	Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise
abstract	Abstract The Nuclear Forensics International Technical Working Group (NF-ITWG) conducted an exercise, Galaxy Serpent 3 (GS3), by the National Nuclear Forensics Libraries Task Group, during 2017. The main goal of the GS3 exercise was to illustrate the usefulness of a National Nuclear Forensic Library for assessing the potential consistency of unknown material with national holdings. Moreover, it offers the possibility to produce a realistic database resembling uranium ore concentrate (UOC) that can be used for algorithm development activities. A database was built based on basalt compositions from different tectonic settings: MORB, IAB, OIB and CRFB. Classes (Affinities) with subtly different chemical compositions. All data are open source and sample names can be traced to original data for error checking. Participants (29 teams) had to use existing techniques, or develop their own algorithms, to allow for dealing with the sparse database and managing incomplete data points. The exercise’s scenario introduced 3 simulated origin-unknown UOC samples, interdicted by authorities. Each team was asked to answer two main questions—“Are the 3 unknown samples consistent with each other?” and “are the 3 unknown samples consistent with the Mines in the database given?”. For the first time, the Israeli National Nuclear Forensic Laboratory (INNFL) participated in the GS exercise. A dedicated team was assigned the task of building capabilities needed and applying them to answer the raised questions. Methodology and tools developed and applied by the Israeli team are described. The results are presented and discussed, including new signatures allowing differentiation between unknown samples and mines. © Springer Nature Switzerland AG 2019
abstractGer	Abstract The Nuclear Forensics International Technical Working Group (NF-ITWG) conducted an exercise, Galaxy Serpent 3 (GS3), by the National Nuclear Forensics Libraries Task Group, during 2017. The main goal of the GS3 exercise was to illustrate the usefulness of a National Nuclear Forensic Library for assessing the potential consistency of unknown material with national holdings. Moreover, it offers the possibility to produce a realistic database resembling uranium ore concentrate (UOC) that can be used for algorithm development activities. A database was built based on basalt compositions from different tectonic settings: MORB, IAB, OIB and CRFB. Classes (Affinities) with subtly different chemical compositions. All data are open source and sample names can be traced to original data for error checking. Participants (29 teams) had to use existing techniques, or develop their own algorithms, to allow for dealing with the sparse database and managing incomplete data points. The exercise’s scenario introduced 3 simulated origin-unknown UOC samples, interdicted by authorities. Each team was asked to answer two main questions—“Are the 3 unknown samples consistent with each other?” and “are the 3 unknown samples consistent with the Mines in the database given?”. For the first time, the Israeli National Nuclear Forensic Laboratory (INNFL) participated in the GS exercise. A dedicated team was assigned the task of building capabilities needed and applying them to answer the raised questions. Methodology and tools developed and applied by the Israeli team are described. The results are presented and discussed, including new signatures allowing differentiation between unknown samples and mines. © Springer Nature Switzerland AG 2019
abstract_unstemmed	Abstract The Nuclear Forensics International Technical Working Group (NF-ITWG) conducted an exercise, Galaxy Serpent 3 (GS3), by the National Nuclear Forensics Libraries Task Group, during 2017. The main goal of the GS3 exercise was to illustrate the usefulness of a National Nuclear Forensic Library for assessing the potential consistency of unknown material with national holdings. Moreover, it offers the possibility to produce a realistic database resembling uranium ore concentrate (UOC) that can be used for algorithm development activities. A database was built based on basalt compositions from different tectonic settings: MORB, IAB, OIB and CRFB. Classes (Affinities) with subtly different chemical compositions. All data are open source and sample names can be traced to original data for error checking. Participants (29 teams) had to use existing techniques, or develop their own algorithms, to allow for dealing with the sparse database and managing incomplete data points. The exercise’s scenario introduced 3 simulated origin-unknown UOC samples, interdicted by authorities. Each team was asked to answer two main questions—“Are the 3 unknown samples consistent with each other?” and “are the 3 unknown samples consistent with the Mines in the database given?”. For the first time, the Israeli National Nuclear Forensic Laboratory (INNFL) participated in the GS exercise. A dedicated team was assigned the task of building capabilities needed and applying them to answer the raised questions. Methodology and tools developed and applied by the Israeli team are described. The results are presented and discussed, including new signatures allowing differentiation between unknown samples and mines. © Springer Nature Switzerland AG 2019
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container_issue	8
title_short	Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise
url	https://dx.doi.org/10.1007/s42452-019-0831-7
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author2	Assulin, Maor Moyal, Amiram Astarchan, Boaz Rubinshtein, Arnon Elish, Eyal
author2Str	Assulin, Maor Moyal, Amiram Astarchan, Boaz Rubinshtein, Arnon Elish, Eyal
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doi_str	10.1007/s42452-019-0831-7
up_date	2024-07-03T18:57:27.452Z
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Analysis of geochemical basalts database as a simulant for uranium ore concentrates data: the Galaxy Serpent 3 Exercise

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