Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression

Verification of fuel parameters is a central undertaking for nuclear inspectors aiming at verifying the completeness and correctness of operator declarations. Traditionally, such verification is done analysing data from one instrument at a time. Here we present a study based on simulated data from v...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Grape, S. [verfasserIn] Branger, E. [verfasserIn] Elter, Zs. [verfasserIn] Pöder Balkeståhl, L. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Safeguards Spent nuclear fuel Fuel parameters Multivariate analysis Machine learning Random forest regression

Übergeordnetes Werk:	Enthalten in: Nuclear instruments & methods in physics research / A - Amsterdam : North-Holland Publ. Co., 1984, 969
Übergeordnetes Werk:	volume:969

DOI / URN:	10.1016/j.nima.2020.163979

Katalog-ID:	ELV004123964

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allfields	10.1016/j.nima.2020.163979 doi (DE-627)ELV004123964 (ELSEVIER)S0168-9002(20)30439-3 DE-627 ger DE-627 rda eng 530 DE-600 33.05 bkl 33.07 bkl 33.40 bkl Grape, S. verfasserin aut Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Verification of fuel parameters is a central undertaking for nuclear inspectors aiming at verifying the completeness and correctness of operator declarations. Traditionally, such verification is done analysing data from one instrument at a time. Here we present a study based on simulated data from various non-destructive assay measurement techniques applied on modelled PWR nuclear fuel assemblies. The data comprised multiple signatures and were analysed using machine learning algorithms. These signatures included activities from gamma-ray emitting fission product radionuclides, the parametrised early die-away time τ from the prototype Differential Die-away Self-Interrogation (DDSI) instrument, as well as the total Cherenkov light intensity which is directly measurable. Safeguards Spent nuclear fuel Fuel parameters Multivariate analysis Machine learning Random forest regression Branger, E. verfasserin aut Elter, Zs. verfasserin aut Pöder Balkeståhl, L. verfasserin aut Enthalten in Nuclear instruments & methods in physics research / A Amsterdam : North-Holland Publ. Co., 1984 969 Online-Ressource (DE-627)266014666 (DE-600)1466532-3 (DE-576)074959743 0168-9002 nnns volume:969 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.05 Experimentalphysik 33.07 Spektroskopie 33.40 Kernphysik AR 969
spelling	10.1016/j.nima.2020.163979 doi (DE-627)ELV004123964 (ELSEVIER)S0168-9002(20)30439-3 DE-627 ger DE-627 rda eng 530 DE-600 33.05 bkl 33.07 bkl 33.40 bkl Grape, S. verfasserin aut Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Verification of fuel parameters is a central undertaking for nuclear inspectors aiming at verifying the completeness and correctness of operator declarations. Traditionally, such verification is done analysing data from one instrument at a time. Here we present a study based on simulated data from various non-destructive assay measurement techniques applied on modelled PWR nuclear fuel assemblies. The data comprised multiple signatures and were analysed using machine learning algorithms. These signatures included activities from gamma-ray emitting fission product radionuclides, the parametrised early die-away time τ from the prototype Differential Die-away Self-Interrogation (DDSI) instrument, as well as the total Cherenkov light intensity which is directly measurable. Safeguards Spent nuclear fuel Fuel parameters Multivariate analysis Machine learning Random forest regression Branger, E. verfasserin aut Elter, Zs. verfasserin aut Pöder Balkeståhl, L. verfasserin aut Enthalten in Nuclear instruments & methods in physics research / A Amsterdam : North-Holland Publ. Co., 1984 969 Online-Ressource (DE-627)266014666 (DE-600)1466532-3 (DE-576)074959743 0168-9002 nnns volume:969 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.05 Experimentalphysik 33.07 Spektroskopie 33.40 Kernphysik AR 969
allfields_unstemmed	10.1016/j.nima.2020.163979 doi (DE-627)ELV004123964 (ELSEVIER)S0168-9002(20)30439-3 DE-627 ger DE-627 rda eng 530 DE-600 33.05 bkl 33.07 bkl 33.40 bkl Grape, S. verfasserin aut Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Verification of fuel parameters is a central undertaking for nuclear inspectors aiming at verifying the completeness and correctness of operator declarations. Traditionally, such verification is done analysing data from one instrument at a time. Here we present a study based on simulated data from various non-destructive assay measurement techniques applied on modelled PWR nuclear fuel assemblies. The data comprised multiple signatures and were analysed using machine learning algorithms. These signatures included activities from gamma-ray emitting fission product radionuclides, the parametrised early die-away time τ from the prototype Differential Die-away Self-Interrogation (DDSI) instrument, as well as the total Cherenkov light intensity which is directly measurable. Safeguards Spent nuclear fuel Fuel parameters Multivariate analysis Machine learning Random forest regression Branger, E. verfasserin aut Elter, Zs. verfasserin aut Pöder Balkeståhl, L. verfasserin aut Enthalten in Nuclear instruments & methods in physics research / A Amsterdam : North-Holland Publ. Co., 1984 969 Online-Ressource (DE-627)266014666 (DE-600)1466532-3 (DE-576)074959743 0168-9002 nnns volume:969 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.05 Experimentalphysik 33.07 Spektroskopie 33.40 Kernphysik AR 969
allfieldsGer	10.1016/j.nima.2020.163979 doi (DE-627)ELV004123964 (ELSEVIER)S0168-9002(20)30439-3 DE-627 ger DE-627 rda eng 530 DE-600 33.05 bkl 33.07 bkl 33.40 bkl Grape, S. verfasserin aut Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Verification of fuel parameters is a central undertaking for nuclear inspectors aiming at verifying the completeness and correctness of operator declarations. Traditionally, such verification is done analysing data from one instrument at a time. Here we present a study based on simulated data from various non-destructive assay measurement techniques applied on modelled PWR nuclear fuel assemblies. The data comprised multiple signatures and were analysed using machine learning algorithms. These signatures included activities from gamma-ray emitting fission product radionuclides, the parametrised early die-away time τ from the prototype Differential Die-away Self-Interrogation (DDSI) instrument, as well as the total Cherenkov light intensity which is directly measurable. Safeguards Spent nuclear fuel Fuel parameters Multivariate analysis Machine learning Random forest regression Branger, E. verfasserin aut Elter, Zs. verfasserin aut Pöder Balkeståhl, L. verfasserin aut Enthalten in Nuclear instruments & methods in physics research / A Amsterdam : North-Holland Publ. Co., 1984 969 Online-Ressource (DE-627)266014666 (DE-600)1466532-3 (DE-576)074959743 0168-9002 nnns volume:969 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.05 Experimentalphysik 33.07 Spektroskopie 33.40 Kernphysik AR 969
allfieldsSound	10.1016/j.nima.2020.163979 doi (DE-627)ELV004123964 (ELSEVIER)S0168-9002(20)30439-3 DE-627 ger DE-627 rda eng 530 DE-600 33.05 bkl 33.07 bkl 33.40 bkl Grape, S. verfasserin aut Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Verification of fuel parameters is a central undertaking for nuclear inspectors aiming at verifying the completeness and correctness of operator declarations. Traditionally, such verification is done analysing data from one instrument at a time. Here we present a study based on simulated data from various non-destructive assay measurement techniques applied on modelled PWR nuclear fuel assemblies. The data comprised multiple signatures and were analysed using machine learning algorithms. These signatures included activities from gamma-ray emitting fission product radionuclides, the parametrised early die-away time τ from the prototype Differential Die-away Self-Interrogation (DDSI) instrument, as well as the total Cherenkov light intensity which is directly measurable. Safeguards Spent nuclear fuel Fuel parameters Multivariate analysis Machine learning Random forest regression Branger, E. verfasserin aut Elter, Zs. verfasserin aut Pöder Balkeståhl, L. verfasserin aut Enthalten in Nuclear instruments & methods in physics research / A Amsterdam : North-Holland Publ. Co., 1984 969 Online-Ressource (DE-627)266014666 (DE-600)1466532-3 (DE-576)074959743 0168-9002 nnns volume:969 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.05 Experimentalphysik 33.07 Spektroskopie 33.40 Kernphysik AR 969
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author	Grape, S.
spellingShingle	Grape, S. ddc 530 bkl 33.05 bkl 33.07 bkl 33.40 misc Safeguards misc Spent nuclear fuel misc Fuel parameters misc Multivariate analysis misc Machine learning misc Random forest regression Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression
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topic_title	530 DE-600 33.05 bkl 33.07 bkl 33.40 bkl Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression Safeguards Spent nuclear fuel Fuel parameters Multivariate analysis Machine learning Random forest regression
topic	ddc 530 bkl 33.05 bkl 33.07 bkl 33.40 misc Safeguards misc Spent nuclear fuel misc Fuel parameters misc Multivariate analysis misc Machine learning misc Random forest regression
topic_unstemmed	ddc 530 bkl 33.05 bkl 33.07 bkl 33.40 misc Safeguards misc Spent nuclear fuel misc Fuel parameters misc Multivariate analysis misc Machine learning misc Random forest regression
topic_browse	ddc 530 bkl 33.05 bkl 33.07 bkl 33.40 misc Safeguards misc Spent nuclear fuel misc Fuel parameters misc Multivariate analysis misc Machine learning misc Random forest regression
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title	Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression
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title_full	Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression
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author_browse	Grape, S. Branger, E. Elter, Zs. Pöder Balkeståhl, L.
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title_sort	determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and random forest regression
title_auth	Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression
abstract	Verification of fuel parameters is a central undertaking for nuclear inspectors aiming at verifying the completeness and correctness of operator declarations. Traditionally, such verification is done analysing data from one instrument at a time. Here we present a study based on simulated data from various non-destructive assay measurement techniques applied on modelled PWR nuclear fuel assemblies. The data comprised multiple signatures and were analysed using machine learning algorithms. These signatures included activities from gamma-ray emitting fission product radionuclides, the parametrised early die-away time τ from the prototype Differential Die-away Self-Interrogation (DDSI) instrument, as well as the total Cherenkov light intensity which is directly measurable.
abstractGer	Verification of fuel parameters is a central undertaking for nuclear inspectors aiming at verifying the completeness and correctness of operator declarations. Traditionally, such verification is done analysing data from one instrument at a time. Here we present a study based on simulated data from various non-destructive assay measurement techniques applied on modelled PWR nuclear fuel assemblies. The data comprised multiple signatures and were analysed using machine learning algorithms. These signatures included activities from gamma-ray emitting fission product radionuclides, the parametrised early die-away time τ from the prototype Differential Die-away Self-Interrogation (DDSI) instrument, as well as the total Cherenkov light intensity which is directly measurable.
abstract_unstemmed	Verification of fuel parameters is a central undertaking for nuclear inspectors aiming at verifying the completeness and correctness of operator declarations. Traditionally, such verification is done analysing data from one instrument at a time. Here we present a study based on simulated data from various non-destructive assay measurement techniques applied on modelled PWR nuclear fuel assemblies. The data comprised multiple signatures and were analysed using machine learning algorithms. These signatures included activities from gamma-ray emitting fission product radionuclides, the parametrised early die-away time τ from the prototype Differential Die-away Self-Interrogation (DDSI) instrument, as well as the total Cherenkov light intensity which is directly measurable.
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title_short	Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression
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author2	Branger, E. Elter, Zs Pöder Balkeståhl, L.
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doi_str	10.1016/j.nima.2020.163979
up_date	2024-07-06T21:55:51.879Z
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These signatures included activities from gamma-ray emitting fission product radionuclides, the parametrised early die-away time τ from the prototype Differential Die-away Self-Interrogation (DDSI) instrument, as well as the total Cherenkov light intensity which is directly measurable.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Safeguards</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spent nuclear fuel</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuel parameters</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multivariate analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random forest regression</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Branger, E.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Elter, Zs.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pöder Balkeståhl, L.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Nuclear instruments & methods in physics research / A</subfield><subfield code="d">Amsterdam : North-Holland Publ. 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Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression

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