Grade Control with Ensembled Machine Learning: A Comparative Case Study at the Carmen de Andacollo Copper Mine

The main goal of grade control is the prediction of material destination based on all available data. The common approach to grade control is based on estimated maps obtained through kriging, inverse distance estimation, or nearest neighbor; however, capturing complex relations from data is not stra...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Natural resources research (New York, N.Y.) Ročník 31; číslo 2; s. 785 - 800
Hlavní autoři:	da Silva, Camilla Zacche, Nisenson, Jed, Boisvert, Jeff
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.04.2022 Springer Nature B.V
Témata:	Algorithms Case studies Chemistry and Earth Sciences Comparative studies Computer Science Copper Decision making Earth and Environmental Science Earth Sciences Fossil Fuels (incl. Carbon Capture) Geography Geostatistics Learning algorithms Machine learning Mathematical Modeling and Industrial Mathematics Mineral Resources Model accuracy Neural networks Original Paper Physics Quality Radial basis function Statistics for Engineering Sustainable Development Workflow Support vector regression Collocated co-kriging Short term modeling Neural networks Kriging Classification
ISSN:	1520-7439, 1573-8981
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	The main goal of grade control is the prediction of material destination based on all available data. The common approach to grade control is based on estimated maps obtained through kriging, inverse distance estimation, or nearest neighbor; however, capturing complex relations from data is not straightforward with such methodologies. Machine learning algorithms provide flexibility and simplicity when integrating data and incorporating complex patterns that cannot be easily accounted for with geostatistical workflows, leading to higher model accuracy and promotes better decision making. The methodology implemented in this case study uses machine learning algorithms to model copper grade, which is incorporated in an intrinsic collocated co-kriging framework as secondary information to generate a final grade model. The workflow presented (1) is not more difficult to implement compared to ordinary kriging, (2) allows for automatic data incorporation in a geostatistical framework and (3) improves grade control decision-making when compared to common approaches. The workflow is demonstrated on 10 blasts from Teck Resources Limited’s Carmen de Andacollo copper mine in Chile and is compared to ordinary kriging and inverse distance. Two machine learning algorithms are implemented and evaluated for grade control decision-making. The algorithms considered are (1) an ensemble of radial basis function neural networks and (2) an ensemble of support vector regressors. These two algorithms are used to obtain an exhaustive secondary model used in copper grade estimation. Incorporating radial basis function neural networks improves the quality of the classified model, with average classification accuracy of 89% over 10 blasts and can reduce the volume of misclassified material on average over 10 blasts by 7% and 1% when compared to inverse distance, ordinary kriging and support vector regressor approach, respectively.
AbstractList	The main goal of grade control is the prediction of material destination based on all available data. The common approach to grade control is based on estimated maps obtained through kriging, inverse distance estimation, or nearest neighbor; however, capturing complex relations from data is not straightforward with such methodologies. Machine learning algorithms provide flexibility and simplicity when integrating data and incorporating complex patterns that cannot be easily accounted for with geostatistical workflows, leading to higher model accuracy and promotes better decision making. The methodology implemented in this case study uses machine learning algorithms to model copper grade, which is incorporated in an intrinsic collocated co-kriging framework as secondary information to generate a final grade model. The workflow presented (1) is not more difficult to implement compared to ordinary kriging, (2) allows for automatic data incorporation in a geostatistical framework and (3) improves grade control decision-making when compared to common approaches. The workflow is demonstrated on 10 blasts from Teck Resources Limited’s Carmen de Andacollo copper mine in Chile and is compared to ordinary kriging and inverse distance. Two machine learning algorithms are implemented and evaluated for grade control decision-making. The algorithms considered are (1) an ensemble of radial basis function neural networks and (2) an ensemble of support vector regressors. These two algorithms are used to obtain an exhaustive secondary model used in copper grade estimation. Incorporating radial basis function neural networks improves the quality of the classified model, with average classification accuracy of 89% over 10 blasts and can reduce the volume of misclassified material on average over 10 blasts by 7% and 1% when compared to inverse distance, ordinary kriging and support vector regressor approach, respectively.
Author	da Silva, Camilla Zacche Boisvert, Jeff Nisenson, Jed
Author_xml	– sequence: 1 givenname: Camilla Zacche orcidid: 0000-0003-4227-3149 surname: da Silva fullname: da Silva, Camilla Zacche email: cdasilva@ualberta.ca organization: Department of Civil and Environmental Engineering, University of Alberta – sequence: 2 givenname: Jed surname: Nisenson fullname: Nisenson, Jed organization: Teck Resources Limited – sequence: 3 givenname: Jeff surname: Boisvert fullname: Boisvert, Jeff organization: Department of Civil and Environmental Engineering, University of Alberta
BookMark	eNp9kM1LwzAchoNMcJv-A54Cnqv5WNrE2xhzChMP6jmkSbp1dGlNMmX_vakVBA875YP3eX8_ngkYudZZAK4xusUIFXcBY8RohgjJ0puIjJ-BMWYFzbjgeNTfCcqKGRUXYBLCDiWIcjYGbuWVsXDRuujbBn7VcQuXLth92VgDn5Xe1s7CtVXe1W5zD-cpuu-UV7H-TJgKFr7GgzlCFWHc9j9-bx1MlXNnlG6bpk1E11kPn1PTJTivVBPs1e85Be8Py7fFY7Z-WT0t5utMUyxihhnOS8EKpGdVPquMEFrhghg-s4pzzXRVGqErJjDXhHAlRMlMTqoCM6UwNXQKbobezrcfBxui3LUH79JISRJEaV7kKKXIkNK-DcHbSna-3it_lBjJ3qscvMrkVf54lTxB_B-k65h09AZV3ZxG6YCGNMdtrP_b6gT1DXyxjhg
CitedBy_id	crossref_primary_10_1016_j_coal_2023_104328 crossref_primary_10_1016_j_resourpol_2023_103340 crossref_primary_10_3390_min15010044 crossref_primary_10_1007_s11004_024_10172_3 crossref_primary_10_1016_j_asoc_2025_113580 crossref_primary_10_2118_208885_PA
Cites_doi	10.1190/segam2018-2997218.1 10.3390/ijgi8040174 10.1007/BF02083491 10.1371/journal.pone.0205872 10.1016/j.petrol.2012.03.019 10.1007/s10852-005-9020-3 10.1007/s11004-005-6660-9 10.1179/1743286314Y.0000000062 10.1007/s11004-010-9264-y 10.1016/j.aca.2013.04.034 10.1007/BF02089242 10.1007/BF02478259 10.1080/14749009.2017.1363991 10.1016/j.egypro.2019.01.219 10.1007/978-1-4302-5990-9 10.1016/j.neucom.2017.01.016 10.1007/s11053-010-9115-z 10.1016/j.ecolind.2014.04.003 10.1023/A:1007553013388 10.1007/s42461-019-0072-8 10.1007/s11053-020-09628-0 10.1016/j.envsoft.2003.03.004 10.1016/j.ins.2017.10.049 10.1007/978-1-4757-2440-0 10.1007/s10596-018-9758-0 10.1016/S0893-6080(05)80023-1 10.1016/j.petrol.2009.08.001 10.2118/24742-MS 10.2112/SI90-023.1 10.5120/ijca2017914643 10.7717/peerj.5518
ContentType	Journal Article
Copyright	International Association for Mathematical Geosciences 2022 International Association for Mathematical Geosciences 2022.
Copyright_xml	– notice: International Association for Mathematical Geosciences 2022 – notice: International Association for Mathematical Geosciences 2022.
DBID	AAYXX CITATION 8FE 8FG ABJCF AEUYN AFKRA ATCPS AZQEC BENPR BGLVJ BHPHI BKSAR CCPQU D1I DWQXO GNUQQ HCIFZ KB. PATMY PCBAR PDBOC PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PYCSY
DOI	10.1007/s11053-022-10029-8
DatabaseName	CrossRef ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest One Sustainability ProQuest Central UK/Ireland Agricultural & Environmental Science Collection ProQuest Central Essentials ProQuest Central Technology Collection Natural Science Collection Earth, Atmospheric & Aquatic Science Collection ProQuest One ProQuest Materials Science Collection ProQuest Central ProQuest Central Student SciTech Premium Collection Materials Science Database Environmental Science Database Earth, Atmospheric & Aquatic Science Database Materials Science Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition Environmental Science Collection
DatabaseTitle	CrossRef ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Central Essentials Materials Science Collection SciTech Premium Collection ProQuest One Community College Earth, Atmospheric & Aquatic Science Collection ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability Natural Science Collection ProQuest Central Korea Agricultural & Environmental Science Collection Materials Science Database ProQuest Central (New) ProQuest Materials Science Collection ProQuest One Academic Eastern Edition Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection ProQuest SciTech Collection Environmental Science Collection ProQuest One Academic UKI Edition Materials Science & Engineering Collection Environmental Science Database ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	ProQuest Central Student
Database_xml	– sequence: 1 dbid: KB. name: Materials Science Database url: http://search.proquest.com/materialsscijournals sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography Engineering Geology Physics Computer Science
EISSN	1573-8981
EndPage	800
ExternalDocumentID	10_1007_s11053_022_10029_8
GrantInformation_xml	– fundername: Mitacs grantid: IT16277 funderid: http://dx.doi.org/10.13039/501100004489
GroupedDBID	-5A -5G -BR -EM -Y2 -~C .86 .VR 06D 0R~ 0VY 123 1N0 2.D 203 29M 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 5QI 5VS 67M 67Z 6NX 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABJCF ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHIR ADIMF ADINQ ADKNI ADKPE ADPHR ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AEOHA AEPYU AESKC AETLH AEUYN AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFRAH AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AOCGG ARMRJ ASPBG ATCPS AVWKF AXYYD AYJHY AZFZN B-. BA0 BDATZ BENPR BGLVJ BGNMA BHPHI BKSAR BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I09 IHE IJ- IKXTQ ITM IWAJR IXC IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KB. KDC KOV LAK LLZTM M4Y MA- N9A NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 PATMY PCBAR PDBOC PF0 PT4 PT5 PYCSY QOK QOS R89 R9I RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCLPG SDH SEV SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TEORI TSG TSK TSV TUC U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z5O Z7Y Z7Z Z81 Z85 Z86 Z8S Z8T Z8U Z8Z ZMTXR ~02 ~A9 ~KM AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB 8FE 8FG AZQEC D1I DWQXO GNUQQ PKEHL PQEST PQQKQ PQUKI
ID	FETCH-LOGICAL-c319t-1516b9570c4f64fd99ca172d84ea88c5cfbd9cf5918c228a99b5d62f715aa13d3
IEDL.DBID	RSV
ISICitedReferencesCount	6
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000763818700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1520-7439
IngestDate	Wed Nov 05 01:47:05 EST 2025 Sat Nov 29 06:27:46 EST 2025 Tue Nov 18 22:22:49 EST 2025 Fri Feb 21 02:47:41 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	2
Keywords	Support vector regression Collocated co-kriging Short term modeling Neural networks Kriging Classification
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c319t-1516b9570c4f64fd99ca172d84ea88c5cfbd9cf5918c228a99b5d62f715aa13d3
Notes	ObjectType-Case Study-2 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Feature-4 ObjectType-Report-1 ObjectType-Article-3
ORCID	0000-0003-4227-3149
PQID	2918336760
PQPubID	2043663
PageCount	16
ParticipantIDs	proquest_journals_2918336760 crossref_primary_10_1007_s11053_022_10029_8 crossref_citationtrail_10_1007_s11053_022_10029_8 springer_journals_10_1007_s11053_022_10029_8
PublicationCentury	2000
PublicationDate	2022-04-01
PublicationDateYYYYMMDD	2022-04-01
PublicationDate_xml	– month: 04 year: 2022 text: 2022-04-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationSubtitle	Official Journal of the International Association for Mathematical Geosciences
PublicationTitle	Natural resources research (New York, N.Y.)
PublicationTitleAbbrev	Nat Resour Res
PublicationYear	2022
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	ChatterjeeSBandopahyaySMuchacaDOre grade estimation using a genetic algorithm and clustering based ensemble neural networksMathematical Geosciences20104230932610.1007/s11004-010-9264-y Larrondo, P., Neufeld, C. T., Deutsch, C. V. (2003). VARFIT: A program for semiautomatic variogram modeling. 5th CCG Annual report. Alberta, Canada. PedregosaFVoroquauxGGramfortAMichelVThirionBGriselODuchenaryEScikit-learn: ML in pythonJournal of ML2011128528252830 Almeida, A. S. (1993). Joint Simulation of multiple variables with a Markov-type coregionalization model. PhD Thesis. Stanford University. JafrastehBFathianpourNA hybrid simultaneous perturbation artificial bee colony and back-propagation algorithm for training a local linear radial basis neural network on ore grade estimationNeurocomputing201723521722710.1016/j.neucom.2017.01.016 Xu, W., Tran, T. T., Srivastava, R. M., & Journel, A. G. (1992). Integrating seismic data in reservoir modelling: The collocated cokriging alternative. Society of Petroleum Engineers, pp. 833–842. ChenLRenCLiLWangYZhangBWangZLiLA comparative assessment of geostatistical, machine learning, and hybrid approaches for mapping topsoil organic carbon contentISPRS International Journal of Geo-Information20198417410.3390/ijgi8040174 WittenIHFrankEHallMAPalCJEnsemble learningData mining20174Springer Deutsch, J. (2015). Variogram program refresh. 17th CCG Annual report, Alberta, Canada. DowdPASaraçCA NN approach to geostatistical simulationMathematical Geology19942649150310.1007/BF02083491 VapnikVNThe nature of statistical learning theory1995Springer10.1007/978-1-4757-2440-0 VasylchukYVDeutschCVImproved grade control in open pit minesMining Technology20181272849110.1080/14749009.2017.1363991 ChandraAYaoXEnsemble learning using multi-objective evolutionary algorithmJournal of Mathematical Modelling and Algorithms2006541744510.1007/s10852-005-9020-3 BabakODeutschCVImproved spatial modeling by merging multiple secondary for intrinsic collocated cokrigingJournal of Petroleum Science and Engineering200969110.1016/j.petrol.2009.08.001 McCullochWSPittsWA logical calculus of the ideas immanent in nervous activityBulletin of Mathematical Biophysics19435411513310.1007/BF02478259 SchapireREThe strength of weak learnabilityMachine Learning199052197227 VapnikVNStatistical learning theory1998Wiley GangappaMMaiCKSammulalPTechniques for machine learning based spatial data analysis: Research directionsInternational Journal of Computer Applications2017170191310.5120/ijca2017914643 TahmasebiPHezarkhaniAA fast and independent architecture of artificial NN for permeability predictionJournal of Petroleum Science and Engineering20128611812610.1016/j.petrol.2012.03.019 WalchACastelloRMohajeriNGuinardFKanevskiMScartezziniJLSpatio-temporal modelling and uncertainty estimation of hourly global solar irradiance using extreme learning machinesEnergy Procedia20191586378638310.1016/j.egypro.2019.01.219 Raschka, S. (2018). Model evaluation, model selection, and algorithm selection in machine learning. Technical report, University of Wisconsin-Maddison. https://arxiv.org/1811.12808 VasylchukYVDeutschCVOptimization of surface mining dig limits with a practical heuristic algorithmMining, Metallurgy and Exploration201936477378410.1007/s42461-019-0072-8 MaZWangPGaoZWangRKhalighiKEnsemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dosePLoS ONE20191310e020587210.1371/journal.pone.0205872 SamsonMDeutschCVA hybrid estimation technique using elliptical radial basis neural networks and cokrigingMathematical Geosciences202110.1016/j.ins.2017.10.049 WolpertDHStacked generalizationNeural Networks19925224125910.1016/S0893-6080(05)80023-1 Kapagerdis, I. K. (1999). Application of NNs systems to grade estimation from exploration data. (PhD). University of Nottingham. LiSSariYAKumralMOptimization of mining-mineral processing integration using unsupervised machine learning algorithmsNatural Resources Research2020293035304610.1007/s11053-020-09628-0 TodeschiniRBallabioDConsonniVSahigaraFFilzmoserPLocally centred Mahalanobis distance: A new distance measure with salient features towards outlier detectionAnalytica Chimica Acta20137871910.1016/j.aca.2013.04.034 AggarwalCCNNs and deep learning2018Springer International Publishing HenglTNussbaumMWrightMNHeuvelinkGBMGralerBRandom Forest as a generic framework for predictive modeling of spatial and spatio-temporal variablesPeerJ20186e551810.7717/peerj.5518 SamantaBRadial basis function network for ore grade estimationNatural Resources and Research20101929110210.1007/s11053-010-9115-z AlmeidaASJournelAGJoint simulation of multiple variables with a Markov-type coregionalization modelMathematical Geology19942656558810.1007/BF02089242 AwadMKahnnaREfficient learning machines: Theories, concepts, and applications for engineers and system designers2015Apress Open10.1007/978-1-4302-5990-9 Costa, J. F. C. L. (1997). Developments in recoverable reserves estimation and ore body modelling. PhD thesis. University of Queensland. Australia. VerlyGGrade control classification of ore and waste: A critical review of estimation and simulation-based proceduresMathematical Geology20053745147510.1007/s11004-005-6660-9 ParkNGeostatistical integration of field measurements and multi-sensor remote sensing images for spatial prediction of grain size of intertidal surface sedimentsJournal of Coastal Research20199019019610.2112/SI90-023.1 WittenIHFrankEHallMAData mining – practical machine learning tools and techniques20113Morgan Kaufman Publishers DaiFZhouQLvZWangXLiuGSpatial prediction of soil organic matter content integrating artificial NN and OK in Tibetan PlateauEcological Indicators20144518419410.1016/j.ecolind.2014.04.003 JafrastehBFathianpurNSuarezAComparison of machine learning methods for copper ore grade estimationComputational Geosciences2018221371138810.1007/s10596-018-9758-0 JournelAMarkov model for cross-covariancesMathmatical Geology19993195596410.1023/A:1007553013388 KanevskiMParkinRPozdnukhovATimoninVMaignanMDemyanovVCanuSEnvironmental data mining and modeling based on machine learning algorithms and geostatisticsEnvironmental Modeling and Software200419984585510.1016/j.envsoft.2003.03.004 Breiman, L. (1994). Bagging predictors. Technical report no. 421. University of California at Berkeley. Maniar, H., Srikanth, R., Kulkarni, M. S., Abubakar, A. (2018). Machine Learning methods in geoscience. Society of Exploration Geophysicists. International Exposition and 88th Annual Meeting. https://doi.org/10.1190/segam2018-2997218.1 DimitrakopoulosRGodoyMGrade control based on economic ore/waste classification functions and stochastic simulation: Examples, comparisons and applicationsMining Technology201412329010610.1179/1743286314Y.0000000062 Isaaks, E. H. (1990). The application of Monte Carlo methods to the analysis of spatially correlated data. PhD thesis. Stanford University, Unites States of America. 10029_CR30 A Walch (10029_CR41) 2019; 158 10029_CR2 M Samson (10029_CR32) 2021 P Tahmasebi (10029_CR34) 2012; 86 A Chandra (10029_CR7) 2006; 5 10029_CR12 10029_CR10 O Babak (10029_CR5) 2009; 69 IH Witten (10029_CR43) 2017 AS Almeida (10029_CR3) 1994; 26 M Awad (10029_CR4) 2015 S Chatterjee (10029_CR8) 2010; 42 WS McCulloch (10029_CR27) 1943; 5 RE Schapire (10029_CR33) 1990; 5 10029_CR17 B Samanta (10029_CR31) 2010; 19 A Journel (10029_CR20) 1999; 31 YV Vasylchuk (10029_CR38) 2018; 127 R Todeschini (10029_CR35) 2013; 787 VN Vapnik (10029_CR36) 1995 DH Wolpert (10029_CR44) 1992; 5 G Verly (10029_CR40) 2005; 37 10029_CR22 VN Vapnik (10029_CR37) 1998 T Hengl (10029_CR16) 2018; 6 10029_CR23 10029_CR45 F Dai (10029_CR11) 2014; 45 PA Dowd (10029_CR14) 1994; 26 N Park (10029_CR28) 2019; 90 10029_CR26 M Gangappa (10029_CR15) 2017; 170 L Chen (10029_CR9) 2019; 8 M Kanevski (10029_CR21) 2004; 19 S Li (10029_CR24) 2020; 29 Z Ma (10029_CR25) 2019; 13 B Jafrasteh (10029_CR18) 2017; 235 YV Vasylchuk (10029_CR39) 2019; 36 B Jafrasteh (10029_CR19) 2018; 22 F Pedregosa (10029_CR29) 2011; 12 R Dimitrakopoulos (10029_CR13) 2014; 123 CC Aggarwal (10029_CR1) 2018 IH Witten (10029_CR42) 2011 10029_CR6
References_xml	– reference: SchapireREThe strength of weak learnabilityMachine Learning199052197227 – reference: WittenIHFrankEHallMAPalCJEnsemble learningData mining20174Springer – reference: JafrastehBFathianpurNSuarezAComparison of machine learning methods for copper ore grade estimationComputational Geosciences2018221371138810.1007/s10596-018-9758-0 – reference: PedregosaFVoroquauxGGramfortAMichelVThirionBGriselODuchenaryEScikit-learn: ML in pythonJournal of ML2011128528252830 – reference: Breiman, L. (1994). Bagging predictors. Technical report no. 421. University of California at Berkeley. – reference: AwadMKahnnaREfficient learning machines: Theories, concepts, and applications for engineers and system designers2015Apress Open10.1007/978-1-4302-5990-9 – reference: DaiFZhouQLvZWangXLiuGSpatial prediction of soil organic matter content integrating artificial NN and OK in Tibetan PlateauEcological Indicators20144518419410.1016/j.ecolind.2014.04.003 – reference: Kapagerdis, I. K. (1999). Application of NNs systems to grade estimation from exploration data. (PhD). University of Nottingham. – reference: VapnikVNThe nature of statistical learning theory1995Springer10.1007/978-1-4757-2440-0 – reference: VasylchukYVDeutschCVImproved grade control in open pit minesMining Technology20181272849110.1080/14749009.2017.1363991 – reference: AggarwalCCNNs and deep learning2018Springer International Publishing – reference: WolpertDHStacked generalizationNeural Networks19925224125910.1016/S0893-6080(05)80023-1 – reference: Costa, J. F. C. L. (1997). Developments in recoverable reserves estimation and ore body modelling. PhD thesis. University of Queensland. Australia. – reference: DimitrakopoulosRGodoyMGrade control based on economic ore/waste classification functions and stochastic simulation: Examples, comparisons and applicationsMining Technology201412329010610.1179/1743286314Y.0000000062 – reference: DowdPASaraçCA NN approach to geostatistical simulationMathematical Geology19942649150310.1007/BF02083491 – reference: AlmeidaASJournelAGJoint simulation of multiple variables with a Markov-type coregionalization modelMathematical Geology19942656558810.1007/BF02089242 – reference: SamantaBRadial basis function network for ore grade estimationNatural Resources and Research20101929110210.1007/s11053-010-9115-z – reference: McCullochWSPittsWA logical calculus of the ideas immanent in nervous activityBulletin of Mathematical Biophysics19435411513310.1007/BF02478259 – reference: JafrastehBFathianpourNA hybrid simultaneous perturbation artificial bee colony and back-propagation algorithm for training a local linear radial basis neural network on ore grade estimationNeurocomputing201723521722710.1016/j.neucom.2017.01.016 – reference: JournelAMarkov model for cross-covariancesMathmatical Geology19993195596410.1023/A:1007553013388 – reference: GangappaMMaiCKSammulalPTechniques for machine learning based spatial data analysis: Research directionsInternational Journal of Computer Applications2017170191310.5120/ijca2017914643 – reference: WittenIHFrankEHallMAData mining – practical machine learning tools and techniques20113Morgan Kaufman Publishers – reference: HenglTNussbaumMWrightMNHeuvelinkGBMGralerBRandom Forest as a generic framework for predictive modeling of spatial and spatio-temporal variablesPeerJ20186e551810.7717/peerj.5518 – reference: Larrondo, P., Neufeld, C. T., Deutsch, C. V. (2003). VARFIT: A program for semiautomatic variogram modeling. 5th CCG Annual report. Alberta, Canada. – reference: WalchACastelloRMohajeriNGuinardFKanevskiMScartezziniJLSpatio-temporal modelling and uncertainty estimation of hourly global solar irradiance using extreme learning machinesEnergy Procedia20191586378638310.1016/j.egypro.2019.01.219 – reference: ChenLRenCLiLWangYZhangBWangZLiLA comparative assessment of geostatistical, machine learning, and hybrid approaches for mapping topsoil organic carbon contentISPRS International Journal of Geo-Information20198417410.3390/ijgi8040174 – reference: LiSSariYAKumralMOptimization of mining-mineral processing integration using unsupervised machine learning algorithmsNatural Resources Research2020293035304610.1007/s11053-020-09628-0 – reference: Maniar, H., Srikanth, R., Kulkarni, M. S., Abubakar, A. (2018). Machine Learning methods in geoscience. Society of Exploration Geophysicists. International Exposition and 88th Annual Meeting. https://doi.org/10.1190/segam2018-2997218.1 – reference: Isaaks, E. H. (1990). The application of Monte Carlo methods to the analysis of spatially correlated data. PhD thesis. Stanford University, Unites States of America. – reference: Deutsch, J. (2015). Variogram program refresh. 17th CCG Annual report, Alberta, Canada. – reference: KanevskiMParkinRPozdnukhovATimoninVMaignanMDemyanovVCanuSEnvironmental data mining and modeling based on machine learning algorithms and geostatisticsEnvironmental Modeling and Software200419984585510.1016/j.envsoft.2003.03.004 – reference: TahmasebiPHezarkhaniAA fast and independent architecture of artificial NN for permeability predictionJournal of Petroleum Science and Engineering20128611812610.1016/j.petrol.2012.03.019 – reference: SamsonMDeutschCVA hybrid estimation technique using elliptical radial basis neural networks and cokrigingMathematical Geosciences202110.1016/j.ins.2017.10.049 – reference: VasylchukYVDeutschCVOptimization of surface mining dig limits with a practical heuristic algorithmMining, Metallurgy and Exploration201936477378410.1007/s42461-019-0072-8 – reference: VerlyGGrade control classification of ore and waste: A critical review of estimation and simulation-based proceduresMathematical Geology20053745147510.1007/s11004-005-6660-9 – reference: BabakODeutschCVImproved spatial modeling by merging multiple secondary for intrinsic collocated cokrigingJournal of Petroleum Science and Engineering200969110.1016/j.petrol.2009.08.001 – reference: Raschka, S. (2018). Model evaluation, model selection, and algorithm selection in machine learning. Technical report, University of Wisconsin-Maddison. https://arxiv.org/1811.12808 – reference: TodeschiniRBallabioDConsonniVSahigaraFFilzmoserPLocally centred Mahalanobis distance: A new distance measure with salient features towards outlier detectionAnalytica Chimica Acta20137871910.1016/j.aca.2013.04.034 – reference: VapnikVNStatistical learning theory1998Wiley – reference: MaZWangPGaoZWangRKhalighiKEnsemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dosePLoS ONE20191310e020587210.1371/journal.pone.0205872 – reference: ParkNGeostatistical integration of field measurements and multi-sensor remote sensing images for spatial prediction of grain size of intertidal surface sedimentsJournal of Coastal Research20199019019610.2112/SI90-023.1 – reference: Almeida, A. S. (1993). Joint Simulation of multiple variables with a Markov-type coregionalization model. PhD Thesis. Stanford University. – reference: ChandraAYaoXEnsemble learning using multi-objective evolutionary algorithmJournal of Mathematical Modelling and Algorithms2006541744510.1007/s10852-005-9020-3 – reference: Xu, W., Tran, T. T., Srivastava, R. M., & Journel, A. G. (1992). Integrating seismic data in reservoir modelling: The collocated cokriging alternative. Society of Petroleum Engineers, pp. 833–842. – reference: ChatterjeeSBandopahyaySMuchacaDOre grade estimation using a genetic algorithm and clustering based ensemble neural networksMathematical Geosciences20104230932610.1007/s11004-010-9264-y – ident: 10029_CR30 – ident: 10029_CR26 doi: 10.1190/segam2018-2997218.1 – volume-title: NNs and deep learning year: 2018 ident: 10029_CR1 – volume: 8 start-page: 174 issue: 4 year: 2019 ident: 10029_CR9 publication-title: ISPRS International Journal of Geo-Information doi: 10.3390/ijgi8040174 – volume: 26 start-page: 491 year: 1994 ident: 10029_CR14 publication-title: Mathematical Geology doi: 10.1007/BF02083491 – volume: 13 start-page: e0205872 issue: 10 year: 2019 ident: 10029_CR25 publication-title: PLoS ONE doi: 10.1371/journal.pone.0205872 – volume: 86 start-page: 118 year: 2012 ident: 10029_CR34 publication-title: Journal of Petroleum Science and Engineering doi: 10.1016/j.petrol.2012.03.019 – volume: 5 start-page: 417 year: 2006 ident: 10029_CR7 publication-title: Journal of Mathematical Modelling and Algorithms doi: 10.1007/s10852-005-9020-3 – volume: 37 start-page: 451 year: 2005 ident: 10029_CR40 publication-title: Mathematical Geology doi: 10.1007/s11004-005-6660-9 – volume: 123 start-page: 90 issue: 2 year: 2014 ident: 10029_CR13 publication-title: Mining Technology doi: 10.1179/1743286314Y.0000000062 – volume: 42 start-page: 309 year: 2010 ident: 10029_CR8 publication-title: Mathematical Geosciences doi: 10.1007/s11004-010-9264-y – volume-title: Data mining year: 2017 ident: 10029_CR43 – volume: 787 start-page: 1 year: 2013 ident: 10029_CR35 publication-title: Analytica Chimica Acta doi: 10.1016/j.aca.2013.04.034 – volume: 26 start-page: 565 year: 1994 ident: 10029_CR3 publication-title: Mathematical Geology doi: 10.1007/BF02089242 – volume: 5 start-page: 115 issue: 4 year: 1943 ident: 10029_CR27 publication-title: Bulletin of Mathematical Biophysics doi: 10.1007/BF02478259 – ident: 10029_CR2 – volume: 127 start-page: 84 issue: 2 year: 2018 ident: 10029_CR38 publication-title: Mining Technology doi: 10.1080/14749009.2017.1363991 – volume: 158 start-page: 6378 year: 2019 ident: 10029_CR41 publication-title: Energy Procedia doi: 10.1016/j.egypro.2019.01.219 – ident: 10029_CR22 – ident: 10029_CR6 – volume-title: Efficient learning machines: Theories, concepts, and applications for engineers and system designers year: 2015 ident: 10029_CR4 doi: 10.1007/978-1-4302-5990-9 – volume: 235 start-page: 217 year: 2017 ident: 10029_CR18 publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.01.016 – volume: 19 start-page: 91 issue: 2 year: 2010 ident: 10029_CR31 publication-title: Natural Resources and Research doi: 10.1007/s11053-010-9115-z – volume: 5 start-page: 197 issue: 2 year: 1990 ident: 10029_CR33 publication-title: Machine Learning – volume: 45 start-page: 184 year: 2014 ident: 10029_CR11 publication-title: Ecological Indicators doi: 10.1016/j.ecolind.2014.04.003 – volume: 31 start-page: 955 year: 1999 ident: 10029_CR20 publication-title: Mathmatical Geology doi: 10.1023/A:1007553013388 – volume: 36 start-page: 773 issue: 4 year: 2019 ident: 10029_CR39 publication-title: Mining, Metallurgy and Exploration doi: 10.1007/s42461-019-0072-8 – ident: 10029_CR10 – ident: 10029_CR12 – volume: 29 start-page: 3035 year: 2020 ident: 10029_CR24 publication-title: Natural Resources Research doi: 10.1007/s11053-020-09628-0 – volume: 12 start-page: 2825 issue: 85 year: 2011 ident: 10029_CR29 publication-title: Journal of ML – volume-title: Data mining – practical machine learning tools and techniques year: 2011 ident: 10029_CR42 – ident: 10029_CR17 – volume: 19 start-page: 845 issue: 9 year: 2004 ident: 10029_CR21 publication-title: Environmental Modeling and Software doi: 10.1016/j.envsoft.2003.03.004 – volume-title: Statistical learning theory year: 1998 ident: 10029_CR37 – ident: 10029_CR23 – year: 2021 ident: 10029_CR32 publication-title: Mathematical Geosciences doi: 10.1016/j.ins.2017.10.049 – volume-title: The nature of statistical learning theory year: 1995 ident: 10029_CR36 doi: 10.1007/978-1-4757-2440-0 – volume: 22 start-page: 1371 year: 2018 ident: 10029_CR19 publication-title: Computational Geosciences doi: 10.1007/s10596-018-9758-0 – volume: 5 start-page: 241 issue: 2 year: 1992 ident: 10029_CR44 publication-title: Neural Networks doi: 10.1016/S0893-6080(05)80023-1 – volume: 69 start-page: 1 year: 2009 ident: 10029_CR5 publication-title: Journal of Petroleum Science and Engineering doi: 10.1016/j.petrol.2009.08.001 – ident: 10029_CR45 doi: 10.2118/24742-MS – volume: 90 start-page: 190 year: 2019 ident: 10029_CR28 publication-title: Journal of Coastal Research doi: 10.2112/SI90-023.1 – volume: 170 start-page: 9 issue: 1 year: 2017 ident: 10029_CR15 publication-title: International Journal of Computer Applications doi: 10.5120/ijca2017914643 – volume: 6 start-page: e5518 year: 2018 ident: 10029_CR16 publication-title: PeerJ doi: 10.7717/peerj.5518
SSID	ssj0007385
Score	2.2967415
Snippet	The main goal of grade control is the prediction of material destination based on all available data. The common approach to grade control is based on...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	785
SubjectTerms	Algorithms Case studies Chemistry and Earth Sciences Comparative studies Computer Science Copper Decision making Earth and Environmental Science Earth Sciences Fossil Fuels (incl. Carbon Capture) Geography Geostatistics Learning algorithms Machine learning Mathematical Modeling and Industrial Mathematics Mineral Resources Model accuracy Neural networks Original Paper Physics Quality Radial basis function Statistics for Engineering Sustainable Development Workflow
SummonAdditionalLinks	– databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1NSwMxEB3UKujBb7FaJQdvurifbeJFaql6sYgo9LZkk6wIuq3bKvjvnUnTrgp68brZDLu8STLJvLwBOEp0GJlQBei8XOEGJUg8wYkBIEUmSCrQN9wWm2j1erzfF7fuwG3kaJXTOdFO1Hqg6Iz8NBTofCQv5p8PXz2qGkXZVVdCYx5qpFSGfl676PZu72ZzMWm1WMVU3CRR6O2uzUwuz2FoQTnM0AssCYR_X5qqePNHitSuPJdr__3mdVh1MSdrT5xkA-ZMsQkrX5QIN2Hpylb4_diC4qqU2rDOhMLO6JyWdYuRecmejWY3lntpmJNlfTxjbdapBMRZBxdFRtzEDybHDINLfFK-mIKhyTbu_sntBthjODQlu0FL2_Bw2b3vXHuuKIOncLSOPYwQmplIWr6K82acayGUxCBI89hIzlWi8kwLlSf46yoMuRQiS3QzzFtBImUQ6WgHFopBYXaBKcEVj3M_x6ZYZT7XJJ4TJ5HUkZ9FUR2CKR6pcorlVDjjOa20lgnDFDFMLYYpr8PxrM9wotfx59uNKXCpG7ujtEKtDidT6Kvm363t_W1tH5ZD8jZL-2nAwrh8MwewqN7HT6Py0HnuJ1Ig8L4 priority: 102 providerName: ProQuest
Title	Grade Control with Ensembled Machine Learning: A Comparative Case Study at the Carmen de Andacollo Copper Mine
URI	https://link.springer.com/article/10.1007/s11053-022-10029-8 https://www.proquest.com/docview/2918336760
Volume	31
WOSCitedRecordID	wos000763818700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 1573-8981 dateEnd: 20241211 omitProxy: false ssIdentifier: ssj0007385 issn: 1520-7439 databaseCode: PCBAR dateStart: 19970301 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: Environmental Science Database customDbUrl: eissn: 1573-8981 dateEnd: 20241211 omitProxy: false ssIdentifier: ssj0007385 issn: 1520-7439 databaseCode: PATMY dateStart: 19970301 isFulltext: true titleUrlDefault: http://search.proquest.com/environmentalscience providerName: ProQuest – providerCode: PRVPQU databaseName: Materials Science Database customDbUrl: eissn: 1573-8981 dateEnd: 20241211 omitProxy: false ssIdentifier: ssj0007385 issn: 1520-7439 databaseCode: KB. dateStart: 19970301 isFulltext: true titleUrlDefault: http://search.proquest.com/materialsscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1573-8981 dateEnd: 20241211 omitProxy: false ssIdentifier: ssj0007385 issn: 1520-7439 databaseCode: BENPR dateStart: 19970301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1573-8981 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0007385 issn: 1520-7439 databaseCode: RSV dateStart: 19990301 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bS8MwFD54RX3wMhXnZeTBN630uiW-bWMqiEO84VtJk1SFrY51Cvv3nmSpVVFB30qbHEJykvOl-fIdgP1I-oHyhYfOSwVuULzIYVQzADhLmJYKdBU1ySYa3S69v2eX9lJYXrDdiyNJs1KXl90QCugzR9_xDGmDTsMshjuqEzZcXd-9r79an8WopOLGSMNte1Xmexufw1GJMb8ci5poc7Lyv3auwrJFl6Q5cYc1mFJZBVaKzA3ETuQKLH2QIazAgs2E_jiuwPypSfWrnww5VOTrkJ0OuVSkPWG1E_3rlnSyXPWTnpLkwtAxFbFKrQ_HpEnapaY4aWOcJJquOCZ8RBBv4pthX2UETTYzqWWze89YYzDAFl6gpQ24PenctM8cm6fBETiBRw6ChnrCooYrwrQeppIxwREXSRoqTqmIRJpIJtKIeVT4PuWMJZGs-2nDizj3Ahlswkz2nKktIIJRQcPUTfFTKBKXSq2nE0YBl4GbBEEVvGK4YmFFzHUujV5cyi_r7o-x-2PT_TGtwsF7ncFEwuPX0ruFF8R2Ouexj00PtLadW4XDYtTLzz9b2_5b8R1Y9LXjGGbQLsyMhi9qD-bE6-gpH9ZgttXpXl7VYPq8dVQzTv8G59z0xA
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3fb9MwED6VDjR42MYAUTaGH-CJBRInae1JCJXup7ZWPGzS3jLHdiakLi1pAfWf4m_kzkmWgbS97YHXOL4o9ue7s-_8HcDb2PDQch0geIXGDUoQe1JQBoCSqSSqQN8KV2yiNxqJ83P5tQW_67swlFZZ60SnqM1E0xn5Ry4RfEQv5n-efveoahRFV-sSGiUsju3iF27ZZp-OdnF-33G-v3c6OPSqqgKeRrjNPTRx3VTGPV9HWTfKjJRaoRU3IrJKCB3rLDVSZzF-T3MulJRpbLo86wWxUkFoQpT7AJaiEH1TCgJ_-XCt-YkZxvGz4paMHP3qkk55VQ8dGYqYci9wKSfib0PYeLf_BGSdndtf_d9GaA1WKo-a9csl8BRaNl-HJzd4Ftfh0YGrX7x4BvlBoYxlgzJBn9EpNNvLZ_YqHVvDhi6z1LKKdPZyh_XZoKFHZwM0-YwyLxdMzRm6zvikuLI5Q5H93BAD-HiCPaZTW7AhSnoOZ_fy6y-gnU9y-xKYlkKLKPMzbIp06gtD1EBRHCoT-mkYdiCo5z_RFR87lQUZJw2TNGEmQcwkDjOJ6MD76z7Tko3kzrc3a6AklWaaJQ1KOrBdQ61pvl3aq7ulvYHlw9PhSXJyNDregMeckO4SnDahPS9-2NfwUP-cf5sVW27NMLi4bwj-AaNUTM4
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3fT9swED5BGdt4GFA2raPb_MAbRORna--tKm1BjKrSAPEWObYDk0qo2mxS__vduUnTIZg08RbF9slyzvFn-7vvAA4i7QfGVx46L1e4QfEiR3BiAEiRCJIKdA23ySbawyG_uRGjlSh-y3YvryQXMQ2k0pTlxxOdHleBbwgL6P7RdzxL4ODrsBESkZ726z-ul_9i0mqxiqm4SSLoXYTNPG3j76WpwpuPrkjtytPffnmfd-BdgTpZZ-Emu7BmsjpslxkdWDHB67C1Ik9YhzdFhvS7eR02BzYFMD1Z0qia7UE2mEptWHfBdmd0pMt62czcJ2Oj2YWlaRpWKLjefmMd1q20xlkX109GNMY5kzlDHIpvpvcmY2iyk2mS0x4_YIvJBHt4gZbew1W_d9k9dYr8DY7CiZ07CCZaiYjargrTVphqIZREvKR5aCTnKlJpooVKI-Fx5ftcCpFEuuWnbS-S0gt08AFq2UNmPgJTgisepm6KRaFKXK5JZyeMAqkDNwmCBnjlp4tVIW5OOTbGcSXLTMMf4_DHdvhj3oDDZZvJQtrjn7WbpUfExTSfxT52PSDNO7cBR6UHVMXPW_v0f9W_wuvRST_-fjY834e3PvmQJQ81oZZPf5nP8Er9zn_Opl-s9_8B9R_-Fg
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Grade+Control+with+Ensembled+Machine+Learning%3A+A+Comparative+Case+Study+at+the+Carmen+de+Andacollo+Copper+Mine&rft.jtitle=Natural+resources+research+%28New+York%2C+N.Y.%29&rft.au=da+Silva%2C+Camilla+Zacche&rft.au=Nisenson%2C+Jed&rft.au=Boisvert%2C+Jeff&rft.date=2022-04-01&rft.pub=Springer+US&rft.issn=1520-7439&rft.eissn=1573-8981&rft.volume=31&rft.issue=2&rft.spage=785&rft.epage=800&rft_id=info:doi/10.1007%2Fs11053-022-10029-8&rft.externalDocID=10_1007_s11053_022_10029_8
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1520-7439&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1520-7439&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1520-7439&client=summon