{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":91,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":91,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"6ef5a36dc04c","filters":{"venue":"Mathematical Geosciences"}},"results":[{"id":"W2043824713","doi":"10.1007/s11004-014-9540-3","title":"Three-Dimensional Modelling of Geological Surfaces Using Generalized Interpolation with Radial Basis Functions","year":2014,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":191,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Mira Geoscience (Canada); Geological Survey of Canada","funders":"Division of Materials Research","keywords":"Interpolation (computer graphics); Covariance; Kriging; Parametric statistics; Applied mathematics; Radial basis function; Surface (topology); Hermite interpolation; Isotropy; Mathematical optimization; Mathematics; Computer science; Algorithm; Geometry; Mathematical analysis; Hermite polynomials","retraction":null,"screen_n_in":null,"score":{"opus":0.05718289125646276,"gpt":0.2277507716716309,"spread":0.1705678804151681,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007023236,0.0001327563,0.0003001933,0.00009153219,0.0002432174,0.00004748962,0.0001993838,0.00006896413,0.002006751],"category_scores_gemma":[0.0001174262,0.00007394916,0.00008455876,0.0003745325,0.0003675568,0.0001402245,0.00001761092,0.00009332685,0.00005854288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001926202,"about_ca_system_score_gemma":0.00002643318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001691189,"about_ca_topic_score_gemma":0.0006021619,"domain_scores_codex":[0.9985679,0.00008064631,0.0003222043,0.0003092521,0.0004494579,0.0002705112],"domain_scores_gemma":[0.9992566,0.0002908964,0.0001217809,0.0001405961,0.000073864,0.0001162492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002212948,0.00003150604,0.05726548,0.000009811539,0.00001089273,5.758515e-7,0.00003289908,0.9405324,0.00003891738,0.0006378107,0.00000644543,0.001411105],"study_design_scores_gemma":[0.0001157241,0.0001680706,0.005354705,0.00002651012,0.00004248299,0.000008271,0.00004442976,0.9737946,0.00001795342,0.02029312,0.00001829788,0.0001157719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5585541,0.00003232555,0.4407499,0.0001001404,0.00004362999,0.00003914714,0.000005859206,0.00001977278,0.0004550512],"genre_scores_gemma":[0.8844647,0.000001538122,0.1153857,0.00004357562,0.00004417606,6.267638e-7,0.0000106091,0.000001525071,0.00004750524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3259106,"threshold_uncertainty_score":0.9989055,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1993304724","doi":"10.1007/s11004-009-9258-9","title":"High-order Statistics of Spatial Random Fields: Exploring Spatial Cumulants for Modeling Complex Non-Gaussian and Non-linear Phenomena","year":2009,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":144,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Cumulant; Geostatistics; Spatial analysis; Gaussian; Spatial dependence; Random field; Statistical physics; Spatial variability; Spatial ecology; Spatial correlation; Mathematics; Field (mathematics); Statistics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.05169258426671069,"gpt":0.278309367307605,"spread":0.2266167830408943,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004074116,0.0001690856,0.0003501764,0.00004378771,0.000213477,0.00005239257,0.0002268111,0.000044821,0.0002423803],"category_scores_gemma":[0.0002522792,0.0001351866,0.00003401349,0.0001316805,0.0002406025,0.0001489295,0.0001052481,0.00007539492,0.0000169395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001779017,"about_ca_system_score_gemma":0.00001653603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00128273,"about_ca_topic_score_gemma":0.0001396944,"domain_scores_codex":[0.9984448,0.00001716476,0.0004329768,0.0003372915,0.0003967152,0.0003710743],"domain_scores_gemma":[0.9993156,0.000234486,0.0001172439,0.0001683412,0.00002740744,0.0001368793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006954946,0.002116211,0.003802522,0.001249519,0.0001202549,0.00004364653,0.0221705,0.2214767,0.01881729,0.04472211,0.001033207,0.6837525],"study_design_scores_gemma":[0.0008481481,0.0002319636,0.003219812,0.00004589487,0.00002152252,0.000001846788,0.0002508677,0.9458076,0.0001839749,0.04916199,0.00004500354,0.0001814042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1915905,0.000004061068,0.8071244,0.0001207148,0.0001117225,0.0003155032,0.00005539483,0.00001315123,0.0006644693],"genre_scores_gemma":[0.8030478,0.00001376945,0.1967009,0.00008407777,0.00006724794,0.0000227721,0.000009206769,0.000006805232,0.00004744928],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7243308,"threshold_uncertainty_score":0.551275,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1998398443","doi":"10.1007/s11004-008-9178-0","title":"Block Simulation of Multiple Correlated Variables","year":2008,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":108,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Block (permutation group theory); Computer science; Multivariate statistics; Algorithm; Multivariate normal distribution; Block size; Kriging; Gaussian; Multivariate random variable; Mathematical optimization; Statistics; Random variable; Mathematics; Machine learning; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.02392817042549622,"gpt":0.241100842954073,"spread":0.2171726725285768,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002290145,0.00007448945,0.000129843,0.00002383435,0.0001511513,0.00000947383,0.0001730341,0.00003596859,0.001019645],"category_scores_gemma":[0.0005889639,0.00005735001,0.0000281738,0.0003066106,0.0004729036,0.00009700355,0.00009588643,0.00004469322,0.0002512843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001487411,"about_ca_system_score_gemma":0.00001005685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001358713,"about_ca_topic_score_gemma":0.000007233202,"domain_scores_codex":[0.9990354,0.00001952921,0.0002353197,0.0001732336,0.0003562011,0.0001803305],"domain_scores_gemma":[0.9993239,0.0003813637,0.00008436149,0.0001318562,0.00001221217,0.00006630919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004036346,0.001350485,0.2267878,0.0001484098,0.00003313068,0.0000486821,0.008766482,0.7103623,0.02040753,0.01864471,0.00259339,0.01081671],"study_design_scores_gemma":[0.0001562213,0.00005042702,0.02972878,0.00002001661,0.000007608546,0.00001431052,0.00009889631,0.9517972,0.0004981975,0.0165631,0.0009484703,0.0001167328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9043096,0.000008814633,0.07266135,0.00003390921,0.00009873872,0.0001334234,0.000005478164,0.00003579331,0.02271291],"genre_scores_gemma":[0.9866028,0.000003646496,0.01257642,0.00003005764,0.000007287548,0.000003813876,0.000001004298,0.000003652011,0.0007712619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.241435,"threshold_uncertainty_score":0.9998935,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1967069051","doi":"10.1007/s11004-013-9497-7","title":"Projection Pursuit Multivariate Transform","year":2013,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":103,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Projection pursuit; Multivariate statistics; Gaussian; Projection (relational algebra); Multivariate normal distribution; Context (archaeology); Geostatistics; Computer science; Mathematics; Algorithm; Artificial intelligence; Data mining; Statistics; Geography; Spatial variability","retraction":null,"screen_n_in":null,"score":{"opus":0.01704581197286067,"gpt":0.247180140883342,"spread":0.2301343289104813,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003052126,0.00009624547,0.000103813,0.00002422167,0.0001763269,0.0001346533,0.0002459346,0.00003469974,0.008577373],"category_scores_gemma":[0.0001245545,0.00006716498,0.00003322527,0.0002608333,0.0002716661,0.000302751,0.00007458845,0.00006418482,0.004386239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003162768,"about_ca_system_score_gemma":0.000006989721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001280147,"about_ca_topic_score_gemma":0.00003908641,"domain_scores_codex":[0.9988533,0.00001682299,0.0001832837,0.0002386427,0.0004054167,0.0003025912],"domain_scores_gemma":[0.9996535,0.00006999284,0.00003910731,0.0001227693,0.000008279003,0.0001063351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000008950598,0.0007940606,0.006227525,0.0001286464,0.00002110578,0.000009946684,0.006066804,0.0001356006,0.02940739,0.07217589,0.02057947,0.8644446],"study_design_scores_gemma":[0.0004241213,0.0002283374,0.1120482,0.00005045924,0.00002256254,0.00003514909,0.001354268,0.3316443,0.00115384,0.5290839,0.02335638,0.0005985484],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3280351,0.000009409669,0.2919751,0.002365677,0.0003910255,0.001170699,0.000005469978,0.0001925337,0.3758551],"genre_scores_gemma":[0.9350221,0.000003524206,0.0580069,0.000253373,0.00003132676,0.0001475841,0.000001256461,0.000008814448,0.006525132],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8638461,"threshold_uncertainty_score":0.996389,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2592389690","doi":"10.1007/s11004-017-9680-3","title":"Simultaneous Stochastic Optimization of Mining Complexes and Mineral Value Chains","year":2017,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":102,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Newmont Corporation; Natural Sciences and Engineering Research Council of Canada; AngloGold Ashanti; Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Barrick Gold Corporation","keywords":"Solver; Computer science; Mathematical optimization; Stochastic programming; Set (abstract data type); Stochastic optimization; Metaheuristic; Production (economics); Linear programming; Robust optimization; Data mining; Operations research; Algorithm; Engineering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02236956444523586,"gpt":0.2508774718105537,"spread":0.2285079073653178,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001507457,0.00006877389,0.0001458687,0.00003105083,0.0001210067,0.00007855277,0.00018896,0.00002997308,0.00003078732],"category_scores_gemma":[0.0002567561,0.00005881807,0.00001640241,0.00001956403,0.00021058,0.00009408729,0.00005364234,0.00002877031,0.0000016054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006421255,"about_ca_system_score_gemma":0.000004856407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000837025,"about_ca_topic_score_gemma":0.00000326457,"domain_scores_codex":[0.9995602,0.000003326802,0.0001549908,0.00009284425,0.00006482625,0.0001238105],"domain_scores_gemma":[0.9996186,0.0001149442,0.00005169291,0.0001610951,0.00001103297,0.00004264059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002382388,0.00002486821,0.000202564,0.0001658012,0.000008087488,0.000002228069,0.001448011,0.96903,0.00085577,0.02611381,0.00005504248,0.002091444],"study_design_scores_gemma":[0.00004532333,0.00002971278,0.0001209691,0.00004706706,0.000004985005,0.000006544596,0.0001109428,0.9971632,0.0001178701,0.002275023,0.000007361006,0.00007100822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6465244,0.00001695174,0.3512857,0.00002775588,0.00005395561,0.00005821045,0.000004175861,0.00006024113,0.001968635],"genre_scores_gemma":[0.88824,0.000005481904,0.1116475,0.000005136794,0.00001573977,0.000003326934,4.444364e-7,0.000005796493,0.00007654773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2417157,"threshold_uncertainty_score":0.2398531,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2017603354","doi":"10.1007/s11004-009-9229-1","title":"Kriging in the Presence of Locally Varying Anisotropy Using Non-Euclidean Distances","year":2009,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Kriging; Variogram; Anisotropy; Covariance; Curvilinear coordinates; Geostatistics; Hydrogeology; Range (aeronautics); Variance (accounting); Covariance function; Geology; Mathematics; Applied mathematics; Spatial variability; Statistics; Geometry; Accounting; Geotechnical engineering; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.02452555596228349,"gpt":0.2715489892885118,"spread":0.2470234333262284,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001187137,0.0001196535,0.0002004035,0.00005508724,0.000172647,0.0001488092,0.001728704,0.00003769397,0.00001933311],"category_scores_gemma":[0.0003980423,0.00007361965,0.00005087634,0.0008335394,0.0002560717,0.0004000914,0.0001263194,0.0001229287,0.000004293232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009056957,"about_ca_system_score_gemma":0.00005514586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002074337,"about_ca_topic_score_gemma":0.000002113314,"domain_scores_codex":[0.9984207,0.00006361876,0.0003496494,0.0003217258,0.0004873817,0.0003568995],"domain_scores_gemma":[0.9990451,0.0003214071,0.000134306,0.0004022004,0.0000497995,0.00004720196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001309275,0.0006746394,0.003012691,0.0003229825,0.0000078984,0.0001203885,0.01600796,0.007179332,0.0412558,0.9140247,0.0001368385,0.01724371],"study_design_scores_gemma":[0.0001107125,0.00008100065,0.001340408,0.0002037547,0.000003531015,0.00003944938,0.0008199255,0.5799498,0.003627823,0.413497,0.0001691648,0.000157453],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.148932,0.00005809614,0.8207178,0.002513952,0.00007802356,0.0001726285,4.974415e-7,0.00003004343,0.02749692],"genre_scores_gemma":[0.9372768,0.000003398535,0.06244272,0.0001724316,0.00001822644,0.000002843385,1.159573e-7,6.87704e-7,0.00008273069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7883448,"threshold_uncertainty_score":0.3212391,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2101655885","doi":"10.1007/s11004-010-9291-8","title":"High-order Stochastic Simulation of Complex Spatially Distributed Natural Phenomena","year":2010,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":85,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cumulant; Legendre polynomials; Spatial analysis; Point process; Kriging; Gaussian; Mathematics; Geostatistics; Stochastic process; Statistical physics; Spatial dependence; Algorithm; Applied mathematics; Computer science; Spatial variability; Statistics; Mathematical analysis; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01535577090465526,"gpt":0.2545418955841851,"spread":0.2391861246795298,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002917874,0.0001135364,0.0001794894,0.00002769417,0.0001351362,0.00004173475,0.0002882405,0.00003655838,0.00274452],"category_scores_gemma":[0.0006444126,0.00008592471,0.00003001459,0.0003498332,0.0006192328,0.0001210951,0.0001544018,0.0001193468,0.0001570336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001554214,"about_ca_system_score_gemma":0.00001337027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002091923,"about_ca_topic_score_gemma":0.0001271069,"domain_scores_codex":[0.9987351,0.00001769014,0.000287931,0.0002329441,0.000471428,0.0002548946],"domain_scores_gemma":[0.9992683,0.0002973191,0.0001251847,0.000189091,0.00002927268,0.00009084167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004324953,0.001177428,0.003062069,0.0001765952,0.000039584,0.000009288955,0.003012124,0.6007508,0.1564562,0.1690291,0.0005994873,0.06564413],"study_design_scores_gemma":[0.0001729256,0.00005140278,0.0325796,0.00001078486,0.00001243246,0.000002213417,0.00008073122,0.9111261,0.0001361356,0.05534228,0.0003245589,0.0001608495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.540457,0.00000205337,0.4570892,0.0001416995,0.0002907498,0.0001787564,0.00003699508,0.00003196443,0.001771664],"genre_scores_gemma":[0.9726874,1.801229e-7,0.02707457,0.00004472123,0.00003313581,0.000006153784,0.00001569315,0.00000517046,0.0001329807],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4322305,"threshold_uncertainty_score":0.9981671,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2081487307","doi":"10.1007/s11004-010-9264-y","title":"Ore Grade Prediction Using a Genetic Algorithm and Clustering Based Ensemble Neural Network Model","year":2010,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta; McGill University","funders":"","keywords":"Artificial neural network; Resampling; Cluster analysis; Ensemble learning; Computer science; Ensemble forecasting; Kriging; Genetic algorithm; Artificial intelligence; Data mining; Machine learning; Algorithm; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.02446558296711545,"gpt":0.2421630346406421,"spread":0.2176974516735267,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002055959,0.0001225126,0.0001321373,0.00004917822,0.0001976703,0.0001381416,0.0001109729,0.00006408486,0.00001404544],"category_scores_gemma":[0.00002829977,0.00009967809,0.00002401261,0.0001823941,0.0001091274,0.0001383532,0.00003404055,0.0001751236,0.000002291614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008394994,"about_ca_system_score_gemma":0.00001309132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000564598,"about_ca_topic_score_gemma":0.000006820688,"domain_scores_codex":[0.9991397,0.000008044636,0.0001813319,0.000178697,0.0001859941,0.000306285],"domain_scores_gemma":[0.9997112,0.00004208134,0.00002301807,0.0001047026,0.0000131139,0.0001058867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[5.357047e-7,0.000009590864,0.0001336937,0.0001016956,0.000002048395,0.000002095703,0.000156507,0.9852594,0.00656568,0.0000942702,0.00002195364,0.007652515],"study_design_scores_gemma":[0.00007726089,0.00001275436,0.0002121677,0.00004881155,0.00001115879,0.00003870023,0.00002252348,0.993268,0.0001604102,0.006022242,0.00001201417,0.0001140134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4813288,0.00003786546,0.5180008,0.00001683313,0.0001910584,0.00004914664,0.000001920103,0.000112911,0.0002606688],"genre_scores_gemma":[0.688949,0.000001556658,0.3108568,0.0000204625,0.0001164575,0.000005254841,5.159665e-7,0.00001103629,0.00003894372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2076202,"threshold_uncertainty_score":0.4064755,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3175039957","doi":"10.1007/s11004-021-09945-x","title":"Three-Dimensional Structural Geological Modeling Using Graph Neural Networks","year":2021,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":80,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Geological Survey of Canada","funders":"Australian Research Council; Natural Resources Canada; RWTH Aachen University","keywords":"Computer science; Interpolation (computer graphics); Polygon mesh; Graph; Artificial neural network; Theoretical computer science; Architecture; Process (computing); Artificial intelligence; Machine learning; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.05065873067761802,"gpt":0.2477139643593166,"spread":0.1970552336816986,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004525433,0.0001989942,0.0003424754,0.0000633634,0.0005204686,0.0001667652,0.000333247,0.0001194093,0.003994626],"category_scores_gemma":[0.0002472792,0.0001240228,0.0001858781,0.0006396426,0.00028412,0.0001821444,0.00006538544,0.0002456473,0.00006619948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002886098,"about_ca_system_score_gemma":0.00003836467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000637962,"about_ca_topic_score_gemma":0.000764931,"domain_scores_codex":[0.9978571,0.00008395901,0.0003878707,0.0005437198,0.0005630523,0.0005643094],"domain_scores_gemma":[0.9991954,0.0001844087,0.00006597665,0.0002138213,0.0001027082,0.0002376634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004216871,0.00001340947,0.02565426,0.000005375049,0.00000834727,0.00003924477,0.00001091149,0.9712055,0.000007166467,0.0006276013,0.000004784655,0.002419243],"study_design_scores_gemma":[0.0000674978,0.00003426865,0.004796754,0.00001272227,0.00002834524,0.000104467,0.00005570355,0.8985023,0.000002511028,0.09621496,0.000002329195,0.0001781018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7838334,0.0005378069,0.2146195,0.0003129318,0.0002228075,0.00004477722,0.000006580988,0.00005812319,0.0003641082],"genre_scores_gemma":[0.9715273,0.000003505438,0.0279609,0.0003159674,0.0001272426,5.521293e-7,0.00002279033,0.00000221446,0.0000395756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1876939,"threshold_uncertainty_score":0.9969159,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2046978636","doi":"10.1007/s11004-011-9318-9","title":"Application of Continuous Wavelet Transform in Examining Soil Spatial Variation: A Review","year":2011,"lang":"en","type":"review","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"University of Saskatchewan","keywords":"Wavelet; Wavelet transform; Scale (ratio); Spatial variability; Continuous wavelet transform; Precision agriculture; Discrete wavelet transform; Mathematics; Spatial analysis; Computer science; Pattern recognition (psychology); Statistics; Artificial intelligence; Geography; Cartography; Agriculture","retraction":null,"screen_n_in":null,"score":{"opus":0.0432726868559676,"gpt":0.2895667633395477,"spread":0.2462940764835801,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001339433,0.0002614921,0.00124601,0.00007641469,0.00005490214,0.00002029064,0.0005398625,0.0001286097,0.001058386],"category_scores_gemma":[0.0002864156,0.000191932,0.0001302663,0.0006459558,0.0003129457,0.0001244973,0.0001264951,0.0001735148,0.0002712265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006512447,"about_ca_system_score_gemma":0.00005610392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007726538,"about_ca_topic_score_gemma":0.0001287643,"domain_scores_codex":[0.9976693,0.00009335967,0.00103813,0.0004615259,0.0004259791,0.0003116657],"domain_scores_gemma":[0.9988282,0.0002657462,0.0004869213,0.0003218108,0.00001261183,0.00008465484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[4.325421e-7,0.00007378787,0.000007553849,0.01174608,0.000004770063,0.000001730238,0.0002325156,4.300444e-7,5.814787e-7,0.003051265,0.00003283625,0.984848],"study_design_scores_gemma":[0.0002229069,0.0001976744,0.0005185425,0.04673872,0.0005417819,0.0000583019,0.00007381761,0.003440831,0.000001792315,0.03495618,0.9123674,0.0008820459],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00000668376,0.8722171,0.09780627,0.000026481,0.0001039278,0.001466471,0.0000288221,0.00002562964,0.02831863],"genre_scores_gemma":[0.0001728869,0.9940235,0.005266582,0.00004358971,0.00002182474,0.0002646373,0.00001375075,0.00001508709,0.000178164],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.983966,"threshold_uncertainty_score":0.9998548,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2724626982","doi":"10.1007/s11004-017-9691-0","title":"Least-Squares Wavelet Analysis of Unequally Spaced and Non-stationary Time Series and Its Applications","year":2017,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Carbon Management Canada","keywords":"Mathematics; Wavelet; Cross-spectrum; Series (stratigraphy); Wavelet transform; Spectrogram; Covariance; Time series; Fourier transform; Algorithm; Statistics; Frequency domain; Mathematical analysis; Computer science; Speech recognition; Artificial intelligence; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.02093225427378673,"gpt":0.2987638343435692,"spread":0.2778315800697825,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007278154,0.00009244983,0.0002593889,0.0001530911,0.0004518528,0.0003802104,0.0005984246,0.00003194036,0.00002223915],"category_scores_gemma":[0.0003190208,0.00007019092,0.00004175905,0.0003467158,0.0003926051,0.000711985,0.0002724218,0.00004333474,0.00001191106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003447524,"about_ca_system_score_gemma":0.00003403467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001316902,"about_ca_topic_score_gemma":0.000003921198,"domain_scores_codex":[0.9990125,0.00004834132,0.0002088285,0.0002887412,0.0002862055,0.0001554065],"domain_scores_gemma":[0.9989559,0.0003162624,0.0001607357,0.0003880224,0.00009688668,0.00008217207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003283976,0.0003277067,0.001663467,0.0004579069,0.0003151694,0.00002213212,0.00732934,0.00009372665,0.05139568,0.8085769,0.0001210474,0.129664],"study_design_scores_gemma":[0.0003102105,0.0002265437,0.06023385,0.00006381808,0.000226944,0.00002085546,0.000235321,0.7540142,0.0066992,0.1773635,0.0002496573,0.0003558498],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1086892,0.0001053947,0.887194,0.001125714,0.00002142674,0.0001566137,0.000009961097,0.00002594934,0.002671737],"genre_scores_gemma":[0.7280561,0.00002368223,0.2702182,0.00005249386,0.00001343463,0.00002114847,0.000001286483,0.000003352474,0.001610329],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7539205,"threshold_uncertainty_score":0.3666378,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2043565541","doi":"10.1007/s11004-008-9209-x","title":"Hierarchical Annealing for Synthesis of Binary Images","year":2009,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Simulated annealing; Binary number; Computer science; Annealing (glass); Algorithm; Theoretical computer science; Mathematics; Materials science","retraction":null,"screen_n_in":null,"score":{"opus":0.02112335712582647,"gpt":0.3077979949068939,"spread":0.2866746377810674,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005197389,0.00009304682,0.0002073385,0.0001000525,0.0001398354,0.00008257926,0.000795185,0.00002378162,0.00001461395],"category_scores_gemma":[0.0008257024,0.00006612784,0.00008213752,0.0003564774,0.0001893942,0.0004477206,0.00008948091,0.0000547149,0.000009820616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000645002,"about_ca_system_score_gemma":0.00003045054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.419263e-7,"about_ca_topic_score_gemma":3.345265e-8,"domain_scores_codex":[0.9988512,0.00002601051,0.0002715825,0.0002824382,0.0002961783,0.0002725956],"domain_scores_gemma":[0.9988821,0.0006159719,0.00007890522,0.000274999,0.00005615179,0.00009183602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001180211,0.0002612203,0.00002727803,0.00008454343,0.000003701776,0.000005115632,0.0005008336,0.0000490552,0.02502816,0.4949468,0.0003945295,0.4786869],"study_design_scores_gemma":[0.000108951,0.000264048,0.0006743516,0.0001232823,0.000004925289,0.00001212211,0.0001197461,0.4877388,0.02813307,0.4822961,0.0003615441,0.0001631057],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005077706,0.00006525663,0.9880889,0.003236906,0.00007461858,0.0001294613,0.000002672978,0.00008145086,0.003243004],"genre_scores_gemma":[0.4198971,0.00000526946,0.5797523,0.0002126589,0.0000165685,0.000007985238,1.074427e-7,0.000002044052,0.000105878],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4876897,"threshold_uncertainty_score":0.2696615,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2089577646","doi":"10.1007/s11004-012-9402-9","title":"Multivariate Block-Support Simulation of the Yandi Iron Ore Deposit, Western Australia","year":2012,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"AngloGold Ashanti","keywords":"Joint (building); Block (permutation group theory); Multivariate statistics; Scale (ratio); Geology; Iron ore; Geostatistics; Mineral exploration; Algorithm; Computer science; Mineralogy; Statistics; Mathematics; Geochemistry; Spatial variability; Engineering; Metallurgy; Materials science; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.03715899103572258,"gpt":0.2981492179378288,"spread":0.2609902269021063,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004257848,0.0001008594,0.0001254356,0.00001469414,0.0001482836,0.00002977694,0.0002970472,0.00004222021,0.000792601],"category_scores_gemma":[0.0001652605,0.0000599718,0.00004641806,0.0002161201,0.0003554916,0.0001825026,0.0002144727,0.00006490093,0.0003143014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002574989,"about_ca_system_score_gemma":0.000005113737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000289162,"about_ca_topic_score_gemma":0.00003167892,"domain_scores_codex":[0.9987754,0.0000380979,0.0002525371,0.0001570323,0.0004599854,0.0003169728],"domain_scores_gemma":[0.9994287,0.0001306176,0.0001226392,0.0002080875,0.000007250971,0.0001027415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008976087,0.0005188422,0.9622983,0.0001245092,0.00001109245,0.000002004383,0.006304035,0.01292292,0.008761102,0.00461304,0.0003875316,0.00404759],"study_design_scores_gemma":[0.0001637519,0.00005450339,0.9498895,0.00004387586,0.00003687858,0.00001154356,0.0001788945,0.04155121,0.001581549,0.005015987,0.001278215,0.0001940425],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983934,0.000003687025,0.01138919,0.00007873183,0.0002233885,0.0001840887,0.000006083518,0.00001548719,0.004165373],"genre_scores_gemma":[0.9949501,4.751412e-7,0.003144446,0.00005244744,0.00002647635,0.000006054933,6.227251e-7,0.000004577559,0.001814848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02862829,"threshold_uncertainty_score":0.8678426,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2050583385","doi":"10.1007/s11004-012-9428-z","title":"Non-stationary Geostatistical Modeling Based on Distance Weighted Statistics and Distributions","year":2012,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; University of Alberta","funders":"","keywords":"Variogram; Geostatistics; Covariance; Mathematics; Gaussian; Kriging; Spatial analysis; Weighting; Covariance function; Transformation (genetics); Range (aeronautics); Statistics; Gaussian process; Histogram; Spatial variability; Computer science; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01594433308531435,"gpt":0.2537960446753275,"spread":0.2378517115900132,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004656712,0.0001742508,0.0001764206,0.00003709162,0.0004036342,0.00008200942,0.0001671212,0.00004689837,0.001040607],"category_scores_gemma":[0.0003656833,0.0001395012,0.00002316413,0.0002673668,0.0005454718,0.0002021229,0.000106088,0.0001243352,0.0003381804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006287562,"about_ca_system_score_gemma":0.00001907808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005404118,"about_ca_topic_score_gemma":0.000009166716,"domain_scores_codex":[0.9982563,0.00003419134,0.0002940343,0.0003094566,0.0005769569,0.0005290816],"domain_scores_gemma":[0.9989081,0.0004926972,0.00006241641,0.0001915591,0.00001839565,0.0003268388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003041303,0.0008441047,0.02790556,0.000113717,0.000009847171,0.00001676229,0.0008602332,0.003242307,0.0001522723,0.9473841,0.003597489,0.01584321],"study_design_scores_gemma":[0.0001345489,0.00005658715,0.01709764,0.00002832386,0.00001580699,0.000004965254,0.0001046659,0.9078734,0.00001156928,0.07366848,0.0008092891,0.0001947246],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.043146,0.00001399444,0.9492928,0.0002381862,0.0001224929,0.0001755243,0.0003506676,0.00003517464,0.006625143],"genre_scores_gemma":[0.8087745,0.00000517557,0.1907372,0.00019472,0.0000283039,0.00002967851,0.00004453885,0.000008968886,0.0001768798],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9046311,"threshold_uncertainty_score":0.9998726,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1988041076","doi":"10.1007/s11004-013-9496-8","title":"A Comparison of Modified Fuzzy Weights of Evidence, Fuzzy Weights of Evidence, and Logistic Regression for Mapping Mineral Prospectivity","year":2013,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University; Geological Survey of Canada","funders":"","keywords":"Prospectivity mapping; Logistic regression; Statistics; Multinomial logistic regression; Fuzzy logic; Mathematics; Econometrics; Data mining; Computer science; Artificial intelligence; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.214179351842446,"gpt":0.3583846096360564,"spread":0.1442052577936103,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001221981,0.0002178377,0.0007320791,0.0001474129,0.0001580398,0.00007111664,0.0009727083,0.0001170094,0.00002282548],"category_scores_gemma":[0.00235119,0.0001189528,0.0001252661,0.0006285314,0.0008939567,0.0006899572,0.0003900054,0.0001166934,0.000003238327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001410223,"about_ca_system_score_gemma":0.0000829353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007417981,"about_ca_topic_score_gemma":0.000003648125,"domain_scores_codex":[0.9974977,0.00009636992,0.0008467176,0.0005504185,0.0006310642,0.00037772],"domain_scores_gemma":[0.9964034,0.0018393,0.0006725276,0.0005209583,0.0004377859,0.0001260615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008637131,0.001885734,0.1018183,0.01081223,0.00007406052,0.000004638897,0.01122101,0.0002471613,0.1245838,0.7373078,0.0009051583,0.01105365],"study_design_scores_gemma":[0.0002733192,0.000452669,0.02994349,0.003095699,0.00002610783,0.00001298523,0.0003728278,0.2100176,0.02967975,0.7257961,0.00005121894,0.0002782485],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8739784,0.00050508,0.1194633,0.001495127,0.0001474005,0.0009284295,0.000003648907,0.00004778519,0.00343077],"genre_scores_gemma":[0.9189398,0.00001597807,0.08016644,0.000009981995,0.00002397774,0.00006411003,4.082801e-7,0.000002152132,0.0007771139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2097705,"threshold_uncertainty_score":0.4850756,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2134699243","doi":"10.1007/s11004-012-9387-4","title":"Dimensional Reduction of Pattern-Based Simulation Using Wavelet Analysis","year":2012,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"AngloGold Ashanti; Newmont Corporation; Natural Sciences and Engineering Research Council of Canada; McGill University; Barrick Gold Corporation","keywords":"Categorical variable; Wavelet; Pattern recognition (psychology); Computer science; Cluster analysis; Data mining; Dimensionality reduction; Cumulative distribution function; Conditional probability; Artificial intelligence; Statistics; Algorithm; Mathematics; Probability density function","retraction":null,"screen_n_in":null,"score":{"opus":0.03397770779474701,"gpt":0.2885186296004755,"spread":0.2545409218057285,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004755209,0.00007068303,0.0001378902,0.00006830268,0.00009656749,0.00001433962,0.00009134702,0.00002717631,0.001625903],"category_scores_gemma":[0.0001093797,0.00005482647,0.00005894063,0.0006159144,0.0002315501,0.000147687,0.0000518896,0.00003237201,0.00004832367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003170853,"about_ca_system_score_gemma":0.000006828625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001670932,"about_ca_topic_score_gemma":0.000003462821,"domain_scores_codex":[0.9989658,0.00003180415,0.0002177738,0.000141615,0.0004333755,0.000209644],"domain_scores_gemma":[0.9995477,0.0001291781,0.0001079719,0.0001224965,0.00001093571,0.00008171373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007483452,0.000526962,0.1223294,0.00005258571,0.00005276111,8.844345e-7,0.001078551,0.8460158,0.01197018,0.001301729,0.00004972878,0.01661391],"study_design_scores_gemma":[0.00004534892,0.00001404657,0.0532289,0.000008489521,0.00008478906,0.000001087794,0.0000634482,0.944153,0.0006322622,0.001660901,0.00003117473,0.00007653061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7275751,0.00000470216,0.2717244,0.00002465115,0.00006902468,0.000053041,0.000004123165,0.000008644995,0.0005363009],"genre_scores_gemma":[0.9728774,1.40101e-7,0.02702414,0.000029266,0.00001948723,0.000002018043,0.000002409487,0.000002808908,0.00004227465],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2453023,"threshold_uncertainty_score":0.9992868,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2043164914","doi":"10.1007/s11004-012-9427-0","title":"A Spectral Analysis Based Methodology to Detect Climatological Influences on Daily Urban Water Demand","year":2012,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Water resources management and optimization","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa; McGill University","funders":"","keywords":"Precipitation; Wavelet; Environmental science; Spectral analysis; Climatology; Range (aeronautics); Meteorology; Statistics; Geography; Mathematics; Computer science; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.04278556756310417,"gpt":0.2707372644374286,"spread":0.2279516968743244,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008971099,0.0001557902,0.0002916318,0.000321768,0.00009549246,0.00009564848,0.0002752241,0.00006079483,0.0004766936],"category_scores_gemma":[0.0001059852,0.00008998113,0.00009828064,0.0006248558,0.00009699079,0.0001481347,0.0000507105,0.00007831844,0.0003098954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000230488,"about_ca_system_score_gemma":0.00000206917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003789238,"about_ca_topic_score_gemma":0.000003971455,"domain_scores_codex":[0.9986304,0.00009754094,0.0002507876,0.0002011883,0.0002737599,0.0005462895],"domain_scores_gemma":[0.9994454,0.0001792156,0.00001928453,0.0001780343,0.00001152311,0.0001665373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006969391,0.0002108711,0.06680755,0.0002910671,0.0003895265,0.00001591993,0.006651354,0.9074326,0.004186527,0.0111726,0.0008515101,0.001920782],"study_design_scores_gemma":[0.0003442219,0.0003691085,0.03889555,0.00003877491,0.0005804895,0.000005292643,0.000337273,0.917691,0.02968982,0.008098048,0.003138937,0.000811463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.651692,0.00001981029,0.3438932,0.0001146268,0.00008670153,0.0001278601,0.000001128192,0.0001698337,0.003894821],"genre_scores_gemma":[0.934859,0.000001339655,0.06472209,0.0002118211,0.00004662873,0.00002747869,0.00000288179,0.000007522121,0.0001211835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.283167,"threshold_uncertainty_score":0.521946,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2119995372","doi":"10.1007/s11004-008-9170-8","title":"Cross-Wavelet Analysis: a Tool for Detection of Relationships between Paleoclimate Proxy Records","year":2008,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":42,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Wavelet; Paleoclimatology; Geology; Proxy (statistics); Hydrogeology; Paleontology; Statistics; Mathematics; Computer science; Climate change; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.06923456866618928,"gpt":0.3008139345869607,"spread":0.2315793659207714,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002024968,0.0001212568,0.0003911052,0.0002732801,0.0006975097,0.00003035279,0.0003105401,0.000161391,0.0007483914],"category_scores_gemma":[0.001186499,0.00008917284,0.0001653635,0.001078779,0.001026361,0.000222315,0.00001623913,0.0002000839,0.0001532641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001017599,"about_ca_system_score_gemma":0.0000614946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004595258,"about_ca_topic_score_gemma":0.0004099154,"domain_scores_codex":[0.9982348,0.0001866185,0.0004820501,0.0003303888,0.0003402428,0.0004258891],"domain_scores_gemma":[0.9977694,0.001625443,0.0001588645,0.0002244655,0.0001180119,0.0001038632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002295126,0.00002102629,0.9988475,0.00006202434,0.00005319346,0.000002432511,0.0002602971,0.00007313165,0.00000856002,0.0002363731,0.000003157284,0.0004093476],"study_design_scores_gemma":[0.000150315,0.0001776294,0.9646471,0.000008426709,0.00009242232,0.0000264261,0.00008497723,0.0202493,0.0002819874,0.01412496,0.00004408621,0.0001124273],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814298,0.00006197182,0.01547013,0.0001126149,0.00007818545,0.0003375837,0.00006199763,0.00003519559,0.002412581],"genre_scores_gemma":[0.992696,0.0000136646,0.006557712,0.00001064211,0.0000350535,0.00001477207,0.00003493929,0.000001800347,0.0006353612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03420046,"threshold_uncertainty_score":0.8194361,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2016119407","doi":"10.1007/s11004-008-9189-x","title":"Integrated Interpretation of Interwell Connectivity Using Injection and Production Fluctuations","year":2008,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Hydrogeology; Plot (graphics); Fluvial; Injector; Geology; Field (mathematics); Permeability (electromagnetism); Flow (mathematics); Matching (statistics); Soil science; Petrology; Mathematics; Statistics; Geotechnical engineering; Geometry; Physics; Geomorphology","retraction":null,"screen_n_in":null,"score":{"opus":0.03536445054688754,"gpt":0.2845583332994662,"spread":0.2491938827525787,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002939873,0.00007068158,0.0001246006,0.0001204642,0.00006462859,0.0000132391,0.00004937382,0.00003373689,0.00002166104],"category_scores_gemma":[0.0004692079,0.00005910429,0.00002100631,0.0003204232,0.0001314164,0.0002172055,0.00001192484,0.00006763719,0.000001551707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000217299,"about_ca_system_score_gemma":0.00001055119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001196031,"about_ca_topic_score_gemma":0.000001997978,"domain_scores_codex":[0.999461,0.00003138923,0.0001926395,0.0001072704,0.0001233667,0.00008431004],"domain_scores_gemma":[0.9996984,0.000103475,0.00002766171,0.00008358252,0.00005526268,0.00003159315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004451122,0.00002473898,0.0005439759,0.0001156823,0.00000943814,3.454846e-7,0.002308981,0.966208,0.02579294,0.0005490498,0.00001109896,0.004431344],"study_design_scores_gemma":[0.0000474113,0.00002536899,0.00138023,0.00004723979,0.000005576446,0.00003047658,0.0001820275,0.9896355,0.004967287,0.00360513,0.00001163086,0.00006210216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5415932,0.0000201593,0.4578928,0.000006172933,0.0001208691,0.0000499064,6.411538e-7,0.00005672196,0.0002595684],"genre_scores_gemma":[0.943446,0.00001116635,0.05646036,0.000001295193,0.00001553876,0.000004024731,7.756268e-7,0.00000531396,0.00005546332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4018529,"threshold_uncertainty_score":0.2410203,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2743001511","doi":"10.1007/s11004-017-9699-5","title":"Which Path to Choose in Sequential Gaussian Simulation","year":2017,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Path (computing); Covariance; Gaussian; Node (physics); Kriging; Realization (probability); Algorithm; Mathematical optimization; Cluster analysis; Data mining; Mathematics; Statistics; Artificial intelligence; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.03013319731057124,"gpt":0.3080154306561378,"spread":0.2778822333455666,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005572286,0.00009170367,0.0001221748,0.00003427177,0.0003456365,0.000243865,0.0004709066,0.00003502772,0.001119281],"category_scores_gemma":[0.0009919878,0.00007273,0.00001921878,0.000171607,0.0001774302,0.0002538814,0.000307427,0.00006525903,0.0009879805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004400191,"about_ca_system_score_gemma":0.00001159009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007177916,"about_ca_topic_score_gemma":0.0009053082,"domain_scores_codex":[0.9988012,0.00002075998,0.0001958008,0.0002948411,0.0003847101,0.0003026517],"domain_scores_gemma":[0.9993708,0.00006879165,0.00007272622,0.0003367279,0.000007614527,0.0001433913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007390825,0.001368256,0.5276408,0.0002541594,0.00001763268,0.0001376264,0.01783353,0.07815687,0.01071578,0.121766,0.002273737,0.2397617],"study_design_scores_gemma":[0.0001594024,0.00004361127,0.6968457,0.00005567769,0.000003847006,0.000002180331,0.0001324116,0.2374083,0.00008576293,0.06417166,0.0008976503,0.0001937971],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8874575,0.000001428341,0.03100941,0.001112034,0.0002124538,0.00025986,0.00000534525,0.00002520806,0.07991672],"genre_scores_gemma":[0.9899498,8.335941e-7,0.009115232,0.0001090972,0.00002583617,0.00001428019,6.248596e-7,0.000004531546,0.0007797675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2395679,"threshold_uncertainty_score":0.9997938,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3096019909","doi":"10.1007/s11004-020-09898-7","title":"MIN3P-HPC: A High-Performance Unstructured Grid Code for Subsurface Flow and Reactive Transport Simulation","year":2020,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick; University of British Columbia","funders":"Mitacs; Nuclear Waste Management Organization","keywords":"Computer science; Grid; Supercomputer; Computational science; Parallel computing; Discretization; Domain decomposition methods; Unstructured grid; Software; Thread (computing); Porous medium; Benchmark (surveying); Materials science; Porosity; Finite element method; Physics; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.01987162045475805,"gpt":0.2332776167731393,"spread":0.2134059963183813,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001900883,0.0001237372,0.0001814486,0.00001276676,0.0002257122,0.00003513641,0.0001509285,0.0000370632,0.0001706449],"category_scores_gemma":[0.00006635037,0.00008972603,0.00002871196,0.0001860321,0.0003275334,0.0003193268,0.000047134,0.000048767,0.00004330289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002018744,"about_ca_system_score_gemma":0.000005746637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000155656,"about_ca_topic_score_gemma":0.00002500266,"domain_scores_codex":[0.998948,0.00001462127,0.0001999458,0.0003213429,0.0003068884,0.0002091877],"domain_scores_gemma":[0.9996302,0.000122264,0.00005561484,0.00007869514,0.00001478819,0.00009842615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009353632,0.00087495,0.2346771,0.001766197,0.0002201287,0.00002816243,0.1324639,0.1405733,0.07602281,0.008920889,0.002007911,0.4015094],"study_design_scores_gemma":[0.0007920368,0.0003873959,0.1921093,0.00003053035,0.00006117243,0.000004896869,0.001109,0.7860162,0.007025923,0.003705571,0.008332793,0.0004250967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8679966,0.000009043679,0.1303938,0.0009367542,0.0000682216,0.0002760368,0.00003163576,0.00003560013,0.0002523178],"genre_scores_gemma":[0.9894752,0.000003497592,0.009808705,0.0002394245,0.0000304343,0.00002956337,0.000004633956,0.000005664752,0.0004028978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.645443,"threshold_uncertainty_score":0.3658921,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1979086443","doi":"10.1007/s11004-011-9326-9","title":"Validation Techniques for Geological Patterns Simulations Based on Variogram and Multiple-Point Statistics","year":2011,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"McGill University","keywords":"Variogram; Geostatistics; Computer science; Kriging; Statistics; Data mining; Algorithm; Similarity (geometry); Spatial analysis; Point (geometry); Point process; Spatial variability; Mathematics; Artificial intelligence; Image (mathematics); Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.04332281542355463,"gpt":0.2731388558928451,"spread":0.2298160404692905,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003672497,0.0001126385,0.0001266937,0.00004158137,0.0002025473,0.00005324926,0.0001410294,0.00004903957,0.001108357],"category_scores_gemma":[0.0006870264,0.00008332445,0.00002385969,0.0001140507,0.0003045946,0.00008408458,0.00007285698,0.00005417874,0.00003313106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001911518,"about_ca_system_score_gemma":0.000006357657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001266751,"about_ca_topic_score_gemma":0.00002570023,"domain_scores_codex":[0.9989993,0.00002981645,0.0002127323,0.000284825,0.0002408899,0.0002324384],"domain_scores_gemma":[0.9989748,0.0007011917,0.00007014914,0.0001420927,0.00001582744,0.00009594675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001661125,0.003545796,0.5449461,0.000447737,0.00003349317,0.00003060526,0.005370098,0.005014373,0.003052021,0.2717389,0.00236477,0.16329],"study_design_scores_gemma":[0.0001728358,0.000411649,0.05394177,0.00002757098,0.00001833521,0.000002100894,0.00008915365,0.7446884,0.0009763085,0.1991384,0.0003395826,0.0001938895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08509818,7.727423e-7,0.9116715,0.00008354659,0.00003973759,0.0004121103,0.0001223881,0.00005991026,0.002511865],"genre_scores_gemma":[0.7226678,0.00000111638,0.2770749,0.0001502211,0.000007857983,0.00003155325,0.00001125049,0.000004608067,0.00005078592],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.739674,"threshold_uncertainty_score":0.9998048,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2026031651","doi":"10.1007/s11004-008-9191-3","title":"Application of Fractal Models to Distinguish between Different Mineral Phases","year":2008,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Geological Survey of Canada; York University","funders":"China University of Geosciences","keywords":"Lacunarity; Fractal dimension; Cassiterite; Fractal; Variogram; Perimeter; Mineralogy; Fractal analysis; Geology; Spatial distribution; Geometry; Materials science; Mathematics; Tin; Kriging; Statistics; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.05079817660434639,"gpt":0.2669833033143658,"spread":0.2161851267100194,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001993165,0.0001122976,0.0002248208,0.00004548861,0.0001431386,0.00003146785,0.0008471443,0.00003846734,0.00001918758],"category_scores_gemma":[0.0002630368,0.00008060784,0.00005353255,0.0003471051,0.0001868643,0.0001687785,0.0002474478,0.00005933259,0.00001447825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008259398,"about_ca_system_score_gemma":0.00002111565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002244314,"about_ca_topic_score_gemma":9.913892e-7,"domain_scores_codex":[0.9987323,0.00001823938,0.0002887845,0.0003266551,0.0003868605,0.0002472055],"domain_scores_gemma":[0.9991588,0.0001925392,0.00009630298,0.0003349216,0.00008367941,0.0001337763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002009873,0.00254699,0.0606855,0.0006576661,0.0000628561,0.00004959046,0.02117434,0.004080921,0.05745328,0.7778781,0.003296647,0.07209403],"study_design_scores_gemma":[0.0002277061,0.000214359,0.01626113,0.00007479735,0.00001167457,0.00004359424,0.000156115,0.4912151,0.02018982,0.469795,0.001379036,0.0004317408],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3537626,0.00000592598,0.6370043,0.0006121607,0.00002770241,0.00009741871,0.000002657386,0.00004751289,0.008439742],"genre_scores_gemma":[0.9679943,6.734496e-7,0.03125986,0.00005110908,0.0000491138,0.00002251369,0.00000159163,0.000001100461,0.0006197527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6142316,"threshold_uncertainty_score":0.3287092,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2883324463","doi":"10.1007/s11004-018-9758-6","title":"Ore–Waste Discrimination in Epithermal Deposits Using Near-Infrared to Short-Wavelength Infrared (NIR-SWIR) Hyperspectral Imagery","year":2018,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Barrick Gold Corporation","keywords":"Hyperspectral imaging; Mineralogy; Sorting; Geology; Illite; Mineral; Clay minerals; Near-infrared spectroscopy; Remote sensing; Chemistry; Optics","retraction":null,"screen_n_in":null,"score":{"opus":0.03211730532485955,"gpt":0.2707120411686947,"spread":0.2385947358438352,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008327261,0.0002303498,0.0002918211,0.000139869,0.0003642223,0.0004753885,0.001130112,0.00009874943,0.00008330851],"category_scores_gemma":[0.000778815,0.0001879839,0.00007063113,0.001167261,0.0004276843,0.0007267063,0.0004837448,0.0001666548,0.00007504791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006337809,"about_ca_system_score_gemma":0.0001273681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002385319,"about_ca_topic_score_gemma":0.00001869359,"domain_scores_codex":[0.9975305,0.00008344559,0.0004654341,0.0006618623,0.0005532033,0.0007056037],"domain_scores_gemma":[0.9988875,0.0001518474,0.00008498216,0.000502134,0.0001568505,0.0002166467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001944918,0.002646138,0.02010605,0.001304392,0.0001173939,0.0008662028,0.1463606,0.002708791,0.3371449,0.2354545,0.002500315,0.2505963],"study_design_scores_gemma":[0.0002441633,0.0002387413,0.008335864,0.0002445751,0.00001279131,0.0001259412,0.001709487,0.8639655,0.04386086,0.08039688,0.000254932,0.0006102741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8001567,0.00001386814,0.1753882,0.001132814,0.0002323073,0.0002529295,0.000001873419,0.0001099891,0.02271139],"genre_scores_gemma":[0.8267671,7.518443e-7,0.1719765,0.0002200623,0.0001033274,0.0000159984,9.557853e-7,0.000004150776,0.0009110955],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8612567,"threshold_uncertainty_score":0.7665761,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1963599322","doi":"10.1007/s11004-014-9580-8","title":"Multivariate Imputation of Unequally Sampled Geological Variables","year":2015,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Missing data; Multivariate statistics; Imputation (statistics); Data mining; Computer science; Statistics; Parametric statistics; Sampling (signal processing); Regression; Econometrics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.04714225100871,"gpt":0.2839453836325165,"spread":0.2368031326238065,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009947941,0.0000958704,0.0001799498,0.00002677346,0.0000672617,0.00003153913,0.0002587301,0.00004690592,0.000914095],"category_scores_gemma":[0.001448563,0.00006692112,0.00002954178,0.0003006555,0.0004339431,0.0001263063,0.0001995975,0.00005224553,0.0002589094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002786631,"about_ca_system_score_gemma":0.00002575067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005401401,"about_ca_topic_score_gemma":0.00001764088,"domain_scores_codex":[0.9986646,0.00005733359,0.0003031508,0.0002336593,0.0004942087,0.0002470581],"domain_scores_gemma":[0.9992787,0.0002890821,0.0001166138,0.0001377657,0.00002531615,0.0001525567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009921102,0.001651668,0.06242556,0.0001716915,0.00004524776,0.00004013463,0.01346134,0.02544812,0.01876341,0.8247485,0.003705923,0.04943925],"study_design_scores_gemma":[0.0003905967,0.0002245333,0.03084076,0.00002405639,0.00001739283,0.00001028008,0.0007359094,0.1785207,0.000336629,0.7876771,0.001013222,0.0002088265],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5428802,0.000009867595,0.4207111,0.0001972749,0.0001514296,0.0001862254,0.000009437222,0.00004779972,0.03580661],"genre_scores_gemma":[0.8767509,0.00000105027,0.1228686,0.00006547583,0.00001266499,0.000008951712,0.000002379675,0.000003569013,0.0002864178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3338707,"threshold_uncertainty_score":0.9999992,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2078155598","doi":"10.1007/s11004-014-9538-x","title":"Inversion and Geodiversity: Searching Model Space for the Answers","year":2014,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Geodiversity; Inversion (geology); A priori and a posteriori; Geology; Hydrogeology; Suite; Geophysics; Workflow; Principal component analysis; Underdetermined system; Environmental geology; Data mining; Computer science; Algorithm; Seismology; Geography; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.03463499317341077,"gpt":0.2272043493168791,"spread":0.1925693561434683,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008989114,0.00007165749,0.0001124671,0.00003611484,0.0007335321,0.0001440115,0.0002450432,0.00003046464,0.0001362593],"category_scores_gemma":[0.0003463566,0.00003580729,0.00006288441,0.0001302241,0.0003374371,0.0001010032,0.00002966057,0.00006790036,0.00005191893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001193609,"about_ca_system_score_gemma":0.0000144726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004186955,"about_ca_topic_score_gemma":0.0001248747,"domain_scores_codex":[0.9992102,0.00003413797,0.00008762366,0.0002093354,0.0002266234,0.0002320943],"domain_scores_gemma":[0.9991487,0.0005934386,0.0000287908,0.0001052133,0.00002227928,0.0001015819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004138733,0.00005983933,0.09982526,0.0001270459,0.00004969321,0.000001426151,0.002892753,0.674031,0.00004746397,0.02962007,0.001354836,0.1919493],"study_design_scores_gemma":[0.00005615294,0.00006076625,0.003383038,0.000005984338,0.00001915041,9.792624e-7,0.000291106,0.9288331,0.000002033971,0.06696247,0.0003259221,0.00005924091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5519569,0.00006868965,0.4416195,0.003422381,0.00004480584,0.0001142705,0.00001041964,0.00003009517,0.002732989],"genre_scores_gemma":[0.9850999,0.00001787203,0.01411087,0.0002292594,0.00002499554,6.944674e-7,0.000002149688,6.897205e-7,0.00051357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.433143,"threshold_uncertainty_score":0.564181,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2058447139","doi":"10.1007/s11004-014-9575-5","title":"Reservoir Geological Uncertainty Reduction: an Optimization-Based Method Using Multiple Static Measures","year":2015,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematical optimization; Computer science; Reduction (mathematics); Ranking (information retrieval); Realization (probability); Cluster analysis; Probability distribution; Reservoir engineering; Kernel (algebra); Reservoir simulation; Kernel density estimation; Algorithm; Data mining; Mathematics; Geology; Statistics; Petroleum engineering; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.123451994545095,"gpt":0.3513221437737568,"spread":0.2278701492286618,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002038836,0.0001914783,0.0002793201,0.0001389189,0.0001289153,0.000135438,0.000327613,0.0001045961,0.0001098673],"category_scores_gemma":[0.001562359,0.0001491437,0.00005492006,0.0005753775,0.0001458837,0.0003025817,0.00002879444,0.0001573416,0.00001130841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008337972,"about_ca_system_score_gemma":0.00007809407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003582235,"about_ca_topic_score_gemma":0.000002580203,"domain_scores_codex":[0.9981453,0.0002519059,0.0003762748,0.0002816421,0.000593146,0.0003517049],"domain_scores_gemma":[0.9987256,0.0004098891,0.00004101923,0.0003290667,0.000163598,0.0003308471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007448655,0.00003912058,0.00008331617,0.00004130892,0.000006014663,0.00000206868,0.0002847723,0.9986359,0.0001678547,0.0003672574,0.00004020064,0.0003246759],"study_design_scores_gemma":[0.0003019304,0.00006209158,0.00001965358,0.00002531205,0.00001269089,0.00001181441,0.0003979027,0.9927597,0.0002508033,0.005739143,0.0002132955,0.0002056703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1314275,0.00004586316,0.8669461,0.000081235,0.0002661082,0.0001535267,0.000003864268,0.0003946022,0.0006811306],"genre_scores_gemma":[0.3899648,0.000001220674,0.609885,0.00001229309,0.00005717082,0.00001494432,0.000004357225,0.00001429459,0.00004590201],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2585373,"threshold_uncertainty_score":0.6081902,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1985135236","doi":"10.1007/s11004-008-9195-z","title":"The Proportional Effect","year":2008,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Correlogram; Variogram; Univariate; Normalization (sociology); Statistics; Inference; Econometrics; Mathematics; Geostatistics; Variable (mathematics); Computer science; Multivariate statistics; Kriging","retraction":null,"screen_n_in":null,"score":{"opus":0.01360567661683021,"gpt":0.2371292302818444,"spread":0.2235235536650142,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005466007,0.0000669472,0.00007109725,0.000007025741,0.0007725335,0.00003730085,0.0002499993,0.00001652117,0.0008945676],"category_scores_gemma":[0.0003957399,0.00003333544,0.00003060224,0.0001719779,0.001013877,0.00006882237,0.0001134992,0.00005363723,0.001382481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001713218,"about_ca_system_score_gemma":0.00001027582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002927389,"about_ca_topic_score_gemma":0.000007901571,"domain_scores_codex":[0.9989706,0.00002783624,0.0001392387,0.0001558315,0.0004741492,0.0002323103],"domain_scores_gemma":[0.9994401,0.0003143133,0.00003922379,0.0001311456,0.000004482962,0.00007076504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00005596004,0.0006285307,0.283224,0.00009371666,0.00003945726,0.0002230044,0.004059107,0.0004497035,0.004564815,0.4198596,0.09373734,0.1930648],"study_design_scores_gemma":[0.0004356386,0.0005058757,0.4443514,0.00003349618,0.00002133614,0.0004799108,0.0002481525,0.04301605,0.001232379,0.3779117,0.1311698,0.000594347],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8608514,0.0000284982,0.01002822,0.0008353448,0.00023385,0.0002609774,0.000001800508,0.0000576985,0.1277022],"genre_scores_gemma":[0.9910308,0.00001113034,0.003402297,0.0001015234,0.00002713287,0.00003314881,4.521208e-7,0.000003586663,0.005389976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1924705,"threshold_uncertainty_score":0.9993951,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2090747669","doi":"10.1007/s11004-008-9176-2","title":"Investigation of the Structure of Geological Process through Multivariate Statistical Analysis—The Creation of a Coal","year":2008,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Geological Survey of Canada; Natural Resources Canada","funders":"","keywords":"Biplot; Multivariate statistics; Raw data; Principal component analysis; Process (computing); Geology; Univariate; Multivariate analysis; Coal; Sample (material); Constant (computer programming); Compositional data; Mining engineering; Statistics; Mineralogy; Data mining; Computer science; Mathematics; Geography; Chemistry; Archaeology","retraction":null,"screen_n_in":null,"score":{"opus":0.03382072907794356,"gpt":0.2734589734939498,"spread":0.2396382444160062,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003669687,0.00009222708,0.0002755503,0.00003118114,0.0001476378,0.00001182179,0.0009452438,0.00006456547,0.00007557516],"category_scores_gemma":[0.001097947,0.00004335637,0.00007943468,0.001101521,0.001709308,0.0001337773,0.0001755947,0.0000992356,6.99528e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004108329,"about_ca_system_score_gemma":0.00008951597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005445648,"about_ca_topic_score_gemma":0.000004757259,"domain_scores_codex":[0.9985154,0.0001244479,0.0004398069,0.0002231568,0.0005438769,0.0001532573],"domain_scores_gemma":[0.9985833,0.0004698077,0.0003622329,0.0003573883,0.0001951993,0.00003203412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002098988,0.0002396935,0.1227541,0.0005470043,0.0001725216,0.000003748892,0.02412781,0.01231533,0.03596252,0.8034916,0.00007419261,0.0002904435],"study_design_scores_gemma":[0.0001456661,0.00008819485,0.1902333,0.00004488453,0.0001035822,0.00002340078,0.0003923914,0.1484866,0.08666795,0.5736852,0.00001844996,0.0001103818],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6386791,0.000009235216,0.3595897,0.0006920035,0.00002838481,0.0001215292,0.000008983404,0.00001028063,0.000860707],"genre_scores_gemma":[0.9727923,0.000001012221,0.02708785,0.0000344835,0.000007325709,0.000002742481,0.000001467127,6.125304e-7,0.00007223337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3341131,"threshold_uncertainty_score":0.6298015,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2804634398","doi":"10.1007/s11004-018-9741-2","title":"High-Order Spatial Simulation Using Legendre-Like Orthogonal Splines","year":2018,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; AngloGold Ashanti; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Newmont Corporation; Barrick Gold Corporation","keywords":"Legendre polynomials; Probability density function; Gaussian; Applied mathematics; Mathematics; Transformation (genetics); Spline (mechanical); Legendre wavelet; Generating function; Probability distribution; Algorithm; Computer science; Statistics; Mathematical analysis; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.02895484684674264,"gpt":0.2867537070524277,"spread":0.257798860205685,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003665792,0.0001368238,0.0001527334,0.00003673175,0.0003561088,0.00009042252,0.0002398292,0.00005007304,0.004849672],"category_scores_gemma":[0.0002207903,0.000106504,0.00003295751,0.0003941976,0.0007643598,0.000201745,0.0002056329,0.00006119698,0.0009366162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003383985,"about_ca_system_score_gemma":0.00002124193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005335431,"about_ca_topic_score_gemma":0.0001936871,"domain_scores_codex":[0.9984573,0.00002844525,0.0002726824,0.0003462324,0.0005459014,0.0003494057],"domain_scores_gemma":[0.9994486,0.0001306654,0.00009015014,0.0001829638,0.00003205919,0.0001155411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001745733,0.002379702,0.0900906,0.0003373108,0.0001194022,0.0001153355,0.01094887,0.3365424,0.05456979,0.1991062,0.003417096,0.3021987],"study_design_scores_gemma":[0.0001395471,0.00008769362,0.01782399,0.00002120051,0.00001690867,0.000008661739,0.0001030787,0.938405,0.0002943804,0.04074134,0.002127075,0.0002310576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5992128,0.000003640007,0.3968831,0.00007700622,0.0004094458,0.0001143937,0.000006750089,0.00003768886,0.003255122],"genre_scores_gemma":[0.9272961,7.725735e-7,0.07180718,0.0001805603,0.0001928908,0.000003919155,0.00000194239,0.000008334924,0.0005082905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6018627,"threshold_uncertainty_score":0.9998413,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2042301468","doi":"10.1007/s11004-008-9192-2","title":"Collocated Cokriging Based on Merged Secondary Attributes","year":2008,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Hydrogeology; Geostatistics; Geology; Statistics; Computer science; Mathematics; Spatial variability; Geotechnical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03331157812737271,"gpt":0.24369631197314,"spread":0.2103847338457673,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006069147,0.0001543761,0.0002017474,0.0001602716,0.000448757,0.0002235165,0.001430567,0.00003166999,0.0002340207],"category_scores_gemma":[0.0001799967,0.0001140388,0.00006110468,0.001005176,0.0002679295,0.0006546311,0.0002360701,0.0001136581,0.0004605247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001889606,"about_ca_system_score_gemma":0.00009178464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008031189,"about_ca_topic_score_gemma":0.000001000687,"domain_scores_codex":[0.9981791,0.00004922659,0.0002525524,0.0004515809,0.0006704115,0.0003971609],"domain_scores_gemma":[0.9990076,0.000230885,0.00007193281,0.0005164954,0.00004401641,0.0001290366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002346169,0.002150415,0.002160739,0.00029643,0.00005719954,0.0005926788,0.001590202,0.0008866247,0.000951462,0.857788,0.06207546,0.0714273],"study_design_scores_gemma":[0.0003582235,0.0001666178,0.001909068,0.00005037772,0.000005359263,0.00001392344,0.00004509898,0.9699392,0.001181587,0.01535308,0.01067564,0.0003018269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01741581,0.0000259533,0.9404759,0.002857301,0.0004542507,0.0002665908,0.00001191045,0.000433307,0.03805899],"genre_scores_gemma":[0.8398095,0.000008879887,0.1541801,0.001838962,0.00006164092,0.00003993683,0.00001167573,0.00001066823,0.00403869],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9690526,"threshold_uncertainty_score":0.5919267,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2553783174","doi":"10.1007/s11004-016-9662-x","title":"Joint High-Order Simulation of Spatially Correlated Variables Using High-Order Spatial Statistics","year":2016,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Decorrelation; Geostatistics; Univariate; Higher-order statistics; Covariance; Joint probability distribution; Spatial analysis; Gaussian; Variogram; Statistics; Joint (building); Mathematics; Order statistic; Covariance matrix; Computer science; Monte Carlo method; Applied mathematics; Kriging; Multivariate statistics; Spatial variability","retraction":null,"screen_n_in":null,"score":{"opus":0.02099343322280555,"gpt":0.2479975998722885,"spread":0.2270041666494829,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005426176,0.0001982255,0.0003209827,0.00006474325,0.0001865712,0.00005199888,0.0002659534,0.00009181556,0.005174981],"category_scores_gemma":[0.001704327,0.0001294437,0.00003105451,0.0004684654,0.0007006202,0.0002148883,0.0002332897,0.00007281297,0.0002100716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007626163,"about_ca_system_score_gemma":0.00006189341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002566117,"about_ca_topic_score_gemma":0.0001348189,"domain_scores_codex":[0.9977752,0.00007262175,0.0005999109,0.0003936808,0.0007493469,0.0004092254],"domain_scores_gemma":[0.9984589,0.0007439557,0.0003014916,0.0002684146,0.00009253529,0.0001346597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007096807,0.001322432,0.01227669,0.0003134357,0.0001065051,0.00004642272,0.00205002,0.4987011,0.1353454,0.1903075,0.0006040033,0.1588556],"study_design_scores_gemma":[0.0004554437,0.000139453,0.01700126,0.0001322947,0.00005182396,0.000004084752,0.00004345427,0.8115022,0.001469105,0.1686912,0.0002003898,0.0003092242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1964119,0.000003635247,0.8021814,0.00009159483,0.0002941089,0.0001905969,0.00008138878,0.00003083454,0.0007145674],"genre_scores_gemma":[0.7990677,0.000004802194,0.2004515,0.0000439565,0.00003096323,0.000005168162,0.000005596833,0.00001334442,0.0003769341],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6026559,"threshold_uncertainty_score":0.9957344,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1982966591","doi":"10.1007/s11004-013-9478-x","title":"Forecasting Recoverable Ore Reserves and Their Uncertainty at Morila Gold Deposit, Mali: An Efficient Simulation Approach and Future Grade Control Drilling","year":2013,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Drilling; Geology; Mining engineering; Well control; Mineral deposit; Hydrogeology; Petroleum engineering; Gold ore; Geochemistry; Engineering; Geotechnical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02941261575192684,"gpt":0.2484949979205484,"spread":0.2190823821686216,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006449696,0.0001987996,0.0002632914,0.00007907845,0.0002146228,0.0002442017,0.0001389924,0.00009704162,0.00002169094],"category_scores_gemma":[0.0001442685,0.0001370205,0.00003550179,0.0002202926,0.00009513402,0.000272908,0.00004374935,0.0001312452,0.000002038871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003324921,"about_ca_system_score_gemma":0.000004408133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002842902,"about_ca_topic_score_gemma":0.000003198047,"domain_scores_codex":[0.998812,0.000073952,0.0002831158,0.0002860309,0.0002141738,0.000330742],"domain_scores_gemma":[0.9990633,0.0004814579,0.00003915253,0.0001927581,0.0000448645,0.0001785359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002982239,0.00001608712,0.0007509289,0.0001717384,0.000007916943,3.984735e-7,0.000797777,0.995442,0.0004986839,0.0003956546,0.000006201721,0.00190965],"study_design_scores_gemma":[0.0002396625,0.00003563684,0.001754046,0.00003673519,0.000007863516,0.0000117546,0.0005025624,0.9950828,0.0001243534,0.00191821,0.0001068068,0.0001795806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7983768,0.0003251445,0.2003264,0.00004690537,0.00009985546,0.0002635793,0.000003905825,0.0001280026,0.0004294477],"genre_scores_gemma":[0.9561773,0.0000214312,0.0435289,0.00000838827,0.0001215046,0.00003129129,0.000003679713,0.0000184253,0.00008906007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1578005,"threshold_uncertainty_score":0.5587536,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2594921564","doi":"10.1007/s11004-017-9676-z","title":"Mine Planning and Oil Field Development: A Survey and Research Potentials","year":2017,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Field (mathematics); Oil field; Petroleum; Petroleum industry; Management science; Scientific field; Computer science; Petroleum engineering; Environmental planning; Data science; Operations research; Engineering; Geology; Environmental science; Work (physics); Environmental engineering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1353723692243705,"gpt":0.3563230034296497,"spread":0.2209506342052792,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001561093,0.00005687688,0.0001103712,0.00004598638,0.0003484395,0.0003058258,0.0002006954,0.00004171593,0.00003338475],"category_scores_gemma":[0.0004880544,0.0000465513,0.000005933974,0.00002661505,0.0001615016,0.0001155585,0.0001518583,0.0000787598,0.00000826196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000506007,"about_ca_system_score_gemma":0.000008618264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004095922,"about_ca_topic_score_gemma":0.00002659481,"domain_scores_codex":[0.9994489,0.00001223212,0.0001241393,0.0001241598,0.00009567551,0.0001948883],"domain_scores_gemma":[0.9995342,0.000218346,0.0000179136,0.0001489691,0.00001408509,0.00006648657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005400172,0.0002004395,0.2681051,0.003166246,0.0001295809,0.00009635061,0.02476865,0.0001351159,0.005128847,0.04393405,0.01435498,0.6399267],"study_design_scores_gemma":[0.0007798018,0.0003692718,0.5149579,0.001388112,0.00001761218,0.0001433455,0.001485799,0.3405941,0.018279,0.1028325,0.01757407,0.001578435],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862538,0.0001210105,0.003048663,0.00008857041,0.00004458929,0.00002729219,0.000001389682,0.00004511114,0.01036959],"genre_scores_gemma":[0.9800531,0.0000420143,0.01937621,0.000009627092,0.00001477047,0.000007622203,3.728719e-7,0.000004903236,0.0004913333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6383482,"threshold_uncertainty_score":0.2949085,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3022049020","doi":"10.1007/s11004-017-9695-9","title":"Optimizing Infill Drilling Decisions Using Multi-Armed Bandits: Application in a Long-Term, Multi-Element Stockpile","year":2017,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; AngloGold Ashanti; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Newmont Corporation; Barrick Gold Corporation","keywords":"Stockpile; Infill; Drilling; Drill; Computer science; Term (time); Sequence (biology); Data mining; Mining engineering; Petroleum engineering; Geology; Operations research; Civil engineering; Engineering; Mechanical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.08718930538874528,"gpt":0.3697845879833213,"spread":0.282595282594576,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000865933,0.0001814517,0.0002693226,0.0001777151,0.0003437419,0.0002740246,0.0005248602,0.00008583969,0.00004177851],"category_scores_gemma":[0.0007220199,0.0001566985,0.00006106259,0.0001898569,0.0001053645,0.0003648185,0.0001062463,0.0001683537,0.00002104453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008010487,"about_ca_system_score_gemma":0.00001786269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003077521,"about_ca_topic_score_gemma":0.00004124989,"domain_scores_codex":[0.9985456,0.00003216285,0.0004501628,0.0002749662,0.0003144892,0.0003826004],"domain_scores_gemma":[0.9989735,0.0002589982,0.00008452709,0.0005100212,0.00004008642,0.0001327998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001618463,0.00006043409,0.005705043,0.00006288542,0.000006421795,0.000003613708,0.0005343265,0.9872125,0.002431456,0.000141503,0.000001480138,0.003838753],"study_design_scores_gemma":[0.00047431,0.000008899779,0.02043073,0.0001972254,0.000007433431,0.000002884264,0.00006341687,0.9777355,0.000582231,0.0002706541,0.0000331427,0.0001935922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3989648,0.00006044933,0.6004355,0.00001113031,0.0001311409,0.0002113518,0.000003154752,0.00008514756,0.0000973192],"genre_scores_gemma":[0.5957929,0.00002012812,0.4040608,0.000004126098,0.00002763582,0.00003477207,0.000002043749,0.00001515315,0.00004247457],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1968281,"threshold_uncertainty_score":0.6389978,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1182963948","doi":"10.1007/s11004-015-9615-9","title":"Dynamic Uncertainty in Cost-Benefit Analysis of Evacuation Prior to a Volcanic Eruption","year":2015,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Probability distribution; Probabilistic logic; Event (particle physics); Computer science; Statistics; Geology; Mathematics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01794073864056205,"gpt":0.2875088809255043,"spread":0.2695681422849422,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008065842,0.0001001878,0.000324339,0.0005340285,0.00002783303,0.00003131432,0.0002161594,0.00005209957,0.0001216063],"category_scores_gemma":[0.0003528552,0.00007894498,0.00008655836,0.002969662,0.00007811599,0.0001494385,0.00002582082,0.00007039036,0.00002593247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001520466,"about_ca_system_score_gemma":0.00003192426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001189321,"about_ca_topic_score_gemma":0.001957031,"domain_scores_codex":[0.9987804,0.00002536435,0.0003706549,0.000188013,0.0004189269,0.0002166417],"domain_scores_gemma":[0.9994669,0.00008717593,0.00003896786,0.0002081249,0.00008273267,0.0001160924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004447614,0.00002703902,0.001237655,0.00003020552,0.00003621181,3.934503e-7,0.001187762,0.9884926,0.0007278891,0.001382042,0.000005276443,0.006868453],"study_design_scores_gemma":[0.00007100011,0.00003193953,0.02008125,0.00001896431,0.0001192492,4.460452e-7,0.0007358693,0.9688041,0.0001095216,0.009912265,0.00002172376,0.00009362913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8951324,0.00002275127,0.1038542,0.0000749048,0.00004469721,0.0001757392,0.000007362571,0.00003359014,0.0006543795],"genre_scores_gemma":[0.9971746,0.000003660535,0.00272528,0.00001808195,0.000004777826,0.0000257077,0.000005060639,0.000003730184,0.00003906248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1020423,"threshold_uncertainty_score":0.3219283,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3195879356","doi":"10.1007/s11004-021-09971-9","title":"Quantifying Mineral Resources and Their Uncertainty Using Two Existing Machine Learning Methods","year":2021,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"Comisión Nacional de Investigación Científica y Tecnológica; Fonds Québécois de la Recherche sur la Nature et les Technologies; National Research Council Canada; Polytechnique Montréal","keywords":"Variogram; Kriging; Geostatistics; Statistics; Tonnage; Computer science; Data mining; Mathematics; Econometrics; Geology; Spatial variability","retraction":null,"screen_n_in":null,"score":{"opus":0.1069464701384109,"gpt":0.3626724595676287,"spread":0.2557259894292179,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001049213,0.0001867794,0.0002849795,0.00007825485,0.0003977666,0.0002944202,0.000135521,0.00004635685,0.00006059739],"category_scores_gemma":[0.000655407,0.0001363036,0.00004835768,0.0004146704,0.0001571887,0.0001620896,0.0001039962,0.000252562,0.000003418703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002113557,"about_ca_system_score_gemma":0.00001661322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005043775,"about_ca_topic_score_gemma":0.00002481837,"domain_scores_codex":[0.998781,0.0001169947,0.0002838515,0.0002800581,0.0001676526,0.0003704924],"domain_scores_gemma":[0.999255,0.0004358632,0.00004799866,0.0001143867,0.00003735703,0.0001094189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007994498,0.00008072933,0.005435301,0.001796331,0.0000779941,0.00006396155,0.02018898,0.246406,0.5920453,0.009267485,0.00002757261,0.1246024],"study_design_scores_gemma":[0.0001004807,0.00001054161,0.00006101306,0.0002017975,0.00001137541,0.0001028481,0.001907015,0.9895428,0.002889854,0.004195952,0.0007746859,0.0002016782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9060185,0.001740095,0.08746979,0.00004649719,0.0001400065,0.00003729848,0.000001933248,0.0002051376,0.004340738],"genre_scores_gemma":[0.8476374,0.00001485733,0.1518597,0.00002296481,0.00008103809,0.000002730367,0.000001565671,0.00001783409,0.0003619509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7431368,"threshold_uncertainty_score":0.5558299,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2805870952","doi":"10.1007/s11004-018-9744-z","title":"A New Computational Model of High-Order Stochastic Simulation Based on Spatial Legendre Moments","year":2018,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Legendre polynomials; Cumulant; Computation; Applied mathematics; Mathematics; Algorithm; Moment (physics); Computational statistics; Mathematical optimization; Spatial analysis; Computer science; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.02385444087046787,"gpt":0.2683188923917033,"spread":0.2444644515212354,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001918918,0.0001006358,0.0001312073,0.00004500642,0.0001237647,0.00002782111,0.0001845599,0.00003176694,0.001693502],"category_scores_gemma":[0.0002441106,0.00008108753,0.00002467888,0.0002454178,0.0003276313,0.00008252065,0.00007130744,0.00004134913,0.0002650014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003067529,"about_ca_system_score_gemma":0.00004489578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002812541,"about_ca_topic_score_gemma":0.0000290689,"domain_scores_codex":[0.9986482,0.00001659395,0.0002280821,0.0002433162,0.0006688319,0.0001949367],"domain_scores_gemma":[0.9993877,0.0002589841,0.00009235903,0.0001305047,0.00002769336,0.0001027687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008258447,0.00007713949,0.00006971027,0.000006302186,0.000001617587,2.125011e-7,0.000227352,0.9895567,0.0001016961,0.006174069,0.00008467386,0.003692315],"study_design_scores_gemma":[0.0001892721,0.0001353912,0.001801817,0.00002084533,0.000006364863,1.896764e-7,0.00002050047,0.8763865,0.00004879428,0.1212971,0.000009965885,0.00008325212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06599788,5.124533e-7,0.9309229,0.0001215971,0.00008271272,0.0001348157,0.0000145267,0.00001701184,0.002708008],"genre_scores_gemma":[0.8660091,3.873535e-8,0.1335193,0.0001294811,0.00002233197,0.000003758695,0.000003592392,0.000004844771,0.0003075698],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8000112,"threshold_uncertainty_score":0.9992191,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2029871590","doi":"10.1007/s11004-013-9490-1","title":"CDFSIM: Efficient Stochastic Simulation Through Decomposition of Cumulative Distribution Functions of Transformed Spatial Patterns","year":2013,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Cumulative distribution function; Decomposition; Hydrogeology; Distribution (mathematics); Mathematics; Spatial distribution; Applied mathematics; Statistical physics; Statistics; Geology; Computer science; Probability density function; Mathematical analysis; Physics; Geotechnical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01731151878289465,"gpt":0.2709764902940033,"spread":0.2536649715111086,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001219824,0.0001210014,0.0002043908,0.000047224,0.00008089751,0.00001860732,0.0001363126,0.00005928905,0.000186899],"category_scores_gemma":[0.00007039808,0.000103222,0.00006667824,0.000260024,0.0001593424,0.0001550759,0.00001667023,0.00007091239,0.0000391156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003956308,"about_ca_system_score_gemma":0.000015951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002329654,"about_ca_topic_score_gemma":0.00001524855,"domain_scores_codex":[0.9988182,0.00001709165,0.0004856258,0.0001534897,0.0003388441,0.0001867715],"domain_scores_gemma":[0.9992806,0.0002586666,0.00008779232,0.0001668191,0.0001564626,0.00004968543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002029974,0.0001115259,0.00004064465,0.0000959097,0.000008823226,2.450283e-8,0.0006812927,0.9927633,0.002460656,0.002105128,0.000005131606,0.001725565],"study_design_scores_gemma":[0.0001221909,0.00006322862,0.002743569,0.00007660386,0.00002775934,6.695385e-7,0.0001656849,0.9838318,0.002276528,0.0105882,0.000003480224,0.0001003111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4197992,0.000005882963,0.5794952,0.0000248434,0.00006194612,0.0003113888,0.0000757121,0.00005055322,0.0001752634],"genre_scores_gemma":[0.9954558,0.000001221613,0.004351723,0.000003113023,0.00002043269,0.00009561457,0.00005482992,0.000009049634,0.000008197608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5756566,"threshold_uncertainty_score":0.4209272,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3184009408","doi":"10.1007/s11004-021-09943-z","title":"High-Order Data-Driven Spatial Simulation of Categorical Variables","year":2021,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; IAMGOLD; AngloGold Ashanti; Barrick Gold Corporation","keywords":"Categorical variable; Spatial analysis; Geostatistics; Variogram; Computer science; Spatial dependence; Data mining; Kriging; Statistics; Grid; Order statistic; Algorithm; Mathematics; Machine learning; Spatial variability","retraction":null,"screen_n_in":null,"score":{"opus":0.03165574062775561,"gpt":0.275940659237515,"spread":0.2442849186097594,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003265603,0.00008904976,0.0001799866,0.00001756956,0.0001019118,0.00004531967,0.000388848,0.00004172965,0.003621015],"category_scores_gemma":[0.0008823549,0.00007044066,0.00001958697,0.0004079953,0.0002982218,0.0001755709,0.0005494819,0.00005806203,0.0001861971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001891647,"about_ca_system_score_gemma":0.00003749576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005853428,"about_ca_topic_score_gemma":0.00009890155,"domain_scores_codex":[0.9986253,0.00004311532,0.0002803705,0.0003462698,0.0004917044,0.0002132668],"domain_scores_gemma":[0.9990773,0.0003349028,0.00008557453,0.0003946107,0.00002820433,0.00007940944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002095744,0.002095828,0.02065988,0.0002995946,0.00007360999,0.0001415538,0.002503686,0.5313202,0.0152377,0.3097998,0.004141344,0.1137058],"study_design_scores_gemma":[0.0001211734,0.00003041677,0.005037395,0.00001429796,0.00002182466,0.000005985504,0.0001290585,0.899048,0.000380569,0.09302052,0.002059543,0.0001311602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08410128,0.00001561624,0.9060233,0.0002817565,0.0002512609,0.0001140158,0.00004333417,0.00002979522,0.009139693],"genre_scores_gemma":[0.9393582,0.000004412112,0.06012078,0.00006073614,0.00003161832,0.00000316755,0.00002694019,0.0000048255,0.0003893461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8552569,"threshold_uncertainty_score":0.9972898,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4297236903","doi":"10.1007/s11004-022-10020-2","title":"Optimization of Subsurface Flow Operations Using a Dynamic Proxy Strategy","year":2022,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Stanford School of Earth, Energy and Environmental Sciences","keywords":"Computer science; Mathematical optimization; Proxy (statistics); Extrapolation; Reservoir simulation; Classification of discontinuities; Algorithm; Mathematics; Machine learning; Statistics; Geology; Petroleum engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02715553290139024,"gpt":0.2931937222039754,"spread":0.2660381893025852,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004163767,0.00007500616,0.0001286897,0.00008071037,0.0001393536,0.00003582314,0.0001663051,0.00001961191,0.0003931217],"category_scores_gemma":[0.00006547656,0.0000688615,0.00003138176,0.0004634031,0.00004370357,0.000121139,0.00003598314,0.0000850432,0.000001457442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003578455,"about_ca_system_score_gemma":0.00002660952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005206024,"about_ca_topic_score_gemma":7.009055e-7,"domain_scores_codex":[0.9992118,0.0000444823,0.0002280973,0.0001031598,0.0002730469,0.0001394294],"domain_scores_gemma":[0.9997273,0.00005989873,0.00001603436,0.000129847,0.00002727971,0.00003961858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[6.025596e-7,0.00001897733,0.00001333731,0.0000466828,0.000003602754,4.014932e-7,0.0002062905,0.9972729,0.001562894,0.0007376948,0.000002190136,0.000134429],"study_design_scores_gemma":[0.0000697815,0.0000246015,0.00002015859,0.000008276005,0.000005367466,0.000004877473,0.0002218296,0.9985993,0.0002091011,0.0007422788,0.00001526326,0.00007915742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3256609,0.00004763158,0.6736216,0.00001114415,0.00008362869,0.00009425023,0.000007936336,0.00007344633,0.0003995456],"genre_scores_gemma":[0.6423602,0.000002598778,0.3574975,0.000001980011,0.000004243582,0.00001378995,0.000002719182,0.000008116441,0.0001088804],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3166993,"threshold_uncertainty_score":0.4304407,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3196239349","doi":"10.1007/s11004-021-09969-3","title":"A Hybrid Estimation Technique Using Elliptical Radial Basis Neural Networks and Cokriging","year":2021,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Kriging; Geostatistics; Artificial neural network; Gaussian; Computer science; Radial basis function; Algorithm; Data mining; Mathematical optimization; Machine learning; Mathematics; Statistics; Spatial variability","retraction":null,"screen_n_in":null,"score":{"opus":0.01795085686238036,"gpt":0.2501824224961213,"spread":0.2322315656337409,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002682304,0.0001165162,0.0001667624,0.00005259333,0.0001613023,0.0002049445,0.00008278407,0.00004330328,0.00004990778],"category_scores_gemma":[0.0001438336,0.00009988461,0.00002933281,0.0002226537,0.0001330994,0.0001944824,0.00004030468,0.0001416232,0.000003424798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002026219,"about_ca_system_score_gemma":0.00001526847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003046482,"about_ca_topic_score_gemma":5.732325e-7,"domain_scores_codex":[0.9991506,0.00002086449,0.0001993484,0.0001859931,0.0001712713,0.0002719733],"domain_scores_gemma":[0.9996563,0.0001097445,0.00002005287,0.00009167742,0.00002071174,0.0001015216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005857715,0.00008997155,0.0004223573,0.0008690904,0.00002575294,0.0001917646,0.00050472,0.9062951,0.02154523,0.01538746,0.00018222,0.05448044],"study_design_scores_gemma":[0.00005010008,0.000008390181,0.00002826276,0.00008925045,0.00001319286,0.0003054792,0.00003679602,0.9901217,0.002388291,0.006796645,0.00003201188,0.0001299326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4421663,0.0001543144,0.5565217,0.0000445372,0.0001046053,0.0000520018,8.634052e-7,0.0001159484,0.0008397062],"genre_scores_gemma":[0.9372922,0.000006405954,0.06253636,0.00002372554,0.00007278428,0.000008433259,0.000001277583,0.00001071397,0.00004812094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4951259,"threshold_uncertainty_score":0.4073176,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2068825924","doi":"10.1007/s11004-014-9543-0","title":"A Multiple Training Image Approach for Spatial Modeling of Geologic Domains","year":2014,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian Natural Resources; University of Alberta","funders":"","keywords":"Geostatistics; Computer science; Data mining; Inference; Spatial analysis; Image (mathematics); Entropy (arrow of time); Artificial intelligence; Pattern recognition (psychology); Statistics; Spatial variability; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.05297652922814356,"gpt":0.2392011028243079,"spread":0.1862245735961644,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001316828,0.0001296997,0.0003584697,0.0000762978,0.0002068265,0.0000482192,0.0003663968,0.00007003408,0.0002964859],"category_scores_gemma":[0.001488607,0.00008049728,0.0001526012,0.0002131681,0.0002716504,0.00009806963,0.00001843417,0.00007652673,0.00002645506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001139953,"about_ca_system_score_gemma":0.0000164706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004547705,"about_ca_topic_score_gemma":0.0001517256,"domain_scores_codex":[0.9985267,0.00006724329,0.0003619099,0.0003442686,0.000313962,0.0003859525],"domain_scores_gemma":[0.9990788,0.0004525118,0.0001284024,0.0001615916,0.00006079264,0.0001178758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004480133,0.0001930611,0.0214425,0.0002379131,0.00002980166,0.000001178775,0.00124565,0.9204336,0.0001942352,0.003638368,0.0000183907,0.05252051],"study_design_scores_gemma":[0.0001610317,0.0001240149,0.00113415,0.00001050145,0.00002241617,0.000002716589,0.0005422453,0.9418001,0.00001371134,0.05605079,0.00002349609,0.0001147974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2490469,0.00001974834,0.744992,0.00008497139,0.00003803934,0.0001129319,0.0000155458,0.00003257235,0.005657282],"genre_scores_gemma":[0.7883462,0.000001552868,0.2114601,0.00006002813,0.00005664557,0.000004424987,0.00001533534,0.000001530796,0.000054169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5392993,"threshold_uncertainty_score":0.3282584,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2033337270","doi":"10.1007/s11004-014-9548-8","title":"Characterization of Non-Gaussian Geologic Facies Distribution Using Ensemble Kalman Filter with Probability Weighted Re-Sampling","year":2014,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian Natural Resources; University of Alberta","funders":"Alberta Innovates - Technology Futures","keywords":"Ensemble Kalman filter; Facies; Data assimilation; Kalman filter; Posterior probability; Sampling (signal processing); Gaussian; Computer science; Algorithm; Ensemble learning; Variogram; Estimator; Importance sampling; Monte Carlo method; Probability distribution; Statistics; Extended Kalman filter; Mathematics; Geology; Filter (signal processing); Artificial intelligence; Kriging; Meteorology; Geography; Bayesian probability","retraction":null,"screen_n_in":null,"score":{"opus":0.03805711156752564,"gpt":0.2739879611499984,"spread":0.2359308495824728,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005988596,0.0001287598,0.000228565,0.00005153929,0.00008231791,0.00004546937,0.0001426397,0.00006349822,0.00004263863],"category_scores_gemma":[0.0001585903,0.00008914555,0.00003424477,0.0003177405,0.0001278912,0.0001820628,0.00002329336,0.0000789872,0.000004033839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002396788,"about_ca_system_score_gemma":0.000009219552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004388342,"about_ca_topic_score_gemma":0.000001165201,"domain_scores_codex":[0.9990406,0.00004193443,0.000288567,0.0001701158,0.0002389147,0.0002198422],"domain_scores_gemma":[0.9994898,0.0001340144,0.00005739508,0.0002040651,0.00005383368,0.00006084012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001365644,0.00008519905,0.002955932,0.0009774758,0.00002039054,6.848534e-7,0.0008951296,0.7562792,0.2310316,0.004860367,0.000003499451,0.002876892],"study_design_scores_gemma":[0.00009785278,0.00004788908,0.004997505,0.00008342729,0.000009342549,0.000001601047,0.00002665344,0.9729836,0.01499797,0.006535234,0.00009236756,0.0001265423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4955793,0.000001805686,0.5039828,0.00001446106,0.00003844213,0.00008069573,0.00000454835,0.00006192635,0.0002360485],"genre_scores_gemma":[0.8685963,0.000001442617,0.1313131,0.000003552038,0.00002206236,0.000008455177,0.0000161106,0.000007655614,0.00003125606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.373017,"threshold_uncertainty_score":0.363525,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2993890771","doi":"10.1007/s11004-019-09843-3","title":"High-Order Sequential Simulation via Statistical Learning in Reproducing Kernel Hilbert Space","year":2019,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Polytechnique Montréal","funders":"IAMGOLD; Fonds Québécois de la Recherche sur la Nature et les Technologies; AngloGold Ashanti; Natural Sciences and Engineering Research Council of Canada; Newmont Corporation; Barrick Gold Corporation","keywords":"Reproducing kernel Hilbert space; Hilbert space; Kernel (algebra); Mathematics; Statistical learning; Order (exchange); Computer science; Space (punctuation); Applied mathematics; Artificial intelligence; Hydrogeology; Geology; Pure mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.06190945092533218,"gpt":0.3932577523745177,"spread":0.3313483014491855,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003892894,0.0001571509,0.0003414715,0.0002661161,0.0001802489,0.0003275058,0.0005634252,0.0000920223,0.003393347],"category_scores_gemma":[0.005192324,0.0001151762,0.00005711034,0.001580206,0.000198844,0.0004182115,0.0001759333,0.0002356479,0.001712973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000477594,"about_ca_system_score_gemma":0.00006716695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001559718,"about_ca_topic_score_gemma":0.00001622702,"domain_scores_codex":[0.9962863,0.0001831712,0.0008348438,0.000910937,0.001425141,0.000359554],"domain_scores_gemma":[0.9960894,0.002701866,0.0002271581,0.0006471783,0.000221454,0.0001129256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003003386,0.0004093883,0.03613262,0.00003931043,0.000007981873,0.000007501833,0.00182238,0.4277706,0.005142721,0.472647,0.0003663434,0.05562415],"study_design_scores_gemma":[0.0001486425,0.00005038111,0.0106585,0.0000203479,0.000003659472,0.000002811408,0.0002670176,0.6601331,0.0003669704,0.3260463,0.002146575,0.0001557903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3836806,0.000007506142,0.6107422,0.0009194724,0.0001326013,0.0003770451,0.000003171881,0.000104038,0.004033447],"genre_scores_gemma":[0.9506196,0.00000110297,0.04369295,0.00008873644,0.00005058247,0.00002378707,0.000004240239,0.00001046343,0.005508488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5670492,"threshold_uncertainty_score":0.9990643,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2994965165","doi":"10.1007/s11004-019-09846-0","title":"A Special Issue on Data Science for Geosciences","year":2019,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Hydrogeology; Geology; Data science; Earth science; Computer science; Geotechnical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.04719193242591725,"gpt":0.2974589098836529,"spread":0.2502669774577356,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003034957,0.0001929088,0.0002622841,0.0001465674,0.0005576172,0.0006628666,0.007677973,0.00005809723,0.000618039],"category_scores_gemma":[0.001906425,0.0001359996,0.00005468805,0.001344696,0.001022508,0.001604099,0.001419259,0.0001211566,0.0009543478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002505693,"about_ca_system_score_gemma":0.0002969106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006261938,"about_ca_topic_score_gemma":0.000001678426,"domain_scores_codex":[0.9964514,0.00002478001,0.0003218685,0.001336146,0.001090301,0.0007754407],"domain_scores_gemma":[0.9972876,0.0004763862,0.0001311228,0.001745371,0.0001557281,0.0002037819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001056864,0.0003135491,0.000302361,0.0001534061,0.000005186268,0.000005627372,0.0009843077,0.00009701664,0.002518063,0.9413779,0.01512112,0.03911094],"study_design_scores_gemma":[0.000280633,0.0003786337,0.0005111932,0.00008545089,0.000005276567,0.00002932865,0.0004128189,0.2927408,0.003387345,0.1665829,0.5351465,0.0004391315],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0230809,0.00002061643,0.1033625,0.01511603,0.005170391,0.001233258,0.00003166574,0.0003133489,0.8516713],"genre_scores_gemma":[0.5705264,0.00001663008,0.3392325,0.003409125,0.008566926,0.0001418109,0.00001451753,0.00001383797,0.07807823],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.774795,"threshold_uncertainty_score":0.9998235,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2162382572","doi":"10.1007/s11004-012-9426-1","title":"Stochastic Distance Based Geological Boundary Modeling with Curvilinear Features","year":2012,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian Natural Resources; University of Alberta","funders":"","keywords":"Kriging; Curvilinear coordinates; Boundary (topology); Geology; Geostatistics; Uncertainty quantification; Stochastic modelling; Hydrogeology; Stochastic simulation; Mathematical optimization; Resource (disambiguation); Computer science; Spatial variability; Mathematics; Statistics; Geotechnical engineering; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.02552391594585105,"gpt":0.2301675110547472,"spread":0.2046435951088962,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007674417,0.0002032709,0.0002988764,0.0000690893,0.000408687,0.0001360835,0.0003757142,0.00008307685,0.002077679],"category_scores_gemma":[0.0002794691,0.0001068561,0.00008560293,0.0004381467,0.0004351382,0.0002598647,0.0000188726,0.0002096077,0.0003771536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000355048,"about_ca_system_score_gemma":0.00004253477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001706362,"about_ca_topic_score_gemma":0.0001808641,"domain_scores_codex":[0.9979748,0.0000769929,0.0002567076,0.000355329,0.0006298449,0.0007062965],"domain_scores_gemma":[0.9990757,0.0002546255,0.00006316137,0.0002207153,0.00004981879,0.0003360125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006316539,0.0002256749,0.06439413,0.00005279671,0.00001847537,0.000009499072,0.0002717883,0.9243301,0.000003997273,0.005475921,0.0000491511,0.005105339],"study_design_scores_gemma":[0.0001036249,0.0001180108,0.00778402,0.00003236555,0.00003157888,0.00001700882,0.0001931832,0.9806025,0.000002697192,0.01078468,0.0001180353,0.0002122783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3373316,0.0006137424,0.6574576,0.0005003258,0.00009172905,0.00009126376,0.00001421394,0.00009152771,0.003808002],"genre_scores_gemma":[0.9734606,0.000003138474,0.02586898,0.0002966262,0.0001210887,0.000003353487,0.00001711381,0.000002706626,0.0002263606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6361291,"threshold_uncertainty_score":0.9988346,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2030159620","doi":"10.1007/s11004-011-9324-y","title":"A GIS and Remote Sensing-based Analysis of Land Use Change Using the Asymmetric Relation Analysis Method: A Case Study from the City of Hangzhou, China","year":2011,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Cochrane; York University","funders":"","keywords":"Urbanization; Land use; Remote sensing; Land cover; Geographic information system; Geography; Land use, land-use change and forestry; China; Physical geography; Change analysis; Land information system; Environmental science; Cartography; Land management; Civil engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.0998428218889575,"gpt":0.2994064711682427,"spread":0.1995636492792852,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001765194,0.0001064238,0.0003780302,0.0001838649,0.0002270098,0.00004926722,0.0002189811,0.00003795897,0.0002068968],"category_scores_gemma":[0.0001451721,0.00004941674,0.0001287903,0.004112921,0.0001278804,0.0001952611,0.0001359166,0.00005574232,0.000001489058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001443373,"about_ca_system_score_gemma":0.00000493554,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2017532,"about_ca_topic_score_gemma":0.03981412,"domain_scores_codex":[0.9985166,0.0003118724,0.0003412553,0.000267789,0.0004150141,0.0001475013],"domain_scores_gemma":[0.9985295,0.000720769,0.0002909087,0.0003916263,0.00001610474,0.00005110339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008963624,0.0001168348,0.9824297,0.00001117297,0.0004277083,0.00001761906,0.01424902,0.0004850323,0.00002628365,0.000005338106,2.867922e-7,0.002222005],"study_design_scores_gemma":[0.00005393672,0.00003177632,0.4792687,0.000007938861,0.002148936,0.000004509736,0.001152807,0.5169191,0.00003856796,0.0003301837,4.436057e-7,0.00004307785],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9561219,0.00002519986,0.0434499,0.0000346891,0.00001511599,0.0002512833,0.00002108543,0.000007096746,0.00007370567],"genre_scores_gemma":[0.981344,0.000002282309,0.01860861,0.00003081522,0.000006355971,0.000001090602,0.000001235231,0.000002919395,0.000002726846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5164341,"threshold_uncertainty_score":0.9777068,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3130665650","doi":"10.1007/s11004-021-09923-3","title":"Training Image Free High-Order Stochastic Simulation Based on Aggregated Kernel Statistics","year":2021,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; IAMGOLD; Newmont Corporation; Barrick Gold Corporation","keywords":"Kernel (algebra); Statistics; Geostatistics; Order statistic; Image (mathematics); Computer science; Hydrogeology; Mathematics; Applied mathematics; Artificial intelligence; Geology; Geotechnical engineering; Combinatorics","retraction":null,"screen_n_in":null,"score":{"opus":0.02584048428505754,"gpt":0.2692367224691768,"spread":0.2433962381841193,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003960546,0.0001766663,0.000213897,0.00004187539,0.0002588941,0.0001581217,0.0003032714,0.00005418437,0.004484051],"category_scores_gemma":[0.004069061,0.0001496524,0.00003378783,0.0005654514,0.0004184793,0.0001315,0.0001558281,0.000127079,0.0005503957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005664756,"about_ca_system_score_gemma":0.00006227171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000706213,"about_ca_topic_score_gemma":0.00004344949,"domain_scores_codex":[0.997992,0.0000637886,0.0003092199,0.0004463302,0.0007757471,0.0004129545],"domain_scores_gemma":[0.9981198,0.001190288,0.0001076842,0.0003693159,0.00004852706,0.0001644369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002094906,0.0006431481,0.0001836738,0.00009300472,0.00001593699,0.0001721163,0.00210942,0.8973169,0.002974326,0.04652309,0.001327451,0.04861995],"study_design_scores_gemma":[0.0002761303,0.00006752287,0.002303663,0.00005519227,0.00001662424,0.00000266007,0.0002981239,0.8892507,0.0001088097,0.1072208,0.0002003704,0.0001993807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02392456,0.000004933791,0.9670386,0.0003215113,0.0001883782,0.000149653,0.0001014241,0.00006522479,0.008205766],"genre_scores_gemma":[0.7858895,7.210589e-7,0.2128596,0.0004321633,0.00002726422,0.00001420717,0.00002298685,0.00001417125,0.0007393415],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.761965,"threshold_uncertainty_score":0.996426,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2982232333","doi":"10.1007/s11004-019-09833-5","title":"Variogram-Based Descriptors for Comparison and Classification of Rock Texture Images","year":2019,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"Fondo de Fomento al Desarrollo Científico y Tecnológico; Comisión Nacional de Investigación Científica y Tecnológica; Natural Sciences and Engineering Research Council of Canada","keywords":"Texture (cosmology); Geology; Variogram; Image texture; Artificial intelligence; Geologist; Texture compression; Pattern recognition (psychology); Computer science; Image (mathematics); Image processing; Kriging; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.02483487868808147,"gpt":0.2633004757859329,"spread":0.2384655970978514,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001786532,0.00007014236,0.0001487011,0.00005513998,0.00004070489,0.00004934995,0.00009350076,0.00003902341,0.00002747415],"category_scores_gemma":[0.00005701578,0.00005187199,0.00002547324,0.0001368625,0.00004729844,0.000083438,0.000007862885,0.00004542708,0.00001016105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006235021,"about_ca_system_score_gemma":0.00000811904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000019962,"about_ca_topic_score_gemma":7.332465e-7,"domain_scores_codex":[0.9994748,0.000005437872,0.0001645858,0.0001101103,0.0001175747,0.0001274725],"domain_scores_gemma":[0.9997182,0.0001087389,0.00003504614,0.00007698273,0.00002389934,0.00003708499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006143072,0.0007680954,0.09631691,0.01809257,0.00008924351,9.137732e-7,0.006078089,0.1051388,0.6073776,0.0734731,0.009335442,0.08326779],"study_design_scores_gemma":[0.0001166196,0.00005101201,0.003696953,0.00008544258,0.000009622031,6.682798e-7,0.000237115,0.9880466,0.003198762,0.004092723,0.0003769316,0.0000874825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7128987,0.0001557353,0.2842287,0.00006463286,0.0001218578,0.000182371,0.000005616837,0.00007868058,0.002263757],"genre_scores_gemma":[0.9869784,0.000001090671,0.01275832,0.000007545089,0.00001111222,0.00001022012,0.000001633487,0.000005672755,0.0002259653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8829079,"threshold_uncertainty_score":0.2115278,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}