{"meta":{"query_hash":"222f394df4c0","filters":{"topic":"Probabilistic and Robust Engineering Design"},"cohort_total":878,"direct_labels_cover":0,"predictions_cover":878,"exported":878,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/222f394df4c0","api":"https://metacan.xera.ac/api/v1/cohort?topic=Probabilistic+and+Robust+Engineering+Design"},"results":[{"id":"W10434500","doi":"10.5006/c2005-05159","title":"Progressive Integrity Assessment Solutions using Quantitative Methods","year":2005,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Energy","funders":"","keywords":"Reliability engineering; Materials science; Computer science; Forensic engineering; Engineering","score_opus":0.5219842898181858,"score_gpt":0.5781098973770318,"score_spread":0.05612560755884599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W10434500","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010761396,0.000119107,0.9967294,0.00012905979,0.000018477964,0.00006116958,0.000020546984,0.00015721435,0.001688843],"genre_scores_gemma":[0.102833934,0.0004236453,0.8937886,0.000093486255,0.000056475186,0.00037736163,0.00013636576,0.00010676791,0.0021834164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9880966,0.0050159534,0.00081700546,0.0010560903,0.0047509726,0.00026341146],"domain_scores_gemma":[0.9778552,0.011414592,0.0021441902,0.0024402074,0.0058449144,0.0003009011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013722488,0.0017617473,0.0009564437,0.0048662582,0.0009647617,0.0045982725,0.002647539,0.0013453729,0.0048009115],"category_scores_gemma":[0.0291779,0.0010623934,0.0015799745,0.0020249179,0.0028725048,0.0041466416,0.005262724,0.0021384503,0.0007994046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007793144,0.00014639708,0.0014346073,0.00063333043,0.00013484043,0.00015366546,0.0006162859,0.41332954,0.007689832,0.29091245,0.0022489764,0.2826222],"study_design_scores_gemma":[0.000035907247,0.0001224756,0.00030269174,0.0001990356,0.00004468239,0.00009074078,0.00017387382,0.7572111,0.0042741154,0.22597371,0.011515925,0.000055810237],"about_ca_topic_score_codex":0.0025399644,"about_ca_topic_score_gemma":0.0015042566,"teacher_disagreement_score":0.013722488,"about_ca_system_score_codex":0.0023703633,"about_ca_system_score_gemma":0.0027594785,"threshold_uncertainty_score":0.07257235},"labels":[],"label_agreement":null},{"id":"W1165189201","doi":"10.1080/02286203.2003.11442250","title":"Time-Dependent Reliability Analysis of Steel Miter Gates","year":2003,"lang":"en","type":"article","venue":"International Journal of Modelling and Simulation","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Army Research Office; Battelle","keywords":"Reliability (semiconductor); Fortran; Continuation; Work (physics); Engineering; Identification (biology); Reliability engineering; Computer science; Mechanical engineering","score_opus":0.06882880172200176,"score_gpt":0.3405485266252818,"score_spread":0.27171972490328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1165189201","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46877435,0.00066356384,0.52782,0.00004163232,0.000015189937,0.000034440163,0.00007021934,0.000118363394,0.0024622853],"genre_scores_gemma":[0.98306376,0.00025972797,0.015786039,0.0000041529156,0.0000051135594,0.000013826361,0.000050209826,0.00001080656,0.0008062996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997323,0.00004656037,0.000008362874,0.000033993514,0.00015871832,0.000020011046],"domain_scores_gemma":[0.9994579,0.0002214488,0.00011326808,0.000042218882,0.0001497699,0.000015371621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038968743,0.00023595935,0.0002569033,0.00064723444,0.00012212567,0.00024146453,0.00038299503,0.00024082948,0.000362485],"category_scores_gemma":[0.0015726269,0.00012459133,0.00031825795,0.00025207375,0.00025621546,0.0002874498,0.00017832435,0.00017928763,0.000096468946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011129289,0.000040265197,0.003552602,0.00015001482,0.00004950494,0.0004096039,0.0001195402,0.82448983,0.11295775,0.014404417,0.00025708397,0.0434581],"study_design_scores_gemma":[0.0000047959793,0.0001937296,0.002698774,0.0000070908704,0.000016246373,0.00014492101,0.000023672443,0.9795053,0.014468708,0.0022914645,0.000633139,0.0000120995355],"about_ca_topic_score_codex":0.00097022334,"about_ca_topic_score_gemma":0.0011855819,"teacher_disagreement_score":0.00097022334,"about_ca_system_score_codex":0.0002666601,"about_ca_system_score_gemma":0.00022371778,"threshold_uncertainty_score":0.0020608306},"labels":[],"label_agreement":null},{"id":"W11797340","doi":"","title":"Estimation of Life Expectancy of Engineering Components from Inspection Data","year":2005,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Life expectancy; Computer science; Estimation; Statistics; Econometrics; Engineering; Mathematics; Systems engineering; Population; Demography; Sociology","score_opus":0.15638121434104577,"score_gpt":0.3304129783260119,"score_spread":0.17403176398496614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W11797340","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60760576,0.00083041884,0.3874337,0.00009946364,0.000020183594,0.000067269924,0.0011664148,0.000581684,0.0021951334],"genre_scores_gemma":[0.974945,0.00017684572,0.022452472,0.0000092782275,0.000007576101,0.000056707177,0.0013890275,0.00003605697,0.0009270424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993241,0.00014799518,0.00006958956,0.00016692615,0.00023727547,0.00005415832],"domain_scores_gemma":[0.9925546,0.0043705567,0.0012130624,0.00062099646,0.0010933859,0.00014743053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019951274,0.000856646,0.00052984396,0.0019561006,0.00013620131,0.00041274875,0.00047048862,0.00057904655,0.0013919782],"category_scores_gemma":[0.010267609,0.0002147925,0.00065552484,0.0006303579,0.00024918595,0.0007766485,0.00041517586,0.00030838008,0.00052730524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007648763,0.00011106857,0.09281352,0.0002917676,0.00019920517,0.00023876711,0.0000889125,0.7080731,0.023106646,0.0028553621,0.00065547746,0.1708014],"study_design_scores_gemma":[0.000010147455,0.00041338848,0.041194525,0.00002598487,0.000062820836,0.00021812126,0.000034991415,0.9391665,0.015612735,0.0024092568,0.0008101808,0.000041346437],"about_ca_topic_score_codex":0.0012813343,"about_ca_topic_score_gemma":0.0013372336,"teacher_disagreement_score":0.0019951274,"about_ca_system_score_codex":0.0006580283,"about_ca_system_score_gemma":0.00037245994,"threshold_uncertainty_score":0.010551393},"labels":[],"label_agreement":null},{"id":"W126162704","doi":"10.1007/978-3-642-04016-0_19","title":"Generalizations of the Local Lemma","year":2002,"lang":"en","type":"book-chapter","venue":"Algorithms and combinatorics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto; University of New Brunswick","funders":"","keywords":"Lemma (botany); Perspective (graphical); Mathematics","score_opus":0.0743401193089485,"score_gpt":0.2668538531313666,"score_spread":0.1925137338224181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W126162704","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006782409,0.014527247,0.5355015,0.004326972,0.0010986502,0.000052098258,0.00043049903,0.00075685466,0.43652377],"genre_scores_gemma":[0.44525152,0.031793218,0.2493548,0.0067359707,0.006529959,0.0006199383,0.0015778596,0.0017832825,0.2563535],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99891925,0.00030471108,0.000050011367,0.00035948586,0.00027146362,0.00009511602],"domain_scores_gemma":[0.99811745,0.0009488138,0.00007160717,0.00061743933,0.00018065158,0.00006412071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013607965,0.0010645016,0.0013451228,0.0019496777,0.0015564568,0.0041867523,0.0015407358,0.0011315844,0.016770907],"category_scores_gemma":[0.0036645164,0.0006823416,0.001620151,0.0026119333,0.0053285626,0.00896546,0.0030516884,0.005141396,0.0068234573],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008354481,0.000007935508,0.000048695096,0.00006924296,0.000008532447,0.000023823593,0.00007128641,0.0007192027,0.00017414219,0.9724816,0.008229324,0.01815775],"study_design_scores_gemma":[0.000005128588,0.000005720317,0.00006684149,0.000026512907,0.000009866624,0.00006830635,0.000018227303,0.0023222463,0.0002545182,0.9555537,0.04166112,0.000007837126],"about_ca_topic_score_codex":0.0010747976,"about_ca_topic_score_gemma":0.0009202689,"teacher_disagreement_score":0.016770907,"about_ca_system_score_codex":0.0018454917,"about_ca_system_score_gemma":0.0007809068,"threshold_uncertainty_score":0.056104243},"labels":[],"label_agreement":null},{"id":"W134881188","doi":"10.1007/0-306-48332-7_177","title":"Global Optimization in the Analysis and Management of Environmental Systems","year":2001,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Environmental resource management; Environmental science","score_opus":0.02257526817568389,"score_gpt":0.2554348670017945,"score_spread":0.23285959882611063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W134881188","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003204294,0.18418245,0.66288364,0.005385557,0.0018322737,0.00006814328,0.00021618932,0.00056093483,0.14166656],"genre_scores_gemma":[0.14962126,0.26171288,0.3942785,0.0022561797,0.00476216,0.00036193084,0.0006560055,0.0011964432,0.18515465],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994898,0.0001670787,0.000016792716,0.00006958278,0.00022961276,0.000027079119],"domain_scores_gemma":[0.99950576,0.00035163845,0.000028295564,0.000040565945,0.000049337283,0.000024414829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087341433,0.001604908,0.0016461452,0.0007649157,0.00038110733,0.0023508978,0.0015157545,0.0016252819,0.006403466],"category_scores_gemma":[0.0012905169,0.0006651053,0.0006001316,0.0025109535,0.0018012355,0.0020304297,0.0011270758,0.0027749147,0.0020366448],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041721403,0.000071014096,0.00019361895,0.0007840185,0.00008444287,0.00006527337,0.00014683412,0.15182015,0.0010593808,0.49526036,0.09116477,0.2593084],"study_design_scores_gemma":[0.000025851645,0.00005294048,0.00037216194,0.00028033543,0.000037879225,0.00008324505,0.00006433704,0.11334924,0.0006884591,0.6991578,0.18585874,0.000029046638],"about_ca_topic_score_codex":0.0023342585,"about_ca_topic_score_gemma":0.004270663,"teacher_disagreement_score":0.006403466,"about_ca_system_score_codex":0.0008751695,"about_ca_system_score_gemma":0.0013690288,"threshold_uncertainty_score":0.02142179},"labels":[],"label_agreement":null},{"id":"W146678604","doi":"10.5555/2557696.2557743","title":"A multi-objective optimization approach to selecting sets of training devices","year":2013,"lang":"en","type":"article","venue":"Summer Computer Simulation Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Variety (cybernetics); Computer science; Training (meteorology); Set (abstract data type); Strengths and weaknesses; Selection (genetic algorithm); Machine learning; Artificial intelligence","score_opus":0.23224495374146045,"score_gpt":0.3592428170631373,"score_spread":0.12699786332167687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W146678604","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058309333,0.00027259212,0.99122953,0.00020651074,0.00002099176,0.00013001484,0.00007254991,0.000081690014,0.0021552062],"genre_scores_gemma":[0.26996386,0.0006145386,0.72312915,0.00021414267,0.000067802306,0.00110948,0.0003075308,0.00010574821,0.0044877566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976967,0.0013178696,0.00010746054,0.00032648037,0.0003940778,0.00015744138],"domain_scores_gemma":[0.9963515,0.0027542983,0.0003284924,0.000102534505,0.00035437767,0.00010872438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049618,0.002572385,0.0023638906,0.0023612755,0.0006744109,0.0017688831,0.0023491213,0.00214219,0.004064515],"category_scores_gemma":[0.0065230057,0.0013934392,0.0019773592,0.0017480247,0.0011562763,0.0016191932,0.0018359954,0.0024457963,0.00042701696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013805749,0.000025098789,0.00014851174,0.000041259653,0.00003648284,0.000018052842,0.000014364555,0.9902182,0.00015522022,0.0032601294,0.00020468641,0.0058641233],"study_design_scores_gemma":[0.000006695258,0.000040314277,0.00006038083,0.000010196877,0.000010796354,0.0000063911766,0.0000075962635,0.99682474,0.00009633575,0.0026653027,0.0002650693,0.000006217653],"about_ca_topic_score_codex":0.005458128,"about_ca_topic_score_gemma":0.0041072154,"teacher_disagreement_score":0.005458128,"about_ca_system_score_codex":0.0014005749,"about_ca_system_score_gemma":0.0023694637,"threshold_uncertainty_score":0.026240826},"labels":[],"label_agreement":null},{"id":"W1486810873","doi":"10.1016/s0167-2681(02)00198-1","title":"Utility of Gains and Losses: Measurement–Theoretical and Experimental Approaches","year":2004,"lang":"en","type":"article","venue":"Journal of Economic Behavior & Organization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Economics; Econometrics; Microeconomics; Mathematical economics","score_opus":0.15854319661426156,"score_gpt":0.32059819588429245,"score_spread":0.1620549992700309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486810873","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6905827,0.001478282,0.2729727,0.0026396776,0.00023197981,0.0005133151,0.0010347035,0.00019152158,0.030355122],"genre_scores_gemma":[0.9855086,0.0002239607,0.012791102,0.00014135051,0.00004838702,0.00027482354,0.00014006112,0.000045125456,0.00082644215],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.985039,0.010006096,0.00058546057,0.0015717242,0.0024349936,0.00036273667],"domain_scores_gemma":[0.7583918,0.21178816,0.008807773,0.016140979,0.00382218,0.0010491613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025482515,0.0011493608,0.0010600146,0.0015221599,0.0007063696,0.004157302,0.0023838063,0.0026947663,0.007779631],"category_scores_gemma":[0.15475816,0.00080896536,0.0008942841,0.001475355,0.006998468,0.007742895,0.0023432781,0.0033145756,0.0005881226],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00791237,0.0040517086,0.03425424,0.00093802303,0.0007977797,0.00015679092,0.002072683,0.08467936,0.019070072,0.7292027,0.0023948667,0.11446935],"study_design_scores_gemma":[0.00047696498,0.0027965768,0.034646325,0.0002028798,0.00043537444,0.0003460485,0.0009324278,0.20735614,0.018633785,0.7313886,0.0025365918,0.00024824048],"about_ca_topic_score_codex":0.0009691092,"about_ca_topic_score_gemma":0.00050735526,"teacher_disagreement_score":0.025482515,"about_ca_system_score_codex":0.002400608,"about_ca_system_score_gemma":0.001321987,"threshold_uncertainty_score":0.13476604},"labels":[],"label_agreement":null},{"id":"W1497469375","doi":"10.1109/epeps.2014.7103642","title":"Non-intrusive pseudo spectral approach for stochastic macromodeling of EM systems using deterministic full-wave solvers","year":2014,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Robustness (evolution); Computer science; Interpolation (computer graphics); Stochastic process; Polynomial chaos; Algorithm; Mathematical optimization; Stochastic modelling; Galerkin method; Spectral method; Mathematics; Monte Carlo method; Finite element method; Engineering; Telecommunications","score_opus":0.11068509273157595,"score_gpt":0.3144500660584012,"score_spread":0.20376497332682525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1497469375","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001439631,0.000014940704,0.9980578,0.00001622844,0.0000043522286,0.0000065937793,0.000006000642,0.000048405247,0.0004061355],"genre_scores_gemma":[0.32892475,0.00026775323,0.66745496,0.00007789842,0.000034883775,0.00018376579,0.000113207905,0.00015802625,0.0027847139],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997373,0.00008467764,0.000010854501,0.000025587398,0.000129493,0.000011986615],"domain_scores_gemma":[0.99943787,0.00032148787,0.00005903761,0.00009239938,0.000072807015,0.000016290309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006339188,0.00045714877,0.00034825265,0.00038072673,0.00026169233,0.0004638634,0.0007612823,0.0005516862,0.0014478733],"category_scores_gemma":[0.0015212384,0.00032429205,0.00052668515,0.00025500642,0.0005025436,0.00083751924,0.00066066434,0.00088064355,0.0004150122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002937499,0.000035239336,0.0002691695,0.000088177985,0.00002655432,0.000056619134,0.00008118945,0.8450529,0.015309284,0.09989686,0.00041011441,0.038744498],"study_design_scores_gemma":[0.0000015361345,0.0000071338827,0.000017168008,0.0000020687871,0.000001424442,0.0000112918515,0.000002339352,0.9939651,0.00111904,0.0042483853,0.0006217698,0.000002804501],"about_ca_topic_score_codex":0.0005646169,"about_ca_topic_score_gemma":0.00079818594,"teacher_disagreement_score":0.0014478733,"about_ca_system_score_codex":0.0003119513,"about_ca_system_score_gemma":0.00058268337,"threshold_uncertainty_score":0.0048436522},"labels":[],"label_agreement":null},{"id":"W1504370722","doi":"10.5539/ijsp.v4n3p1","title":"Modeling a Mixture of Linear and Changepoint Trajectories for Longitudinal Time-Series Data","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Virginia Commonwealth University; University of Saskatchewan","keywords":"Shock (circulatory); Trajectory; Phase transition; Series (stratigraphy); Phase (matter); Shock wave; Bent molecular geometry; Longitudinal data; Mathematics; Mechanics; Econometrics; Statistical physics; Physics; Geology; Thermodynamics; Computer science; Materials science; Medicine; Internal medicine","score_opus":0.20005515729918957,"score_gpt":0.37758343664087013,"score_spread":0.17752827934168056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1504370722","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062329803,0.0005255467,0.9344021,0.0005576981,0.00007726661,0.00018233599,0.00088386907,0.00036744014,0.0006740011],"genre_scores_gemma":[0.8160591,0.0014821326,0.16313161,0.00041800254,0.0002519879,0.0010812947,0.0043027634,0.0001851358,0.013088011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99773264,0.0007780274,0.00015502218,0.0008317367,0.00029010497,0.00021244722],"domain_scores_gemma":[0.9879347,0.009009309,0.001348054,0.00066714425,0.00079502363,0.00024572655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009208939,0.0014256903,0.0013391573,0.002229535,0.00058051094,0.0017156326,0.0028538867,0.0026275632,0.0033152953],"category_scores_gemma":[0.019963916,0.0011469913,0.0019565085,0.0020084297,0.0013895412,0.002615619,0.0018049593,0.0025322372,0.00085401314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027295237,0.00016589616,0.02295036,0.00026672584,0.00030858078,0.00040421792,0.00042070152,0.8759858,0.0015224918,0.05839999,0.0016279275,0.037674326],"study_design_scores_gemma":[0.000010351269,0.000068198155,0.0014934978,0.000020685618,0.000025940208,0.000041636606,0.000029718847,0.98717207,0.00017208736,0.010215188,0.0007314361,0.000019263949],"about_ca_topic_score_codex":0.011043617,"about_ca_topic_score_gemma":0.009333466,"teacher_disagreement_score":0.011043617,"about_ca_system_score_codex":0.0013435705,"about_ca_system_score_gemma":0.0012206342,"threshold_uncertainty_score":0.04870206},"labels":[],"label_agreement":null},{"id":"W1510546709","doi":"10.1214/lnms/1215091945","title":"General saddlepoint approximation methods for smooth functions of M-estimates with bootstrap applications","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes-monograph series","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Applied mathematics; Mathematics; Statistics","score_opus":0.07275725466865257,"score_gpt":0.3401941809746421,"score_spread":0.26743692630598953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1510546709","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023686126,0.00015957502,0.99882287,0.00002362293,0.0000169574,0.000012278694,0.000014066507,0.000096675525,0.0006170201],"genre_scores_gemma":[0.023573663,0.0009833504,0.968984,0.000096311516,0.0001360197,0.00031450932,0.00018231937,0.0004100405,0.0053198924],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99887544,0.00059365993,0.000073658484,0.000109781795,0.0003137741,0.000033582288],"domain_scores_gemma":[0.99826175,0.0011233496,0.00010653572,0.00021609118,0.00025423692,0.000038135957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003173725,0.0015301119,0.0014154157,0.0017618925,0.0005659018,0.0013026051,0.0022846002,0.0016411251,0.005141747],"category_scores_gemma":[0.008617104,0.00088912767,0.001874387,0.0017324795,0.0010732869,0.0015391726,0.0020573048,0.002530897,0.0029640251],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047914105,0.00006513621,0.00044386458,0.00033681083,0.000120576624,0.00022396963,0.00022337162,0.2008186,0.0052263313,0.61728704,0.007858353,0.16734809],"study_design_scores_gemma":[0.00001570126,0.000028979215,0.00014877718,0.000062432824,0.000017887918,0.000112886286,0.00002070966,0.7986576,0.0013288641,0.18582188,0.013760152,0.000024066003],"about_ca_topic_score_codex":0.0011984606,"about_ca_topic_score_gemma":0.0013965254,"teacher_disagreement_score":0.005141747,"about_ca_system_score_codex":0.0006911739,"about_ca_system_score_gemma":0.00063187396,"threshold_uncertainty_score":0.017200828},"labels":[],"label_agreement":null},{"id":"W1520584263","doi":"","title":"Sensitivity analysis in convex quadratic optimization: Simultaneous perturbation of the objective and right-hand-side vectors","year":2007,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Convexity; Quadratic equation; Perturbation (astronomy); Mathematical optimization; Optimization problem; Parametric statistics; Piecewise; Differentiable function; Quadratic programming; Applied mathematics; Convex optimization; Convex function; Sensitivity (control systems); Regular polygon; Mathematical analysis","score_opus":0.02062638904830234,"score_gpt":0.2804876325850727,"score_spread":0.25986124353677037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1520584263","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016229223,0.0002002786,0.9816064,0.00013960156,0.000018961191,0.000024386527,0.00001563789,0.00005152063,0.0017139299],"genre_scores_gemma":[0.9143566,0.00045255834,0.08270318,0.00011654347,0.00005417868,0.000097006814,0.00005098963,0.00008795108,0.0020809793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99784184,0.0010757487,0.00006277304,0.00027318017,0.00062749634,0.00011894748],"domain_scores_gemma":[0.9938538,0.0051412135,0.0003771718,0.0002420013,0.00032240496,0.00006334403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028787246,0.0010309627,0.0008182403,0.0005911869,0.00031489064,0.0010244255,0.0006268522,0.0008328211,0.0012373793],"category_scores_gemma":[0.010290574,0.00046640882,0.000794755,0.0005460815,0.0016419181,0.001324558,0.00108822,0.0012619123,0.00016709878],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008839865,0.000027605302,0.0007122954,0.00015215632,0.00008784768,0.0002239678,0.00010736249,0.93734825,0.0083962185,0.034088455,0.0003521086,0.018415282],"study_design_scores_gemma":[0.000002664804,0.00004166555,0.00020526035,0.000008434362,0.000010224448,0.000050593768,0.000015401441,0.98466694,0.0022519983,0.012423576,0.00031277575,0.000010481537],"about_ca_topic_score_codex":0.0016494611,"about_ca_topic_score_gemma":0.0004611577,"teacher_disagreement_score":0.0028787246,"about_ca_system_score_codex":0.00081916444,"about_ca_system_score_gemma":0.00050668954,"threshold_uncertainty_score":0.015224338},"labels":[],"label_agreement":null},{"id":"W1522624584","doi":"10.1109/mwsym.1975.1123333","title":"Solution of Large, Sparse Systms in Design and Analysis","year":2005,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Finite element method; Nonlinear system; Tearing; Linear system; Mathematical optimization; Applied mathematics; Algorithm; Mathematics; Engineering; Mathematical analysis; Mechanical engineering; Structural engineering","score_opus":0.0910958553823635,"score_gpt":0.3343416417639194,"score_spread":0.2432457863815559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1522624584","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046799376,0.0007913065,0.99725395,0.00014280493,0.000033262546,0.000011468247,0.000013706414,0.00004573029,0.0012397331],"genre_scores_gemma":[0.051183164,0.007856797,0.9332685,0.0003178544,0.0005216862,0.00035847517,0.00015942716,0.000098907534,0.006235317],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992625,0.00025081093,0.00004332093,0.00007810398,0.00032040404,0.000044745128],"domain_scores_gemma":[0.99905664,0.0006837004,0.00006299131,0.00009314566,0.00007931184,0.000024172487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001722027,0.001280837,0.0011701477,0.0010913606,0.0004927945,0.0010402773,0.000977026,0.0014869955,0.0023650911],"category_scores_gemma":[0.002797815,0.00065696833,0.0009460764,0.0015220983,0.0020117757,0.001579509,0.0016425358,0.001857562,0.00128286],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050313,0.00006117625,0.00044274257,0.0006408652,0.00009894324,0.00020133196,0.000269935,0.27220696,0.013439092,0.5364646,0.008552023,0.16757205],"study_design_scores_gemma":[0.000024234436,0.000115224495,0.00023991453,0.00011328243,0.000023050636,0.00021093882,0.00005167425,0.60907835,0.0039764056,0.35346168,0.032658737,0.000046501078],"about_ca_topic_score_codex":0.0007336487,"about_ca_topic_score_gemma":0.0012688901,"teacher_disagreement_score":0.0023650911,"about_ca_system_score_codex":0.00050163956,"about_ca_system_score_gemma":0.0006587706,"threshold_uncertainty_score":0.009107053},"labels":[],"label_agreement":null},{"id":"W1539568852","doi":"10.1023/a:1011101700032","title":"Confidence Intervals of Quantiles in Hydrology Computed by an Analytical Method","year":2001,"lang":"en","type":"article","venue":"Natural Hazards","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Institut National d'Optique","funders":"","keywords":"Quantile; Quantile function; Confidence interval; Statistics; CDF-based nonparametric confidence interval; Estimator; Coverage probability; Mathematics; Confidence distribution; Confidence region; Robust confidence intervals; Confidence and prediction bands; Probability density function; Econometrics; Cumulative distribution function","score_opus":0.08328000088307935,"score_gpt":0.41402734880604963,"score_spread":0.3307473479229703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539568852","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03705654,0.0004729859,0.9610943,0.000107890766,0.00004091125,0.000024770801,0.000103780796,0.00037698037,0.0007217265],"genre_scores_gemma":[0.8041055,0.0006136813,0.19360685,0.00011224577,0.00014122114,0.00014328468,0.00049083127,0.0002778078,0.0005086061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9913099,0.0048536276,0.00051278726,0.0010079773,0.0018221712,0.00049368944],"domain_scores_gemma":[0.8259859,0.15148355,0.005552518,0.0083161425,0.0077039474,0.0009579122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029300658,0.0009238085,0.0012647441,0.004011514,0.00056462333,0.0031348446,0.0021940176,0.0016989188,0.0018689674],"category_scores_gemma":[0.14769703,0.0007750911,0.0014179765,0.0022569322,0.0023308229,0.0024547963,0.0024370826,0.0024866182,0.00025934065],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094251113,0.00011260447,0.011138159,0.0004391521,0.0004194309,0.00022432122,0.00053256453,0.7271209,0.0045227464,0.17516285,0.0013972139,0.07798753],"study_design_scores_gemma":[0.00007821824,0.00012527569,0.003647781,0.00013760025,0.00010299799,0.00011859542,0.000079429694,0.935668,0.005173706,0.05346658,0.0013347464,0.000067052686],"about_ca_topic_score_codex":0.0024889174,"about_ca_topic_score_gemma":0.0009966424,"teacher_disagreement_score":0.029300658,"about_ca_system_score_codex":0.0012033472,"about_ca_system_score_gemma":0.0013560588,"threshold_uncertainty_score":0.15495855},"labels":[],"label_agreement":null},{"id":"W1547533744","doi":"10.1017/s0001924000010150","title":"Bayesian sensitivity analysis of flight parameters that affect main landing gear yield locations","year":2014,"lang":"en","type":"article","venue":"The Aeronautical Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"Engineering and Physical Sciences Research Council","keywords":"Landing gear; Descent (aeronautics); Sensitivity (control systems); Structural engineering; Bending moment; Shock (circulatory); Engineering; Control theory (sociology); Aerospace engineering; Computer science","score_opus":0.09483672752970208,"score_gpt":0.3242031557245695,"score_spread":0.2293664281948674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1547533744","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80314535,0.00019375015,0.1918071,0.00021258036,0.00002111602,0.00011135678,0.00032418,0.00023742614,0.003947085],"genre_scores_gemma":[0.9980299,0.000018851544,0.0016081489,0.000013760474,0.0000014565254,0.000016363501,0.00006311927,0.000010070489,0.00023844282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988121,0.0005881887,0.000037933987,0.00015210873,0.0002513888,0.00015825573],"domain_scores_gemma":[0.98756486,0.010755837,0.0006597419,0.00036333175,0.00054001465,0.00011616143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004606858,0.0008654675,0.00075048674,0.0010584621,0.00032260068,0.00074286567,0.00055778143,0.00091668835,0.0011176446],"category_scores_gemma":[0.013319694,0.0006970835,0.0013448027,0.00029847166,0.00063527905,0.0007130506,0.0008041085,0.0009291906,0.00010827399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007032127,0.000023126944,0.0016883271,0.000014445377,0.000049776678,0.000043336484,0.000014079739,0.9945673,0.0018299301,0.0006232154,0.00004822902,0.0010279411],"study_design_scores_gemma":[0.000005875247,0.000060555394,0.0020778186,0.000004870124,0.000026841224,0.000014887962,0.000012250312,0.9955793,0.0015029033,0.0006592493,0.0000400501,0.000015451913],"about_ca_topic_score_codex":0.006642947,"about_ca_topic_score_gemma":0.0028638819,"teacher_disagreement_score":0.006642947,"about_ca_system_score_codex":0.0011016845,"about_ca_system_score_gemma":0.0005891308,"threshold_uncertainty_score":0.024363697},"labels":[],"label_agreement":null},{"id":"W1563464067","doi":"10.1007/s00158-004-0400-5","title":"An investigation of structural optimization in crashworthiness design using a stochastic approach","year":2004,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nordic Life Science Pipeline (Canada)","funders":"Saab; Stiftelsen för Strategisk Forskning","keywords":"Crashworthiness; Response surface methodology; Convergence (economics); Mathematical optimization; Design of experiments; Stochastic optimization; Engineering design process; Computer science; Engineering; Mathematics; Finite element method; Structural engineering; Mechanical engineering; Machine learning","score_opus":0.07156319175293331,"score_gpt":0.32834496683094255,"score_spread":0.25678177507800926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1563464067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07298865,0.0005264221,0.91807306,0.00067483896,0.000043176617,0.00004193466,0.000037560676,0.00004232709,0.007572084],"genre_scores_gemma":[0.91207904,0.00071299804,0.08415845,0.00013938852,0.00012059988,0.000092159,0.000041718526,0.00005348536,0.002602175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988765,0.0006384912,0.00003037034,0.00009039013,0.000275858,0.00008836819],"domain_scores_gemma":[0.99553865,0.0036060624,0.00034510257,0.000113719536,0.00031200593,0.00008436682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028618427,0.00069510355,0.0010876226,0.0008807921,0.0005456687,0.0010033322,0.001094581,0.0012343992,0.0014962592],"category_scores_gemma":[0.008617551,0.0007080103,0.0011035474,0.00066690566,0.0012736765,0.0010997835,0.0009548646,0.0009757892,0.00009591326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013038758,0.000023711162,0.00019996215,0.000033066564,0.00001625114,0.000017797549,0.000013447484,0.9608419,0.0004669379,0.035544485,0.000084993466,0.0027442824],"study_design_scores_gemma":[0.000003643636,0.000023503275,0.00012300914,0.0000036342349,0.0000051826337,0.0000057585667,0.0000048328834,0.99391633,0.00008907045,0.00569382,0.00012842889,0.0000027158462],"about_ca_topic_score_codex":0.003744195,"about_ca_topic_score_gemma":0.0036190415,"teacher_disagreement_score":0.003744195,"about_ca_system_score_codex":0.0010522457,"about_ca_system_score_gemma":0.0012562273,"threshold_uncertainty_score":0.01513505},"labels":[],"label_agreement":null},{"id":"W1566165412","doi":"","title":"On the estimation of hazard models with flexible baseline hazards and nonparametric unobserved heterogeneity","year":2003,"lang":"en","type":"article","venue":"Economics bulletin","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"sort; Baseline (sea); Nonparametric statistics; Hazard; Econometrics; Estimation; Computer science; Proportional hazards model; Specification; Statistics; Hazard ratio; Mathematics; Engineering; Confidence interval","score_opus":0.07349646085740313,"score_gpt":0.2719194194510167,"score_spread":0.19842295859361359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1566165412","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030369654,0.00009640205,0.9962083,0.00015753938,0.000013172859,0.000030986717,0.000044584463,0.000046292338,0.00036584635],"genre_scores_gemma":[0.27118996,0.0013449698,0.7217278,0.00032856682,0.0002395002,0.0007669131,0.00058663957,0.00017473933,0.0036410505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98651266,0.010852072,0.00028964813,0.0007759192,0.0011832974,0.00038636345],"domain_scores_gemma":[0.9403232,0.05140114,0.0029005657,0.0038857195,0.0011881702,0.00030121402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022291735,0.001123693,0.0014504167,0.0016827951,0.0006662056,0.0017453857,0.0031443466,0.0016617593,0.0040889727],"category_scores_gemma":[0.06994571,0.0009208899,0.0023532263,0.0029625248,0.0023781725,0.0028211128,0.004087305,0.0030414504,0.0004849637],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014723593,0.00009818594,0.0058252914,0.00023704562,0.00031197548,0.00027573114,0.00046868794,0.51911277,0.0006657725,0.37318426,0.0017467793,0.09792621],"study_design_scores_gemma":[0.00006405207,0.00009044607,0.0012658022,0.000063946536,0.00005266885,0.00012868631,0.000073111325,0.64420193,0.0004121494,0.35085526,0.0027291689,0.00006276391],"about_ca_topic_score_codex":0.0054332684,"about_ca_topic_score_gemma":0.004356868,"teacher_disagreement_score":0.022291735,"about_ca_system_score_codex":0.0010260855,"about_ca_system_score_gemma":0.0030081817,"threshold_uncertainty_score":0.11789137},"labels":[],"label_agreement":null},{"id":"W1578600300","doi":"10.1109/icsmc.1996.561475","title":"Reliability-based optimization on a graph-theoretic modelling foundation","year":2002,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Reliability (semiconductor); Mathematical optimization; Graph; Structural reliability; Reliability theory; Graph theory; Reliability engineering; Theoretical computer science; Mathematics; Artificial intelligence; Engineering; Probabilistic logic; Failure rate","score_opus":0.10292605096290218,"score_gpt":0.29177974442525223,"score_spread":0.18885369346235004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1578600300","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019726788,0.00010203572,0.99395764,0.0001900928,0.000011380545,0.000013048631,0.000037409012,0.00010321604,0.0036123989],"genre_scores_gemma":[0.41416058,0.0013404645,0.57491994,0.00022464972,0.00011188476,0.00043573417,0.00029347625,0.0003877664,0.008125516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993316,0.00024375298,0.000022383469,0.00008903058,0.00026797492,0.000045352277],"domain_scores_gemma":[0.9992411,0.00048901397,0.00006704045,0.00009902136,0.00008302996,0.000020813091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009986279,0.00078863895,0.00075974077,0.0007328347,0.00036473747,0.0009002953,0.0011143209,0.0007212173,0.0030846202],"category_scores_gemma":[0.0024265007,0.0006761073,0.0009417039,0.000737073,0.0012730021,0.0017388023,0.0010216429,0.001421866,0.0007005882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007813204,0.000009870328,0.000037034722,0.00003236776,0.000009102586,0.000014525943,0.000019978354,0.709058,0.00084282603,0.28119454,0.00055400404,0.008219961],"study_design_scores_gemma":[0.0000043938635,0.000009461996,0.000021170365,0.000007585083,0.0000042099864,0.00000783202,0.000003079149,0.8292227,0.0002697392,0.16856852,0.0018761521,0.0000051201805],"about_ca_topic_score_codex":0.0019133833,"about_ca_topic_score_gemma":0.0021612686,"teacher_disagreement_score":0.0030846202,"about_ca_system_score_codex":0.0012394801,"about_ca_system_score_gemma":0.001347712,"threshold_uncertainty_score":0.010319114},"labels":[],"label_agreement":null},{"id":"W1579664569","doi":"10.1023/a:1022463111224","title":"Beta Approximation to the Distribution of Kolmogorov-Smirnov Statistic","year":2002,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Kolmogorov–Smirnov test; Mathematics; Statistic; Beta distribution; Goodness of fit; Anderson–Darling test; Test statistic; Statistics; Simple (philosophy); BETA (programming language); F-distribution; Distribution (mathematics); Applied mathematics; Statistical hypothesis testing; Probability distribution; Mathematical analysis; Computer science","score_opus":0.18488499937142475,"score_gpt":0.36081387621232186,"score_spread":0.1759288768408971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579664569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068650884,0.00121071,0.98744595,0.00037271003,0.00017747025,0.00001601318,0.00010845218,0.0002743182,0.0035293535],"genre_scores_gemma":[0.6196562,0.0082819965,0.348626,0.00088486535,0.0019138106,0.00061300106,0.0012951633,0.0010254006,0.017703611],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9961506,0.0018001033,0.00013094574,0.0005853702,0.000971761,0.00036117822],"domain_scores_gemma":[0.9848371,0.009201782,0.0011569418,0.0014696905,0.0027230952,0.000611356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007495352,0.0011934179,0.001794646,0.0037616664,0.0008879978,0.0036870276,0.0024446296,0.0019435541,0.0049935845],"category_scores_gemma":[0.038966767,0.00076407206,0.0011142705,0.0028278816,0.0026100818,0.0044785347,0.002297584,0.0054650353,0.0028995685],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020512969,0.000050293216,0.0027455695,0.0003044406,0.00010138293,0.00024400628,0.00026439666,0.06295088,0.003551356,0.8773595,0.0068122786,0.045410763],"study_design_scores_gemma":[0.000038217655,0.000072218616,0.0017599976,0.00016108429,0.00004067108,0.0005562485,0.00008738945,0.42750207,0.0020898974,0.55869496,0.008928013,0.00006926041],"about_ca_topic_score_codex":0.0011458587,"about_ca_topic_score_gemma":0.00073165435,"teacher_disagreement_score":0.007495352,"about_ca_system_score_codex":0.0016273678,"about_ca_system_score_gemma":0.0018529783,"threshold_uncertainty_score":0.03963971},"labels":[],"label_agreement":null},{"id":"W1580227917","doi":"","title":"Dealing With Uncertainty in Engineering and Management Practices","year":2011,"lang":"en","type":"dissertation","venue":"oURspace (University of Regina)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University","keywords":"Engineering; Computer science","score_opus":0.03993353667638691,"score_gpt":0.26127581997013294,"score_spread":0.22134228329374603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1580227917","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0128336605,0.0012302953,0.9721683,0.0012573433,0.000048690363,0.00013532171,0.000074207914,0.00007658411,0.012175616],"genre_scores_gemma":[0.45901707,0.003437194,0.5340432,0.00023774484,0.00013300721,0.0004567035,0.00015209989,0.00004010248,0.0024829248],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.989763,0.0044060643,0.00093714625,0.001249852,0.0032268616,0.00041697407],"domain_scores_gemma":[0.9909869,0.0046341177,0.0014257599,0.0014200226,0.0013875135,0.00014574079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009400988,0.0012207191,0.000950002,0.003540508,0.0013878002,0.006118932,0.002412517,0.0017142694,0.0020769057],"category_scores_gemma":[0.016275223,0.00063808396,0.0012550659,0.003537049,0.0026222179,0.004755868,0.0034287747,0.0014777861,0.0003105193],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027036562,0.00007052051,0.0030626582,0.00034787235,0.00016090357,0.00033909618,0.0014448006,0.28407332,0.0010030927,0.55715084,0.0010259564,0.15129384],"study_design_scores_gemma":[0.000012490472,0.00006928231,0.0012481703,0.00041711947,0.000057001213,0.00019264735,0.0009819492,0.27937785,0.0016312365,0.69241697,0.023513272,0.00008199185],"about_ca_topic_score_codex":0.002999046,"about_ca_topic_score_gemma":0.0025304577,"teacher_disagreement_score":0.009400988,"about_ca_system_score_codex":0.0038118712,"about_ca_system_score_gemma":0.0039828527,"threshold_uncertainty_score":0.049717784},"labels":[],"label_agreement":null},{"id":"W1587490865","doi":"10.48550/arxiv.1208.0028","title":"On Bayesian credible sets in restricted parameter space problems and lower bounds for frequentist coverage","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Frequentist inference; Bayesian probability; Mathematics; Parameter space; Space (punctuation); Econometrics; Credible interval; Statistics; Frequentist probability; Mathematical economics; Applied mathematics; Bayesian inference; Computer science","score_opus":0.15207328561988573,"score_gpt":0.24890648326482387,"score_spread":0.09683319764493814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1587490865","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045819557,0.0015725747,0.98656505,0.001084113,0.000038864957,0.00007812591,0.00013302585,0.00009396046,0.0058522844],"genre_scores_gemma":[0.34914893,0.0060182908,0.6353786,0.0011029288,0.00110285,0.0019843387,0.00073078414,0.00048313197,0.004050186],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9635239,0.02680925,0.0009859309,0.0029964254,0.004805507,0.00087887724],"domain_scores_gemma":[0.58053493,0.39239752,0.008969275,0.011478295,0.0052266656,0.00139336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.064388014,0.002780088,0.004812992,0.008104406,0.0022072534,0.007405064,0.0068563805,0.0053924033,0.008380152],"category_scores_gemma":[0.3061131,0.0017508634,0.0038196417,0.006751369,0.011050988,0.010507202,0.008473452,0.012729946,0.001259621],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006624165,0.000035009416,0.0006007654,0.00024312432,0.000098824195,0.00011994766,0.000380609,0.07890922,0.00024821592,0.9019586,0.0010400628,0.016299356],"study_design_scores_gemma":[0.000023141576,0.000026429718,0.00022144869,0.00022060683,0.000029377237,0.000075768825,0.000059182003,0.14496621,0.00029098638,0.8524219,0.0016249783,0.000039977014],"about_ca_topic_score_codex":0.0020362632,"about_ca_topic_score_gemma":0.0013175757,"teacher_disagreement_score":0.064388014,"about_ca_system_score_codex":0.0043087634,"about_ca_system_score_gemma":0.002233439,"threshold_uncertainty_score":0.34052044},"labels":[],"label_agreement":null},{"id":"W1594949792","doi":"10.2139/ssrn.400760","title":"Appendix: Omitted Proofs of 'Testing for a Unit Root in Panels with Dynamic Factors'","year":2003,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Mathematical proof; Unit root; Dynamic testing; Root (linguistics); Econometrics; Computer science; Mathematics; Calculus (dental); Programming language; Linguistics; Geometry; Philosophy; Medicine","score_opus":0.055185573031781024,"score_gpt":0.3075310634080508,"score_spread":0.2523454903762698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1594949792","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005029279,0.0022398923,0.41847742,0.033607777,0.029119596,0.0035956453,0.2717733,0.0055612107,0.23059595],"genre_scores_gemma":[0.14556178,0.003899164,0.3574686,0.026664939,0.015626641,0.0108046215,0.16698678,0.0036142573,0.26937327],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961994,0.0011493537,0.00045720593,0.00037154224,0.0015265844,0.0002958452],"domain_scores_gemma":[0.87942064,0.086087875,0.0027466773,0.0072628167,0.023715919,0.00076607044],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004648052,0.0014915294,0.0019235143,0.0026816973,0.0012918789,0.0011990378,0.0029749775,0.0027585654,0.40598634],"category_scores_gemma":[0.11129199,0.0014575989,0.0025315508,0.0030087824,0.0008281411,0.0025144517,0.0015868412,0.0029412925,0.15305103],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005105665,0.00017469727,0.00055522914,0.0005193103,0.000048045735,0.00021916752,0.000044270797,0.0024825071,0.00026557362,0.030969163,0.94111985,0.023551054],"study_design_scores_gemma":[0.0010134351,0.00022856228,0.012540944,0.0009009708,0.000171811,0.001729962,0.00017110462,0.017035065,0.0037363616,0.33872288,0.6235337,0.00021518834],"about_ca_topic_score_codex":0.010856336,"about_ca_topic_score_gemma":0.009151574,"teacher_disagreement_score":0.40598634,"about_ca_system_score_codex":0.002036836,"about_ca_system_score_gemma":0.0043311296,"threshold_uncertainty_score":0.84728837},"labels":[],"label_agreement":null},{"id":"W1623277192","doi":"10.1520/stp13443s","title":"Component Design: The Interface Between Threshold and Endurance Limit","year":2000,"lang":"en","type":"book-chapter","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Limit (mathematics); Component (thermodynamics); Interface (matter); Interface design; Computer science; Physics; Human–computer interaction; Mathematics; Operating system; Mathematical analysis","score_opus":0.1758517349512376,"score_gpt":0.328814959409466,"score_spread":0.15296322445822838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1623277192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014423552,0.009105525,0.92428255,0.00053237064,0.00011848264,0.00003240875,0.000078364385,0.00054124463,0.050885543],"genre_scores_gemma":[0.50357455,0.012267369,0.43511513,0.00043476518,0.00027018186,0.00019643948,0.00021546795,0.00080782035,0.047118206],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991788,0.00014952061,0.000050596907,0.00013723345,0.00044893177,0.000034835717],"domain_scores_gemma":[0.9990471,0.00054668466,0.000056651028,0.00010444252,0.00020893037,0.00003621912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008356622,0.0005060416,0.00055682665,0.0006522951,0.0002557949,0.0017593957,0.0010916444,0.00087457977,0.004419631],"category_scores_gemma":[0.0021439989,0.00036460464,0.00023972802,0.00053999096,0.0010758466,0.0021272637,0.0006502285,0.0007063558,0.0015993607],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007399835,0.000031384432,0.0012336235,0.0008271358,0.00002453028,0.00015914027,0.00048858806,0.05604855,0.028609911,0.5183991,0.005530349,0.38857368],"study_design_scores_gemma":[0.000014203899,0.000199196,0.001760401,0.00032848184,0.00004559431,0.0013816939,0.00017721693,0.16612072,0.021287154,0.6749595,0.1336688,0.000057011646],"about_ca_topic_score_codex":0.00052306673,"about_ca_topic_score_gemma":0.0005270178,"teacher_disagreement_score":0.004419631,"about_ca_system_score_codex":0.00061199645,"about_ca_system_score_gemma":0.00048631866,"threshold_uncertainty_score":0.014785111},"labels":[],"label_agreement":null},{"id":"W1637407644","doi":"10.1007/978-3-540-78929-1_51","title":"Level Set Methods for Computing Reachable Sets of Hybrid Systems with Differential Algebraic Equation Dynamics","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Reachability; Computer science; Hybrid system; Differential equation; Nonlinear system; Set (abstract data type); Algebraic number; Differential algebraic equation; Dynamical systems theory; Algebraic equation; Applied mathematics; Reset (finance); Differential (mechanical device); State (computer science); Algorithm; Mathematics; Ordinary differential equation; Mathematical analysis","score_opus":0.1324122096792108,"score_gpt":0.35314320903776925,"score_spread":0.22073099935855844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1637407644","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068010725,0.00013853915,0.9912826,0.000033425116,0.000016325592,0.00002940109,0.000073616546,0.00033566423,0.0012893508],"genre_scores_gemma":[0.23453781,0.00032131883,0.76153725,0.00004950938,0.000034570206,0.00033693583,0.00043483806,0.0003554966,0.0023922345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994541,0.00015709932,0.0000403409,0.00006618403,0.00023792997,0.0000442724],"domain_scores_gemma":[0.9980013,0.0015481707,0.00007879974,0.00013024906,0.00016711222,0.00007440949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013653374,0.0009424633,0.0017629082,0.0018041802,0.0008921756,0.0019702653,0.0022137994,0.0011884074,0.004473189],"category_scores_gemma":[0.0044176965,0.0009772945,0.0017585058,0.0013058698,0.0012318833,0.002007734,0.0022387719,0.0024086654,0.0007693128],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007418259,0.000046527723,0.0004747625,0.00017424708,0.00007433194,0.000047439742,0.000119035794,0.8513737,0.001670321,0.08351152,0.0009968013,0.06143721],"study_design_scores_gemma":[0.0000045972456,0.000006362734,0.000032933007,0.0000074258896,0.0000051560464,0.0000045953607,0.000006453454,0.9712219,0.00030982206,0.028111583,0.0002848583,0.000004319841],"about_ca_topic_score_codex":0.0032607133,"about_ca_topic_score_gemma":0.0038205779,"teacher_disagreement_score":0.004473189,"about_ca_system_score_codex":0.0011231407,"about_ca_system_score_gemma":0.00093099434,"threshold_uncertainty_score":0.0149642825},"labels":[],"label_agreement":null},{"id":"W1652045230","doi":"10.48550/arxiv.1503.02352","title":"Infinite-dimensional $\\ell^1$ minimization and function approximation from pointwise data","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Alfred P. Sloan Foundation","keywords":"Pointwise; Mathematics; Minification; Function (biology); A priori and a posteriori; Applied mathematics; Aliasing; Truncation (statistics); Algorithm; Mathematical optimization; Mathematical analysis; Computer science; Filter (signal processing)","score_opus":0.2935977031256472,"score_gpt":0.24677706120341852,"score_spread":0.04682064192222868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1652045230","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061294306,0.00013973002,0.99299026,0.00013445101,0.000011368906,0.000007711096,0.00002541915,0.000032051285,0.0005295477],"genre_scores_gemma":[0.34217972,0.00075319613,0.6521545,0.0002506901,0.00011200271,0.00021804859,0.00032559078,0.00010621692,0.003900021],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99866676,0.00059369806,0.00006904979,0.00021585128,0.0003784024,0.00007623626],"domain_scores_gemma":[0.99672914,0.002340558,0.00025617666,0.0003167199,0.0002797422,0.00007769836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039840313,0.00086509285,0.0010725883,0.0009360879,0.00048960856,0.0011410329,0.0015761242,0.0015440616,0.0010849197],"category_scores_gemma":[0.007971824,0.00055056094,0.0009361209,0.0006923272,0.002504746,0.0018619231,0.001995099,0.0018353193,0.00033409317],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008964048,0.00004492828,0.0008319802,0.0002527468,0.00006144788,0.00008179266,0.000119095166,0.7287631,0.005701601,0.23370637,0.000727161,0.029620074],"study_design_scores_gemma":[0.0000031991415,0.000024972374,0.000096384065,0.000017834873,0.0000054384673,0.000020453415,0.000010202263,0.9537899,0.0019325749,0.04349062,0.0005973752,0.000011023862],"about_ca_topic_score_codex":0.0011947562,"about_ca_topic_score_gemma":0.0009372427,"teacher_disagreement_score":0.0039840313,"about_ca_system_score_codex":0.0012825455,"about_ca_system_score_gemma":0.0007181536,"threshold_uncertainty_score":0.021069825},"labels":[],"label_agreement":null},{"id":"W17620124","doi":"10.1186/1471-2377-7-19","title":"A whirlwind tour of statistical methods in structural dynamics.","year":2004,"lang":"en","type":"article","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Janeway Children's Health and Rehabilitation Centre","funders":"","keywords":"Computer science; Process (computing); Set (abstract data type); Statistical inference; Exploratory data analysis; Inference; Whirlwind; Multivariate statistics; Variance (accounting); Data science; Industrial engineering; Machine learning; Data mining; Management science; Artificial intelligence; Engineering; Mathematics; Statistics","score_opus":0.028668578728016484,"score_gpt":0.31958165500372626,"score_spread":0.2909130762757098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W17620124","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006537917,0.08885261,0.8811102,0.012592068,0.0042365845,0.00007749687,0.0003878108,0.0012715124,0.010817966],"genre_scores_gemma":[0.02850584,0.068547554,0.85623884,0.0068697617,0.011778651,0.0006304525,0.0010619884,0.001898522,0.024468413],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956654,0.0023004182,0.0003729259,0.00046868465,0.001090172,0.00010247553],"domain_scores_gemma":[0.98261136,0.0136438515,0.00029571183,0.0015151849,0.0015779835,0.00035586275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01000152,0.0020113068,0.0018681376,0.0034442146,0.000831525,0.0026217028,0.002391996,0.002667448,0.009943956],"category_scores_gemma":[0.020729687,0.0014617606,0.0024158268,0.0037197517,0.004418601,0.004826796,0.0031586166,0.008511669,0.0068262336],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014629916,0.00019388531,0.00081407983,0.0014001088,0.00053949485,0.00037996582,0.0003971199,0.017491248,0.0015161166,0.46970716,0.21984449,0.2875701],"study_design_scores_gemma":[0.00005211304,0.00006960352,0.00039972115,0.00036543465,0.00005064136,0.00017522153,0.00007367529,0.032461658,0.00046137744,0.75595874,0.20986365,0.00006824295],"about_ca_topic_score_codex":0.0035168133,"about_ca_topic_score_gemma":0.0058589163,"teacher_disagreement_score":0.01000152,"about_ca_system_score_codex":0.0015797513,"about_ca_system_score_gemma":0.0017107332,"threshold_uncertainty_score":0.052893758},"labels":[],"label_agreement":null},{"id":"W1765951233","doi":"10.1016/j.jhydrol.2015.09.034","title":"A fractional factorial probabilistic collocation method for uncertainty propagation of hydrologic model parameters in a reduced dimensional space","year":2015,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Latin hypercube sampling; Collocation (remote sensing); Factorial; Monte Carlo method; Fractional factorial design; Probabilistic logic; Mathematics; Hydrological modelling; Uncertainty quantification; Equifinality; Hydrogeology; Computer science; Mathematical optimization; Applied mathematics; Statistics; Factorial experiment; Machine learning","score_opus":0.1396165955386345,"score_gpt":0.37969600166510714,"score_spread":0.24007940612647263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1765951233","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033570554,0.00004430543,0.99626523,0.00002373515,0.000017347511,0.0000075691764,0.000011667924,0.0000510661,0.00022198356],"genre_scores_gemma":[0.2442827,0.00021091296,0.7534686,0.000070834474,0.000053266867,0.00011486227,0.00011424819,0.00014490557,0.0015397302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999363,0.00030808593,0.000037854887,0.00008757993,0.00016723573,0.00003621199],"domain_scores_gemma":[0.99829215,0.001017981,0.00012926066,0.00014875045,0.0003617288,0.000050081602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016382649,0.0006775897,0.0007961015,0.00065075693,0.0005895138,0.0007836232,0.0010163268,0.0015373608,0.0019206118],"category_scores_gemma":[0.004362459,0.0005429439,0.0008713922,0.00075572345,0.0007085763,0.0011222616,0.00085810176,0.0009847925,0.00038170436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014970219,0.00006311609,0.00044409855,0.00011279775,0.00006292053,0.00008514901,0.00010962457,0.88660455,0.009556402,0.023874454,0.0009376701,0.07799952],"study_design_scores_gemma":[0.0000022627548,0.000008461674,0.000027914908,0.0000022615445,0.000002844243,0.000005207565,0.0000023998575,0.9984806,0.0003318772,0.0009118159,0.00021970896,0.000004665655],"about_ca_topic_score_codex":0.006111489,"about_ca_topic_score_gemma":0.004472609,"teacher_disagreement_score":0.006111489,"about_ca_system_score_codex":0.0005248817,"about_ca_system_score_gemma":0.0010145139,"threshold_uncertainty_score":0.012151837},"labels":[],"label_agreement":null},{"id":"W1769081502","doi":"10.1155/2015/764643","title":"Inverse Problems via the “Generalized Collage Theorem” for Vector-Valued Lax-Milgram-Based Variational Problems","year":2015,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Inverse; Mathematics; Milgram experiment; Inverse problem; Applied mathematics; Calculus (dental); Mathematical analysis; Pure mathematics; Algebra over a field; Geometry; Political science; Law","score_opus":0.09478737871887571,"score_gpt":0.30026705104290763,"score_spread":0.20547967232403191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1769081502","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012700952,0.00037165525,0.9738038,0.0004631897,0.00014254116,0.000035921254,0.000034658322,0.00006400511,0.012383211],"genre_scores_gemma":[0.6304676,0.0011614504,0.3450082,0.0006638624,0.00050011254,0.0003385033,0.000164976,0.00029883496,0.021396564],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99913824,0.0003674181,0.000037507787,0.000103967956,0.0002870902,0.00006573324],"domain_scores_gemma":[0.99901354,0.00047347014,0.00012424069,0.0001240994,0.00018601709,0.00007857649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020587523,0.0010868618,0.0011245512,0.0011109314,0.0008947465,0.0023087051,0.0010755203,0.001623882,0.0040154196],"category_scores_gemma":[0.004095681,0.00038502493,0.001243516,0.00056811655,0.0033339509,0.00281199,0.0038194472,0.0021125164,0.0006544911],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013955779,0.000011068699,0.00012949045,0.000057104426,0.000018154284,0.00012391667,0.00011264078,0.047988784,0.0014105628,0.9429316,0.00089872023,0.006304043],"study_design_scores_gemma":[0.000014134695,0.000035526184,0.00008021081,0.000027174887,0.000010059496,0.00012461442,0.000048175185,0.4012251,0.000978735,0.5937705,0.003662688,0.00002299637],"about_ca_topic_score_codex":0.0010715475,"about_ca_topic_score_gemma":0.0007404968,"teacher_disagreement_score":0.0040154196,"about_ca_system_score_codex":0.0008382282,"about_ca_system_score_gemma":0.00097058475,"threshold_uncertainty_score":0.01343292},"labels":[],"label_agreement":null},{"id":"W1806842414","doi":"10.3968/j.pam.1925252820130501.718","title":"First Excursion Probabilities of Non-Linear Dynamical Systems by Importance Sampling","year":2013,"lang":"en","type":"article","venue":"Progress in applied mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Excursion; Mathematics; Linearization; Monte Carlo method; Poisson distribution; Sampling (signal processing); Linear dynamical system; Applied mathematics; Gaussian; Importance sampling; Linear model; Statistics; Linear system; Nonlinear system; Computer science; Mathematical analysis; Physics","score_opus":0.05653516897583648,"score_gpt":0.31950922688675387,"score_spread":0.2629740579109174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1806842414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01457687,0.000080532365,0.9848993,0.000019323437,0.000006981633,0.000018676425,0.000009390765,0.00008561,0.00030327684],"genre_scores_gemma":[0.7890737,0.00038948204,0.20814525,0.000041120773,0.000054304324,0.00017691788,0.00014857981,0.00010285904,0.0018677657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992679,0.00028413217,0.000033675675,0.00012491037,0.0002311836,0.00005819623],"domain_scores_gemma":[0.9949752,0.003952042,0.00039871523,0.00022382091,0.00034422474,0.0001060262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001727443,0.00066729507,0.0007720657,0.001241205,0.00031956227,0.0006588519,0.0008990955,0.0004881253,0.0011592131],"category_scores_gemma":[0.0073414985,0.00048477607,0.00072950404,0.00041708094,0.00091373257,0.0010878205,0.0008033939,0.0009840318,0.00018133855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012398661,0.000059748858,0.0026230358,0.00017236816,0.00008906767,0.00018840509,0.00013251568,0.911623,0.0059637628,0.034127854,0.00029709897,0.044599246],"study_design_scores_gemma":[0.0000027551434,0.000015309382,0.00035973024,0.00000456401,0.000005031186,0.000022091517,0.0000042837555,0.9941683,0.0008709337,0.004444334,0.00009531207,0.0000074298123],"about_ca_topic_score_codex":0.0021446755,"about_ca_topic_score_gemma":0.0013604648,"teacher_disagreement_score":0.0021446755,"about_ca_system_score_codex":0.00046567558,"about_ca_system_score_gemma":0.0004724726,"threshold_uncertainty_score":0.009135723},"labels":[],"label_agreement":null},{"id":"W1815257002","doi":"10.2139/ssrn.258952","title":"F Versus T Tests For Unit Roots","year":2001,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Unit (ring theory); Mathematics; Mathematics education","score_opus":0.10988503860456514,"score_gpt":0.3672038573240642,"score_spread":0.257318818719499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1815257002","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7248666,0.002905693,0.2272619,0.002554816,0.0041615977,0.0014701594,0.007922414,0.002178262,0.026678603],"genre_scores_gemma":[0.9475912,0.00022541275,0.04042546,0.0010629254,0.00065852614,0.0016027959,0.0022131794,0.00080557447,0.005414985],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8514343,0.09403731,0.010488431,0.027662437,0.010001491,0.0063761324],"domain_scores_gemma":[0.40335053,0.5643911,0.008365811,0.016835475,0.0042326762,0.0028244602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06038025,0.0031454933,0.006669917,0.0065927445,0.0035186703,0.0041455016,0.0058981073,0.010083501,0.05584498],"category_scores_gemma":[0.28288582,0.001037649,0.0072066826,0.004471563,0.0066850507,0.009097142,0.0043463893,0.008666595,0.006048328],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.09733455,0.005799626,0.1934257,0.008723862,0.030717611,0.018265188,0.010428715,0.029897396,0.023242008,0.100800574,0.052713566,0.4286513],"study_design_scores_gemma":[0.017221045,0.070779085,0.2959,0.0025892474,0.01793156,0.02441837,0.016149377,0.20986114,0.03147312,0.25132287,0.06061202,0.0017422138],"about_ca_topic_score_codex":0.00091660843,"about_ca_topic_score_gemma":0.0010371963,"teacher_disagreement_score":0.06038025,"about_ca_system_score_codex":0.0012556011,"about_ca_system_score_gemma":0.0021707222,"threshold_uncertainty_score":0.3193251},"labels":[],"label_agreement":null},{"id":"W1822088304","doi":"10.48550/arxiv.math/0202274","title":"Estimation of Weibull Shape Parameter by Shrinkage Towards an Interval Under Failure Censored Sampling","year":2002,"lang":"en","type":"book","venue":"ArXiv.org","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Estimator; Mathematics; Weibull distribution; Statistics; Shrinkage estimator; Shape parameter; Shrinkage; Interval (graph theory); Efficiency; Mean squared error; Applied mathematics; Bias of an estimator; Minimum-variance unbiased estimator; Combinatorics","score_opus":0.13254681219681114,"score_gpt":0.33162176446016634,"score_spread":0.1990749522633552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1822088304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005698395,0.00037348448,0.99306655,0.000042515614,0.000018677823,0.000011801196,0.000020426898,0.00013707884,0.00063093286],"genre_scores_gemma":[0.28899366,0.0021698116,0.7032189,0.00016559269,0.00022838324,0.00019907599,0.00041627674,0.0002525667,0.0043557603],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988996,0.00040868451,0.00005098119,0.00020237274,0.00039517277,0.000043187323],"domain_scores_gemma":[0.9961366,0.0023717608,0.00025674226,0.0005905395,0.00059505797,0.000049336788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035473362,0.000609709,0.0009741371,0.0008930341,0.00019927506,0.0005777659,0.0012807583,0.0007346793,0.0011351752],"category_scores_gemma":[0.013080409,0.00031778225,0.0005915973,0.000946759,0.00071585504,0.0012665922,0.0009403716,0.0011922346,0.0006307795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021714068,0.000055599598,0.0043845205,0.0003984392,0.00011041515,0.00015339452,0.00029658957,0.2602199,0.020852134,0.10346016,0.0039563878,0.6058954],"study_design_scores_gemma":[0.000019394254,0.00010404283,0.001717836,0.000054748347,0.000033764172,0.00026846962,0.000027225396,0.9294554,0.008920537,0.05260396,0.006749261,0.000045390483],"about_ca_topic_score_codex":0.00028289476,"about_ca_topic_score_gemma":0.00019299462,"teacher_disagreement_score":0.0035473362,"about_ca_system_score_codex":0.0002666479,"about_ca_system_score_gemma":0.00024056832,"threshold_uncertainty_score":0.018760383},"labels":[],"label_agreement":null},{"id":"W1825717997","doi":"10.6000/1929-6029.2015.04.02.5","title":"Reliability Analysis for Two Components Connected in Parallel with Lindley Probability Model","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Reliability (semiconductor); Estimator; Statistics; Confidence interval; Moment (physics); Extension (predicate logic); Maximum likelihood; Applied mathematics; Combinatorics; Power (physics); Physics; Computer science; Thermodynamics","score_opus":0.357863702380387,"score_gpt":0.5117207094614673,"score_spread":0.1538570070810803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1825717997","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09859561,0.0014669293,0.8922125,0.00073135487,0.00006798179,0.00008387956,0.00030691747,0.00032019557,0.006214578],"genre_scores_gemma":[0.9561172,0.0012378332,0.03371205,0.00009047458,0.00009439691,0.00016030556,0.00031775062,0.00006136991,0.008208537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99791867,0.0005876256,0.000101537196,0.00053156144,0.0006133676,0.00024720753],"domain_scores_gemma":[0.99576,0.0023137575,0.0007855028,0.000266937,0.00072384335,0.00014994471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002868267,0.0011615814,0.00156797,0.0023798405,0.0007947823,0.0016139768,0.0024471774,0.001394016,0.0039636274],"category_scores_gemma":[0.0070280693,0.0006308504,0.0013520953,0.0018507342,0.0014765032,0.002983124,0.0012672233,0.0013286087,0.0005482814],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016754415,0.000043202497,0.0042155995,0.00014681264,0.00013529877,0.0005663766,0.00031060947,0.8998012,0.0014494712,0.081337735,0.0011093224,0.010716809],"study_design_scores_gemma":[0.0000060859056,0.00003796885,0.0006822567,0.000010363745,0.00003903292,0.000111305846,0.00003945892,0.9761579,0.00020495018,0.02227763,0.00041590867,0.00001716359],"about_ca_topic_score_codex":0.009497521,"about_ca_topic_score_gemma":0.0049606063,"teacher_disagreement_score":0.009497521,"about_ca_system_score_codex":0.0023599188,"about_ca_system_score_gemma":0.0011537563,"threshold_uncertainty_score":0.01888448},"labels":[],"label_agreement":null},{"id":"W1827652896","doi":"10.1260/1369433011502408","title":"Design of RC Columns Subjected to Safety Constraint","year":2001,"lang":"en","type":"article","venue":"Advances in Structural Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Constraint (computer-aided design); Reliability engineering; Probabilistic logic; Structural engineering; Code (set theory); Probabilistic design; Optimal design; Computer science; Structural reliability; Mathematical optimization; Engineering; Engineering design process; Mathematics; Mechanical engineering","score_opus":0.03276672404270279,"score_gpt":0.31225085382939327,"score_spread":0.27948412978669046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1827652896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1699745,0.00053994945,0.8019686,0.00022249151,0.00005362838,0.00014811507,0.00029048376,0.00059690204,0.026205404],"genre_scores_gemma":[0.87134045,0.0003807328,0.118941545,0.00007682792,0.000027295655,0.00020280338,0.00021067873,0.00012164045,0.008697965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970716,0.000059556427,0.0000094528605,0.00004148492,0.00011994562,0.00006241531],"domain_scores_gemma":[0.99971956,0.000068994545,0.00006093412,0.000022839751,0.00009487601,0.00003277961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024448326,0.0004603026,0.00046218067,0.0003825117,0.00025262978,0.0005813653,0.0004707228,0.00066402,0.0032088095],"category_scores_gemma":[0.00062918564,0.00030138344,0.00025612555,0.0002491109,0.00043104705,0.00026489797,0.00029323637,0.00029846054,0.000615332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000848937,0.000023685136,0.0003535538,0.00010152929,0.000015807735,0.00011360531,0.000026844637,0.93852174,0.03695322,0.005907494,0.00081885385,0.017078772],"study_design_scores_gemma":[0.000031475847,0.00016207038,0.00058381713,0.000015891692,0.000017760498,0.000062825064,0.000034290657,0.97881776,0.0149480915,0.001724713,0.0035862967,0.000014992912],"about_ca_topic_score_codex":0.003739669,"about_ca_topic_score_gemma":0.0034933717,"teacher_disagreement_score":0.003739669,"about_ca_system_score_codex":0.0005730467,"about_ca_system_score_gemma":0.0011319094,"threshold_uncertainty_score":0.010734558},"labels":[],"label_agreement":null},{"id":"W18503642","doi":"","title":"Bayesian model selection for a finite element model of a large civil aircraft","year":2004,"lang":"en","type":"article","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Simon Fraser University; Deutsches Zentrum für Luft- und Raumfahrt","keywords":"Metamodeling; Finite element method; Stiffness; Deflection (physics); Bayesian probability; Surrogate model; Computer science; Natural frequency; Model selection; Inverse; Bayesian inference; Engineering; Structural engineering; Mathematics; Artificial intelligence; Machine learning; Vibration; Acoustics; Physics","score_opus":0.03146740585099436,"score_gpt":0.27406108963134346,"score_spread":0.2425936837803491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W18503642","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048618007,0.00017324011,0.94880706,0.00039857693,0.0000197686,0.00010197012,0.0002459901,0.00022689738,0.0014085403],"genre_scores_gemma":[0.8079789,0.0003758925,0.1850263,0.0001898217,0.00005254074,0.0009787122,0.0012186092,0.00010182793,0.0040773246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985839,0.0007785909,0.00005003469,0.0001984431,0.0003056415,0.000083414416],"domain_scores_gemma":[0.99644727,0.0027358988,0.0003332071,0.00009007765,0.0003264122,0.000067190485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004139262,0.0011188681,0.0012658095,0.0012251006,0.0005648903,0.0011105097,0.0013754396,0.0017214213,0.0018595373],"category_scores_gemma":[0.007333004,0.0010905021,0.0012167455,0.00087409996,0.0009840567,0.00089807843,0.00089625205,0.0017864633,0.0005350423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056389228,0.000020759418,0.00044463083,0.000020466245,0.0000191423,0.000025203315,0.000024432387,0.9918749,0.00035660883,0.0038588352,0.00012675932,0.0031719487],"study_design_scores_gemma":[0.000011373947,0.000014022504,0.000109759865,0.000003878643,0.0000043245623,0.000004060644,0.0000036551187,0.9975013,0.00009852125,0.0021402445,0.000103469494,0.0000054214497],"about_ca_topic_score_codex":0.009975542,"about_ca_topic_score_gemma":0.009596024,"teacher_disagreement_score":0.009975542,"about_ca_system_score_codex":0.0013155926,"about_ca_system_score_gemma":0.0018422806,"threshold_uncertainty_score":0.02189076},"labels":[],"label_agreement":null},{"id":"W1852595912","doi":"10.1109/iscas.2002.1009899","title":"Statistical analysis of switched linear networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Electronic circuit; Computer science; Monte Carlo method; PSL; Algorithm; Frequency domain; Moment (physics); Network analysis; Laplace transform; Mathematics; Statistics; Engineering","score_opus":0.07970003996051041,"score_gpt":0.3523401571832003,"score_spread":0.2726401172226899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1852595912","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017854897,0.00017637055,0.98050874,0.00013241138,0.000015308406,0.000019980494,0.000058264544,0.00022204468,0.0010121124],"genre_scores_gemma":[0.8227377,0.0007718113,0.17218858,0.0001463204,0.00018877351,0.00025087208,0.0003957691,0.000116655225,0.003203392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99900144,0.00033758313,0.000035937643,0.00016206918,0.00039810748,0.00006488925],"domain_scores_gemma":[0.99402386,0.004465473,0.0004945711,0.00030698927,0.00063773774,0.000071314375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014998191,0.00034971605,0.00045263526,0.0012585716,0.00030052953,0.0007568453,0.0005682162,0.00038956173,0.0015182253],"category_scores_gemma":[0.009042146,0.00026978969,0.0003946307,0.0006516935,0.00091584673,0.00099351,0.0004535792,0.0005922729,0.00020730591],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064878186,0.000027475371,0.0023468614,0.000078667865,0.00006678518,0.0000761712,0.00005425446,0.8086452,0.0044103325,0.11327412,0.0007464209,0.07020875],"study_design_scores_gemma":[0.000003325389,0.000013536987,0.0004302329,0.0000037030977,0.000004600798,0.000015671792,0.0000056051726,0.9754528,0.0007996941,0.0228022,0.0004634363,0.0000052495375],"about_ca_topic_score_codex":0.0016230706,"about_ca_topic_score_gemma":0.0011697559,"teacher_disagreement_score":0.0016230706,"about_ca_system_score_codex":0.0010019802,"about_ca_system_score_gemma":0.00090442476,"threshold_uncertainty_score":0.007931888},"labels":[],"label_agreement":null},{"id":"W1857089916","doi":"10.1139/cjfr-2015-0148","title":"Global sensitivity analysis for the Rothermel model based on high-dimensional model representation","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sensitivity (control systems); Environmental science; Variance-based sensitivity analysis; Parametric model; Parametric statistics; Variance (accounting); Terrain; Meteorology; Mathematics; Statistics; Engineering; Geography; One-way analysis of variance","score_opus":0.2921601362052443,"score_gpt":0.4227481578946782,"score_spread":0.1305880216894339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1857089916","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.343745,0.0004343857,0.6464119,0.00056896853,0.00006238568,0.00015943873,0.0006958329,0.00052147615,0.0074006733],"genre_scores_gemma":[0.9828439,0.00010405975,0.015072749,0.00006232452,0.000010722436,0.00012031239,0.0003463221,0.00006639036,0.0013731585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859875,0.0006722658,0.00005630971,0.0002905762,0.000211512,0.00017053689],"domain_scores_gemma":[0.9939551,0.0048541804,0.000387388,0.0002487888,0.00046799445,0.000086606946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042106127,0.0016486924,0.0011815368,0.0013166124,0.0005379069,0.0015228739,0.0010352831,0.001433866,0.0031432589],"category_scores_gemma":[0.009483917,0.0005375978,0.0027555265,0.0005891212,0.0009231177,0.001325242,0.0015155915,0.0019229623,0.00018123897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018498808,0.00001033604,0.00082859624,0.000017305782,0.000032480602,0.00004412326,0.000022105827,0.99515,0.00042227548,0.0022921266,0.00009070755,0.0010713505],"study_design_scores_gemma":[0.000002409751,0.000018268656,0.00035098055,0.000003739389,0.000011480557,0.000008887601,0.000016744574,0.99773896,0.00017464897,0.0015898793,0.00007572458,0.00000828636],"about_ca_topic_score_codex":0.016473003,"about_ca_topic_score_gemma":0.0062406585,"teacher_disagreement_score":0.016473003,"about_ca_system_score_codex":0.0017121809,"about_ca_system_score_gemma":0.0010829588,"threshold_uncertainty_score":0.032754242},"labels":[],"label_agreement":null},{"id":"W1866622903","doi":"10.1142/s0218539315500217","title":"Stability Analysis of Uncertain Systems Using a Singular Value Decomposition-Based Metric","year":2015,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Singular value decomposition; Metric (unit); Stability (learning theory); Singular value; Nonlinear system; Computer science; Reliability (semiconductor); Domain (mathematical analysis); Variance (accounting); Decomposition; Key (lock); Mathematics; Matrix (chemical analysis); Mathematical optimization; Control theory (sociology); Algorithm; Engineering; Artificial intelligence; Machine learning","score_opus":0.15387654579331983,"score_gpt":0.4052082435584159,"score_spread":0.25133169776509606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1866622903","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005477942,0.00032428704,0.9933037,0.00004578847,0.000022002572,0.000023662607,0.00003471302,0.000057037792,0.0007108857],"genre_scores_gemma":[0.5963715,0.0015382167,0.39960828,0.000066912195,0.00017016668,0.00028938186,0.00033132642,0.000089538924,0.0015346818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982008,0.00064145913,0.00015010311,0.00023747802,0.0007164454,0.00005370026],"domain_scores_gemma":[0.997908,0.0011294633,0.00032895096,0.00016411222,0.00042169035,0.00004777573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002487366,0.0010764053,0.00092465023,0.0015304185,0.0003060055,0.0011712896,0.0005282704,0.00054234464,0.0011363925],"category_scores_gemma":[0.0047188844,0.00017884387,0.000869413,0.0009280351,0.0010351267,0.001348,0.00087238586,0.0009525547,0.0002520103],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015492087,0.00007212272,0.0015445449,0.00046401416,0.00018908239,0.00014010958,0.00017859874,0.76151943,0.029596591,0.095316194,0.0009810667,0.109843284],"study_design_scores_gemma":[0.0000037232026,0.00012579016,0.0007220012,0.00002807395,0.000017567358,0.000042559433,0.000026159816,0.972736,0.0033202902,0.021832423,0.0011229784,0.000022413882],"about_ca_topic_score_codex":0.00081522414,"about_ca_topic_score_gemma":0.00044146876,"teacher_disagreement_score":0.002487366,"about_ca_system_score_codex":0.0006493785,"about_ca_system_score_gemma":0.0006337237,"threshold_uncertainty_score":0.013154566},"labels":[],"label_agreement":null},{"id":"W1885427747","doi":"10.1186/s40323-015-0045-5","title":"Adaptive surrogate modeling for response surface approximations with application to bayesian inference","year":2015,"lang":"en","type":"article","venue":"Advanced Modeling and Simulation in Engineering Sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; National Nuclear Security Administration; King Abdullah University of Science and Technology; University of Texas at Austin; U.S. Department of Energy","keywords":"Surrogate model; Inference; Bayesian inference; Nonlinear system; Uncertainty quantification; Computer science; Mathematical optimization; A priori and a posteriori; Bayesian probability; Model selection; Statistical inference; Frequentist inference; Applied mathematics; Mathematics; Algorithm; Machine learning; Artificial intelligence; Statistics","score_opus":0.11236184361025425,"score_gpt":0.36793324368981406,"score_spread":0.2555714000795598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1885427747","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009811587,0.00010291338,0.9983121,0.00007561439,0.000009194542,0.000011417766,0.000014937843,0.000056779772,0.00043582064],"genre_scores_gemma":[0.22437583,0.0010678335,0.76968795,0.00017742156,0.000112869515,0.0007261046,0.00030950338,0.0002634777,0.0032789938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774855,0.0014737429,0.00008971967,0.00013661868,0.0004888757,0.000062519306],"domain_scores_gemma":[0.99316156,0.0054343943,0.00040148036,0.00040432604,0.00050121243,0.00009714473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00531868,0.0011837729,0.0017812389,0.0014062077,0.00050625816,0.0013372685,0.001617181,0.002065715,0.0024906052],"category_scores_gemma":[0.016508548,0.0010625321,0.0016625308,0.0012918358,0.0014517627,0.0013124401,0.002295186,0.0029105602,0.0007357471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023402295,0.000023459812,0.00021528397,0.00007844827,0.0000366294,0.0000454333,0.00004571716,0.90329677,0.0009043827,0.08302522,0.0003334035,0.011971872],"study_design_scores_gemma":[0.0000031923246,0.0000042460115,0.000016593553,0.0000059449108,0.0000017456018,0.00000469146,0.0000018697003,0.9859314,0.00009464145,0.013620443,0.00031201745,0.0000032099456],"about_ca_topic_score_codex":0.0027229204,"about_ca_topic_score_gemma":0.0018872854,"teacher_disagreement_score":0.00531868,"about_ca_system_score_codex":0.0008767525,"about_ca_system_score_gemma":0.001301501,"threshold_uncertainty_score":0.028128266},"labels":[],"label_agreement":null},{"id":"W1897767665","doi":"10.48550/arxiv.1008.5372","title":"Penalty Decomposition Methods for $L0$-Norm Minimization","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Norm (philosophy); Minification; Decomposition; Mathematics; Mathematical optimization; Mathematical economics; Applied mathematics; Computer science; Political science; Law; Chemistry","score_opus":0.23208059577484833,"score_gpt":0.3338933066642917,"score_spread":0.10181271088944335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1897767665","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00061961357,0.00022567883,0.998104,0.0000875008,0.000032100044,0.000018429708,0.000019639852,0.0000598173,0.00083316554],"genre_scores_gemma":[0.047734007,0.0010935798,0.9446027,0.00021148259,0.00018688296,0.00030459298,0.00031335684,0.00029710698,0.0052561867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986085,0.0006643363,0.00006574401,0.00017544751,0.00041979356,0.00006619902],"domain_scores_gemma":[0.9976483,0.0014674088,0.00016118577,0.00021564144,0.00042343812,0.00008411461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027887577,0.0017316566,0.0012353543,0.0009561974,0.0004573212,0.00110952,0.0013166687,0.0017043632,0.003987588],"category_scores_gemma":[0.007171437,0.0005754149,0.0009811074,0.0011968429,0.0012613307,0.0017276478,0.0018738528,0.0031439476,0.0018230322],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014625285,0.00012435268,0.00049790763,0.0007208298,0.00012506687,0.00015841023,0.0001491272,0.5114919,0.011081223,0.2724015,0.010861432,0.19224195],"study_design_scores_gemma":[0.000010527127,0.000031164298,0.00005642097,0.000025662202,0.0000062182653,0.00004629366,0.000009541027,0.962594,0.0014198314,0.031523634,0.0042638974,0.000012889991],"about_ca_topic_score_codex":0.0012267401,"about_ca_topic_score_gemma":0.0010489818,"teacher_disagreement_score":0.003987588,"about_ca_system_score_codex":0.00071330066,"about_ca_system_score_gemma":0.0010869105,"threshold_uncertainty_score":0.014748514},"labels":[],"label_agreement":null},{"id":"W1904859131","doi":"10.9744/jti.6.2.111-120","title":"PERANCANGAN BERBASIS KOMPUTER UNTUK REKAYASA PRODUK DAN PROSES KOMPLEKS","year":2004,"lang":"id","type":"article","venue":"Jurnal Teknik Industri","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada)","funders":"","keywords":"Computer science","score_opus":0.0809213162880789,"score_gpt":0.3056886298801906,"score_spread":0.22476731359211166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1904859131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047688443,0.0009098199,0.9269727,0.0006630365,0.00014161505,0.00022128389,0.00041386718,0.0018004592,0.02118878],"genre_scores_gemma":[0.4313361,0.0012613189,0.5341449,0.00024691658,0.00004670237,0.00037855573,0.0005709739,0.0005931264,0.03142151],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99842095,0.0003384327,0.00011293108,0.00025008162,0.00075887964,0.000118675125],"domain_scores_gemma":[0.99846053,0.00040798355,0.0001813639,0.0003054432,0.0005881949,0.000056438137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002147973,0.0014023901,0.00096810784,0.001492465,0.00055382703,0.0029833864,0.0010844639,0.0008833611,0.010664357],"category_scores_gemma":[0.0028966183,0.0005804089,0.0014004736,0.0011728688,0.0006443322,0.002505953,0.0013248136,0.001540416,0.0026497762],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040486793,0.00038257748,0.003897957,0.0014559185,0.00013863374,0.0003177699,0.00063819945,0.29026482,0.07828434,0.06563392,0.0032729954,0.555308],"study_design_scores_gemma":[0.000071910035,0.0009191432,0.0030711389,0.00031697136,0.00023793174,0.0003158195,0.0005250773,0.6997561,0.1485853,0.053235162,0.092831686,0.0001337698],"about_ca_topic_score_codex":0.0034499215,"about_ca_topic_score_gemma":0.004340195,"teacher_disagreement_score":0.010664357,"about_ca_system_score_codex":0.0014395015,"about_ca_system_score_gemma":0.0023536885,"threshold_uncertainty_score":0.035675764},"labels":[],"label_agreement":null},{"id":"W191358350","doi":"10.1007/978-94-007-5134-7_9","title":"Enhanced Monte Carlo for Reliability-Based Design and Calibration","year":2012,"lang":"en","type":"book-chapter","venue":"Computational methods in applied sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Monte Carlo method; Quantile; Reliability (semiconductor); Parametric statistics; Range (aeronautics); Margin (machine learning); Calibration; Random variable; Limit (mathematics); Computer science; Set (abstract data type); Algorithm; Applied mathematics; Mathematical optimization; Mathematics; Reliability engineering; Statistical physics; Statistics; Engineering; Physics; Power (physics)","score_opus":0.21955476304849586,"score_gpt":0.42730840963601613,"score_spread":0.20775364658752027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W191358350","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029950144,0.00048080512,0.9968907,0.000036338497,0.000027880935,0.000010061105,0.000017351038,0.00022416307,0.0020131138],"genre_scores_gemma":[0.0647215,0.0015968947,0.9238018,0.00015629204,0.00015526725,0.000218143,0.00019375839,0.00044451078,0.00871193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845994,0.0006040647,0.000055057582,0.00014934406,0.00068875396,0.000042803793],"domain_scores_gemma":[0.9975579,0.0015285698,0.0001197462,0.00042092978,0.00033977837,0.00003305231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00212513,0.0012068436,0.0014623982,0.0009532039,0.00028342364,0.0012760516,0.0020372004,0.0015816002,0.0070082736],"category_scores_gemma":[0.006350402,0.0009819013,0.0009861543,0.0011567165,0.0010207327,0.0016655354,0.0013102962,0.0023828775,0.0022478686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046717774,0.00004418327,0.00015340594,0.00019244406,0.00007664481,0.000047358848,0.000043070748,0.71681464,0.0024683683,0.15201485,0.003972662,0.124125645],"study_design_scores_gemma":[0.000008493678,0.000013743703,0.00006650633,0.00003607571,0.000016172957,0.000043572967,0.0000027356252,0.91768247,0.0015687728,0.07056427,0.009983135,0.000014037245],"about_ca_topic_score_codex":0.0013630502,"about_ca_topic_score_gemma":0.0016551141,"teacher_disagreement_score":0.0070082736,"about_ca_system_score_codex":0.0010281727,"about_ca_system_score_gemma":0.0008095348,"threshold_uncertainty_score":0.02344507},"labels":[],"label_agreement":null},{"id":"W1916674000","doi":"10.1139/cjce-2012-0427","title":"Plotting positions for fitting distributions and extreme value analysis","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Centre For Cold Ocean Resources Engineering","funders":"","keywords":"Extreme value theory; Position (finance); Probability distribution; Computer science; Basis (linear algebra); Selection (genetic algorithm); Value (mathematics); Advice (programming); Statistics; Distribution (mathematics); Mathematics; Artificial intelligence","score_opus":0.04924482234170223,"score_gpt":0.2589512555132052,"score_spread":0.20970643317150295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1916674000","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011285391,0.00009056041,0.9945064,0.00011272897,0.00009207947,0.00007096077,0.00012379143,0.0019245454,0.0019503286],"genre_scores_gemma":[0.033861075,0.00030052106,0.9608769,0.000112014146,0.00011462883,0.00048709213,0.0004274603,0.0017283934,0.0020919347],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98431593,0.009730096,0.0009685227,0.0014301679,0.0031939116,0.00036142915],"domain_scores_gemma":[0.9504197,0.033522934,0.0031418274,0.006953564,0.0054983567,0.00046354285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012667883,0.0025693548,0.0014632383,0.0059810868,0.0014830438,0.0043532294,0.0027984616,0.002471665,0.02226046],"category_scores_gemma":[0.111575685,0.0012140562,0.0016138016,0.0066294614,0.0021840576,0.0046496824,0.0029813007,0.004578934,0.008545248],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004420899,0.00020543432,0.0041773706,0.0008810491,0.0001664183,0.000731175,0.0016929867,0.14112008,0.00714081,0.3004844,0.0287446,0.5142137],"study_design_scores_gemma":[0.00013346758,0.00044241405,0.002909352,0.00055250543,0.00009034072,0.0010009552,0.00067747664,0.5496919,0.021303074,0.3179069,0.104994275,0.0002973352],"about_ca_topic_score_codex":0.0016499575,"about_ca_topic_score_gemma":0.0012987161,"teacher_disagreement_score":0.02226046,"about_ca_system_score_codex":0.00095090974,"about_ca_system_score_gemma":0.0016910294,"threshold_uncertainty_score":0.07446867},"labels":[],"label_agreement":null},{"id":"W1919107580","doi":"10.14359/12613","title":"Calibration of Design Code for Buildings (ACI 318): Part 1—Statistical Models for Resistance","year":2003,"lang":"en","type":"article","venue":"ACI Structural Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":286,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Calibration; Building code; Code (set theory); Structural engineering; Reinforced concrete; Engineering; Civil engineering; Computer science; Reliability engineering; Programming language; Statistics; Mathematics","score_opus":0.13851322761240295,"score_gpt":0.3533893990982879,"score_spread":0.21487617148588498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1919107580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038734633,0.000100041696,0.8948425,0.00025325987,0.00026710064,0.001244045,0.021088414,0.022321958,0.021148095],"genre_scores_gemma":[0.25785664,0.00016262174,0.6671324,0.00016828645,0.00009424991,0.0043436512,0.039783422,0.009879614,0.020579185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963568,0.0012266159,0.00021204038,0.0005328072,0.0014659126,0.00020586536],"domain_scores_gemma":[0.98194045,0.006132698,0.0012836445,0.0036629268,0.0067039826,0.0002763335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003753286,0.0013163183,0.0006271355,0.0019177584,0.0006949089,0.0014710614,0.0018421271,0.0012817632,0.032353282],"category_scores_gemma":[0.0333517,0.0010744823,0.0012736298,0.0020869402,0.00053438736,0.0014971487,0.0011266179,0.0016507729,0.02403737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003484078,0.00037182393,0.03846146,0.0004818675,0.00010065374,0.00011162816,0.00041590392,0.49154428,0.005283678,0.020828124,0.108240366,0.33381176],"study_design_scores_gemma":[0.0000989115,0.00023510924,0.024580028,0.0001817889,0.0000343606,0.00020641052,0.0001910302,0.87661594,0.0078041772,0.012735341,0.077195294,0.000121667166],"about_ca_topic_score_codex":0.011710348,"about_ca_topic_score_gemma":0.009496126,"teacher_disagreement_score":0.032353282,"about_ca_system_score_codex":0.0018676798,"about_ca_system_score_gemma":0.0022084864,"threshold_uncertainty_score":0.1082325},"labels":[],"label_agreement":null},{"id":"W1923533812","doi":"10.1109/isuma.1995.527682","title":"Tail effects of uncertainty modeling in QRA","year":2002,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Research Council Canada","keywords":"Reliability (semiconductor); Computer science; Risk analysis (engineering); Uncertainty quantification; Reliability engineering; Risk assessment; Engineering; Machine learning; Computer security","score_opus":0.12419413722793701,"score_gpt":0.3004483593726884,"score_spread":0.17625422214475137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1923533812","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026182568,0.0006079503,0.9672638,0.0004114304,0.000046207635,0.000026157382,0.000046797904,0.00029310124,0.0051220264],"genre_scores_gemma":[0.938028,0.000745178,0.057000346,0.00021318946,0.00010302564,0.000059183752,0.000045057055,0.00016912751,0.0036370405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99700135,0.0016439243,0.00014094311,0.00023579028,0.00078416173,0.00019381712],"domain_scores_gemma":[0.97869545,0.016381733,0.0018027357,0.0016165645,0.0012201875,0.0002833553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00602932,0.0006938653,0.00078529393,0.00091402396,0.000695039,0.001929932,0.0009994698,0.0010912421,0.002296644],"category_scores_gemma":[0.023189481,0.00057807466,0.0009355632,0.00078736834,0.00202814,0.0036066961,0.002495295,0.0020793828,0.00043437164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013003185,0.000026487249,0.0016445452,0.00010888081,0.00004794392,0.00025840173,0.00035249908,0.7722612,0.0037987144,0.19349024,0.00048724687,0.027393788],"study_design_scores_gemma":[0.0000042068054,0.0000541561,0.00031201204,0.000029282137,0.00002182446,0.000069690905,0.000030204363,0.9018477,0.0014233983,0.095206074,0.00096643774,0.000034916855],"about_ca_topic_score_codex":0.002150684,"about_ca_topic_score_gemma":0.0015126689,"teacher_disagreement_score":0.00602932,"about_ca_system_score_codex":0.0008180045,"about_ca_system_score_gemma":0.0005997839,"threshold_uncertainty_score":0.03188646},"labels":[],"label_agreement":null},{"id":"W1944582417","doi":"10.1016/j.ress.2015.09.002","title":"An efficient method for evaluating the effect of input parameters on the integrity of safety systems","year":2015,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Variance (accounting); Computation; Sensitivity (control systems); Computer science; Reliability engineering; Function (biology); Relation (database); Reliability (semiconductor); Algorithm; Mathematical optimization; Mathematics; Data mining; Engineering","score_opus":0.09838165600084074,"score_gpt":0.3772021076489894,"score_spread":0.27882045164814867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1944582417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002139424,0.000042500134,0.9970048,0.000010953729,0.000008129902,0.000025897534,0.000027880884,0.00032552483,0.00041479114],"genre_scores_gemma":[0.15816432,0.00013962129,0.83821815,0.000037426376,0.000030470756,0.00023324283,0.00014706246,0.00016960978,0.0028601352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987293,0.0003730083,0.000048746264,0.00013614942,0.00065326237,0.0000595634],"domain_scores_gemma":[0.99639064,0.0023625193,0.00024299578,0.000371593,0.00056912564,0.00006309924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015094882,0.0012364337,0.0012549459,0.0013326558,0.00050338404,0.00097211596,0.001171246,0.0010414991,0.0034960667],"category_scores_gemma":[0.0061759446,0.0005031135,0.0006629997,0.0006609824,0.0007203683,0.001136675,0.0009375535,0.0010552403,0.00072231854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037981424,0.0001595969,0.0011234545,0.00031867597,0.00017037848,0.00011836945,0.00008942625,0.45756432,0.06704766,0.020467358,0.001887117,0.45067388],"study_design_scores_gemma":[0.000022110837,0.000058443828,0.00026387192,0.00000881876,0.000022307091,0.000051814324,0.000005624142,0.9832614,0.011545738,0.0038458328,0.00089727476,0.000016726099],"about_ca_topic_score_codex":0.0023269344,"about_ca_topic_score_gemma":0.0030981293,"teacher_disagreement_score":0.0034960667,"about_ca_system_score_codex":0.00063602056,"about_ca_system_score_gemma":0.0012212064,"threshold_uncertainty_score":0.011695564},"labels":[],"label_agreement":null},{"id":"W1959135937","doi":"10.1109/aps.2015.7305277","title":"Uncertainty quantification of ray-tracing based wireless propagation models with a Control Variate-Polynomial Chaos Expansion method","year":2015,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Polynomial chaos; Control variates; Monte Carlo method; Random variate; Convergence (economics); Ray tracing (physics); Polynomial; Applied mathematics; Uncertainty quantification; Algorithm; Computer science; Tracing; Polynomial expansion; Taylor series; Mathematical optimization; Mathematics; Hybrid Monte Carlo; Statistics; Random variable; Mathematical analysis; Physics; Machine learning; Optics","score_opus":0.12414624621861206,"score_gpt":0.33549123751935434,"score_spread":0.21134499130074227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1959135937","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007887213,0.000050860377,0.9915309,0.00002639163,0.0000035940445,0.000012451717,0.000012890214,0.00005789264,0.00041777914],"genre_scores_gemma":[0.8370588,0.0003424698,0.16056654,0.000036069036,0.00002636752,0.00014558881,0.00008392824,0.00007054133,0.0016698263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992914,0.00027363913,0.000029135606,0.00007128262,0.00029139736,0.000043014737],"domain_scores_gemma":[0.9982589,0.0012086357,0.00018572612,0.000086869615,0.00022945381,0.000030411073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013300754,0.00064274005,0.0006218893,0.00085456175,0.00033050292,0.0007236083,0.0008376022,0.0006887623,0.00053340266],"category_scores_gemma":[0.0035668788,0.0003243137,0.00072186266,0.0006166817,0.00071139575,0.00083328306,0.00089845073,0.0008461802,0.000089907015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017630642,0.000007431529,0.00016251569,0.000019335903,0.00000945953,0.000017473567,0.00002156677,0.9827079,0.001547915,0.008926978,0.000066834094,0.0064950106],"study_design_scores_gemma":[4.847747e-7,0.0000034110521,0.000020789523,8.887965e-7,0.0000011294355,0.0000033887895,8.2487185e-7,0.9990466,0.00024306723,0.0006368292,0.00004077244,0.0000018043802],"about_ca_topic_score_codex":0.0041057137,"about_ca_topic_score_gemma":0.0016930237,"teacher_disagreement_score":0.0041057137,"about_ca_system_score_codex":0.00075562735,"about_ca_system_score_gemma":0.0008995887,"threshold_uncertainty_score":0.008163631},"labels":[],"label_agreement":null},{"id":"W1964866647","doi":"10.1016/s0960-0779(01)00237-5","title":"Moment Lyapunov exponents of a two-dimensional system in wind-induced vibration under real noise excitation","year":2002,"lang":"en","type":"article","venue":"Chaos Solitons & Fractals","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Lyapunov exponent; Noise (video); Vibration; Moment (physics); Excitation; Physics; Acoustics; Mathematics; Mathematical analysis; Classical mechanics; Computer science; Nonlinear system; Quantum mechanics","score_opus":0.119084762467201,"score_gpt":0.3319226984672924,"score_spread":0.21283793600009138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964866647","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9837863,0.00010199218,0.012598684,0.00018366612,0.000031921743,0.0000119532115,0.0000561846,0.000058782938,0.003170521],"genre_scores_gemma":[0.9990018,0.0000191554,0.00042458376,0.0000045405914,0.0000058731266,0.0000034633345,0.000013129343,0.0000049827236,0.0005225902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999523,0.000010126268,0.0000035924556,0.000008305511,0.000013179714,0.00001248054],"domain_scores_gemma":[0.99961084,0.00016135289,0.000075071424,0.00001994274,0.00005965715,0.00007317564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025155587,0.00027927855,0.0003352926,0.00048153565,0.000500215,0.0007061407,0.00025283845,0.00049351016,0.0012880408],"category_scores_gemma":[0.0010365621,0.00016421363,0.00024926531,0.00012798268,0.00053786533,0.0005793587,0.00045163254,0.00040707493,0.00007892695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001512679,0.00021757664,0.013251222,0.00025072644,0.00021714567,0.0046722614,0.001358427,0.6548437,0.19416282,0.115375586,0.0016716213,0.012466262],"study_design_scores_gemma":[0.000021139274,0.00013285721,0.0089835785,0.000011585797,0.000023361094,0.00022052262,0.00013881791,0.9771739,0.0038435503,0.009129455,0.00027943985,0.000041753025],"about_ca_topic_score_codex":0.0008197457,"about_ca_topic_score_gemma":0.0005630525,"teacher_disagreement_score":0.0012880408,"about_ca_system_score_codex":0.00032564873,"about_ca_system_score_gemma":0.0002139312,"threshold_uncertainty_score":0.0043088794},"labels":[],"label_agreement":null},{"id":"W1965106675","doi":"10.1002/pen.21794","title":"Identification of transient responses of a plasticating twin screw extruder due to excitation in feed rate","year":2010,"lang":"en","type":"article","venue":"Polymer Engineering and Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Materials science; Transient (computer programming); Plastics extrusion; Excitation; Identification (biology); Composite material; Electrical engineering; Computer science; Engineering; Biology","score_opus":0.03369265051823991,"score_gpt":0.3035738396656173,"score_spread":0.2698811891473774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965106675","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97123533,0.000035514364,0.028001372,0.000030427665,0.0000054690595,0.000013822865,0.000038237125,0.00014482516,0.0004950956],"genre_scores_gemma":[0.9992716,0.000010466458,0.0005618747,0.0000018439157,3.716677e-7,0.0000040458985,0.0000151898785,0.0000020753143,0.00013256159],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998431,0.000033476328,0.000011248083,0.000034662993,0.000048753456,0.000028724273],"domain_scores_gemma":[0.9994437,0.0003417355,0.0000905547,0.000044126646,0.00006028217,0.000019589093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004726397,0.00031966742,0.00035694597,0.00032150335,0.00012139387,0.00028316892,0.00022972979,0.000410799,0.00049940794],"category_scores_gemma":[0.0014569459,0.00019594205,0.00027273592,0.0001220741,0.00022765725,0.00018184882,0.00019599567,0.00023772004,0.000104163264],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026204349,0.00025005176,0.01654077,0.00019389232,0.00013135224,0.0008896264,0.00034492317,0.5483875,0.38771755,0.0010239892,0.00020542037,0.04169451],"study_design_scores_gemma":[0.000023288485,0.00031747468,0.010941601,0.0000096108715,0.000022753737,0.00008553741,0.000045650588,0.89510983,0.09310631,0.00016721193,0.00015150355,0.00001917755],"about_ca_topic_score_codex":0.0012684291,"about_ca_topic_score_gemma":0.0005902638,"teacher_disagreement_score":0.0012684291,"about_ca_system_score_codex":0.00026204888,"about_ca_system_score_gemma":0.00014574286,"threshold_uncertainty_score":0.002522111},"labels":[],"label_agreement":null},{"id":"W1965873940","doi":"10.1002/j.2334-5837.2003.tb02648.x","title":"6.4.3 Risk Informed Design for System Life Cycle","year":2003,"lang":"en","type":"article","venue":"INCOSE International Symposium","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Golder Associates (Canada)","funders":"","keywords":"Probabilistic risk assessment; Risk analysis (engineering); Probabilistic logic; Nuclear power; Reliability engineering; Nuclear power plant; Risk assessment; Reliability (semiconductor); Component (thermodynamics); Computer science; Engineering; Systems engineering; Power (physics); Computer security; Business","score_opus":0.05365556540440572,"score_gpt":0.31902050188632125,"score_spread":0.2653649364819155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965873940","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006937704,0.00029084642,0.9734085,0.00032378818,0.0000413493,0.00014450532,0.000072696486,0.00045503522,0.01832568],"genre_scores_gemma":[0.45199195,0.0006715592,0.52838755,0.0002916927,0.000051965413,0.00054478867,0.00023903586,0.00033643798,0.017485073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99784195,0.0008339542,0.000091882896,0.0001370454,0.0009599249,0.00013522626],"domain_scores_gemma":[0.99877983,0.00043701185,0.000136952,0.00019349037,0.00040995542,0.00004279363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027305863,0.001040214,0.00050769537,0.0005546047,0.00037763,0.0018045307,0.0010103394,0.0010982973,0.007961973],"category_scores_gemma":[0.002832131,0.00056087936,0.0011500261,0.00023910415,0.0006380216,0.0010489813,0.0010509493,0.0011834635,0.0013953124],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000108344735,0.000054317858,0.00069214666,0.00020763252,0.00004948722,0.00010656963,0.00012561066,0.8711061,0.011567468,0.060166296,0.0013725484,0.05444345],"study_design_scores_gemma":[0.00004457278,0.0002358552,0.00038930515,0.00010686725,0.000054556218,0.00014082443,0.000048272403,0.91185874,0.008818862,0.046447884,0.031822868,0.000031452786],"about_ca_topic_score_codex":0.0011971821,"about_ca_topic_score_gemma":0.0012348237,"teacher_disagreement_score":0.007961973,"about_ca_system_score_codex":0.0011810816,"about_ca_system_score_gemma":0.0021791903,"threshold_uncertainty_score":0.026635408},"labels":[],"label_agreement":null},{"id":"W1966407428","doi":"10.1007/s10086-011-1232-8","title":"Seismic performance of post-and-beam timber buildings II: reliability evaluations","year":2011,"lang":"en","type":"article","venue":"Journal of Wood Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Shear wall; Beam (structure); Structural engineering; Seismic analysis; Shear (geology); Polynomial; Sampling (signal processing); Computer science; Engineering; Geology; Mathematics; Physics","score_opus":0.08241421613898828,"score_gpt":0.33416311546145694,"score_spread":0.2517488993224687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966407428","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99359375,0.00009485132,0.004896465,0.000020246249,0.000004286791,0.000007956207,0.00021502205,0.000042666165,0.0011247532],"genre_scores_gemma":[0.99879116,0.000025695315,0.0005999325,0.0000020586572,0.0000020890789,0.0000033540332,0.00014360853,0.000010033826,0.0004219561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963546,0.00010352062,0.00002127228,0.000044046432,0.00012624575,0.000069435606],"domain_scores_gemma":[0.9979348,0.0010960022,0.0002100822,0.00017173214,0.00050133315,0.00008609891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095887354,0.0005706939,0.00042114712,0.00078466814,0.00020206565,0.00025237267,0.0006060517,0.00050725444,0.0017728996],"category_scores_gemma":[0.0023096115,0.00020088881,0.0004440156,0.00050706277,0.00037048175,0.0004622014,0.00031442987,0.00023333354,0.00038532124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019576626,0.0002612169,0.025750732,0.00013688504,0.000087942244,0.00025924735,0.00024297315,0.87620384,0.051977705,0.00085260166,0.0009222506,0.041346975],"study_design_scores_gemma":[0.000052377447,0.003367537,0.06182457,0.000023878954,0.00014163696,0.0002557384,0.0002787153,0.8943119,0.038539417,0.0006390108,0.0005099278,0.000055281904],"about_ca_topic_score_codex":0.0029396329,"about_ca_topic_score_gemma":0.003476352,"teacher_disagreement_score":0.0029396329,"about_ca_system_score_codex":0.0003909516,"about_ca_system_score_gemma":0.00023814493,"threshold_uncertainty_score":0.00593096},"labels":[],"label_agreement":null},{"id":"W1967178250","doi":"10.1061/(asce)0733-9399(2008)134:10(867)","title":"Second-Order Sensitivities of Inelastic Finite-Element Response by Direct Differentiation","year":2008,"lang":"en","type":"article","venue":"Journal of Engineering Mechanics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Finite element method; Sensitivity (control systems); Computer science; Order (exchange); Work (physics); Mathematical optimization; Algorithm; Mathematics; Electronic engineering; Engineering; Structural engineering","score_opus":0.037679594230281964,"score_gpt":0.25477009444488713,"score_spread":0.21709050021460516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967178250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010605659,0.00008316819,0.9845088,0.00005301911,0.000023018585,0.00003369563,0.000019654788,0.0002488595,0.0044241394],"genre_scores_gemma":[0.52474064,0.0004983939,0.46146125,0.00018054349,0.00001920385,0.00016731513,0.000084547806,0.0003453084,0.012502851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956197,0.00009648418,0.000018919489,0.000040656767,0.00024954215,0.000032445263],"domain_scores_gemma":[0.99904794,0.00063548627,0.000056116693,0.00010443165,0.0001410569,0.000015063009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094512047,0.0006167687,0.00044713682,0.0005601307,0.00023608089,0.0006003444,0.00063904066,0.0004904245,0.002543648],"category_scores_gemma":[0.002603891,0.0004085226,0.0004864188,0.0002800678,0.00059760356,0.0007722191,0.0008600365,0.0007697854,0.0007488753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049323153,0.000061834246,0.00075708865,0.00024032465,0.00002435464,0.00018761698,0.00038964,0.754126,0.06362835,0.09638484,0.00085842493,0.083292104],"study_design_scores_gemma":[0.0000028287457,0.000012925209,0.00014170617,0.000014439313,0.000003175283,0.00008156863,0.000016875796,0.97541225,0.014758908,0.0070890225,0.0024521938,0.000014082677],"about_ca_topic_score_codex":0.001214823,"about_ca_topic_score_gemma":0.0014256279,"teacher_disagreement_score":0.002543648,"about_ca_system_score_codex":0.000544337,"about_ca_system_score_gemma":0.0006724368,"threshold_uncertainty_score":0.0085093975},"labels":[],"label_agreement":null},{"id":"W1969887802","doi":"10.1080/03610910802454070","title":"An Exponential Model for Damage Accumulation","year":2008,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Exponential function; Applied mathematics; Mathematics; Compatibility (geochemistry); Computer science; Mathematical analysis; Engineering","score_opus":0.5403654422120929,"score_gpt":0.529754199156716,"score_spread":0.010611243055376973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969887802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043081645,0.000550017,0.9489758,0.0004633772,0.00005685294,0.00005435629,0.00030195448,0.00023282578,0.006283122],"genre_scores_gemma":[0.89391404,0.0017561782,0.06456892,0.0002392283,0.00013994459,0.0002497536,0.0006236577,0.00012041413,0.038387902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995327,0.00008345705,0.000027443533,0.00013589766,0.00013984801,0.00008066472],"domain_scores_gemma":[0.9988944,0.0004697889,0.00018259863,0.00012205663,0.00028796686,0.000043291304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001165767,0.0006918322,0.0006004806,0.0010521825,0.00032390183,0.0006636986,0.0019740167,0.0015393719,0.0052894563],"category_scores_gemma":[0.0038138707,0.00036801083,0.00065865787,0.0006985828,0.000798752,0.0024388582,0.0007123751,0.0010621526,0.0013806196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007114291,0.000078954596,0.002285234,0.00012380311,0.000034337467,0.00038889653,0.00020365036,0.7374273,0.010273162,0.22175677,0.002112029,0.02524478],"study_design_scores_gemma":[0.0000096815265,0.00004993511,0.0005506119,0.000014604023,0.000014392673,0.00016844086,0.000019200597,0.95642877,0.0007476661,0.039720662,0.002253991,0.000022039958],"about_ca_topic_score_codex":0.0029912172,"about_ca_topic_score_gemma":0.0028660207,"teacher_disagreement_score":0.0052894563,"about_ca_system_score_codex":0.00060215377,"about_ca_system_score_gemma":0.00057234225,"threshold_uncertainty_score":0.01769501},"labels":[],"label_agreement":null},{"id":"W1970168921","doi":"10.1007/s00477-013-0729-7","title":"Degradation kinetics of dense nonaqueous phase liquids in the environment under impacts of mixed white and colored noises","year":2013,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Colors of noise; Colored; White noise; Noise (video); Gaussian noise; Statistical physics; Additive white Gaussian noise; Computer science; Mathematics; Stochastic modelling; Applied mathematics; Statistics; Algorithm; Artificial intelligence; Physics; Materials science","score_opus":0.06412306695445309,"score_gpt":0.37500044746822625,"score_spread":0.31087738051377317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970168921","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99436414,0.0001539863,0.004993374,0.000030132915,0.000008548372,0.0000057153948,0.000058182115,0.000018194434,0.0003676832],"genre_scores_gemma":[0.9989931,0.00009281175,0.00042629917,0.000010179907,0.0000012685292,0.000002702861,0.00003222958,0.0000029359371,0.00043852354],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982446,0.00002136137,0.000008199609,0.000040448904,0.000051884545,0.00005365185],"domain_scores_gemma":[0.99958926,0.00015868653,0.000102561,0.000018603369,0.0001003065,0.00003048365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032129255,0.00022006413,0.00027075136,0.00021556103,0.00022875363,0.0005866628,0.00023600955,0.00042670342,0.0005515571],"category_scores_gemma":[0.00079969753,0.00012915063,0.00031228398,0.00016293686,0.00043897837,0.0005210283,0.00029444604,0.00025796067,0.000102118174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017534986,0.0001677849,0.015484491,0.00020679466,0.00006860407,0.0007277881,0.00028766616,0.11324181,0.8531177,0.0020475641,0.00022176203,0.01267455],"study_design_scores_gemma":[0.00002796781,0.00077183574,0.012170871,0.000013246534,0.00005347831,0.00017256936,0.00033543838,0.37570715,0.6091106,0.0009868314,0.00059906836,0.000050915223],"about_ca_topic_score_codex":0.0038179457,"about_ca_topic_score_gemma":0.0019858482,"teacher_disagreement_score":0.0038179457,"about_ca_system_score_codex":0.00047449162,"about_ca_system_score_gemma":0.00039104797,"threshold_uncertainty_score":0.0075914264},"labels":[],"label_agreement":null},{"id":"W1970222772","doi":"10.1021/ie050790r","title":"Relative Gain Array for Norm-Bounded Uncertain Systems","year":2006,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Bounded function; Norm (philosophy); Mathematics; Representation (politics); Control theory (sociology); Set (abstract data type); Computer science; Mathematical optimization; Applied mathematics; Mathematical analysis; Artificial intelligence","score_opus":0.32655603056691906,"score_gpt":0.41566614149565706,"score_spread":0.089110110928738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970222772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001993307,0.00013834392,0.9962878,0.000045049124,0.00002373295,0.000008814871,0.000011628919,0.00007715295,0.0014141067],"genre_scores_gemma":[0.6205752,0.0013113072,0.372796,0.0002320664,0.0003524354,0.00020585548,0.00017793759,0.00015920073,0.004189957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99658644,0.0012165175,0.00018643096,0.0006257078,0.0012325326,0.00015231875],"domain_scores_gemma":[0.9961169,0.0023375482,0.00042276637,0.00041018537,0.0006352469,0.00007738331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024217153,0.0011693247,0.0010182749,0.000853118,0.00035058713,0.0019936753,0.0012016194,0.0010430227,0.0025674077],"category_scores_gemma":[0.010172399,0.0003579345,0.0007652471,0.0007683574,0.0015376147,0.0036010307,0.0016172865,0.0018561594,0.0010869959],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018114489,0.000027285925,0.0003351788,0.00023938404,0.000059906797,0.00018202409,0.00013263275,0.70783633,0.018438298,0.17856584,0.0007227424,0.09327925],"study_design_scores_gemma":[0.000005130088,0.00013941267,0.000094019866,0.00001814251,0.000011060106,0.000095896736,0.000015190614,0.94861275,0.003637054,0.0451624,0.0021820555,0.000026953152],"about_ca_topic_score_codex":0.0003907681,"about_ca_topic_score_gemma":0.00024382841,"teacher_disagreement_score":0.0025674077,"about_ca_system_score_codex":0.0006194388,"about_ca_system_score_gemma":0.00040615472,"threshold_uncertainty_score":0.012807429},"labels":[],"label_agreement":null},{"id":"W1970493804","doi":"10.1115/1.3094027","title":"A Stochastic Model for Piping Failure Frequency Analysis Using OPDE Data","year":2009,"lang":"en","type":"article","venue":"Journal of Engineering for Gas Turbines and Power","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nuclear Safety Commission; University of Waterloo; Toronto Metropolitan University","funders":"Canadian Nuclear Safety Commission; University Network of Excellence in Nuclear Engineering","keywords":"Piping; Probabilistic logic; Point estimation; Statistical model; Failure rate; Nuclear power plant; Computer science; Process (computing); Reliability engineering; Task (project management); Engineering; Statistics; Artificial intelligence; Mathematics","score_opus":0.10764673885655142,"score_gpt":0.34823238722377003,"score_spread":0.24058564836721863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970493804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02129716,0.000106242944,0.9769507,0.00016040845,0.000017610155,0.000064465974,0.00061025814,0.00018743849,0.0006055955],"genre_scores_gemma":[0.8434539,0.00091935374,0.14552751,0.0001261471,0.000109090346,0.0009982393,0.0025247242,0.00010693975,0.0062340265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980165,0.00065344636,0.00016059763,0.00045842706,0.00054263405,0.00016837903],"domain_scores_gemma":[0.9940958,0.003948602,0.000900531,0.00044760914,0.000518775,0.000088639244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041446667,0.0009630714,0.0013737581,0.0014475529,0.00041779262,0.0013412628,0.003033474,0.0017661293,0.0019159156],"category_scores_gemma":[0.011158315,0.0010458012,0.0012580102,0.0015581346,0.0009311864,0.0015724113,0.0011712043,0.0019368541,0.000611428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031917392,0.000026119218,0.0012951414,0.000039588147,0.000031169046,0.00007115537,0.00003478896,0.97323114,0.00067291275,0.018386327,0.00026570912,0.0059140986],"study_design_scores_gemma":[0.0000039870747,0.000019149515,0.00033108974,0.0000042405336,0.000008088226,0.000022241873,0.0000042374086,0.99557674,0.00014697119,0.0036436983,0.00022863138,0.0000110011],"about_ca_topic_score_codex":0.008292445,"about_ca_topic_score_gemma":0.0059977337,"teacher_disagreement_score":0.008292445,"about_ca_system_score_codex":0.0009644052,"about_ca_system_score_gemma":0.0010457016,"threshold_uncertainty_score":0.02191937},"labels":[],"label_agreement":null},{"id":"W1970821085","doi":"10.1016/j.ast.2015.02.019","title":"Surrogate models and mixtures of experts in aerodynamic performance prediction for aircraft mission analysis","year":2015,"lang":"en","type":"article","venue":"Aerospace Science and Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":134,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Surrogate model; Kriging; Aerodynamics; Computer science; Interpolation (computer graphics); Lift (data mining); Mathematical optimization; Transonic; Sampling (signal processing); Function (biology); Algorithm; Mathematics; Machine learning; Aerospace engineering; Artificial intelligence; Engineering","score_opus":0.07051560220608662,"score_gpt":0.323149665249358,"score_spread":0.2526340630432714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970821085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012169287,0.0005771666,0.98624927,0.00019939088,0.000050777046,0.000014590383,0.000055146564,0.00008394649,0.0006005038],"genre_scores_gemma":[0.7914572,0.0013557296,0.20138372,0.00025768578,0.00033384564,0.0002164653,0.00042968174,0.0001399152,0.004425861],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748296,0.0016022212,0.00011607452,0.00023409826,0.00045948222,0.000105265986],"domain_scores_gemma":[0.98454756,0.01270848,0.0010301445,0.0005921803,0.00084754225,0.00027419886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069333403,0.0010571103,0.001911147,0.0011852296,0.00043894074,0.0013772348,0.0015119936,0.0024614271,0.0010210956],"category_scores_gemma":[0.02571183,0.0013679777,0.0012881279,0.0009658612,0.0015316133,0.0027777452,0.0019182997,0.0022966953,0.00033049218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064955275,0.000022471095,0.00022797564,0.000034130484,0.00003393688,0.00001583843,0.000022222986,0.97997093,0.00022492655,0.011686593,0.00032229198,0.0073737674],"study_design_scores_gemma":[0.0000023306995,0.0000073304836,0.000038762544,0.0000035070188,0.0000032687265,0.0000036172532,0.0000015438942,0.9924821,0.000068583766,0.007309419,0.00007592117,0.000003582145],"about_ca_topic_score_codex":0.0019967128,"about_ca_topic_score_gemma":0.0018432821,"teacher_disagreement_score":0.0069333403,"about_ca_system_score_codex":0.0005591004,"about_ca_system_score_gemma":0.000752175,"threshold_uncertainty_score":0.036667466},"labels":[],"label_agreement":null},{"id":"W1971415046","doi":"10.1002/mren.200800052","title":"Parameter Estimation in a Simplified MWD Model for HDPE Produced by a Ziegler‐Natta Catalyst","year":2009,"lang":"en","type":"article","venue":"Macromolecular Reaction Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Mitacs","keywords":"Natta; Ethylene; Estimation theory; Catalysis; Scaling; Materials science; Thermodynamics; Polymerization; Ziegler–Natta catalyst; Polymer chemistry; Mathematics; Applied mathematics; Chemistry; Statistics; Physics; Polymer; Organic chemistry; Composite material","score_opus":0.04266934624098108,"score_gpt":0.3117358376345396,"score_spread":0.26906649139355854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971415046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41445327,0.00024385961,0.5791316,0.0003940825,0.000018543444,0.00010905583,0.0005735814,0.0003963753,0.0046796543],"genre_scores_gemma":[0.9885294,0.00010140447,0.007694658,0.000025642756,0.000004328408,0.00012151281,0.00019631708,0.000016436254,0.0033103467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976355,0.00006465221,0.000012112115,0.00006748525,0.000057468824,0.00003470432],"domain_scores_gemma":[0.9993285,0.00044111357,0.00010124743,0.000029764278,0.00008551206,0.000013902132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007043154,0.0005574084,0.0010461904,0.0004016029,0.00026538232,0.00067701034,0.0007956613,0.00088310987,0.0013097753],"category_scores_gemma":[0.0016903807,0.00048371483,0.0007584868,0.00022005237,0.0005911788,0.0005254341,0.00045940353,0.0006100214,0.00021028933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025012436,0.0000070832616,0.00021250578,0.000012038777,0.000007723283,0.000021389333,0.0000102556605,0.99708194,0.000890972,0.00057067606,0.000032638363,0.0011277021],"study_design_scores_gemma":[0.000005018663,0.0000074459763,0.00017675897,0.000001151402,0.0000054597162,0.0000026300288,0.000001687061,0.99917054,0.00032732368,0.00025144592,0.000047545327,0.0000030417211],"about_ca_topic_score_codex":0.022149608,"about_ca_topic_score_gemma":0.00918798,"teacher_disagreement_score":0.022149608,"about_ca_system_score_codex":0.0010780463,"about_ca_system_score_gemma":0.0007550508,"threshold_uncertainty_score":0.044041395},"labels":[],"label_agreement":null},{"id":"W1972863150","doi":"10.12989/was.2014.19.4.371","title":"Plotting positions and approximating first two moments of order statistics for Gumbel distribution: estimating quantiles of wind speed","year":2014,"lang":"en","type":"article","venue":"Wind and Structures","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Gumbel distribution; Quantile; Weibull distribution; Statistics; Mathematics; Order statistic; Position (finance); Covariance; Shape parameter; Extreme value theory","score_opus":0.04124081250159518,"score_gpt":0.3247753014376546,"score_spread":0.2835344889360594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972863150","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057723187,0.00009059743,0.9932985,0.00003080841,0.000013625984,0.000011426399,0.000023769577,0.0002864984,0.0004724756],"genre_scores_gemma":[0.30056503,0.00054702157,0.69619066,0.0000627774,0.000053774762,0.00016845715,0.0002281602,0.00027698025,0.0019071164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985079,0.0007190449,0.00006563579,0.00022056229,0.00039963357,0.00008724177],"domain_scores_gemma":[0.9941345,0.0038742654,0.0005577383,0.0006350269,0.00072654913,0.00007200828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028432992,0.0009131561,0.00056161394,0.0013410106,0.00038720435,0.0012143905,0.0011026873,0.0010986018,0.0023648276],"category_scores_gemma":[0.018387936,0.00045677592,0.0006971502,0.0014386881,0.0009253791,0.0014312603,0.000734853,0.0013917973,0.0010555132],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020643126,0.0000516279,0.004481405,0.00018847945,0.000052891653,0.00020226753,0.00037346006,0.73283297,0.013356216,0.07580973,0.0019444879,0.17049994],"study_design_scores_gemma":[0.000011299009,0.000054757398,0.0010208992,0.000026471424,0.000011412002,0.0000965044,0.000029559747,0.9761154,0.004843757,0.015389127,0.0023710884,0.000029738902],"about_ca_topic_score_codex":0.0022727102,"about_ca_topic_score_gemma":0.0018120282,"teacher_disagreement_score":0.0028432992,"about_ca_system_score_codex":0.0007871028,"about_ca_system_score_gemma":0.0009198853,"threshold_uncertainty_score":0.015037},"labels":[],"label_agreement":null},{"id":"W1974088515","doi":"10.1115/gt2007-27193","title":"A Simple Sub-Idle Component Map Extrapolation Method","year":2007,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Extrapolation; Component (thermodynamics); Computer science; Idle; Convergence (economics); Algorithm; Mathematical optimization; Mathematics; Statistics; Physics","score_opus":0.10663475642604292,"score_gpt":0.3896104303498626,"score_spread":0.28297567392381967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974088515","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009817555,0.00008020353,0.98672,0.000026254012,0.00002648219,0.000047639223,0.000045600904,0.0011997224,0.0020363864],"genre_scores_gemma":[0.3469896,0.00024928898,0.64582443,0.000066049615,0.000043453554,0.00018442266,0.00037346303,0.0004052517,0.0058639506],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99949765,0.00007449099,0.000026216569,0.00008435085,0.00028312573,0.000034207857],"domain_scores_gemma":[0.9991743,0.00024681026,0.00006552143,0.00018762444,0.0002930648,0.000032714623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057974405,0.0007598863,0.0005647073,0.0009173423,0.0003868712,0.000614551,0.0009916098,0.00048122546,0.004551449],"category_scores_gemma":[0.0024155546,0.0003217192,0.0005333615,0.00059360714,0.00028022754,0.0010854293,0.00090926426,0.00075987115,0.0019070611],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045300284,0.00015894801,0.0019962983,0.00026133808,0.00005316592,0.00030627762,0.0003088226,0.23687817,0.048016664,0.01508474,0.0032796415,0.6932029],"study_design_scores_gemma":[0.00002157859,0.00014211399,0.0008799079,0.000025134248,0.000023394852,0.00017891798,0.000039264738,0.9620474,0.021124085,0.0060810708,0.009402088,0.00003503413],"about_ca_topic_score_codex":0.0014436692,"about_ca_topic_score_gemma":0.0010757656,"teacher_disagreement_score":0.004551449,"about_ca_system_score_codex":0.00024103439,"about_ca_system_score_gemma":0.00064602506,"threshold_uncertainty_score":0.015226066},"labels":[],"label_agreement":null},{"id":"W1974159704","doi":"10.1016/j.camwa.2007.01.006","title":"An optimization method for solving mixed discrete-continuous programming problems","year":2007,"lang":"en","type":"article","venue":"Computers & Mathematics with Applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Mathematics; Mathematical optimization; Nonlinear programming; Maxima and minima; Heuristic; Nonlinear system; Continuous optimization; Discrete optimization; Optimization problem; Extension (predicate logic); Constraint (computer-aided design); Series (stratigraphy); Computer science","score_opus":0.04486506723494484,"score_gpt":0.3460140202889045,"score_spread":0.30114895305395967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974159704","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00095944334,0.000118949334,0.99757963,0.000057404293,0.00006877645,0.000027278129,0.0000137433835,0.000067769215,0.0011069501],"genre_scores_gemma":[0.048668046,0.0002385809,0.9459671,0.00012858683,0.00011479027,0.0003281026,0.000063252264,0.00015397728,0.004337611],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991635,0.00028681365,0.000041322462,0.00010909426,0.0003482178,0.000051018058],"domain_scores_gemma":[0.998884,0.0007756802,0.00006290869,0.000041165782,0.00017379042,0.000062567124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002098849,0.0013464034,0.0016294871,0.00093656476,0.0006558326,0.0010716629,0.0018438183,0.0018865902,0.0043300563],"category_scores_gemma":[0.0032284001,0.00094135676,0.0013972371,0.0010172045,0.00087251264,0.0012104148,0.0017709217,0.0021666144,0.00073690136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017159918,0.00019903916,0.00033392108,0.00046118154,0.00014351985,0.00012810579,0.000086645836,0.7469083,0.0067685414,0.06816891,0.0037324599,0.17289777],"study_design_scores_gemma":[0.000017662896,0.000031560634,0.00003387528,0.000010803509,0.000012393905,0.000021057083,0.0000035391647,0.9921733,0.00031790108,0.0059428853,0.0014270978,0.000007916185],"about_ca_topic_score_codex":0.0026854964,"about_ca_topic_score_gemma":0.0029790394,"teacher_disagreement_score":0.0043300563,"about_ca_system_score_codex":0.0005859961,"about_ca_system_score_gemma":0.0014189504,"threshold_uncertainty_score":0.014485538},"labels":[],"label_agreement":null},{"id":"W1974605152","doi":"10.1007/s11069-010-9563-0","title":"Stochastic methods for safety assessment of the flood defense system in the Scheldt Estuary of the Netherlands","year":2010,"lang":"en","type":"article","venue":"Natural Hazards","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Reliability (semiconductor); Flood myth; Finite element method; Dimension (graph theory); Hydrogeology; Natural hazard; Computer science; Reliability engineering; Operations research; Civil engineering; Uncertainty quantification; Risk analysis (engineering); Environmental science; Engineering; Mathematics; Geography; Machine learning; Geotechnical engineering; Meteorology; Structural engineering","score_opus":0.03651192648145984,"score_gpt":0.3905249083800529,"score_spread":0.35401298189859304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974605152","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56591266,0.000639727,0.42774367,0.000725814,0.00006363217,0.00010308711,0.00044534146,0.00012846956,0.00423759],"genre_scores_gemma":[0.9817617,0.00020936396,0.0151416715,0.00002075896,0.000025310112,0.00008042678,0.00025041055,0.000027444274,0.0024829495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937123,0.00032240344,0.000038844373,0.0000869991,0.000102275044,0.000078391124],"domain_scores_gemma":[0.99835086,0.0012278846,0.00015203036,0.000028712137,0.00019170462,0.000048897567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016088792,0.0005209342,0.0006180748,0.0010801645,0.0004267968,0.0010894804,0.0009468135,0.000826453,0.00085318554],"category_scores_gemma":[0.004578858,0.00055324694,0.00074638485,0.0005250904,0.0004982321,0.0005430676,0.0009964553,0.00053051685,0.00008146733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020802583,0.0000074581535,0.0007736697,0.000014728973,0.000016386302,0.00001734789,0.000015424883,0.99396086,0.0001771281,0.0015086179,0.00007698274,0.0034106376],"study_design_scores_gemma":[0.000004158787,0.000008761996,0.00047262124,0.0000019278211,0.0000035050168,0.0000030873723,0.000014162637,0.99827576,0.000092611284,0.0010322619,0.00008733527,0.0000037263167],"about_ca_topic_score_codex":0.056354154,"about_ca_topic_score_gemma":0.037450302,"teacher_disagreement_score":0.056354154,"about_ca_system_score_codex":0.001650861,"about_ca_system_score_gemma":0.0023140989,"threshold_uncertainty_score":0.11205232},"labels":[],"label_agreement":null},{"id":"W1975712130","doi":"10.1115/1.4023431","title":"Framework for a Combined Netting Analysis and Tsai-Wu-Based Design Approach for Braided and Filament-Wound Composites","year":2013,"lang":"en","type":"article","venue":"Journal of Pressure Vessel Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Netting; Piping; Structural engineering; Fiber; Materials science; Composite number; Filament winding; Composite material; Ultimate tensile strength; Mechanical engineering; Engineering","score_opus":0.056931170741906366,"score_gpt":0.3176793081698345,"score_spread":0.26074813742792813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975712130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00060296594,0.00001927998,0.99856794,0.000014463469,0.0000036346332,0.00002372632,0.0000070774586,0.00003564539,0.0007253568],"genre_scores_gemma":[0.1495293,0.00025862953,0.8455656,0.00007131826,0.000038597715,0.00081126223,0.000111487614,0.00009656338,0.0035172289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992575,0.00018179137,0.000037419723,0.00008953313,0.00038508693,0.000048606555],"domain_scores_gemma":[0.99948394,0.00020520086,0.00006569086,0.00003218421,0.00019335332,0.000019624847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001750406,0.0014248759,0.0011335814,0.0014107152,0.0004457212,0.0010671538,0.0017561913,0.001207648,0.0030796912],"category_scores_gemma":[0.0015808684,0.00062640733,0.0011839212,0.0005829953,0.0008691371,0.00066711573,0.0009772792,0.0010025038,0.00083798415],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001510661,0.00003730118,0.00016870338,0.000095636555,0.000026955213,0.000069254944,0.00004891835,0.92707056,0.0052028047,0.04310793,0.00031333973,0.023843449],"study_design_scores_gemma":[0.0000060344937,0.00004277336,0.000045779056,0.000014667897,0.000009485292,0.000018827956,0.000007228793,0.989441,0.0007746049,0.0082706995,0.0013613015,0.000007718824],"about_ca_topic_score_codex":0.001804116,"about_ca_topic_score_gemma":0.0028122505,"teacher_disagreement_score":0.0030796912,"about_ca_system_score_codex":0.0008611238,"about_ca_system_score_gemma":0.0017764262,"threshold_uncertainty_score":0.010302544},"labels":[],"label_agreement":null},{"id":"W1976161574","doi":"10.1016/j.jspi.2011.01.021","title":"Approximate bounded influence estimation for longitudinal data with outliers and measurement errors","year":2011,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Bounded function; Outlier; Longitudinal data; Statistics; Estimation; Econometrics; Observational error; Applied mathematics; Data mining; Mathematical analysis; Computer science","score_opus":0.3446604200967033,"score_gpt":0.3935651152379615,"score_spread":0.048904695141258225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976161574","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004884391,0.00026900964,0.9943294,0.00010504994,0.000018187902,0.000019565858,0.000037530655,0.00008837204,0.00024853263],"genre_scores_gemma":[0.6552393,0.0017309354,0.336272,0.00021655137,0.00038048235,0.00064659055,0.0010778425,0.0003027317,0.004133535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99288344,0.003660987,0.00046045857,0.0011277316,0.0013979289,0.0004694714],"domain_scores_gemma":[0.93352044,0.057332966,0.0029804055,0.002714609,0.0027893605,0.0006622668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01260447,0.0019470017,0.0042426023,0.0022296326,0.0010940415,0.0025651641,0.0035798668,0.0024207295,0.0015392854],"category_scores_gemma":[0.08906422,0.0027488638,0.0023585283,0.0024861703,0.0033610177,0.0033510986,0.0043751667,0.002830194,0.00040618045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016975332,0.0000530378,0.0021739637,0.00016866426,0.00023856008,0.00013447637,0.00015393844,0.93672717,0.0006394979,0.0333253,0.0006538763,0.02556177],"study_design_scores_gemma":[0.000006892125,0.000013065641,0.00018421141,0.000009308793,0.000012598521,0.000015251089,0.0000068168465,0.98791134,0.00023080049,0.011373641,0.00022882647,0.0000072597413],"about_ca_topic_score_codex":0.014322254,"about_ca_topic_score_gemma":0.011253582,"teacher_disagreement_score":0.014322254,"about_ca_system_score_codex":0.0021284018,"about_ca_system_score_gemma":0.0031294164,"threshold_uncertainty_score":0.06665963},"labels":[],"label_agreement":null},{"id":"W1976590847","doi":"10.1139/t09-119","title":"Reliability-based calibration of resistance factors for static bearing capacity of driven steel pipe piles","year":2010,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pile; Foundation (evidence); Reliability (semiconductor); Geotechnical engineering; Standard penetration test; Bearing capacity; Structural engineering; Resistance Factors; Engineering; Load testing; Monte Carlo method; Penetration test; Bearing (navigation); Reliability engineering; Computer science; Mathematics; Statistics","score_opus":0.05558133598230719,"score_gpt":0.281619967843575,"score_spread":0.22603863186126782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976590847","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6620622,0.00014131953,0.33297276,0.000051244373,0.00001886391,0.00023728535,0.0005471459,0.001234986,0.0027341244],"genre_scores_gemma":[0.94593185,0.000051605348,0.05314461,0.000006272738,0.000004017574,0.000084158906,0.0004250551,0.00007026316,0.00028201228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99745244,0.0008148584,0.00018418426,0.0003691084,0.0010541762,0.00012513586],"domain_scores_gemma":[0.98947936,0.003127089,0.0018319521,0.0017622937,0.0036772045,0.00012210329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031223954,0.00066651776,0.00036384547,0.0028825141,0.0002099049,0.00045555228,0.0007500923,0.00034516648,0.000659526],"category_scores_gemma":[0.023124764,0.0003696379,0.00051704864,0.0011735248,0.00036377364,0.0007637354,0.0005012942,0.0003634544,0.00030796233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003343683,0.000111369234,0.07890833,0.00014922334,0.00007527955,0.00016540944,0.0003126378,0.74772424,0.029476177,0.003739562,0.0010794546,0.13792399],"study_design_scores_gemma":[0.000035505047,0.00037552323,0.06112926,0.00004118296,0.000037216636,0.00026450327,0.00010966006,0.9057183,0.028259635,0.0017851017,0.0021502231,0.00009384465],"about_ca_topic_score_codex":0.0040772646,"about_ca_topic_score_gemma":0.0040408038,"teacher_disagreement_score":0.99592274,"about_ca_system_score_codex":0.0010553091,"about_ca_system_score_gemma":0.0010456194,"threshold_uncertainty_score":0.01651299},"labels":[],"label_agreement":null},{"id":"W1978028164","doi":"10.1115/1.3179237","title":"Stability Based Robust Eigenvalue Design for Tolerance","year":2009,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; City University of Hong Kong","keywords":"Eigenvalues and eigenvectors; Robustness (evolution); Jacobian matrix and determinant; Control theory (sociology); Sensitivity (control systems); Eigenvalue perturbation; Mathematics; Stability (learning theory); Mathematical optimization; Computer science; Applied mathematics; Engineering","score_opus":0.2655176531858352,"score_gpt":0.34952875224190716,"score_spread":0.08401109905607196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978028164","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011696342,0.000035330944,0.9977064,0.000017547007,0.000008468662,0.00001133883,0.000004137969,0.00007539792,0.0009716882],"genre_scores_gemma":[0.37729472,0.00035183755,0.6164719,0.00010736069,0.000082552775,0.00040492526,0.00008164509,0.00020308394,0.005002027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988852,0.0002968534,0.000053088035,0.0001961647,0.0005000168,0.000068643],"domain_scores_gemma":[0.99922085,0.00026762215,0.00015104553,0.00009834759,0.00023928398,0.00002279029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012352013,0.0012086862,0.0009760667,0.0006805106,0.00040595923,0.000888465,0.00093280844,0.000667953,0.0027188254],"category_scores_gemma":[0.002611039,0.00042667214,0.0008249104,0.000498654,0.0008514808,0.0010127958,0.0014253923,0.00091522257,0.0009712328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095706906,0.00006124546,0.00025853064,0.00025723383,0.00007356566,0.00010138719,0.00017179664,0.68038553,0.0622979,0.14226939,0.0014489564,0.11257875],"study_design_scores_gemma":[0.000014664072,0.00014106114,0.000084982166,0.000016913193,0.000016722797,0.00006534552,0.000015070403,0.96251434,0.010081316,0.023289125,0.0037358953,0.000024543466],"about_ca_topic_score_codex":0.00049095566,"about_ca_topic_score_gemma":0.0003600351,"teacher_disagreement_score":0.0027188254,"about_ca_system_score_codex":0.0005108843,"about_ca_system_score_gemma":0.0006986074,"threshold_uncertainty_score":0.00909543},"labels":[],"label_agreement":null},{"id":"W1978121545","doi":"10.1115/detc2007-34366","title":"Stability of SDOF Linear Viscoelastic System Under the Excitation of Narrow-Band Noise","year":2007,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Moment (physics); Eigenvalues and eigenvectors; Lyapunov exponent; Mathematical analysis; Viscoelasticity; Noise (video); Mathematics; Bounded function; Parametric statistics; Excitation; Stochastic differential equation; Monte Carlo method; Physics; Differential equation; Second moment of area; Stability (learning theory); Classical mechanics; Nonlinear system; Geometry; Computer science; Quantum mechanics","score_opus":0.09091219288828785,"score_gpt":0.3309681456810059,"score_spread":0.24005595279271807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978121545","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7164302,0.00032053798,0.27908054,0.00020428197,0.000030812505,0.000019778994,0.000046783785,0.00009542708,0.003771537],"genre_scores_gemma":[0.99799395,0.00007798577,0.0013697691,0.000007342757,0.000006769227,0.000010024928,0.000013925018,0.0000040187206,0.0005162453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998467,0.000036188114,0.00000834853,0.000034639157,0.000049236114,0.000024928386],"domain_scores_gemma":[0.99927205,0.00034583136,0.00022107868,0.000032108816,0.000094455,0.00003444274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004920796,0.00031597115,0.000489157,0.00033382032,0.0003337172,0.00056463707,0.00028530133,0.00046032522,0.0004466022],"category_scores_gemma":[0.0018746993,0.000120506724,0.00029737584,0.00014833,0.0007851622,0.00049564056,0.0004952826,0.0002949349,0.00005234633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014682864,0.000018243196,0.0031891312,0.00008395729,0.0000478653,0.00038550104,0.0002512221,0.93498176,0.030462297,0.02248359,0.00013793037,0.0078116395],"study_design_scores_gemma":[0.0000030419328,0.00003361814,0.0007409164,0.0000036253373,0.0000049017,0.00003821329,0.000016873066,0.99617445,0.0008919456,0.0020145557,0.00007190765,0.00000588736],"about_ca_topic_score_codex":0.0016899793,"about_ca_topic_score_gemma":0.00051864696,"teacher_disagreement_score":0.0016899793,"about_ca_system_score_codex":0.00026714028,"about_ca_system_score_gemma":0.00031159224,"threshold_uncertainty_score":0.0033602715},"labels":[],"label_agreement":null},{"id":"W1978289729","doi":"10.1016/j.ress.2013.07.010","title":"An effective approximation for variance-based global sensitivity analysis","year":2013,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":109,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multiplicative function; Variance-based sensitivity analysis; Sensitivity (control systems); Computation; Applied mathematics; Mathematics; Quadrature (astronomy); Variance (accounting); Gaussian; Variance reduction; Function (biology); Product (mathematics); Algorithm; Algebraic number; Mathematical optimization; Computer science; Monte Carlo method; Statistics; Mathematical analysis; One-way analysis of variance; Analysis of variance","score_opus":0.015399285122781744,"score_gpt":0.27555076612929963,"score_spread":0.26015148100651786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978289729","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012243113,0.00011698419,0.9973627,0.00003450275,0.000024360785,0.000008233326,0.000015851881,0.00008798831,0.0011252325],"genre_scores_gemma":[0.3716254,0.0008713852,0.615337,0.00035024498,0.0002679803,0.00026417323,0.0002611852,0.000734,0.010288575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985689,0.00062833965,0.00004937055,0.00014559603,0.0005102143,0.000097588905],"domain_scores_gemma":[0.99751663,0.0017523362,0.00008381909,0.00026067908,0.00033751404,0.000049046197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026342473,0.0013054779,0.0014123202,0.0015493141,0.00044223724,0.0013643743,0.001175709,0.0010787843,0.0029441703],"category_scores_gemma":[0.00850059,0.00066362746,0.0015317752,0.00091546366,0.000860758,0.0016398497,0.0017128567,0.0020241395,0.0007180904],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006660361,0.00005751148,0.00030837196,0.00015651918,0.00012277388,0.00010802981,0.00006296612,0.82107824,0.009497217,0.10691123,0.002128411,0.05950213],"study_design_scores_gemma":[0.0000017683393,0.000008141578,0.0000410026,0.0000067430615,0.000010966165,0.000016854947,0.0000021582666,0.986414,0.0005071881,0.012430543,0.00055478426,0.000005704427],"about_ca_topic_score_codex":0.0018777425,"about_ca_topic_score_gemma":0.0019827054,"teacher_disagreement_score":0.0029441703,"about_ca_system_score_codex":0.00063677685,"about_ca_system_score_gemma":0.00075091585,"threshold_uncertainty_score":0.013931394},"labels":[],"label_agreement":null},{"id":"W1978478904","doi":"10.1016/s0167-7152(01)00164-x","title":"A unified treatment of direct and indirect estimation of a probability density and its derivatives","year":2002,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Mathematics; Kernel density estimation; Minimax; Density estimation; Regularization (linguistics); Estimation; Probability density function; Applied mathematics; Kernel (algebra); Convolution (computer science); Statistics; Mathematical optimization; Econometrics; Computer science; Estimator; Artificial intelligence; Combinatorics","score_opus":0.09335610905585086,"score_gpt":0.2991652901597176,"score_spread":0.20580918110386676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978478904","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034906028,0.00062366104,0.99673504,0.00014973953,0.00006279317,0.000010990521,0.00002388015,0.000028563358,0.0020161776],"genre_scores_gemma":[0.111560546,0.0061728284,0.8635109,0.00067439204,0.0010935257,0.0003827104,0.00031584434,0.0003625738,0.015926639],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967986,0.001388201,0.00022966047,0.00038940614,0.0010470374,0.00014719114],"domain_scores_gemma":[0.9958224,0.0024125217,0.00026068417,0.00073152885,0.0006942628,0.00007863131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005230216,0.0018540325,0.001892371,0.0017957072,0.0009859138,0.004984083,0.0038892175,0.0033176409,0.0038421731],"category_scores_gemma":[0.015889293,0.0014513755,0.0020652423,0.0021562653,0.0027451694,0.005771131,0.003875726,0.0038365507,0.0015497649],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001777976,0.000018888695,0.00025848497,0.00013961211,0.000062106126,0.00019685927,0.0001278715,0.06765551,0.0012126327,0.8948863,0.0018899805,0.03353399],"study_design_scores_gemma":[0.000009052521,0.000038335158,0.00026447928,0.000083537154,0.00006374224,0.00031994976,0.000034940556,0.5512152,0.0011725004,0.4376602,0.009085669,0.00005244684],"about_ca_topic_score_codex":0.0029679367,"about_ca_topic_score_gemma":0.0031394735,"teacher_disagreement_score":0.005230216,"about_ca_system_score_codex":0.0010373673,"about_ca_system_score_gemma":0.0018948881,"threshold_uncertainty_score":0.02766037},"labels":[],"label_agreement":null},{"id":"W1978688925","doi":"10.1139/l02-112","title":"Harmonizing structural safety levels with life-quality objectives","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Risk analysis (engineering); Probabilistic logic; Reliability engineering; Limit state design; Quality (philosophy); Computer science; Engineering; Operations research; Business","score_opus":0.09424857687804362,"score_gpt":0.2847670100393858,"score_spread":0.1905184331613422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978688925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044147607,0.0001411842,0.94691926,0.00016199311,0.000013353527,0.00015251376,0.00006751255,0.000103750855,0.0082928445],"genre_scores_gemma":[0.8493402,0.0002350555,0.14778149,0.000074764095,0.000024953673,0.00047966963,0.00011861644,0.000092162285,0.0018530551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99459225,0.0024926271,0.00024172517,0.00033641804,0.00191041,0.00042648517],"domain_scores_gemma":[0.9922017,0.00430982,0.001042955,0.0006198214,0.0016689438,0.00015675652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008256981,0.0010258358,0.00087529083,0.0018632634,0.00034340506,0.0022544726,0.0010474689,0.0009309181,0.001845144],"category_scores_gemma":[0.018362172,0.00040588397,0.0006894075,0.0010808216,0.0011717834,0.0022531631,0.0015288957,0.00091531023,0.00036967176],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012123606,0.00011070043,0.0022593453,0.00019710409,0.0000607079,0.000045194854,0.00021969392,0.80713755,0.005780714,0.120641924,0.00054580404,0.06288002],"study_design_scores_gemma":[0.000050433628,0.0009463862,0.0039140894,0.00012569729,0.00007980318,0.00010917263,0.0002817334,0.7786488,0.010668992,0.19929309,0.0058112144,0.000070548],"about_ca_topic_score_codex":0.0007171599,"about_ca_topic_score_gemma":0.00072180625,"teacher_disagreement_score":0.008256981,"about_ca_system_score_codex":0.0015592626,"about_ca_system_score_gemma":0.0015862468,"threshold_uncertainty_score":0.043667614},"labels":[],"label_agreement":null},{"id":"W1979158317","doi":"10.1016/j.laa.2003.09.013","title":"Perturbed cones for analysis of uncertain multi-criteria optimization problems","year":2003,"lang":"en","type":"article","venue":"Linear Algebra and its Applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Syncrude (Canada); University of Alberta","funders":"","keywords":"Mathematics; Cone (formal languages); Convex cone; Convex hull; Regular polygon; Bounded function; Conic optimization; Basis (linear algebra); Convex optimization; Mathematical optimization; Optimization problem; Dual cone and polar cone; Combinatorics; Convex analysis; Mathematical analysis; Geometry; Algorithm","score_opus":0.1296109580914765,"score_gpt":0.3745569035399836,"score_spread":0.24494594544850712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979158317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006672501,0.0015819632,0.9875436,0.00026806298,0.00010329665,0.000044162596,0.00013194414,0.00007166519,0.0035827185],"genre_scores_gemma":[0.6505066,0.0054590036,0.33196762,0.00035736294,0.00044047364,0.00054052327,0.0008386563,0.00031676725,0.009573054],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979963,0.0010678072,0.000097825636,0.00014615926,0.00061362237,0.00007833562],"domain_scores_gemma":[0.99564517,0.002581461,0.00051686965,0.00022629146,0.0008141992,0.00021594838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004017493,0.0018403218,0.0019404117,0.0016977878,0.00059217156,0.0031271337,0.0018025006,0.0010016065,0.0026337225],"category_scores_gemma":[0.009104284,0.00067109807,0.0012873263,0.0019476811,0.0014864702,0.0026836428,0.0013929304,0.0028723655,0.00044899774],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015424169,0.00009710394,0.00033563233,0.00033510683,0.00014844417,0.00016863846,0.00008487101,0.47645485,0.002930103,0.49002427,0.0027681694,0.026498606],"study_design_scores_gemma":[0.000007349578,0.000021789585,0.00006744079,0.000028823597,0.000010820946,0.000019028012,0.00001655473,0.8801083,0.00030098675,0.11839373,0.0010136269,0.000011552722],"about_ca_topic_score_codex":0.0028490562,"about_ca_topic_score_gemma":0.0015018437,"teacher_disagreement_score":0.004017493,"about_ca_system_score_codex":0.0013747946,"about_ca_system_score_gemma":0.0013955961,"threshold_uncertainty_score":0.021246731},"labels":[],"label_agreement":null},{"id":"W1979628867","doi":"10.1002/nme.2431","title":"Domain decomposition of stochastic PDEs: Theoretical formulations","year":2008,"lang":"en","type":"article","venue":"International Journal for Numerical Methods in Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Carleton University","funders":"Sandia National Laboratories; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Air Force Office of Scientific Research; Ontario Innovation Trust","keywords":"Domain decomposition methods; Discretization; Decomposition; Probabilistic logic; Domain (mathematical analysis); Decomposition method (queueing theory); Applied mathematics; Schur complement; Complement (music); Mathematics; Mortar methods; Mathematical optimization; Partial differential equation; Computer science; Algorithm; Finite element method; Mathematical analysis; Discrete mathematics","score_opus":0.08926284794522757,"score_gpt":0.46822116124608487,"score_spread":0.3789583133008573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979628867","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005867327,0.0005707835,0.98912257,0.00033291444,0.000055769724,0.000019458073,0.000044685483,0.000021498847,0.003965109],"genre_scores_gemma":[0.6476935,0.0038324406,0.3356067,0.000388827,0.0004953633,0.00039474262,0.00037019528,0.000104850464,0.011113312],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99936885,0.00022567518,0.000027830392,0.00007295114,0.00024620327,0.000058377103],"domain_scores_gemma":[0.9990914,0.000459809,0.000101339516,0.00006483688,0.00022065014,0.00006192634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001676715,0.0006238031,0.00064493367,0.0010193596,0.00034012974,0.0018515218,0.00070125394,0.0007815999,0.002844734],"category_scores_gemma":[0.002442823,0.00030867866,0.00084606657,0.0005707178,0.0016779525,0.0015762944,0.0015441969,0.0016011373,0.00041005167],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008448224,0.000011930665,0.00013247476,0.000062548,0.00000919179,0.0000340854,0.000051651507,0.11711249,0.0009384559,0.8746767,0.0006682201,0.006293838],"study_design_scores_gemma":[0.0000051573165,0.000011540428,0.000060749415,0.000024355595,0.0000035173766,0.000031775806,0.000024469631,0.7813939,0.0002552867,0.2162465,0.0019357338,0.00000693783],"about_ca_topic_score_codex":0.0010085683,"about_ca_topic_score_gemma":0.0004887557,"teacher_disagreement_score":0.002844734,"about_ca_system_score_codex":0.00092255586,"about_ca_system_score_gemma":0.0008324471,"threshold_uncertainty_score":0.009516537},"labels":[],"label_agreement":null},{"id":"W1980287044","doi":"10.1109/tap.2013.2279094","title":"Application of Polynomial Chaos to Quantify Uncertainty in Deterministic Channel Models","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thales (Canada); University of Toronto","funders":"","keywords":"Polynomial chaos; Randomness; Monte Carlo method; Channel (broadcasting); Algorithm; Polynomial; Ray tracing (physics); Radio channel; Uncertainty quantification; Computer science; Applied mathematics; Statistical physics; Finite-difference time-domain method; Mathematics; Mathematical optimization; Mathematical analysis; Telecommunications; Statistics; Physics; Optics","score_opus":0.06381199489590063,"score_gpt":0.30330857791684285,"score_spread":0.23949658302094223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980287044","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018794302,0.00007856329,0.9800158,0.000056757694,0.000011009741,0.00001618497,0.000028537817,0.0000835092,0.0009153189],"genre_scores_gemma":[0.9158345,0.00025500933,0.08263965,0.000028631723,0.00003290504,0.00007854087,0.000055248598,0.00005047117,0.0010250135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990876,0.00032852608,0.000035759233,0.000083943734,0.00039206678,0.00007218977],"domain_scores_gemma":[0.996479,0.0025604605,0.00036388898,0.00028735708,0.00025236857,0.0000569134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013264271,0.00064339215,0.00055276835,0.0008455777,0.00034939396,0.00072422676,0.00065946754,0.0004975067,0.00051898765],"category_scores_gemma":[0.0055883904,0.00032569998,0.0005775878,0.0005767755,0.0009343228,0.001035218,0.0011242956,0.00089633226,0.000091459275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013111473,0.000009071232,0.00024183736,0.000016433085,0.000011467287,0.000032574273,0.000026823502,0.97034925,0.0018398964,0.023214282,0.00006540631,0.004179916],"study_design_scores_gemma":[6.416968e-7,0.0000064529822,0.000034928067,9.1751895e-7,0.0000010222376,0.0000067503734,0.0000016415959,0.9969355,0.00029959486,0.0026481084,0.00006140897,0.0000031469247],"about_ca_topic_score_codex":0.00222432,"about_ca_topic_score_gemma":0.0013580389,"teacher_disagreement_score":0.00222432,"about_ca_system_score_codex":0.00081064645,"about_ca_system_score_gemma":0.0007719109,"threshold_uncertainty_score":0.0070148706},"labels":[],"label_agreement":null},{"id":"W1980799771","doi":"10.1115/detc2007-35566","title":"Diagonal Quadratic Approximation for Parallelization of Analytical Target Cascading","year":2007,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mathematical optimization; Computer science; Quadratic equation; Coordinate descent; Diagonal; Convergence (economics); Separable space; Block (permutation group theory); Algorithm; Parallel computing; Mathematics","score_opus":0.11784527882359182,"score_gpt":0.3797180227505404,"score_spread":0.2618727439269486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980799771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067937043,0.000059268907,0.99113643,0.000043571017,0.000014124639,0.000017362521,0.000011328023,0.00031433968,0.001609795],"genre_scores_gemma":[0.421743,0.00017435064,0.5724663,0.00007625516,0.000029128554,0.00018683058,0.0001091328,0.00022837431,0.0049866843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994128,0.00019027476,0.000021785618,0.00007598686,0.0002362256,0.00006296956],"domain_scores_gemma":[0.99897695,0.0005239048,0.00008501888,0.00011893445,0.00024954192,0.00004576773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011746974,0.0007140886,0.0007966233,0.00043768057,0.00037780573,0.00048343875,0.0008389497,0.00044946044,0.003045047],"category_scores_gemma":[0.002628538,0.00034790818,0.0004898385,0.00051977206,0.0006254531,0.0006208047,0.0008464423,0.0009333698,0.0007019844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005333681,0.000045292963,0.0002500262,0.000047103655,0.00001933805,0.000036290116,0.00004888061,0.93805736,0.002795954,0.013350052,0.0010061602,0.044290207],"study_design_scores_gemma":[0.0000028201096,0.0000053604185,0.00001124154,7.4118714e-7,9.3042496e-7,0.0000025512702,0.000001970232,0.9984738,0.00025952212,0.0010473405,0.00019283322,8.3298863e-7],"about_ca_topic_score_codex":0.008379696,"about_ca_topic_score_gemma":0.007635692,"teacher_disagreement_score":0.008379696,"about_ca_system_score_codex":0.0007702792,"about_ca_system_score_gemma":0.0016098775,"threshold_uncertainty_score":0.016661823},"labels":[],"label_agreement":null},{"id":"W1981478437","doi":"10.1191/0142331205tm152oa","title":"Control design to shape the stationary probability density function","year":2005,"lang":"en","type":"article","venue":"Transactions of the Institute of Measurement and Control","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probability density function; Control theory (sociology); Process (computing); Dimension (graph theory); Controller (irrigation); Focus (optics); Function (biology); Mathematics; Set (abstract data type); Applied mathematics; Computer science; Mathematical optimization; Control (management); Artificial intelligence; Physics","score_opus":0.10239927824513341,"score_gpt":0.267948535273408,"score_spread":0.1655492570282746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981478437","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040867315,0.000036834794,0.9945208,0.000037086505,0.000015270738,0.000021510148,0.000005845233,0.0001427249,0.0011331983],"genre_scores_gemma":[0.7107672,0.00033643795,0.28578898,0.00014553843,0.00007633421,0.00021945657,0.000048815702,0.000085046595,0.0025322502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99958235,0.00007882634,0.00002041865,0.00011659982,0.00016665002,0.000035145604],"domain_scores_gemma":[0.9992101,0.000371565,0.00012694887,0.00007570653,0.00019301279,0.000022694225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011121093,0.0005132224,0.00046988216,0.00046309686,0.00023848303,0.0005351357,0.0007388731,0.00051242684,0.0013466728],"category_scores_gemma":[0.0031714523,0.00023720563,0.00044561477,0.0002853476,0.00069506647,0.00057526084,0.0005581064,0.00087581103,0.00033880386],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105949934,0.00016530494,0.00065244705,0.00024290514,0.000051657054,0.00008482901,0.00025432603,0.6597639,0.05675944,0.11534781,0.001108545,0.16546296],"study_design_scores_gemma":[0.000021588306,0.00011864683,0.00020989908,0.000012390065,0.000015291664,0.000043676795,0.000010809322,0.9765304,0.009519024,0.011149167,0.002354585,0.000014521775],"about_ca_topic_score_codex":0.0007965929,"about_ca_topic_score_gemma":0.0005971936,"teacher_disagreement_score":0.0013466728,"about_ca_system_score_codex":0.0004896942,"about_ca_system_score_gemma":0.0009488654,"threshold_uncertainty_score":0.0058814287},"labels":[],"label_agreement":null},{"id":"W1982566213","doi":"10.1007/s10589-015-9751-7","title":"Scenario generation for stochastic optimization problems via the sparse grid method","year":2015,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Office of Naval Research; U.S. Department of Energy","keywords":"Sparse grid; Mathematics; Mathematical optimization; Discretization; Univariate; Quasi-Monte Carlo method; Grid; Applied mathematics; Monte Carlo method; Markov chain Monte Carlo; Hybrid Monte Carlo; Multivariate statistics; Statistics; Mathematical analysis","score_opus":0.15939405150286576,"score_gpt":0.35937311730166466,"score_spread":0.1999790657987989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982566213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004753843,0.000074793,0.99339926,0.00011618331,0.000027610015,0.000033348082,0.000073000534,0.00009389791,0.0014281508],"genre_scores_gemma":[0.5494677,0.00042108353,0.44528937,0.00018011092,0.00009484157,0.0005449465,0.0006654689,0.00024436775,0.0030922042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928916,0.00041737466,0.000024938494,0.00006198752,0.00015683734,0.000049682232],"domain_scores_gemma":[0.9969338,0.002330297,0.00017688394,0.00020630937,0.00024288445,0.00010975801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017582573,0.00065193366,0.001225603,0.0008082603,0.00042557047,0.00084495754,0.0013700246,0.0010262639,0.004466115],"category_scores_gemma":[0.006727468,0.0007314751,0.0009541502,0.0011151041,0.0008278121,0.0015417851,0.001804383,0.0014421332,0.0005561602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042364354,0.000017244152,0.00018221248,0.00003831515,0.000022239405,0.000038440685,0.000018950015,0.94658524,0.00030528556,0.04320983,0.0007642915,0.0087755285],"study_design_scores_gemma":[0.0000063194766,0.000004393358,0.000014874521,0.0000021467786,0.0000011791725,0.0000045751535,0.0000021194237,0.98663586,0.000036567475,0.013128743,0.00016133387,0.0000019053027],"about_ca_topic_score_codex":0.0028915738,"about_ca_topic_score_gemma":0.002850915,"teacher_disagreement_score":0.004466115,"about_ca_system_score_codex":0.0005974285,"about_ca_system_score_gemma":0.0010506966,"threshold_uncertainty_score":0.0149406195},"labels":[],"label_agreement":null},{"id":"W1982732541","doi":"10.3182/20140824-6-za-1003.01231","title":"Integration of fault diagnosis and control by finding a trade-off between the detectability of stochastic fault and economics","year":2014,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fault detection and isolation; Control theory (sociology); Fault (geology); Monte Carlo method; Polynomial; Polynomial chaos; Process (computing); Key (lock); Controller (irrigation); Continuous stirred-tank reactor; Engineering; Mathematical optimization; Computer science; Control (management); Control engineering; Mathematics; Artificial intelligence","score_opus":0.03334671370732933,"score_gpt":0.2742653972105703,"score_spread":0.24091868350324097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982732541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033439614,0.00063144043,0.959321,0.0012603139,0.000045359466,0.000048215556,0.000026762265,0.00014300014,0.0050842776],"genre_scores_gemma":[0.9392819,0.00031359267,0.057999242,0.00014104122,0.000091708236,0.000052985215,0.000029097117,0.000037395766,0.002053132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986744,0.0004483734,0.000071877905,0.00023483566,0.00042797741,0.00014248448],"domain_scores_gemma":[0.99376017,0.004830102,0.00046338825,0.00033526128,0.0004984854,0.000112564165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030841406,0.00114008,0.0016864993,0.0012016927,0.00037461755,0.002411129,0.0012418778,0.0019354518,0.0026631118],"category_scores_gemma":[0.013942343,0.0006449193,0.00059990626,0.00069675304,0.0012571382,0.004347927,0.0017762114,0.0014498112,0.00022545308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033085595,0.0002764832,0.002912467,0.0002442661,0.00020487631,0.000119149605,0.00012230503,0.70730484,0.007567427,0.16391473,0.00050505804,0.1164975],"study_design_scores_gemma":[0.000021893671,0.00013093854,0.0006648228,0.000023308783,0.000043540807,0.000046042718,0.000026033196,0.9109899,0.0019853017,0.085565835,0.00048673467,0.000015687132],"about_ca_topic_score_codex":0.0018154554,"about_ca_topic_score_gemma":0.002053338,"teacher_disagreement_score":0.0030841406,"about_ca_system_score_codex":0.0013749466,"about_ca_system_score_gemma":0.0017242334,"threshold_uncertainty_score":0.016310692},"labels":[],"label_agreement":null},{"id":"W1983263007","doi":"10.1016/j.strusafe.2005.10.002","title":"Analysis of approximations for multinormal integration in system reliability computation","year":2005,"lang":"en","type":"article","venue":"Structural Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reliability (semiconductor); Computation; Bivariate analysis; Simple (philosophy); Context (archaeology); Computer science; Conditional probability; Reliability engineering; Numerical integration; Algorithm; Mathematics; Applied mathematics; Statistics; Engineering; Machine learning","score_opus":0.04459091172091458,"score_gpt":0.3426442450358452,"score_spread":0.2980533333149306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983263007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023293257,0.00033343126,0.9746337,0.00014546419,0.000037949136,0.000016066437,0.000028447266,0.0001880688,0.0013235896],"genre_scores_gemma":[0.6554996,0.00049950805,0.3402862,0.00016362275,0.00008739204,0.00012603479,0.00019206907,0.00035720976,0.0027884988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975969,0.0011424546,0.00011715191,0.0002050075,0.000720612,0.00021789639],"domain_scores_gemma":[0.9797365,0.016475767,0.0007469455,0.0011684333,0.0015253696,0.00034709385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007731828,0.0007604575,0.0014416415,0.0011182523,0.0007259051,0.0015909978,0.0023414465,0.0010462302,0.0026637856],"category_scores_gemma":[0.039847404,0.00093325955,0.00093524856,0.0011845151,0.0014173769,0.0026448357,0.0020423594,0.0026705842,0.00034950254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010600059,0.0000309426,0.0009716916,0.00005554693,0.00003701197,0.000040299914,0.00010579547,0.9218992,0.00046637573,0.0599536,0.0005100789,0.015823418],"study_design_scores_gemma":[0.0000016274867,0.0000037770467,0.000033011747,0.0000041920043,0.0000026683383,0.0000036220626,0.0000033954395,0.9925155,0.00006901966,0.0072678616,0.00009380775,0.0000015016076],"about_ca_topic_score_codex":0.010408167,"about_ca_topic_score_gemma":0.008691033,"teacher_disagreement_score":0.010408167,"about_ca_system_score_codex":0.0022078813,"about_ca_system_score_gemma":0.0015827918,"threshold_uncertainty_score":0.040890276},"labels":[],"label_agreement":null},{"id":"W1984251841","doi":"10.1139/l02-079","title":"Reliability assessment in highway bridge design","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bridge (graph theory); Reliability (semiconductor); Reliability engineering; Code (set theory); Limit (mathematics); Engineering; Design methods; Computer science; Set (abstract data type)","score_opus":0.0809470172987901,"score_gpt":0.2778785742101629,"score_spread":0.19693155691137276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984251841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.126407,0.0014563795,0.8623463,0.0003336235,0.00003810338,0.00006568597,0.000061609935,0.00028828546,0.009003065],"genre_scores_gemma":[0.9052653,0.00078768877,0.09144624,0.000031824893,0.00003118105,0.000118466625,0.00008706497,0.00005089839,0.0021814546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848396,0.0009571121,0.00003473694,0.00006569721,0.00041491873,0.00004360543],"domain_scores_gemma":[0.9972818,0.00170412,0.00018141083,0.00015670317,0.00063766056,0.000038209644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024453378,0.00048431812,0.0003865912,0.00089786144,0.00032222047,0.00044712276,0.000540096,0.00068678835,0.0011479881],"category_scores_gemma":[0.009746472,0.00030381163,0.00036056718,0.0005123182,0.0007379375,0.00065039983,0.0005150381,0.0005016068,0.00030567954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006491977,0.000037447262,0.0024584574,0.00010991634,0.000019232055,0.00007233108,0.00014606363,0.91128737,0.0024466596,0.020891834,0.0008030283,0.061662704],"study_design_scores_gemma":[0.00000848189,0.00009122875,0.0007614046,0.000033676733,0.000009070515,0.000039115126,0.000038346792,0.9820077,0.001345113,0.0142646395,0.0013897319,0.000011562022],"about_ca_topic_score_codex":0.0058765234,"about_ca_topic_score_gemma":0.0036775111,"teacher_disagreement_score":0.0058765234,"about_ca_system_score_codex":0.00080905174,"about_ca_system_score_gemma":0.00101461,"threshold_uncertainty_score":0.01293236},"labels":[],"label_agreement":null},{"id":"W1986201712","doi":"10.1115/1.4025491","title":"High Dimensional Model Representation With Principal Component Analysis","year":2013,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Principal component analysis; Monte Carlo method; Dimension (graph theory); Sampling (signal processing); Basis (linear algebra); Mathematical optimization; Computer science; Benchmark (surveying); Representation (politics); Applied mathematics; Mathematics; Basis function; Algorithm; Statistics","score_opus":0.11214772167806922,"score_gpt":0.32224623186512097,"score_spread":0.21009851018705175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986201712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014202534,0.00008641582,0.9977221,0.00004054858,0.000009673107,0.000017396105,0.000029496261,0.00035086332,0.00032323416],"genre_scores_gemma":[0.132261,0.0005452466,0.86438966,0.00007867009,0.00005041054,0.00029600103,0.0005934995,0.00023918327,0.0015462853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987062,0.0004550934,0.00006485764,0.00021984623,0.00048666526,0.000067266614],"domain_scores_gemma":[0.9985825,0.00066345127,0.00014169831,0.00029356012,0.00028573253,0.000033175995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001467263,0.0017110548,0.0012127224,0.0014961419,0.00063845864,0.0014149622,0.0010481307,0.0011338249,0.002728638],"category_scores_gemma":[0.0041194297,0.00089773495,0.001803672,0.0018793797,0.0007454587,0.0016565606,0.0014920492,0.0020946476,0.0011110323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059240814,0.000063589825,0.00055433286,0.00015237975,0.0001049049,0.000056136236,0.00007883321,0.8221904,0.004205968,0.018491795,0.002431055,0.15161133],"study_design_scores_gemma":[0.0000026775695,0.000014804122,0.00008622336,0.00000457335,0.0000072852354,0.000013250348,0.000006825829,0.9929617,0.0006754099,0.005183093,0.0010342936,0.000009813758],"about_ca_topic_score_codex":0.0059592356,"about_ca_topic_score_gemma":0.003901036,"teacher_disagreement_score":0.0059592356,"about_ca_system_score_codex":0.0005887853,"about_ca_system_score_gemma":0.0017849334,"threshold_uncertainty_score":0.011849105},"labels":[],"label_agreement":null},{"id":"W1987791609","doi":"10.1093/imamat/hxl035","title":"Utilization of divergent integrals and a new symbolism in contact and crack analysis","year":2007,"lang":"en","type":"article","venue":"IMA Journal of Applied Mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Art history; Mathematics; Volume (thermodynamics); Art; Physics","score_opus":0.12317652965689005,"score_gpt":0.35889784409126724,"score_spread":0.2357213144343772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987791609","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11190724,0.002683979,0.81758016,0.0036796946,0.00075410097,0.000052696796,0.00008549587,0.00017113441,0.06308544],"genre_scores_gemma":[0.88603705,0.0014986921,0.10049495,0.00055696204,0.001054554,0.000071534196,0.00006881647,0.00026213104,0.009955375],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962149,0.0016450536,0.00022278397,0.00037847002,0.0012879927,0.00025068875],"domain_scores_gemma":[0.9895219,0.0057806484,0.0010895272,0.0013929423,0.0014374346,0.00077752303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006449841,0.00073170173,0.0011246058,0.004375512,0.0020129567,0.0040545226,0.0014206452,0.0013706165,0.0031737203],"category_scores_gemma":[0.02141626,0.00042072558,0.0011546391,0.0023076565,0.013449364,0.00905233,0.0059262384,0.004277175,0.0004551046],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019962365,0.000010431752,0.00030989383,0.000020482623,0.0000055094647,0.00007448546,0.00018526112,0.0011206433,0.0003706737,0.99280965,0.0004490502,0.0046240855],"study_design_scores_gemma":[0.000006003684,0.00003003592,0.000221623,0.000022252934,0.000006733972,0.00012588764,0.0001110944,0.023893943,0.00028493648,0.9735214,0.0017558881,0.000020192836],"about_ca_topic_score_codex":0.00094270706,"about_ca_topic_score_gemma":0.00054861285,"teacher_disagreement_score":0.006449841,"about_ca_system_score_codex":0.0015724241,"about_ca_system_score_gemma":0.0010265856,"threshold_uncertainty_score":0.034110367},"labels":[],"label_agreement":null},{"id":"W1988575859","doi":"10.2514/2.2750","title":"Risk Analysis of Fuselage Splices Containing Multisite Damage and Corrosion","year":2001,"lang":"en","type":"article","venue":"Journal of Aircraft","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"Southwest Research Institute; Boeing","keywords":"Fuselage; Corrosion; Structural engineering; Materials science; Aerospace engineering; Engineering; Forensic engineering; Composite material","score_opus":0.04214507120302709,"score_gpt":0.3228797367466402,"score_spread":0.28073466554361315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988575859","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1774749,0.0022805058,0.81204027,0.0005980352,0.00003644227,0.00008479177,0.0001669637,0.00014895377,0.0071690963],"genre_scores_gemma":[0.9742164,0.0007895837,0.021655982,0.000025110345,0.00005420634,0.000049413975,0.00016254745,0.000023731547,0.0030230323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990395,0.00033224098,0.000038050388,0.00008200242,0.0004316749,0.00007659659],"domain_scores_gemma":[0.99726784,0.0017904161,0.00044046392,0.00011065349,0.00031344243,0.000077202996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002135515,0.00080525177,0.000665317,0.0014017886,0.00028892249,0.00085267634,0.0007662588,0.00083221943,0.0013300633],"category_scores_gemma":[0.005112194,0.00041594397,0.0008187114,0.00039440583,0.00073708675,0.0011516901,0.0007524782,0.00044780454,0.00014625992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009188828,0.000036806225,0.0041759457,0.000096923046,0.00011987418,0.0003725785,0.00008507537,0.9344649,0.003966433,0.036061466,0.00064896134,0.019879151],"study_design_scores_gemma":[0.000005864585,0.00006475876,0.0021134599,0.000011190308,0.000027921968,0.00026612208,0.000033396176,0.97569793,0.0012554138,0.019999448,0.0005107222,0.000013734574],"about_ca_topic_score_codex":0.0012771854,"about_ca_topic_score_gemma":0.00094635313,"teacher_disagreement_score":0.002135515,"about_ca_system_score_codex":0.00069596665,"about_ca_system_score_gemma":0.000540398,"threshold_uncertainty_score":0.0112938285},"labels":[],"label_agreement":null},{"id":"W1988997080","doi":"10.1080/00401706.2012.727751","title":"Global Sensitivity Analysis for Mixture Experiments","year":2012,"lang":"en","type":"article","venue":"Technometrics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus","funders":"Los Alamos National Laboratory; Natural Sciences and Engineering Research Council of Canada; National Nuclear Security Administration; U.S. Department of Energy","keywords":"Sensitivity (control systems); Computer science; Process (computing); Code (set theory); Gaussian process; Function (biology); Mixture model; Design of experiments; Gaussian; Algorithm; Source code; Computer experiment; Simulation; Mathematical optimization; Statistics; Mathematics; Artificial intelligence; Engineering; Programming language","score_opus":0.14795226542832704,"score_gpt":0.38829418152598505,"score_spread":0.240341916097658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988997080","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022086982,0.00031784998,0.97320235,0.00017993736,0.00004344218,0.00029719522,0.00030995978,0.00048244427,0.0030797631],"genre_scores_gemma":[0.76324,0.00072917284,0.22770672,0.00047411185,0.000084621315,0.002152597,0.00083897676,0.00060372433,0.0041701733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99191415,0.0050617727,0.00025968486,0.00089172786,0.0014266932,0.0004459879],"domain_scores_gemma":[0.9630823,0.031527285,0.0012166356,0.0024580678,0.0014845119,0.00023125864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01845956,0.0025497964,0.002092452,0.0030755654,0.0006945642,0.0015888021,0.0016444965,0.001534907,0.0068294965],"category_scores_gemma":[0.04537893,0.0006851965,0.0035548038,0.0012736049,0.0015090613,0.0020544922,0.0033617509,0.0023471932,0.00043063238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036436046,0.00015526598,0.0023993957,0.00055819494,0.0004868733,0.00024454528,0.00017615934,0.8838383,0.011133375,0.0685901,0.0015577456,0.030495668],"study_design_scores_gemma":[0.000025235895,0.00021891446,0.0007853809,0.000045153243,0.000114218696,0.00007130922,0.000052273386,0.93927956,0.0061817016,0.05180243,0.0013796884,0.000044166452],"about_ca_topic_score_codex":0.0016898103,"about_ca_topic_score_gemma":0.0008556984,"teacher_disagreement_score":0.01845956,"about_ca_system_score_codex":0.0019441628,"about_ca_system_score_gemma":0.0012884069,"threshold_uncertainty_score":0.09762466},"labels":[],"label_agreement":null},{"id":"W1989099374","doi":"10.1109/ccece.2012.6334899","title":"Simplifying oscillometric blood pressure measurement models using global sensitivity analysis","year":2012,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Sensitivity (control systems); Blood pressure; Computer science; Control theory (sociology); Reliability engineering; Electronic engineering; Medicine; Engineering; Internal medicine; Artificial intelligence","score_opus":0.2990325830190381,"score_gpt":0.3639528237963283,"score_spread":0.0649202407772902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989099374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010809244,0.00011758095,0.9869653,0.00005556663,0.0000126866735,0.00003629415,0.000079310164,0.00030214497,0.0016218498],"genre_scores_gemma":[0.8551312,0.0006909316,0.13810313,0.00011456075,0.000040512274,0.0004633396,0.00033735562,0.00023278459,0.004886308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905556,0.00037612984,0.000041809268,0.0001876431,0.0002626433,0.00007622028],"domain_scores_gemma":[0.99836284,0.0011579542,0.00015098465,0.00010794895,0.00020182082,0.00001835138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001552824,0.0019386061,0.001179525,0.0008995047,0.00030445185,0.0010740228,0.0008474132,0.0007767862,0.0015873762],"category_scores_gemma":[0.004266239,0.0008017569,0.0016565771,0.0005001613,0.0006539352,0.0009494378,0.0010825827,0.0012182131,0.00038745048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013187202,0.0000065443264,0.00012782347,0.00002581899,0.000015204686,0.000025204954,0.000016217658,0.99306196,0.0014608419,0.0018549731,0.000079850164,0.0033123374],"study_design_scores_gemma":[0.0000022136662,0.000013750165,0.00009198877,0.0000045241204,0.000011932356,0.0000085488655,0.000002465593,0.99720037,0.0005585036,0.001895555,0.00020450217,0.000005678015],"about_ca_topic_score_codex":0.0068604085,"about_ca_topic_score_gemma":0.004122872,"teacher_disagreement_score":0.0068604085,"about_ca_system_score_codex":0.0009757673,"about_ca_system_score_gemma":0.000841359,"threshold_uncertainty_score":0.01364094},"labels":[],"label_agreement":null},{"id":"W1989553357","doi":"10.1115/imece2005-80592","title":"Risk-Informed Load and Resistance Factor Design (LRFD) Methods for Piping","year":2005,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Golder Associates (Canada)","funders":"","keywords":"Piping; Reliability (semiconductor); Reliability engineering; Safety factor; Engineering; First-order reliability method; Structural engineering; Load factor; Structural reliability; Computer science; Mechanical engineering; Probabilistic logic","score_opus":0.17411650579969296,"score_gpt":0.4323369323243679,"score_spread":0.2582204265246749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989553357","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006313253,0.000032374428,0.99871325,0.000013689475,0.000003308269,0.000016584498,0.000009289584,0.00006491471,0.00051526976],"genre_scores_gemma":[0.11493168,0.00029532702,0.8822948,0.000038717935,0.000026557005,0.000301416,0.00008077126,0.00014516783,0.0018855621],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99810433,0.0008723937,0.00008272025,0.00018909699,0.00069366687,0.000057808655],"domain_scores_gemma":[0.99799997,0.0012001293,0.00029034432,0.00017954147,0.00030627399,0.000023877095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026775238,0.0013610091,0.0007986508,0.001241613,0.00040037438,0.0006556879,0.0008781412,0.00062829605,0.004100449],"category_scores_gemma":[0.006418702,0.0006119171,0.0009082604,0.00067080493,0.00075835816,0.0010117538,0.00083417364,0.0010230171,0.0005757188],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046142937,0.000026444468,0.00029031013,0.00014632176,0.000027500206,0.00003300352,0.000080376914,0.80707455,0.003328209,0.03818645,0.000743477,0.15001719],"study_design_scores_gemma":[0.000021936346,0.00008146604,0.00015825003,0.000033293785,0.000016790285,0.000060484497,0.000011572824,0.9649878,0.0023166584,0.026189534,0.0061018155,0.000020460559],"about_ca_topic_score_codex":0.001450876,"about_ca_topic_score_gemma":0.0017026642,"teacher_disagreement_score":0.004100449,"about_ca_system_score_codex":0.00087265234,"about_ca_system_score_gemma":0.0012913382,"threshold_uncertainty_score":0.014160275},"labels":[],"label_agreement":null},{"id":"W1989692164","doi":"10.1016/j.ress.2008.06.016","title":"Parameter uncertainty effects on variance-based sensitivity analysis","year":2008,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Sensitivity (control systems); Variance (accounting); Autoregressive model; Nonlinear system; Variance-based sensitivity analysis; Econometrics; Process (computing); Variable (mathematics); Mathematics; Computer science; Statistics; Engineering; One-way analysis of variance; Analysis of variance","score_opus":0.027056283606562977,"score_gpt":0.25569763125592365,"score_spread":0.22864134764936067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989692164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15541281,0.0007471833,0.83544576,0.00034606762,0.000075167336,0.00008539015,0.00025680705,0.0005153099,0.0071154386],"genre_scores_gemma":[0.96921676,0.00020822616,0.029540492,0.00007890458,0.00002574301,0.000050663613,0.0001195463,0.00020602884,0.00055354583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931641,0.004654395,0.00020424239,0.00043310583,0.0011969971,0.0003471382],"domain_scores_gemma":[0.94928485,0.04654611,0.00067450566,0.0018989486,0.0014730169,0.00012258008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010107977,0.0014609381,0.001264558,0.0025451523,0.00053418905,0.0018700769,0.0010181832,0.0013930863,0.0019717792],"category_scores_gemma":[0.052523017,0.0010393185,0.0018610557,0.0012146588,0.0010711041,0.0025919839,0.0016342315,0.0016622941,0.00018585662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014543747,0.000029240357,0.0006926028,0.00009383508,0.00012997926,0.00011020282,0.000054708777,0.9758218,0.004675986,0.009236929,0.00020736287,0.008801907],"study_design_scores_gemma":[0.000006833818,0.000032717657,0.0006215276,0.00001861306,0.000062021936,0.00004102681,0.000009163858,0.9867517,0.0031060057,0.009182838,0.00013291584,0.000034693476],"about_ca_topic_score_codex":0.0023511404,"about_ca_topic_score_gemma":0.0016940291,"teacher_disagreement_score":0.010107977,"about_ca_system_score_codex":0.00086351397,"about_ca_system_score_gemma":0.00058607623,"threshold_uncertainty_score":0.053456724},"labels":[],"label_agreement":null},{"id":"W1991159422","doi":"10.1115/omae2006-92095","title":"On the Quantification of Robustness of Structures","year":2006,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Robustness (evolution); Computer science; Reliability engineering; Data mining; Engineering","score_opus":0.08946853264836394,"score_gpt":0.3156248785542778,"score_spread":0.22615634590591385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991159422","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010274929,0.006737779,0.96411896,0.00062509463,0.00010042406,0.000040814713,0.00018388013,0.00016708342,0.017751005],"genre_scores_gemma":[0.74065214,0.016332407,0.23343678,0.0005416754,0.0011664894,0.00038074827,0.00054941856,0.00035908155,0.006581122],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9908923,0.0030805205,0.0006662243,0.0014165052,0.0034204628,0.00052391825],"domain_scores_gemma":[0.9850425,0.010035796,0.0020632786,0.0015396622,0.0010195032,0.0002992133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067071915,0.0027069063,0.0015139537,0.006012737,0.0007586903,0.004156455,0.0018524103,0.002329493,0.0033728885],"category_scores_gemma":[0.020471886,0.00066363945,0.0020932476,0.003121704,0.008018318,0.0077942125,0.0034616662,0.0025430382,0.0006786052],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043927423,0.000016047637,0.00060792913,0.00030714707,0.00009852172,0.00019601814,0.00020396855,0.20836355,0.0035529283,0.7483261,0.0007615914,0.037522312],"study_design_scores_gemma":[0.0000073352985,0.00014634753,0.0012836691,0.00027147986,0.000056714125,0.00026536762,0.000073355535,0.15361813,0.0030103202,0.8317396,0.009424025,0.00010375398],"about_ca_topic_score_codex":0.0010062763,"about_ca_topic_score_gemma":0.00036770065,"teacher_disagreement_score":0.0067071915,"about_ca_system_score_codex":0.0023525446,"about_ca_system_score_gemma":0.0006926041,"threshold_uncertainty_score":0.03547144},"labels":[],"label_agreement":null},{"id":"W1991647997","doi":"10.1016/j.ress.2012.10.016","title":"Restrictions of point estimate methods and remedy","year":2012,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Hydro (Canada)","funders":"BC Hydro","keywords":"Point (geometry); Reliability (semiconductor); Variable (mathematics); Point estimation; Computer science; Random variable; Reliability engineering; Mathematics; Mathematical optimization; Engineering; Statistics","score_opus":0.0463721193684948,"score_gpt":0.3619071078684022,"score_spread":0.3155349884999074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991647997","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002485145,0.0005984863,0.9872715,0.0018500211,0.00028201527,0.000039822055,0.00017360711,0.00017483345,0.007124618],"genre_scores_gemma":[0.37486413,0.004077579,0.56879896,0.0034477012,0.0030304536,0.0016502362,0.001384791,0.0009088179,0.041837312],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97388476,0.015561747,0.0017450759,0.0028844269,0.0050303927,0.00089362153],"domain_scores_gemma":[0.8905184,0.08126169,0.003993948,0.016694026,0.0068039075,0.0007279825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025475197,0.0015570705,0.0035011617,0.0029919797,0.0016798906,0.0034394441,0.0041360613,0.004591963,0.012720331],"category_scores_gemma":[0.1568998,0.00181507,0.0030953325,0.0021019692,0.005888084,0.007608663,0.011274299,0.009044034,0.002785516],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020751198,0.00005631685,0.0014659904,0.0004000356,0.00014391108,0.00023483195,0.0002793554,0.032012794,0.00088281755,0.8995633,0.007462525,0.05729052],"study_design_scores_gemma":[0.00006767232,0.00008238568,0.0005545171,0.0001362497,0.00005893289,0.0002992114,0.00007181806,0.12584473,0.0013164701,0.8559302,0.015594335,0.000043572076],"about_ca_topic_score_codex":0.0014810002,"about_ca_topic_score_gemma":0.00063334784,"teacher_disagreement_score":0.025475197,"about_ca_system_score_codex":0.0009928831,"about_ca_system_score_gemma":0.002051172,"threshold_uncertainty_score":0.1347273},"labels":[],"label_agreement":null},{"id":"W1991674311","doi":"10.1007/s00477-006-0090-1","title":"Probabilistic risk analysis using ordered weighted averaging (OWA) operators","year":2006,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Probabilistic logic; Parametric statistics; Reliability (semiconductor); Computational intelligence; Uncertainty quantification; Mathematics; Sensitivity (control systems); Computer science; Probabilistic risk assessment; Mathematical optimization; Statistics; Artificial intelligence; Engineering","score_opus":0.05705977011967664,"score_gpt":0.37168577180019496,"score_spread":0.31462600168051835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991674311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034934229,0.000115966715,0.9957053,0.000037274185,0.000019865163,0.000007978591,0.000011216061,0.00003645027,0.0005726257],"genre_scores_gemma":[0.45029074,0.0007116293,0.54410344,0.00012961289,0.00023131406,0.00020935922,0.00015284923,0.00018309592,0.003988011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969591,0.0014012625,0.00017970863,0.000252423,0.0010362418,0.00017132095],"domain_scores_gemma":[0.99530375,0.0030923118,0.0003447725,0.0003565281,0.00076799653,0.00013459664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058288067,0.0012464301,0.0015776715,0.0016045934,0.00048451617,0.0018727311,0.0012776612,0.00086986704,0.0015280277],"category_scores_gemma":[0.0100299865,0.00047271,0.001611032,0.0014541799,0.0009515664,0.0032697283,0.0018461812,0.001544219,0.00019333407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008610741,0.00007321346,0.0005087472,0.00014648677,0.00022172909,0.000105402956,0.00007508409,0.6469415,0.004145292,0.26950634,0.0011785814,0.07701155],"study_design_scores_gemma":[0.000005006705,0.000026962476,0.00008272555,0.000005912971,0.00002329919,0.000024175255,0.000006407754,0.9059375,0.00062239286,0.09281501,0.00043886653,0.000011811745],"about_ca_topic_score_codex":0.0014297467,"about_ca_topic_score_gemma":0.0010102513,"teacher_disagreement_score":0.0058288067,"about_ca_system_score_codex":0.0006640902,"about_ca_system_score_gemma":0.0010084967,"threshold_uncertainty_score":0.030826092},"labels":[],"label_agreement":null},{"id":"W1991691074","doi":"10.1016/j.strusafe.2008.03.002","title":"Extreme quantile estimation from censored sample using partial cross-entropy and fractional partial probability weighted moments","year":2008,"lang":"en","type":"article","venue":"Structural Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University Network of Excellence in Nuclear Engineering","keywords":"Quantile; Mathematics; Quantile function; Order statistic; Statistics; Entropy (arrow of time); Extreme value theory; Random variable; Principle of maximum entropy; Applied mathematics; Mathematical optimization; Moment-generating function","score_opus":0.1162957971666997,"score_gpt":0.34510093656552704,"score_spread":0.22880513939882735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991691074","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026875578,0.000071361705,0.9726458,0.000029100804,0.00000622183,0.00000651017,0.000028239217,0.0001259613,0.00021119733],"genre_scores_gemma":[0.8685475,0.0001747067,0.1298885,0.00004307193,0.000051744857,0.00005318963,0.0003196367,0.00007220769,0.0008493889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99895537,0.0005091716,0.000047249712,0.00016806251,0.00022782008,0.00009230846],"domain_scores_gemma":[0.99424285,0.004307985,0.0005264411,0.00046591728,0.00035358014,0.000103239654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033769864,0.00050284667,0.001249278,0.0015687608,0.00033829326,0.0010319828,0.0010323604,0.00087871787,0.0012896482],"category_scores_gemma":[0.011391136,0.0005560759,0.0010128664,0.0010620797,0.0008886932,0.0019513773,0.0012864708,0.00092813146,0.00015384628],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020721204,0.000060928713,0.006227829,0.00010325128,0.0001971791,0.00020872713,0.00011311861,0.86026585,0.0032587857,0.063897945,0.00055474933,0.06490447],"study_design_scores_gemma":[0.0000035390538,0.000010261962,0.00096892053,0.000004807172,0.000007637615,0.000027512637,0.0000056690374,0.98349977,0.0005224944,0.014848702,0.00009131972,0.000009410712],"about_ca_topic_score_codex":0.0012041952,"about_ca_topic_score_gemma":0.0010121212,"teacher_disagreement_score":0.0033769864,"about_ca_system_score_codex":0.00056206423,"about_ca_system_score_gemma":0.0004944567,"threshold_uncertainty_score":0.017859459},"labels":[],"label_agreement":null},{"id":"W1992433881","doi":"10.1016/j.apnum.2013.05.005","title":"An efficient determination of critical parameters of nonlinear Schrödinger equation with a point-like potential using generalized polynomial chaos methods","year":2013,"lang":"en","type":"article","venue":"Applied Numerical Mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Mathematics; Soliton; Convergence (economics); Nonlinear system; Mathematical analysis; Polynomial; Spectral method; Critical point (mathematics); Nonlinear Schrödinger equation; Space (punctuation); Schrödinger equation; Applied mathematics; Quantum mechanics; Physics","score_opus":0.08286114314658849,"score_gpt":0.373086999757461,"score_spread":0.2902258566108725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992433881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10479137,0.00033679276,0.88932985,0.000251349,0.00005598708,0.00008813499,0.000067515546,0.00020367213,0.0048753093],"genre_scores_gemma":[0.78967303,0.00038024626,0.20565067,0.00006928719,0.00006467411,0.00020737758,0.00010616526,0.0001588055,0.0036898062],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980026,0.000088875146,0.0000111866575,0.000022632477,0.000058863003,0.000018132854],"domain_scores_gemma":[0.9993451,0.00037655942,0.00006645242,0.000055956152,0.00010862994,0.000047224712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006140106,0.0006116771,0.0010061446,0.000942748,0.0007636604,0.0006801186,0.0010108708,0.0008579988,0.0013759992],"category_scores_gemma":[0.0021518345,0.00038310877,0.00059871306,0.000587075,0.0009786199,0.0014974909,0.001282386,0.0009684298,0.00025914935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002747534,0.000086698965,0.0016029265,0.00054267125,0.000107697626,0.00040603557,0.00040603592,0.3929978,0.042468216,0.5142617,0.0017406865,0.045104805],"study_design_scores_gemma":[0.00001028599,0.000014852465,0.00014883,0.0000067917686,0.000006921439,0.000028792034,0.0000147126575,0.969435,0.0015605374,0.028408024,0.00035225356,0.000012998495],"about_ca_topic_score_codex":0.0009320243,"about_ca_topic_score_gemma":0.00081884407,"teacher_disagreement_score":0.0013759992,"about_ca_system_score_codex":0.00044118543,"about_ca_system_score_gemma":0.000836203,"threshold_uncertainty_score":0.004603207},"labels":[],"label_agreement":null},{"id":"W1993188372","doi":"10.1121/1.1558374","title":"A substructure approach for the midfrequency vibration of stochastic systems","year":2003,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Office of Naval Research","keywords":"Substructure; Finite element method; Probabilistic logic; Basis (linear algebra); Vibration; Applied mathematics; Coupling (piping); Mathematics; Computer science; Stochastic process; Orthogonal basis; Mathematical optimization; Algorithm; Geometry; Structural engineering; Physics; Artificial intelligence","score_opus":0.04549947068937182,"score_gpt":0.2866772193099117,"score_spread":0.24117774862053987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993188372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008793119,0.000052143285,0.9986362,0.000019440737,0.000008673928,0.0000061268856,0.000006131866,0.000023668232,0.0003681498],"genre_scores_gemma":[0.1238007,0.0006744879,0.8719559,0.00007907511,0.00009139446,0.00017850124,0.0000863472,0.00007562054,0.0030579655],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969625,0.000077445846,0.00001454719,0.00005480631,0.000139078,0.000017865028],"domain_scores_gemma":[0.99969316,0.00013776119,0.000040277042,0.00005994735,0.000050735995,0.000018120709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006050847,0.00065482175,0.0005128718,0.00061586645,0.00028279904,0.00053645147,0.0008758202,0.00060022477,0.0026320869],"category_scores_gemma":[0.0011421095,0.0002934187,0.0007954782,0.0004394293,0.00080760324,0.0010709092,0.00090226874,0.0013038185,0.0006311517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037414153,0.000045208744,0.00046762143,0.00016629837,0.00004522086,0.00011207725,0.00014746406,0.33130974,0.024170227,0.5185978,0.0007759978,0.124124974],"study_design_scores_gemma":[0.00000490156,0.00005699517,0.00016109047,0.000016496055,0.000007696441,0.00006394032,0.000010805304,0.9329607,0.0019720474,0.060444977,0.004286718,0.0000136502495],"about_ca_topic_score_codex":0.000603286,"about_ca_topic_score_gemma":0.0006638169,"teacher_disagreement_score":0.0026320869,"about_ca_system_score_codex":0.0003355049,"about_ca_system_score_gemma":0.0005183724,"threshold_uncertainty_score":0.008805215},"labels":[],"label_agreement":null},{"id":"W1993573560","doi":"10.1016/s0022-460x(03)00037-3","title":"Spectral finite elements for vibrating rods and beams with random field properties","year":2003,"lang":"en","type":"article","venue":"Journal of Sound and Vibration","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Canada Research Chairs; National Science Foundation","keywords":"Randomness; Rod; Random field; Helmholtz equation; Vibration; Beam (structure); Mathematics; Wavelength; Stiffness; Fourier series; Mathematical analysis; Physics; Materials science; Optics; Acoustics; Composite material","score_opus":0.06065044832714069,"score_gpt":0.2934499920727194,"score_spread":0.23279954374557868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993573560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027501216,0.000112105095,0.9703379,0.00009829637,0.00002455467,0.000022669285,0.00002946836,0.000085411135,0.0017883711],"genre_scores_gemma":[0.72823644,0.00030451253,0.26346603,0.000110991634,0.000038178856,0.00018645462,0.00014423797,0.00014774692,0.0073653604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962664,0.00015566789,0.000020607084,0.00003607462,0.00013609213,0.0000249552],"domain_scores_gemma":[0.9979019,0.0015429192,0.00017610325,0.00010414199,0.00021537457,0.00005969835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010858853,0.0005748593,0.00075694587,0.0007036649,0.00032706192,0.0006188635,0.0010509874,0.0015690372,0.0017316352],"category_scores_gemma":[0.004274783,0.0006624985,0.0006582202,0.00036957872,0.0011521572,0.0008231797,0.0009193618,0.0006266912,0.0003008595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005827595,0.000056678593,0.00040254567,0.000047391524,0.000016793207,0.00004332316,0.00006257424,0.9392146,0.00291687,0.044748418,0.00026586885,0.012166817],"study_design_scores_gemma":[0.0000024885715,0.0000047853005,0.00002223122,0.000003772334,0.0000017263428,0.0000060695857,0.0000063233897,0.99582326,0.0002656184,0.0037448,0.00011677957,0.0000022144284],"about_ca_topic_score_codex":0.0018616094,"about_ca_topic_score_gemma":0.002287235,"teacher_disagreement_score":0.0018616094,"about_ca_system_score_codex":0.0004000844,"about_ca_system_score_gemma":0.0005095199,"threshold_uncertainty_score":0.005792916},"labels":[],"label_agreement":null},{"id":"W1995275365","doi":"10.1504/ijrs.2007.014965","title":"Statistical approximations of vibration power flow for complex structures with uncertainties","year":2007,"lang":"en","type":"article","venue":"International Journal of Reliability and Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Research, Development and Engineering Command; Automotive Research Center","keywords":"Probabilistic logic; Vibration; Flow (mathematics); Reliability (semiconductor); Interpolation (computer graphics); Probabilistic analysis of algorithms; Power (physics); Component (thermodynamics); Computer science; Control theory (sociology); Mathematics; Mathematical optimization; Algorithm; Acoustics; Geometry; Artificial intelligence; Physics","score_opus":0.0443880060602353,"score_gpt":0.3404300358415755,"score_spread":0.2960420297813402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995275365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005041433,0.000044487115,0.9944285,0.00002048812,0.00000431465,0.000004876097,0.00001037732,0.000050288112,0.0003952868],"genre_scores_gemma":[0.700378,0.00057680975,0.29580444,0.000050384013,0.00006286954,0.00016940184,0.0001660295,0.0001379821,0.0026541485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968195,0.000109302855,0.000009564768,0.00004232564,0.00013723384,0.000019602434],"domain_scores_gemma":[0.9978788,0.0017048953,0.00014812248,0.00012327326,0.00012206831,0.000022892731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011128024,0.00047163072,0.0004920728,0.00062534644,0.0002055971,0.0004801917,0.0007519248,0.0005259236,0.0013625718],"category_scores_gemma":[0.004842527,0.0003683822,0.0005708761,0.0004472016,0.0006833109,0.0009171514,0.00050772727,0.00091419194,0.00028251554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000073565097,0.000006113873,0.00013091961,0.000017198663,0.000005107017,0.000016324524,0.000015848775,0.97475004,0.0006023198,0.016887927,0.000093642935,0.007467141],"study_design_scores_gemma":[4.8256027e-7,0.0000029176056,0.000024218141,0.0000010335314,4.3406345e-7,0.000003490282,0.000001240032,0.99642724,0.0000868809,0.0033547108,0.000096206095,0.0000012299737],"about_ca_topic_score_codex":0.001206946,"about_ca_topic_score_gemma":0.0009457241,"teacher_disagreement_score":0.0013625718,"about_ca_system_score_codex":0.000407098,"about_ca_system_score_gemma":0.00046717664,"threshold_uncertainty_score":0.005885124},"labels":[],"label_agreement":null},{"id":"W1995713695","doi":"10.1007/s11222-011-9230-7","title":"Variance decompositions of nonlinear time series using stochastic simulation and sensitivity analysis","year":2011,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Sobol sequence; Series (stratigraphy); Nonlinear system; Mathematics; Monte Carlo method; Variance decomposition of forecast errors; Variance-based sensitivity analysis; Variance (accounting); Decomposition; Sensitivity (control systems); Applied mathematics; Time series; Statistics; Mathematical optimization; One-way analysis of variance; Analysis of variance; Engineering","score_opus":0.07037939029807398,"score_gpt":0.3309448722627854,"score_spread":0.2605654819647114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995713695","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037234392,0.00014036061,0.9611545,0.00010626384,0.000017043756,0.000023451466,0.00005221471,0.00015553522,0.0011161149],"genre_scores_gemma":[0.86484116,0.00035907974,0.1321544,0.0000461889,0.000035560424,0.00010747754,0.00020483567,0.00022888105,0.0020224617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924195,0.00039213858,0.000039839917,0.0000916433,0.00018172423,0.000052749827],"domain_scores_gemma":[0.9957553,0.0033833287,0.00027993784,0.00021154367,0.00031783857,0.000052100917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003148895,0.0006774231,0.0008119414,0.0013855129,0.00034142943,0.0012311795,0.0004472534,0.00067430746,0.0012067436],"category_scores_gemma":[0.01072068,0.0006757127,0.0014818375,0.0007167933,0.0006706249,0.0013608795,0.00073897553,0.0009590394,0.00013847706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002923942,0.000020273787,0.0003952869,0.000030298334,0.00004262465,0.000028218969,0.000034443157,0.9705071,0.0015418661,0.020345673,0.00013415416,0.00689086],"study_design_scores_gemma":[0.0000012206813,0.000003165856,0.00010127992,0.0000031185223,0.0000038046687,0.000004163345,0.0000022189263,0.99452657,0.00023946162,0.0050576753,0.000053236763,0.000004066901],"about_ca_topic_score_codex":0.003044018,"about_ca_topic_score_gemma":0.002081133,"teacher_disagreement_score":0.003148895,"about_ca_system_score_codex":0.0008819578,"about_ca_system_score_gemma":0.0007989537,"threshold_uncertainty_score":0.01665318},"labels":[],"label_agreement":null},{"id":"W1995973104","doi":"10.1109/mwsym.2014.6848540","title":"A multi-resolution FDTD method for uncertainty quantification in the time-domain modeling of microwave structures","year":2014,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Finite-difference time-domain method; Robustness (evolution); Polynomial chaos; Computer science; Algorithm; Grid; Uncertainty quantification; Microwave imaging; Monte Carlo method; Sparse grid; Wavelet; Microwave; Computational science; Mathematics; Optics; Artificial intelligence; Physics; Machine learning; Telecommunications","score_opus":0.1212125312571866,"score_gpt":0.3719642011458725,"score_spread":0.25075166988868586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995973104","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016616107,0.000062179424,0.9978598,0.000027348,0.00000883385,0.000007652419,0.000011606673,0.00004751072,0.00031343714],"genre_scores_gemma":[0.13270795,0.00027950594,0.86561084,0.000034819943,0.00001755391,0.00006960606,0.00006033521,0.00003854823,0.0011807936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997956,0.000057205838,0.000011326114,0.000025479916,0.000101910635,0.000008487463],"domain_scores_gemma":[0.9996556,0.00018605411,0.000033221626,0.00005663798,0.000056752713,0.0000117798245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004392873,0.00036790033,0.0002999536,0.00038026588,0.00028206877,0.00037848178,0.00054834096,0.00061499485,0.0007353528],"category_scores_gemma":[0.0012350755,0.00022859711,0.00045636093,0.0003968312,0.00037626358,0.0005746377,0.0004972841,0.00075122004,0.00020003368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000472548,0.00004794639,0.000559905,0.0001908534,0.00003570189,0.000166345,0.00016449719,0.7243551,0.05225111,0.049513888,0.0012577376,0.17140958],"study_design_scores_gemma":[0.0000024774388,0.000009205188,0.00004947337,0.000005772283,0.0000031221973,0.000053902382,0.0000035507983,0.99320626,0.0029586672,0.0018889749,0.0018131045,0.000005617528],"about_ca_topic_score_codex":0.0010377582,"about_ca_topic_score_gemma":0.0010768628,"teacher_disagreement_score":0.0010377582,"about_ca_system_score_codex":0.00031064783,"about_ca_system_score_gemma":0.00053901033,"threshold_uncertainty_score":0.0024599433},"labels":[],"label_agreement":null},{"id":"W1996282723","doi":"10.1002/env.955","title":"Derivation of sample oriented quantile function using maximum entropy and self‐determined probability weighted moments","year":2009,"lang":"en","type":"article","venue":"Environmetrics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Specialized Research Fund for the Doctoral Program of Higher Education of China; University Network of Excellence in Nuclear Engineering","keywords":"Quantile; Outlier; Principle of maximum entropy; Mathematics; Quantile function; Statistics; Sample size determination; Estimator; Moment (physics); Random variable; Sample (material); Probability density function; Applied mathematics; Moment-generating function","score_opus":0.05873134701679925,"score_gpt":0.285537651561781,"score_spread":0.22680630454498177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996282723","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002239183,0.000068057394,0.99732804,0.000028910024,0.00000789812,0.000011805778,0.000018827724,0.00004627252,0.00025104114],"genre_scores_gemma":[0.4302939,0.00060237397,0.5658416,0.00016113714,0.00013825284,0.00032164287,0.00035377577,0.00021018571,0.0020771506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99857914,0.0006104566,0.000068949696,0.00016471274,0.0004894213,0.000087359556],"domain_scores_gemma":[0.99639684,0.002608928,0.0002973358,0.00020804093,0.00041726345,0.00007167549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004315817,0.00058068166,0.0008709191,0.0017167578,0.00033504335,0.0011314702,0.0014348583,0.00089328515,0.0021472322],"category_scores_gemma":[0.013144198,0.00039851552,0.0011165049,0.0011972591,0.0009303887,0.0015685367,0.0012398497,0.0012317683,0.00043682402],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007755976,0.00006009821,0.0030602273,0.0002499447,0.000105610416,0.0002547014,0.00016813233,0.6870636,0.0077184225,0.17834443,0.0015120002,0.12138534],"study_design_scores_gemma":[0.0000044211392,0.000017945104,0.0005827073,0.000013032079,0.000005460555,0.000045802535,0.000007685077,0.97673804,0.001187405,0.020682031,0.00070172275,0.000013737511],"about_ca_topic_score_codex":0.0010507602,"about_ca_topic_score_gemma":0.0006168996,"teacher_disagreement_score":0.004315817,"about_ca_system_score_codex":0.00088183937,"about_ca_system_score_gemma":0.000828561,"threshold_uncertainty_score":0.022824526},"labels":[],"label_agreement":null},{"id":"W1996334888","doi":"10.1007/s00170-015-7128-9","title":"Global sensitivity analysis of a CNC machine tool: application of MDRM","year":2015,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Machine tool; Sensitivity (control systems); Reliability (semiconductor); Kinematics; Computer science; Trajectory; Monte Carlo method; Propagation of uncertainty; Algorithm; Multiplicative function; Numerical control; Control theory (sociology); Engineering; Mathematics; Artificial intelligence; Statistics; Machining; Mechanical engineering","score_opus":0.033982559701895856,"score_gpt":0.3320304744580831,"score_spread":0.29804791475618725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996334888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24763866,0.0005541579,0.7219509,0.00039460746,0.00007995832,0.00020436423,0.0004358436,0.0005327013,0.028208831],"genre_scores_gemma":[0.9851167,0.0000775954,0.01284452,0.00003313956,0.000008025314,0.000047362697,0.000046522116,0.000056272278,0.0017698504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999398,0.00019594074,0.000015905604,0.00013239162,0.00019039847,0.00006729736],"domain_scores_gemma":[0.9984668,0.0011385862,0.00010173055,0.000102611375,0.00017150838,0.000018667044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017547907,0.00088564324,0.001007249,0.0010424972,0.00033888864,0.0009796508,0.0008248668,0.0010272352,0.0031811332],"category_scores_gemma":[0.0033168902,0.0003931985,0.0011064343,0.0005385572,0.00081497023,0.0007916635,0.001181233,0.0006588026,0.00014272715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057088702,0.000014657942,0.0002855546,0.00007417824,0.000021374051,0.00008981451,0.000022779113,0.98597753,0.005545906,0.00340693,0.00013300561,0.0043712193],"study_design_scores_gemma":[0.000003510282,0.00003159682,0.0002759165,0.0000050559784,0.000013181228,0.00001847135,0.000009263413,0.9966216,0.0016203357,0.0011866359,0.00020772404,0.0000066652283],"about_ca_topic_score_codex":0.004492023,"about_ca_topic_score_gemma":0.0024855812,"teacher_disagreement_score":0.004492023,"about_ca_system_score_codex":0.00095415115,"about_ca_system_score_gemma":0.000630518,"threshold_uncertainty_score":0.0106419325},"labels":[],"label_agreement":null},{"id":"W1996469383","doi":"10.1115/imece2007-41178","title":"Effect of Notch Location on the Stress Concentration and Reliability of Notched Composite Laminates Based on a Probabilistic Approach","year":2007,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Composite laminates; Materials science; Composite material; Delamination (geology); Stiffness; Composite number; Enhanced Data Rates for GSM Evolution; Stress (linguistics); Fibre-reinforced plastic; Structural engineering; Stress concentration; Specific strength; Epoxy; Fracture mechanics; Computer science; Engineering","score_opus":0.027002645136130737,"score_gpt":0.2948090372534956,"score_spread":0.2678063921173649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996469383","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83720374,0.00029233206,0.16157492,0.000033186447,0.000008489967,0.000014873438,0.000032038322,0.00011492967,0.00072559627],"genre_scores_gemma":[0.9945833,0.00006026977,0.005250337,0.0000036107135,0.0000017314071,0.0000049750834,0.000011901097,0.0000036875688,0.000080184414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937445,0.00020762964,0.000039196566,0.00010414806,0.00022962285,0.000045083878],"domain_scores_gemma":[0.9957918,0.0028221817,0.00063127064,0.00020205927,0.0004965742,0.000056063298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013061796,0.00044109393,0.0003483848,0.00060376,0.00017721207,0.00033327242,0.0003823486,0.0005203938,0.000374112],"category_scores_gemma":[0.003654233,0.00033528556,0.00045344885,0.00026288844,0.00041733807,0.0002969047,0.0002921854,0.00029571794,0.000070865295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023582081,0.00004102464,0.004536322,0.000060979575,0.000053963733,0.00008629731,0.000048555343,0.9411884,0.040998135,0.0006657848,0.000023252536,0.012061475],"study_design_scores_gemma":[0.0000049274076,0.0003169823,0.0043219337,0.000006385302,0.000056821988,0.000065006156,0.000021172831,0.97506285,0.019761594,0.00029017223,0.000076391276,0.000015657179],"about_ca_topic_score_codex":0.001052381,"about_ca_topic_score_gemma":0.0012710552,"teacher_disagreement_score":0.0013061796,"about_ca_system_score_codex":0.00039353862,"about_ca_system_score_gemma":0.0002211398,"threshold_uncertainty_score":0.0069078207},"labels":[],"label_agreement":null},{"id":"W1997246930","doi":"10.1115/1.1795813","title":"Stochastic Stability of Coupled Oscillators in Resonance: A Perturbation Approach","year":2004,"lang":"en","type":"article","venue":"Journal of Applied Mechanics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Science Foundation","keywords":"Eigenvalues and eigenvectors; Lyapunov exponent; Moment (physics); Perturbation (astronomy); Mathematics; Mathematical analysis; Galerkin method; Lyapunov function; Physics; Classical mechanics; Nonlinear system; Quantum mechanics","score_opus":0.055086911594303824,"score_gpt":0.28153786439761946,"score_spread":0.22645095280331565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997246930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053804666,0.00041184752,0.93774885,0.00043774702,0.00008039177,0.000040224015,0.00003461728,0.00011042954,0.0073311],"genre_scores_gemma":[0.9428027,0.0007063747,0.04972091,0.000130454,0.00015744448,0.00014593237,0.000045951067,0.000063605374,0.0062265485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997161,0.00012813856,0.000007983274,0.000030491881,0.00009580601,0.00002140481],"domain_scores_gemma":[0.9994785,0.00031804244,0.0000629411,0.000034771376,0.00007668178,0.000029104123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051151233,0.00053572207,0.00050212035,0.00072862586,0.00030296965,0.0004692175,0.0004988934,0.00048079513,0.0010944897],"category_scores_gemma":[0.0014299605,0.00024635825,0.00053293846,0.0002677857,0.0009352994,0.00062356243,0.0010331101,0.00059539516,0.00019264696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066666136,0.000034386925,0.0003096152,0.000062565494,0.00005534106,0.00021974673,0.00009893725,0.70718074,0.022627763,0.26009813,0.0006056121,0.008640587],"study_design_scores_gemma":[0.000002717862,0.0000130105,0.000054877848,0.0000028208729,0.00000280289,0.000017051998,0.0000055240653,0.9761303,0.00044850435,0.023094686,0.00022245737,0.000005192775],"about_ca_topic_score_codex":0.0005540644,"about_ca_topic_score_gemma":0.00028872397,"teacher_disagreement_score":0.0010944897,"about_ca_system_score_codex":0.0005108794,"about_ca_system_score_gemma":0.0002856538,"threshold_uncertainty_score":0.0037067533},"labels":[],"label_agreement":null},{"id":"W1997794981","doi":"10.1016/j.jval.2012.10.018","title":"Need for Speed: An Efficient Algorithm for Calculation of Single-Parameter Expected Value of Partial Perfect Information","year":2013,"lang":"en","type":"article","venue":"Value in Health","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Advancing Health Outcomes; University of British Columbia; Vancouver Coastal Health","funders":"Canadian Institutes of Health Research","keywords":"Estimator; Maximization; Probabilistic logic; Sensitivity (control systems); Computer science; Sequence (biology); Computation; Monte Carlo method; Mathematical optimization; Measure (data warehouse); Algorithm; Fraction (chemistry); Expected value; Mathematics; Data mining; Statistics; Artificial intelligence","score_opus":0.1062230686482538,"score_gpt":0.341457258818786,"score_spread":0.2352341901705322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997794981","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016119939,0.000095333446,0.9961235,0.00010841604,0.000053315736,0.00005177177,0.00004858029,0.0010665737,0.0008404715],"genre_scores_gemma":[0.052734587,0.00013040366,0.9437772,0.00012271969,0.00007225501,0.00030238385,0.00015076049,0.0005580458,0.0021515498],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984977,0.0004910228,0.00011434529,0.00021577935,0.0005644802,0.000116681644],"domain_scores_gemma":[0.9914955,0.0061624083,0.00019597974,0.0010352222,0.0009760916,0.00013482136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040831417,0.0017346125,0.0014082285,0.00146132,0.000841387,0.002381648,0.0024057832,0.0014839598,0.014281397],"category_scores_gemma":[0.031640485,0.0008522211,0.0011393798,0.0015698688,0.0010898187,0.004277991,0.0025898693,0.0029937183,0.004952613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006874145,0.00011127834,0.0013954536,0.00033176018,0.00014851984,0.00023611404,0.00028308376,0.17807737,0.005773705,0.17240693,0.016925953,0.6236224],"study_design_scores_gemma":[0.000111007794,0.000049751925,0.00027888812,0.000043412736,0.000043698306,0.00015233936,0.000037053745,0.86725813,0.0046081143,0.12088854,0.006489933,0.000039090228],"about_ca_topic_score_codex":0.003130144,"about_ca_topic_score_gemma":0.0034281914,"teacher_disagreement_score":0.014281397,"about_ca_system_score_codex":0.0011589768,"about_ca_system_score_gemma":0.002596596,"threshold_uncertainty_score":0.047776043},"labels":[],"label_agreement":null},{"id":"W1999715097","doi":"10.1142/s0218539303001056","title":"Reliability Modelling with Fuzzy Covariates","year":2003,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Covariate; Randomness; Reliability (semiconductor); Fuzzy logic; Computer science; Multiplicative function; Reliability engineering; Mathematics; Data mining; Machine learning; Statistics; Artificial intelligence; Engineering","score_opus":0.06305387135057407,"score_gpt":0.3256653467923376,"score_spread":0.26261147544176355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999715097","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024860986,0.00024408106,0.97272813,0.00031696816,0.000039656195,0.00003261625,0.00013596096,0.00010455072,0.0015371635],"genre_scores_gemma":[0.9031048,0.00093371014,0.08699375,0.00013782684,0.0001915274,0.00023228541,0.0003217339,0.000058079047,0.008026318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99717194,0.0012841132,0.00012596419,0.0005989636,0.00060428336,0.00021479216],"domain_scores_gemma":[0.9922391,0.005082735,0.0011571737,0.0006474034,0.00071171945,0.00016182917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044850577,0.0012285267,0.0012356556,0.0012510254,0.0005228736,0.001569827,0.0023418218,0.001959023,0.0027392688],"category_scores_gemma":[0.014895667,0.00065822364,0.0015854238,0.0012370392,0.0014512851,0.00310243,0.0014402975,0.0018880401,0.00053495436],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006733557,0.000035161283,0.0021387765,0.00008934289,0.00007824744,0.0001743543,0.00024628532,0.7082551,0.0011136238,0.27609447,0.00043790424,0.0112695545],"study_design_scores_gemma":[0.000012710158,0.00007397014,0.0005201187,0.000012797415,0.000026742451,0.000049979346,0.000021250826,0.9090679,0.00023767418,0.089010395,0.0009441138,0.000022328093],"about_ca_topic_score_codex":0.003538056,"about_ca_topic_score_gemma":0.0019408291,"teacher_disagreement_score":0.0044850577,"about_ca_system_score_codex":0.0012102113,"about_ca_system_score_gemma":0.0009018969,"threshold_uncertainty_score":0.02371955},"labels":[],"label_agreement":null},{"id":"W2000972170","doi":"10.3141/2195-01","title":"Risk-Based Highway Design","year":2010,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Sight; Geometric design; Computer science; Terrain; Probabilistic logic; Constraint (computer-aided design); Reliability (semiconductor); Collision; Reliability engineering; Transport engineering; Engineering; Geography; Artificial intelligence; Computer security","score_opus":0.20675253221707118,"score_gpt":0.4262546356982999,"score_spread":0.21950210348122873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000972170","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046472717,0.00044061188,0.968997,0.00026689263,0.000061037674,0.00016079523,0.0002027005,0.0003618647,0.02486175],"genre_scores_gemma":[0.47755095,0.0018281172,0.48841473,0.00023158579,0.00011746352,0.00081097573,0.0009166578,0.0003612647,0.029768117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969952,0.0008806934,0.00012870049,0.0003495592,0.0014563301,0.00018949507],"domain_scores_gemma":[0.998214,0.0005933347,0.00023144601,0.00020243974,0.00069946056,0.000059358514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032611766,0.0013971054,0.0008646684,0.0019421347,0.0005971643,0.0022053018,0.002207122,0.0011783089,0.011664181],"category_scores_gemma":[0.005115564,0.00061636086,0.0009864926,0.0008638856,0.0010010779,0.0017439082,0.0018076755,0.0011836168,0.0020595335],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041858453,0.00003153182,0.0005964064,0.00014015299,0.000034670255,0.00006006053,0.00006913571,0.7967877,0.0011337001,0.13003407,0.0033838772,0.067686796],"study_design_scores_gemma":[0.000027644515,0.00017064727,0.00047204696,0.00009390851,0.00004436973,0.00014974398,0.00006850035,0.8340935,0.001599407,0.13044465,0.03280197,0.000033536457],"about_ca_topic_score_codex":0.002342187,"about_ca_topic_score_gemma":0.002498687,"teacher_disagreement_score":0.011664181,"about_ca_system_score_codex":0.0019850384,"about_ca_system_score_gemma":0.0023322173,"threshold_uncertainty_score":0.039020598},"labels":[],"label_agreement":null},{"id":"W2001811464","doi":"10.1016/j.camwa.2013.09.006","title":"Data partition methodology for validation of predictive models","year":2013,"lang":"en","type":"article","venue":"Computers & Mathematics with Applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"U.S. Department of Energy","keywords":"Mathematics; Partition (number theory); Statistics; Data mining; Computer science; Combinatorics","score_opus":0.3862517955785712,"score_gpt":0.3967841771877205,"score_spread":0.010532381609149322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001811464","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015448593,0.00026285546,0.98288834,0.00007800657,0.000028691977,0.0001133375,0.00032908205,0.00039719354,0.0004539332],"genre_scores_gemma":[0.4628961,0.00037287173,0.5289763,0.00024087314,0.00006630553,0.0009701663,0.0047523486,0.00049469725,0.0012302649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98643726,0.00884956,0.00058426993,0.0011885257,0.0025348323,0.00040552867],"domain_scores_gemma":[0.95724523,0.03147236,0.0010697958,0.00622478,0.0036457912,0.00034201515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020301403,0.0014185234,0.002173612,0.0040122443,0.0016142274,0.002209875,0.0029483081,0.001821341,0.002506127],"category_scores_gemma":[0.06897237,0.00092714245,0.0024238625,0.0022748434,0.0022123682,0.0026279343,0.0036511698,0.0029332836,0.0006497818],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014224151,0.00032525486,0.0077155484,0.0006508175,0.0007962888,0.00016002065,0.00045924424,0.6371651,0.007897365,0.089491986,0.0035825993,0.25033328],"study_design_scores_gemma":[0.00008665779,0.00014706275,0.0011559981,0.00006885577,0.00007310739,0.00005638359,0.00006460042,0.9462932,0.004839534,0.045307927,0.0018828736,0.000023795055],"about_ca_topic_score_codex":0.0055089346,"about_ca_topic_score_gemma":0.0035806512,"teacher_disagreement_score":0.020301403,"about_ca_system_score_codex":0.0015562456,"about_ca_system_score_gemma":0.0026555539,"threshold_uncertainty_score":0.10736537},"labels":[],"label_agreement":null},{"id":"W2002377257","doi":"10.1115/imece2006-15069","title":"A Component-Based Parametric Reduced-Order Modeling Technique and Its Application to Probabilistic Vibration Analysis and Design Optimization","year":2006,"lang":"en","type":"article","venue":"Design Engineering and Computers and Information in Engineering, Parts A and B","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Research, Development and Engineering Command","keywords":"Parametric statistics; Probabilistic logic; Finite element method; Mathematical optimization; Basis (linear algebra); Parametric model; Computer science; Vibration; Modal analysis; Algorithm; Mathematics; Engineering; Structural engineering","score_opus":0.019726418576879387,"score_gpt":0.22976012519843575,"score_spread":0.21003370662155635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002377257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006467328,0.000055856137,0.99849355,0.000021422517,0.0000058204478,0.0000081556655,0.000009495384,0.000095433854,0.0006634355],"genre_scores_gemma":[0.14834598,0.00072862086,0.84715146,0.000054264285,0.00003745934,0.00023754322,0.00014176281,0.00018652843,0.0031163737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997038,0.00007898659,0.000010913322,0.00003871633,0.00015359203,0.000013833648],"domain_scores_gemma":[0.99973494,0.0001287456,0.000031424526,0.00004736584,0.00005047302,0.0000069525104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061067205,0.0009036323,0.00055430393,0.0006395183,0.00029502405,0.00041993425,0.000798728,0.0006221365,0.0020025421],"category_scores_gemma":[0.0009459714,0.00052166014,0.0010483989,0.0005997416,0.0004414776,0.0005633431,0.00051115337,0.0011178965,0.00067514344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025455955,0.0000254774,0.00022052562,0.00014652453,0.000029939487,0.000058181246,0.00007125803,0.86072475,0.015029445,0.03980725,0.000857804,0.083003476],"study_design_scores_gemma":[0.000002066154,0.000014350569,0.000046677604,0.0000055643277,0.0000048221927,0.000024215442,0.0000028860388,0.9933268,0.0013159157,0.0031426016,0.0021089618,0.0000050370236],"about_ca_topic_score_codex":0.0013768708,"about_ca_topic_score_gemma":0.0012817156,"teacher_disagreement_score":0.0020025421,"about_ca_system_score_codex":0.0003102024,"about_ca_system_score_gemma":0.0005846655,"threshold_uncertainty_score":0.006699145},"labels":[],"label_agreement":null},{"id":"W2004389039","doi":"10.1088/1742-6596/256/1/012001","title":"Primal and dual-primal iterative substructuring methods of stochastic PDEs","year":2010,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Preconditioner; Domain decomposition methods; Lagrange multiplier; Polynomial chaos; Mathematics; Applied mathematics; Discretization; Schur complement; Finite element method; Iterative method; Mathematical optimization; Mathematical analysis; Monte Carlo method; Eigenvalues and eigenvectors","score_opus":0.0750320210051482,"score_gpt":0.3680820055984526,"score_spread":0.2930499845933044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004389039","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003550505,0.00007466458,0.9954183,0.00005143645,0.000023999713,0.000021737316,0.000014220282,0.00004947303,0.0007956596],"genre_scores_gemma":[0.13976428,0.00024703259,0.85634035,0.00006921804,0.00004954557,0.0002206428,0.00010231998,0.00007908409,0.0031275535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99958843,0.00014847134,0.000021492258,0.000049855804,0.00015771328,0.000034048542],"domain_scores_gemma":[0.9993494,0.00029308934,0.000084764426,0.00007977877,0.00014113945,0.000051757223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010493791,0.0006688254,0.0010208378,0.00048525943,0.00035452287,0.00065316254,0.0010494256,0.00081396016,0.0016508412],"category_scores_gemma":[0.001744321,0.00045899034,0.00091104035,0.00040343383,0.00081735745,0.0006540742,0.0013384726,0.0012620396,0.00040495893],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005370631,0.00004697648,0.0005212366,0.00013321145,0.00003662152,0.00009542336,0.00010292261,0.86621875,0.0066049313,0.063541785,0.00096383836,0.06168071],"study_design_scores_gemma":[0.0000041359594,0.000009060383,0.00001805672,0.0000029548587,0.0000014531284,0.000010847664,0.0000024358053,0.9967379,0.00049317424,0.0021871484,0.0005305081,0.0000023066118],"about_ca_topic_score_codex":0.0011019653,"about_ca_topic_score_gemma":0.00080760475,"teacher_disagreement_score":0.0016508412,"about_ca_system_score_codex":0.00042806644,"about_ca_system_score_gemma":0.0012229163,"threshold_uncertainty_score":0.005549729},"labels":[],"label_agreement":null},{"id":"W2005353159","doi":"10.1137/110836651","title":"Efficient Rigorous Numerics for Higher-Dimensional PDEs via One-Dimensional Estimates","year":2013,"lang":"en","type":"article","venue":"SIAM Journal on Numerical Analysis","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Mathematics; Bounded function; Applied mathematics; Projection (relational algebra); Partial differential equation; Numerical analysis; Interval arithmetic; Domain (mathematical analysis); Domain decomposition methods; Mathematical analysis; Algorithm; Finite element method","score_opus":0.05104941173884355,"score_gpt":0.312140407990924,"score_spread":0.2610909962520805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005353159","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003348026,0.00007377974,0.99450475,0.00008024426,0.000025938352,0.000018881767,0.000014644174,0.00010266589,0.0018310073],"genre_scores_gemma":[0.20087107,0.00043589668,0.79352653,0.00010765776,0.00008667853,0.00023213551,0.00012010343,0.0003535703,0.00426629],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991955,0.00025899438,0.000046803525,0.00005303357,0.00038961731,0.000056042398],"domain_scores_gemma":[0.99774975,0.0012616544,0.0002124764,0.00033791404,0.000367963,0.00007024666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022054738,0.0009340664,0.0008737402,0.0010908829,0.0005147907,0.0015838643,0.0012501308,0.0007356896,0.0030430355],"category_scores_gemma":[0.006610813,0.0004504289,0.0011861885,0.00047864753,0.0016487067,0.0020748323,0.0027302566,0.0025679376,0.0009590664],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003944806,0.000061408646,0.0005718724,0.00022286015,0.000038282164,0.00013311536,0.00022723281,0.34467375,0.009356471,0.6020506,0.0013740994,0.041250695],"study_design_scores_gemma":[0.0000062206263,0.000015452904,0.000046626094,0.000011373401,0.0000036863476,0.000017757828,0.000009847797,0.9521981,0.0012065705,0.044788145,0.0016867644,0.0000095279875],"about_ca_topic_score_codex":0.0011269442,"about_ca_topic_score_gemma":0.0011523041,"teacher_disagreement_score":0.0030430355,"about_ca_system_score_codex":0.0008182446,"about_ca_system_score_gemma":0.001104039,"threshold_uncertainty_score":0.0116637945},"labels":[],"label_agreement":null},{"id":"W2007180500","doi":"10.1016/j.csda.2009.01.003","title":"Parametric sensitivity: A case study comparison","year":2009,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"American University of Sharjah; University of Sharjah","keywords":"Sensitivity (control systems); Parametric statistics; Mathematics; Applied mathematics; Parametric model; Nonlinear system; Regression analysis; Statistics; Engineering","score_opus":0.25161246762953865,"score_gpt":0.4585212768646224,"score_spread":0.20690880923508376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007180500","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73427004,0.0027377403,0.20445901,0.0014100147,0.0001651408,0.00087869575,0.0015113009,0.000497718,0.054070357],"genre_scores_gemma":[0.9733385,0.0005373759,0.023183525,0.00010402359,0.000039855233,0.0001478135,0.0002406973,0.00009090993,0.002317371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9930565,0.004588169,0.00031054195,0.000466848,0.0013004219,0.0002776402],"domain_scores_gemma":[0.9553426,0.03797792,0.0010084568,0.002828604,0.0024932602,0.00034911645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008707248,0.00076362264,0.0008457079,0.003181106,0.0008333959,0.0022168725,0.001553923,0.002881,0.0068831616],"category_scores_gemma":[0.039613344,0.00038288988,0.0017882149,0.0019788907,0.0009536067,0.0018462174,0.0019154889,0.00087036577,0.0005816333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008873543,0.0041549946,0.07098399,0.0032739835,0.0018609853,0.02700927,0.0041727186,0.3978198,0.02015734,0.07387233,0.013717414,0.37410358],"study_design_scores_gemma":[0.0011030005,0.009506587,0.065682456,0.0006533248,0.002269452,0.0560883,0.007987157,0.6703465,0.051281266,0.09708106,0.03731337,0.00068748614],"about_ca_topic_score_codex":0.0020623538,"about_ca_topic_score_gemma":0.0020797262,"teacher_disagreement_score":0.008707248,"about_ca_system_score_codex":0.001095029,"about_ca_system_score_gemma":0.0006937006,"threshold_uncertainty_score":0.04604882},"labels":[],"label_agreement":null},{"id":"W2007267362","doi":"10.1007/s11081-008-9046-2","title":"An asymmetric suboptimization approach to aerostructural optimization","year":2008,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Multidisciplinary design optimization; Aerodynamics; Solver; Computer science; Sensitivity (control systems); Mathematical optimization; Multidisciplinary approach; Optimization problem; Mathematics; Algorithm; Engineering; Aerospace engineering","score_opus":0.04735350519812372,"score_gpt":0.26526903577634636,"score_spread":0.21791553057822263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007267362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047727935,0.00017570134,0.9873942,0.0002558542,0.00006787949,0.000021477865,0.000026181558,0.000062126994,0.00722374],"genre_scores_gemma":[0.60506076,0.0009152595,0.37422705,0.00063030934,0.0005917237,0.00034024275,0.00019749581,0.0005364813,0.017500672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999089,0.00035907424,0.000030553183,0.00007951814,0.0003703153,0.00007156991],"domain_scores_gemma":[0.99914956,0.00041616577,0.00007694769,0.00015004101,0.00016430278,0.000043028074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013556075,0.0011859648,0.0016458734,0.0008333494,0.00057519006,0.0011462616,0.001781294,0.0008892514,0.003838669],"category_scores_gemma":[0.0030934496,0.0005781681,0.00094873563,0.00093070266,0.0010124611,0.0014240784,0.0018752337,0.0019726944,0.0005518813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082484614,0.000057907575,0.00014414825,0.00006578517,0.00005447981,0.00006526688,0.000033841417,0.769961,0.0029935748,0.18466121,0.0021067443,0.039773505],"study_design_scores_gemma":[0.0000050045187,0.000009353552,0.000024402418,0.0000021891644,0.0000058120513,0.000007491359,0.0000024942601,0.9649846,0.00028379622,0.03410783,0.0005642428,0.0000027832966],"about_ca_topic_score_codex":0.0012291376,"about_ca_topic_score_gemma":0.0012777129,"teacher_disagreement_score":0.003838669,"about_ca_system_score_codex":0.00078213075,"about_ca_system_score_gemma":0.0009837886,"threshold_uncertainty_score":0.012841582},"labels":[],"label_agreement":null},{"id":"W2008680512","doi":"10.2139/ssrn.1251542","title":"Econometric Analysis of Structural Systems with Permanent and Transitory Shocks","year":2008,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Economics; Econometric analysis; Econometrics; Econometric model; Macroeconomics","score_opus":0.02991096993416458,"score_gpt":0.2604991312323486,"score_spread":0.230588161298184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008680512","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8065835,0.00060327945,0.18390314,0.0024167465,0.00007021552,0.000038566293,0.0004573231,0.00016758459,0.005759611],"genre_scores_gemma":[0.99557436,0.00016321086,0.0022235818,0.000021094755,0.000029865658,0.00000892876,0.00012162796,0.0000102453405,0.0018471775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994734,0.00025200305,0.000024087763,0.000062736595,0.00008144209,0.00010634985],"domain_scores_gemma":[0.98988205,0.008581359,0.0008104117,0.00031147883,0.00025532444,0.00015926725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020919892,0.00028527225,0.0006413993,0.0007886705,0.00021558096,0.0013209041,0.0006117238,0.0011634717,0.003953713],"category_scores_gemma":[0.012838788,0.0003776003,0.0006934217,0.0008870482,0.0010158176,0.0009959614,0.0007354684,0.0010847708,0.00025301208],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018288555,0.000082439976,0.010991708,0.000057032823,0.00010009553,0.00018503146,0.00009835835,0.8585059,0.0010803753,0.11490438,0.0011191426,0.01269257],"study_design_scores_gemma":[0.000029876544,0.000045068806,0.007861969,0.0000043756545,0.000026544154,0.000020964038,0.00007698259,0.9524195,0.0002037381,0.038865592,0.00042997143,0.000015490334],"about_ca_topic_score_codex":0.0075068534,"about_ca_topic_score_gemma":0.0053766975,"teacher_disagreement_score":0.0075068534,"about_ca_system_score_codex":0.0011206719,"about_ca_system_score_gemma":0.000773183,"threshold_uncertainty_score":0.014926314},"labels":[],"label_agreement":null},{"id":"W2008697620","doi":"10.1088/1742-6596/341/1/012033","title":"Domain decomposition method of stochastic PDEs: a two-level scalable preconditioner","year":2012,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Preconditioner; Domain decomposition methods; Polynomial chaos; Solver; Computer science; Probabilistic logic; Uncertainty quantification; Mathematics; Iterative method; Finite element method; Applied mathematics; Algorithm; Mathematical optimization; Monte Carlo method","score_opus":0.11953218047764079,"score_gpt":0.3807752335863998,"score_spread":0.261243053108759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008697620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031158773,0.00006673952,0.995141,0.00010361899,0.000030798463,0.000033312066,0.000041364456,0.00019294216,0.001274313],"genre_scores_gemma":[0.10616501,0.0002187305,0.89043224,0.00011704107,0.000053017968,0.0003011563,0.00021701698,0.00013993347,0.0023559174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995546,0.00013732856,0.00002014029,0.000045824334,0.0002070009,0.000035181947],"domain_scores_gemma":[0.999602,0.00015422747,0.00003776538,0.00008117178,0.000087584,0.00003718389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068647234,0.00048808227,0.0007073709,0.00030643205,0.00034103062,0.00044560246,0.00062139553,0.0008212548,0.0026761708],"category_scores_gemma":[0.0013937518,0.0002547865,0.00067722716,0.00038363232,0.00053204695,0.00058091944,0.0013242611,0.0013156534,0.0007460045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010604419,0.00008031482,0.0006737426,0.00023717312,0.000050325583,0.00023918015,0.0001595669,0.7408715,0.037561096,0.10139484,0.0050454065,0.1135808],"study_design_scores_gemma":[0.000015916281,0.00002026248,0.00007237509,0.0000074295936,0.0000030299097,0.000023937551,0.0000048679967,0.9885761,0.0020535288,0.00559193,0.0036243931,0.0000062179674],"about_ca_topic_score_codex":0.0019999156,"about_ca_topic_score_gemma":0.0018445908,"teacher_disagreement_score":0.0026761708,"about_ca_system_score_codex":0.00036704174,"about_ca_system_score_gemma":0.0013567591,"threshold_uncertainty_score":0.008952618},"labels":[],"label_agreement":null},{"id":"W2008748574","doi":"10.1016/j.jhydrol.2014.12.056","title":"Global sensitivity analysis for urban water quality modelling: Terminology, convergence and comparison of different methods","year":2015,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":106,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Sensitivity (control systems); Convergence (economics); Environmental science; Water quality; Quality (philosophy); Terminology; Computer science; Engineering; Economics","score_opus":0.2995497154953138,"score_gpt":0.4689704764429834,"score_spread":0.1694207609476696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008748574","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054477756,0.0015028133,0.9902559,0.00024662548,0.000051593157,0.000031545456,0.00006257352,0.0000877569,0.0023134714],"genre_scores_gemma":[0.59461206,0.008236901,0.3903275,0.00043819836,0.00042956756,0.00046068538,0.00041330242,0.0009542569,0.004127632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957716,0.0030993628,0.00019240161,0.0002828201,0.00053524465,0.000118508855],"domain_scores_gemma":[0.99215275,0.0062752366,0.00029173528,0.000685923,0.00052968675,0.0000646476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011809697,0.0015675535,0.0013575655,0.0031754724,0.0004659167,0.00186514,0.001367734,0.0013497891,0.0016053406],"category_scores_gemma":[0.016729746,0.00054601335,0.0034638152,0.00226379,0.0021112897,0.002531617,0.0033606181,0.0024327673,0.00022976549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009839143,0.000055742734,0.0011841608,0.00047650078,0.00036921518,0.000085978114,0.00023329965,0.70758224,0.00374515,0.20562193,0.0012084933,0.07933891],"study_design_scores_gemma":[0.0000114768045,0.00007715004,0.0006693904,0.00013272575,0.00011027081,0.00008634999,0.00005311823,0.8564543,0.0023227027,0.13668436,0.003350743,0.00004736754],"about_ca_topic_score_codex":0.0018060387,"about_ca_topic_score_gemma":0.0011059543,"teacher_disagreement_score":0.011809697,"about_ca_system_score_codex":0.00096870295,"about_ca_system_score_gemma":0.0007787974,"threshold_uncertainty_score":0.06245637},"labels":[],"label_agreement":null},{"id":"W2009109739","doi":"10.1007/s00362-008-0148-x","title":"Life expectancy of a bathtub shaped failure distribution","year":2008,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Bathtub; Weibull distribution; Life expectancy; Parametric statistics; Residual; Reliability (semiconductor); Confidence interval; Statistics; Reliability engineering; Failure rate; Mathematics; Computer science; Econometrics; Engineering; Algorithm; Demography; Geography; Sociology","score_opus":0.06641643713144421,"score_gpt":0.30814972472064694,"score_spread":0.24173328758920273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009109739","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65514696,0.0010171523,0.33449036,0.0012258018,0.00010492915,0.00007373576,0.00059829926,0.00033076567,0.007011932],"genre_scores_gemma":[0.9890675,0.00036108226,0.0044699195,0.00010771339,0.000054875287,0.00006252496,0.0003516038,0.00006349523,0.0054612784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993531,0.00022239336,0.00002693557,0.00014246917,0.00011678695,0.00013833182],"domain_scores_gemma":[0.9877652,0.008001938,0.001550113,0.00064426096,0.0012128352,0.0008256766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040104208,0.00054743077,0.0010380993,0.0017884015,0.00052586244,0.0009931674,0.0017079448,0.0024010113,0.0039038346],"category_scores_gemma":[0.017537987,0.0005819208,0.00082631706,0.0010169573,0.0019397147,0.0024829083,0.0012423599,0.0013845527,0.0006441302],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003923032,0.00010224065,0.015266924,0.0002840453,0.00016166373,0.0007716047,0.00049100514,0.7612896,0.0067900694,0.19663212,0.0028846583,0.0149337845],"study_design_scores_gemma":[0.00002466909,0.00012897482,0.0056686103,0.000049803388,0.0000610006,0.00026387305,0.00013115903,0.9039636,0.00075006514,0.088353306,0.0005448391,0.000060070743],"about_ca_topic_score_codex":0.002185415,"about_ca_topic_score_gemma":0.0010149538,"teacher_disagreement_score":0.0040104208,"about_ca_system_score_codex":0.00093167764,"about_ca_system_score_gemma":0.00059893477,"threshold_uncertainty_score":0.021209419},"labels":[],"label_agreement":null},{"id":"W2010174052","doi":"10.1115/detc2014-35623","title":"An Efficient Reliability Analysis Method for Structures With Epistemic Uncertainty Using Evidence Theory","year":2014,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Program for New Century Excellent Talents in University; National Natural Science Foundation of China","keywords":"Reliability (semiconductor); Probabilistic logic; Reliability theory; Computer science; Measure (data warehouse); Limit (mathematics); Limit state design; Uncertainty quantification; Vertex (graph theory); Extreme point; Point (geometry); Mathematical optimization; Point estimation; Reliability engineering; Mathematics; Data mining; Theoretical computer science; Statistics; Artificial intelligence; Machine learning; Failure rate; Engineering; Structural engineering","score_opus":0.10048749074402141,"score_gpt":0.41102641973560006,"score_spread":0.31053892899157864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010174052","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008663484,0.00006696614,0.99875236,0.000017122195,0.0000039168112,0.000008934192,0.000006255454,0.000026327933,0.00025175136],"genre_scores_gemma":[0.1654156,0.0005132899,0.8322303,0.00003028129,0.00004763961,0.00021423036,0.00011357233,0.00007965376,0.0013553832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986093,0.0004993231,0.0000826855,0.00022390373,0.00051443494,0.00007028874],"domain_scores_gemma":[0.99756455,0.0015248673,0.00019658096,0.00013839325,0.00052583165,0.0000497163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021916286,0.0010050696,0.0013743646,0.002405329,0.00064618356,0.0014138846,0.00158742,0.0011224722,0.0020887414],"category_scores_gemma":[0.0058719013,0.0006024896,0.0020664434,0.0014458102,0.0010310226,0.0018649171,0.0016022957,0.0018395775,0.00043234063],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072773146,0.000033519107,0.00077560236,0.00036247104,0.00009145503,0.00017039543,0.00027903612,0.66464126,0.008232488,0.13299277,0.0011973048,0.19115092],"study_design_scores_gemma":[0.0000085449365,0.000033843353,0.0001526542,0.000025150055,0.00002343094,0.00007423668,0.000020929776,0.9697511,0.0012928514,0.027456125,0.0011435109,0.000017678407],"about_ca_topic_score_codex":0.0017843082,"about_ca_topic_score_gemma":0.0012542863,"teacher_disagreement_score":0.002405329,"about_ca_system_score_codex":0.0008639379,"about_ca_system_score_gemma":0.0015474233,"threshold_uncertainty_score":0.0115906},"labels":[],"label_agreement":null},{"id":"W2010956547","doi":"10.1002/cjce.20406","title":"Selection of simplified models: I. Analysis of model‐selection criteria using mean‐squared error","year":2010,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Queen's University; Honeywell (Canada)","funders":"","keywords":"Overfitting; Mean squared error; Selection (genetic algorithm); Model selection; Computer science; Statistics; Nonlinear system; Information Criteria; Mathematics; Data mining; Artificial intelligence; Artificial neural network","score_opus":0.10043549021238123,"score_gpt":0.3153066754645507,"score_spread":0.21487118525216947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010956547","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14551124,0.0010650932,0.8506956,0.00041697678,0.00003528024,0.0003802962,0.0003782681,0.00032383227,0.0011934212],"genre_scores_gemma":[0.7986132,0.0005132907,0.19813858,0.00017454328,0.00004769218,0.0006616657,0.0010063843,0.00012207766,0.00072271185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98665106,0.009120156,0.0008420424,0.0006906103,0.002410121,0.00028607468],"domain_scores_gemma":[0.9115943,0.07607386,0.0040847627,0.0031735378,0.0046394654,0.00043407883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024752386,0.0014763571,0.002794135,0.002849594,0.0005749823,0.0015851491,0.0014749195,0.0009383641,0.0013443724],"category_scores_gemma":[0.10377922,0.000656855,0.0022272523,0.0014418646,0.0009465317,0.0017073416,0.0021366216,0.0013145542,0.00021451415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038265143,0.000051134513,0.0049992977,0.00034695744,0.00049576233,0.00015865779,0.00007163376,0.9523678,0.0014208272,0.0051391055,0.00052831863,0.034037832],"study_design_scores_gemma":[0.000024314173,0.00013537671,0.0014989567,0.00002796519,0.00008175718,0.000043170614,0.000019092915,0.99349207,0.0008266934,0.0035409522,0.00028735635,0.00002222775],"about_ca_topic_score_codex":0.005606771,"about_ca_topic_score_gemma":0.0040537044,"teacher_disagreement_score":0.024752386,"about_ca_system_score_codex":0.0012323809,"about_ca_system_score_gemma":0.002377186,"threshold_uncertainty_score":0.13090473},"labels":[],"label_agreement":null},{"id":"W2011437645","doi":"10.1109/eucap.2014.6902136","title":"Quantifying uncertainty in ray-tracing models of radiowave propagation using polynomial chaos","year":2014,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Randomness; Polynomial chaos; Ray tracing (physics); Monte Carlo method; Tracing; Algorithm; Uncertainty quantification; Statistical physics; Polynomial; Computer science; Measurement uncertainty; Propagation of uncertainty; Set (abstract data type); Power (physics); Distributed ray tracing; Mathematical optimization; Applied mathematics; Mathematics; Physics; Mathematical analysis; Statistics; Optics","score_opus":0.19654790065952796,"score_gpt":0.35122727538445675,"score_spread":0.1546793747249288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011437645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09217666,0.000046787733,0.90657604,0.00006802681,0.0000078510775,0.000030106848,0.00004433176,0.000187685,0.000862498],"genre_scores_gemma":[0.96491987,0.00008014493,0.034442436,0.000013149946,0.000008067263,0.000041338015,0.000059927963,0.000037178692,0.00039787625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988188,0.00050673133,0.000046074463,0.00011279178,0.00043000563,0.00008565604],"domain_scores_gemma":[0.9951846,0.0036905305,0.00041719084,0.00038807665,0.0002667741,0.000052730495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018393524,0.00077867607,0.0007057169,0.00064889906,0.00039981504,0.00102527,0.0008566268,0.0007066066,0.00043303418],"category_scores_gemma":[0.008964698,0.00042533444,0.00060546375,0.0006060211,0.0010399865,0.0012981807,0.0009715659,0.0009007186,0.000093496375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016995713,0.000005328023,0.00026121477,0.0000064193064,0.0000070961723,0.000011250376,0.000014271167,0.99509513,0.0009517846,0.0023192763,0.000017067372,0.0012941955],"study_design_scores_gemma":[0.0000010950185,0.000008328385,0.00006373241,0.0000010846087,0.0000014700753,0.000005286215,0.0000017184856,0.9982886,0.0006575929,0.00094116724,0.000026156176,0.0000037690568],"about_ca_topic_score_codex":0.0032885738,"about_ca_topic_score_gemma":0.0016215282,"teacher_disagreement_score":0.0032885738,"about_ca_system_score_codex":0.0011364785,"about_ca_system_score_gemma":0.0007716101,"threshold_uncertainty_score":0.009727538},"labels":[],"label_agreement":null},{"id":"W2012127087","doi":"10.1115/pvp2010-25277","title":"Incorporation of Strain Hardening Effect Into Limit Load Analysis","year":2010,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Limit load; Hardening (computing); Tangent; Limit analysis; Strain hardening exponent; Materials science; Limit (mathematics); Bilinear interpolation; Structural engineering; Composite material; Mechanics; Computer science; Finite element method; Mathematics; Mathematical analysis; Geometry; Physics; Engineering","score_opus":0.03529531517577953,"score_gpt":0.32771078926742325,"score_spread":0.2924154740916437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012127087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01339967,0.00012728278,0.98359525,0.00002006068,0.000012916363,0.000018126337,0.000009494221,0.000229507,0.0025876518],"genre_scores_gemma":[0.55660594,0.0006460818,0.43356606,0.000055631976,0.00006194063,0.00009390445,0.00009613086,0.00022368325,0.008650594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955565,0.000080094025,0.000021178525,0.000065619875,0.0002521296,0.000025322417],"domain_scores_gemma":[0.9994137,0.000270171,0.00006238348,0.00009576516,0.00013446988,0.000023373648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000601444,0.0008293229,0.00065522006,0.00134411,0.00035880963,0.0006736407,0.0010339781,0.00061385875,0.002355251],"category_scores_gemma":[0.0015064175,0.00032394967,0.00056377525,0.00036080374,0.000733149,0.002110509,0.000868794,0.0006326673,0.00071583147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013142453,0.00008708471,0.0010932431,0.00029828845,0.00004519658,0.00025331616,0.00028585075,0.4686601,0.12990631,0.08546976,0.0006120459,0.31315738],"study_design_scores_gemma":[0.0000036809383,0.00008769015,0.00030189767,0.000011717705,0.000014387506,0.000086452004,0.000015169015,0.9566218,0.026524687,0.013582268,0.0027272038,0.000023064036],"about_ca_topic_score_codex":0.00082031,"about_ca_topic_score_gemma":0.0009252434,"teacher_disagreement_score":0.002355251,"about_ca_system_score_codex":0.00032479395,"about_ca_system_score_gemma":0.00038112345,"threshold_uncertainty_score":0.007879078},"labels":[],"label_agreement":null},{"id":"W2012240233","doi":"10.1615/int.j.uncertaintyquantification.2014007972","title":"SOME A PRIORI ERROR ESTIMATES FOR FINITE ELEMENT APPROXIMATIONS OF ELLIPTIC AND PARABOLIC LINEAR STOCHASTIC PARTIAL DIFFERENTIAL EQUATIONS","year":2014,"lang":"en","type":"article","venue":"International Journal for Uncertainty Quantification","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Mathematics; Stochastic partial differential equation; Superconvergence; Finite element method; Elliptic partial differential equation; Sobolev space; Partial differential equation; Parabolic partial differential equation; Discretization; Applied mathematics; A priori and a posteriori; Mathematical analysis","score_opus":0.1271159786429568,"score_gpt":0.39258019949758094,"score_spread":0.2654642208546242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012240233","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012963349,0.00045417558,0.9844998,0.00032897497,0.000048487727,0.000028824217,0.000040277286,0.000054757103,0.0015813886],"genre_scores_gemma":[0.6312055,0.0023519353,0.35783473,0.00033505246,0.00017678113,0.00038389344,0.00038338563,0.00021356886,0.00711514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987968,0.0004693879,0.000102412385,0.00014430704,0.0004197146,0.00006738098],"domain_scores_gemma":[0.9898816,0.006997762,0.00092734414,0.0005016261,0.0014335454,0.00025810546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006342488,0.0013241532,0.00115605,0.0016095319,0.00054580125,0.0016346409,0.0012795182,0.002180434,0.0013802837],"category_scores_gemma":[0.017956235,0.00061778916,0.0012814159,0.00052007264,0.002784977,0.0022269757,0.0029110694,0.0027702346,0.00030660504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010980634,0.000050975643,0.0010650983,0.00034812515,0.00006196944,0.000114238675,0.00024274204,0.6627955,0.010380924,0.3111285,0.0005649953,0.0131371785],"study_design_scores_gemma":[0.0000026345642,0.000024989518,0.000083016464,0.000029309975,0.0000048126476,0.00001684325,0.000011685541,0.9825487,0.0016037844,0.015272153,0.00039006502,0.00001191979],"about_ca_topic_score_codex":0.0018300923,"about_ca_topic_score_gemma":0.00085301156,"teacher_disagreement_score":0.006342488,"about_ca_system_score_codex":0.0011800397,"about_ca_system_score_gemma":0.0011595567,"threshold_uncertainty_score":0.033542693},"labels":[],"label_agreement":null},{"id":"W2012705912","doi":"10.1115/detc2010-28172","title":"Probabilistic Design Optimization of Frequency Dispersion for Rotating Blades","year":2010,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Sensitivity (control systems); Probabilistic logic; Finite element method; Stiffness; Mathematical optimization; Optimization problem; Natural frequency; Perturbation (astronomy); Computer science; Mathematics; Control theory (sociology); Algorithm; Engineering; Structural engineering; Electronic engineering; Acoustics; Vibration; Physics; Artificial intelligence","score_opus":0.0826113474499843,"score_gpt":0.3250784580761942,"score_spread":0.24246711062620988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012705912","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058824685,0.00007643532,0.9934784,0.00003232286,0.0000068182267,0.000011262625,0.0000068866084,0.000030726955,0.00047468345],"genre_scores_gemma":[0.6434319,0.00032840628,0.35392645,0.00005995162,0.000034470653,0.0002534149,0.00009116291,0.000082950246,0.0017913787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985728,0.00049022306,0.00006234217,0.00021477355,0.00058716856,0.00007262027],"domain_scores_gemma":[0.99829537,0.0011095898,0.00024730156,0.00010196404,0.00022004748,0.000025735444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021618817,0.0008087083,0.0009936321,0.00063199166,0.0004043072,0.000653392,0.0007249363,0.00079642626,0.0008840962],"category_scores_gemma":[0.0040228604,0.00089902565,0.0010545483,0.00042526916,0.0008155676,0.00079973484,0.00078429625,0.00072609825,0.00018529836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014528894,0.0000065525774,0.00016350979,0.000027854045,0.000016273809,0.000014615709,0.000017907132,0.9860184,0.0017160651,0.0041448455,0.0000640186,0.007795276],"study_design_scores_gemma":[0.000004233124,0.00002280599,0.00009554548,0.000003877937,0.0000054887796,0.000015588019,0.0000034248437,0.9964414,0.0005945867,0.0024814326,0.00032621302,0.0000053511094],"about_ca_topic_score_codex":0.0009997821,"about_ca_topic_score_gemma":0.0009414976,"teacher_disagreement_score":0.0021618817,"about_ca_system_score_codex":0.00071627164,"about_ca_system_score_gemma":0.000872203,"threshold_uncertainty_score":0.011433244},"labels":[],"label_agreement":null},{"id":"W2013964516","doi":"10.2514/1.38447","title":"Accurate Stick Model Development for Static Analysis of Complex Aircraft Wing-Box Structures","year":2009,"lang":"en","type":"article","venue":"AIAA Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada); Concordia University; McGill University","funders":"","keywords":"Finite element method; Wing; Stiffness; Bending stiffness; Structural engineering; Bending; Wing configuration; Aeroelasticity; Process (computing); Engineering; Beam (structure); Mechanical engineering; Aerodynamics; Computer science; Aerospace engineering","score_opus":0.16127519995366688,"score_gpt":0.38284129459472804,"score_spread":0.22156609464106117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013964516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011005341,0.000063843596,0.98661834,0.000019217849,0.000013595898,0.000037191407,0.00011489283,0.0003856343,0.0017419215],"genre_scores_gemma":[0.52610826,0.00061517104,0.46662745,0.000043004773,0.00002762008,0.00045789153,0.00086357543,0.00026469864,0.0049922704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997305,0.00003530818,0.000014206058,0.000019469819,0.00018541353,0.0000150904325],"domain_scores_gemma":[0.9995252,0.00020667764,0.000052857147,0.0001029732,0.00009846596,0.000013847489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041547042,0.00048528484,0.000563717,0.00059238524,0.0003216548,0.0005068412,0.0008682796,0.00065605424,0.0033490798],"category_scores_gemma":[0.0013367241,0.0003754663,0.00074674457,0.00048703497,0.00032146904,0.0006784326,0.00063999544,0.00054899126,0.0008184158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027392878,0.000015900145,0.0005901561,0.0000973701,0.000018510184,0.000085912754,0.00006466976,0.9445864,0.011410087,0.011028098,0.0004963001,0.03157926],"study_design_scores_gemma":[0.0000023790255,0.000013061888,0.000111486,0.000006581762,0.0000031597083,0.00001736867,0.0000065608992,0.9954562,0.0017986683,0.0014782093,0.0011037062,0.00000270192],"about_ca_topic_score_codex":0.0023807702,"about_ca_topic_score_gemma":0.0030393547,"teacher_disagreement_score":0.0033490798,"about_ca_system_score_codex":0.00036043485,"about_ca_system_score_gemma":0.0007224883,"threshold_uncertainty_score":0.011203766},"labels":[],"label_agreement":null},{"id":"W2014114366","doi":"10.1016/j.strusafe.2014.03.008","title":"Influence of load spectrum assumptions on the expected reliability of hydroelectric turbines: A case study","year":2014,"lang":"en","type":"article","venue":"Structural Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Hydro-Québec","funders":"École de technologie supérieure","keywords":"Reliability (semiconductor); Hydroelectricity; Reliability engineering; Sensitivity (control systems); Turbine; Engineering; Environmental science; Computer science; Statistics; Structural engineering; Mathematics; Power (physics); Mechanical engineering","score_opus":0.03289225143629886,"score_gpt":0.3070243338106859,"score_spread":0.274132082374387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014114366","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9853441,0.00017530308,0.010759016,0.0002563511,0.00001483957,0.000031805546,0.00029182053,0.00007276461,0.003053979],"genre_scores_gemma":[0.9989986,0.000030942683,0.0007223275,0.000005658324,0.000004193616,0.000005284101,0.000055756624,0.000012012168,0.00016518078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99673957,0.0018594223,0.000120318946,0.00035588304,0.0005823105,0.00034243692],"domain_scores_gemma":[0.9025563,0.090124086,0.0029659355,0.0014698205,0.0023902908,0.0004936515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076344353,0.0008949516,0.00087764265,0.0011702369,0.0006933586,0.0015822072,0.0016953398,0.0019042487,0.0015932339],"category_scores_gemma":[0.036571752,0.0006849425,0.0009366768,0.0007184384,0.0014647048,0.0017303333,0.00073747983,0.0016110674,0.00015780328],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004700427,0.0001175174,0.005996206,0.000058806323,0.00005661479,0.0009321707,0.000084998916,0.9852454,0.0016053781,0.0018998019,0.00024049367,0.0032925366],"study_design_scores_gemma":[0.000054121952,0.0004068487,0.009960031,0.000020213734,0.000120230994,0.00035264058,0.00020656684,0.98249835,0.0024018318,0.0037354871,0.00020160437,0.00004209876],"about_ca_topic_score_codex":0.006795678,"about_ca_topic_score_gemma":0.005163999,"teacher_disagreement_score":0.0076344353,"about_ca_system_score_codex":0.0018338513,"about_ca_system_score_gemma":0.0006168581,"threshold_uncertainty_score":0.040375233},"labels":[],"label_agreement":null},{"id":"W2015280363","doi":"10.1109/tmtt.2013.2281777","title":"Efficient Analysis of Geometrical Uncertainty in the FDTD Method Using Polynomial Chaos With Application to Microwave Circuits","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Polynomial chaos; Finite-difference time-domain method; Monte Carlo method; Curvilinear coordinates; Polynomial; Electronic circuit; Algorithm; Microstrip; Microwave; Mathematics; Computer science; Electronic engineering; Mathematical analysis; Geometry; Optics; Physics; Engineering; Telecommunications; Electrical engineering","score_opus":0.034406392402908714,"score_gpt":0.3221099803124872,"score_spread":0.2877035879095785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015280363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010773732,0.000060593866,0.98856294,0.000032611155,0.0000068652776,0.000008149812,0.000012873894,0.00008490354,0.00045730954],"genre_scores_gemma":[0.5723871,0.00023668825,0.42613363,0.000027241393,0.000018692459,0.00006706301,0.000057100486,0.00007025389,0.0010022619],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978465,0.0000464269,0.000008901698,0.0000167632,0.00012832126,0.000014925447],"domain_scores_gemma":[0.9993142,0.00047103406,0.000057198584,0.000052573232,0.000092978386,0.000012121481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000427292,0.00030367958,0.00037330063,0.00043412411,0.00025308566,0.00040061455,0.00041419608,0.00037691125,0.00046345766],"category_scores_gemma":[0.0015315958,0.00023388816,0.00032660915,0.0004082371,0.00044892158,0.0004951113,0.00038969412,0.0004279618,0.00009230082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035588535,0.000010437923,0.0005721491,0.00005328672,0.000016411454,0.0000799387,0.000060584804,0.93661076,0.012596648,0.01916773,0.00024031094,0.03055617],"study_design_scores_gemma":[0.0000012184913,0.000004312382,0.000043804237,0.0000012203789,0.0000010956444,0.000013021906,0.0000018378906,0.9978404,0.0010065774,0.00083937746,0.00024502838,0.0000021661901],"about_ca_topic_score_codex":0.0017724169,"about_ca_topic_score_gemma":0.0013213747,"teacher_disagreement_score":0.0017724169,"about_ca_system_score_codex":0.00055080664,"about_ca_system_score_gemma":0.00053747767,"threshold_uncertainty_score":0.003996372},"labels":[],"label_agreement":null},{"id":"W2015882984","doi":"10.1016/j.probengmech.2007.12.016","title":"Estimation of minimum cross-entropy quantile function using fractional probability weighted moments","year":2007,"lang":"en","type":"article","venue":"Probabilistic Engineering Mechanics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University Network of Excellence in Nuclear Engineering","keywords":"Quantile; Quantile function; Mathematics; Applied mathematics; Random variable; Entropy (arrow of time); Monte Carlo method; Principle of maximum entropy; Cross entropy; Mathematical optimization; Probability distribution; Probability density function; Joint entropy; Statistics; Moment-generating function","score_opus":0.06401099151942956,"score_gpt":0.3301996762323622,"score_spread":0.2661886847129326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015882984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029906504,0.00007977382,0.9695423,0.000060258426,0.0000070751576,0.0000062272675,0.000022270116,0.00015599304,0.00021954888],"genre_scores_gemma":[0.84461176,0.00011154903,0.15441813,0.000033949294,0.000041492927,0.000034992074,0.0001368261,0.000087107175,0.00052421074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991492,0.0003561286,0.00004217458,0.00016660879,0.00019333963,0.00009255558],"domain_scores_gemma":[0.99553716,0.003197868,0.0004949321,0.00030638123,0.00036668126,0.00009700541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029019406,0.00054126815,0.0014629582,0.0014008365,0.00041815123,0.0012054174,0.0012071714,0.0012560543,0.0010165094],"category_scores_gemma":[0.011108695,0.0005351474,0.00084500347,0.00089151063,0.0006901326,0.0018379233,0.0010155066,0.0007979038,0.00019324155],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035580373,0.0000617994,0.0035749841,0.00011351689,0.00011315881,0.000114036106,0.000082079954,0.86356735,0.011260725,0.046647046,0.00058301346,0.073526435],"study_design_scores_gemma":[0.000004718511,0.000011366847,0.00059314794,0.0000031503544,0.000005785774,0.000020451233,0.0000035750932,0.99140066,0.0014884742,0.0063798004,0.00008047993,0.00000843796],"about_ca_topic_score_codex":0.0010421517,"about_ca_topic_score_gemma":0.0005648728,"teacher_disagreement_score":0.0029019406,"about_ca_system_score_codex":0.0007879039,"about_ca_system_score_gemma":0.0006315645,"threshold_uncertainty_score":0.015347123},"labels":[],"label_agreement":null},{"id":"W2016381991","doi":"10.1142/s0218539306002264","title":"QUALITY AND PERFORMANCE RELIABILITY ASSESSMENT OF MULTI-RESPONSE DEGRADING SYSTEMS","year":2006,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Computer science; Quality (philosophy); Function (biology); Component (thermodynamics); Limit (mathematics); Set (abstract data type); Sampling (signal processing); Engineering; Mathematics","score_opus":0.08019962239881956,"score_gpt":0.3833879144698951,"score_spread":0.30318829207107556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016381991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05564377,0.00022229792,0.9429247,0.000025904543,0.000009992617,0.000032183587,0.000021011976,0.0001853301,0.0009347642],"genre_scores_gemma":[0.87661725,0.00021651771,0.12208392,0.000016851765,0.000021043063,0.00004595798,0.00006546077,0.00004807425,0.0008850091],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816847,0.0004627983,0.00006507102,0.00016967274,0.0010759275,0.000058065823],"domain_scores_gemma":[0.99697065,0.0013920163,0.00051562284,0.000395008,0.00064279535,0.00008382149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017970889,0.00051400624,0.00053460675,0.0012078282,0.0002220591,0.0006395875,0.00074986974,0.00041449745,0.0006267757],"category_scores_gemma":[0.0062088426,0.00022280117,0.00047539274,0.00038281523,0.0005329979,0.0006749829,0.0006351213,0.00046778444,0.00015647202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045593944,0.0001410733,0.008204211,0.00037138772,0.000111180045,0.00016482543,0.00026596413,0.6177654,0.116528,0.013703628,0.00034658928,0.24194185],"study_design_scores_gemma":[0.000008716564,0.00020058958,0.0032162433,0.000009258086,0.000022223187,0.00013809612,0.000020580746,0.9706303,0.022451235,0.0027014106,0.0005782754,0.000023180502],"about_ca_topic_score_codex":0.00056597946,"about_ca_topic_score_gemma":0.00048600862,"teacher_disagreement_score":0.0017970889,"about_ca_system_score_codex":0.0004652215,"about_ca_system_score_gemma":0.00032619064,"threshold_uncertainty_score":0.00950408},"labels":[],"label_agreement":null},{"id":"W2017020721","doi":"10.1016/j.strusafe.2009.10.001","title":"Feasibility of FORM in finite element reliability analysis","year":2009,"lang":"en","type":"article","venue":"Structural Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Finite element method; Context (archaeology); Computer science; Reliability engineering; Conjunction (astronomy); Limit (mathematics); Limit state design; Random variable; Mathematical optimization; Engineering; Structural engineering; Mathematics","score_opus":0.056084762938245644,"score_gpt":0.3530236284965948,"score_spread":0.29693886555834914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017020721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016494196,0.00027743296,0.96900284,0.0007695594,0.00008834658,0.00011239126,0.00011892776,0.00010674645,0.013029561],"genre_scores_gemma":[0.5545304,0.0011179083,0.4230319,0.00052693713,0.00036105988,0.0008045187,0.00063625525,0.00048500777,0.018505985],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99556524,0.0023251092,0.00021116875,0.0005545082,0.0011108465,0.00023312405],"domain_scores_gemma":[0.97579944,0.018203443,0.0010754013,0.0023062106,0.0022702438,0.00034537516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008408845,0.0012215403,0.0018508257,0.0021789125,0.0015780193,0.0034537024,0.002199532,0.0023252082,0.011287903],"category_scores_gemma":[0.060746755,0.0016682206,0.0017294374,0.0016978033,0.006160502,0.010404641,0.004877509,0.0036334447,0.002047741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016456506,0.00006422392,0.0006731636,0.00020846032,0.000022102675,0.000070564434,0.000246677,0.044867866,0.0008865703,0.912733,0.0014642376,0.03859854],"study_design_scores_gemma":[0.00002534775,0.00007892793,0.00013296581,0.00006036801,0.000014702745,0.00006343151,0.00006495172,0.104662105,0.00056715496,0.8920351,0.0022815992,0.000013345293],"about_ca_topic_score_codex":0.00086194923,"about_ca_topic_score_gemma":0.000571341,"teacher_disagreement_score":0.011287903,"about_ca_system_score_codex":0.00060122734,"about_ca_system_score_gemma":0.0011834415,"threshold_uncertainty_score":0.044470727},"labels":[],"label_agreement":null},{"id":"W2018192541","doi":"10.2118/2005-180","title":"A Numerical Approach to Simulateand Design VAPEX Experiments","year":2005,"lang":"en","type":"article","venue":"Canadian International Petroleum Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Engineering drawing; Engineering","score_opus":0.12511295834078434,"score_gpt":0.32879285523013857,"score_spread":0.20367989688935423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018192541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07676264,0.00030189185,0.91452783,0.0001685073,0.000094576506,0.00065723434,0.0003568373,0.00059143786,0.006539133],"genre_scores_gemma":[0.5692672,0.0003136818,0.42440587,0.000064399894,0.000021816359,0.0024211947,0.00033459358,0.000083216786,0.00308807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995436,0.00013959769,0.000037862414,0.00006761062,0.00016435706,0.000046989495],"domain_scores_gemma":[0.9987685,0.0007300671,0.00013508231,0.00013071917,0.00020636112,0.0000291867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011872804,0.000778307,0.000709664,0.00053577643,0.0005420431,0.0007769868,0.0010702804,0.0013896258,0.0039191213],"category_scores_gemma":[0.0022319676,0.0004680786,0.000625212,0.00047043344,0.00056571903,0.00051618274,0.000835226,0.0009304313,0.00037509855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047754802,0.000071869516,0.00041818456,0.00010499141,0.00001051202,0.00003942216,0.00002109054,0.98104984,0.008302835,0.0027321132,0.00010446905,0.00709688],"study_design_scores_gemma":[0.00001276825,0.0000664369,0.000063455685,0.000005357401,0.000004533648,0.000005189185,0.0000061340015,0.995021,0.0034199408,0.0006706409,0.0007199714,0.0000046505797],"about_ca_topic_score_codex":0.0025551307,"about_ca_topic_score_gemma":0.0018179916,"teacher_disagreement_score":0.0039191213,"about_ca_system_score_codex":0.0010937466,"about_ca_system_score_gemma":0.0011031007,"threshold_uncertainty_score":0.013110757},"labels":[],"label_agreement":null},{"id":"W2018433689","doi":"10.1115/1.1451167","title":"Probabilistic Assessment of Structures using Monte Carlo Simulations","year":2002,"lang":"en","type":"article","venue":"Applied Mechanics Reviews","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Probabilistic logic; Czech; Computer science; Order (exchange); Curiosity; Operations research; Psychology; Mathematics; Artificial intelligence; Philosophy; Social psychology","score_opus":0.25556896972085863,"score_gpt":0.39467421073037695,"score_spread":0.13910524100951832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018433689","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003185214,0.0027416146,0.9766044,0.0004603953,0.0001545621,0.0001000004,0.0003017577,0.00091235543,0.01553966],"genre_scores_gemma":[0.2918263,0.007509015,0.6701463,0.00037235438,0.00032147096,0.00079089997,0.0014823074,0.0013766475,0.026174678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988613,0.0004063964,0.000039473052,0.00010568061,0.0005474547,0.0000396235],"domain_scores_gemma":[0.9979232,0.0013963534,0.00012457596,0.00019185856,0.00030713374,0.000056934383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019754677,0.0010207099,0.0011667946,0.00195155,0.00048999535,0.0020450328,0.0014974695,0.0017077406,0.010536073],"category_scores_gemma":[0.0051434576,0.0010768494,0.0014635252,0.0010701836,0.0010999307,0.0018603889,0.0016435487,0.00136298,0.0026778395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004042084,0.00001967692,0.00046339785,0.00024306342,0.00004667476,0.00010063381,0.000089711095,0.8879304,0.0014880197,0.04999693,0.0054584565,0.054122593],"study_design_scores_gemma":[0.00000851262,0.000016527345,0.00025072423,0.000121066245,0.000012549142,0.00006755112,0.000022012502,0.9411312,0.000667224,0.04650834,0.011169719,0.000024720874],"about_ca_topic_score_codex":0.00295562,"about_ca_topic_score_gemma":0.0023858142,"teacher_disagreement_score":0.010536073,"about_ca_system_score_codex":0.0010530354,"about_ca_system_score_gemma":0.0010271805,"threshold_uncertainty_score":0.03524667},"labels":[],"label_agreement":null},{"id":"W2019474460","doi":"10.1115/ipc2012-90546","title":"Providing Safety: Using Probabilistic or Deterministic Methods","year":2012,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TransCanada (Canada)","funders":"","keywords":"Reliability (semiconductor); Probabilistic logic; Reliability engineering; Consistency (knowledge bases); Computer science; Process (computing); System safety; Risk analysis (engineering); Engineering","score_opus":0.39742545650212036,"score_gpt":0.49837877410213177,"score_spread":0.10095331760001142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019474460","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011373683,0.0029208136,0.9815061,0.0013920025,0.00015603424,0.000051636514,0.00008870398,0.0002795661,0.012467755],"genre_scores_gemma":[0.21167128,0.015012719,0.7574017,0.001200334,0.0011537618,0.00044975439,0.00032403597,0.00048557355,0.012300783],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9952087,0.0019172713,0.00021906168,0.0005864064,0.00186567,0.00020298186],"domain_scores_gemma":[0.9919395,0.0049137566,0.00095120474,0.0009270105,0.0011062131,0.00016234844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063737817,0.0016960019,0.0009988706,0.0024081385,0.0007930158,0.00339516,0.002590208,0.0020774405,0.0066351388],"category_scores_gemma":[0.0139983725,0.00089655904,0.0018537422,0.0015039558,0.0033745286,0.0050887302,0.0026081386,0.0025403027,0.0018143567],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049361697,0.00005829724,0.0008780348,0.00059622154,0.000094116906,0.000107057465,0.0002551757,0.25979656,0.0012577627,0.5791589,0.005155441,0.15259305],"study_design_scores_gemma":[0.000027903714,0.0000675792,0.00035005723,0.00038981644,0.000069231566,0.00017541871,0.00012028698,0.2698426,0.00103669,0.68120944,0.046621997,0.00008901538],"about_ca_topic_score_codex":0.0039983615,"about_ca_topic_score_gemma":0.0028285712,"teacher_disagreement_score":0.0066351388,"about_ca_system_score_codex":0.0023411803,"about_ca_system_score_gemma":0.0032343932,"threshold_uncertainty_score":0.033708155},"labels":[],"label_agreement":null},{"id":"W2021782183","doi":"10.1139/l02-086","title":"Load factor calibration for the proposed 2005 edition of the National Building Code of Canada: Companion-action load combinations","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Wind engineering; Structural load; Building code; Load factor; Dynamic load testing; Deflection (physics); Serviceability (structure); Engineering; Structural engineering","score_opus":0.06668229095776652,"score_gpt":0.27534284637586304,"score_spread":0.20866055541809653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021782183","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1071075,0.0023499888,0.25804588,0.006432592,0.0045351987,0.006084587,0.06726084,0.0094577335,0.5387257],"genre_scores_gemma":[0.35847747,0.0022031048,0.33769828,0.0029946696,0.00033969182,0.004100118,0.07328104,0.0034123643,0.21749328],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9791445,0.0013187538,0.0009410948,0.00096233416,0.016464045,0.0011692696],"domain_scores_gemma":[0.9261757,0.0019088732,0.0013264606,0.0026105104,0.06725266,0.00072580285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057416167,0.0011575128,0.0004976981,0.0048977793,0.0032890053,0.0029099528,0.0029984985,0.00122468,0.014432346],"category_scores_gemma":[0.028020531,0.0006972559,0.00083705847,0.006524959,0.0015392818,0.001564511,0.0012223095,0.0024660279,0.006040091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037752136,0.00027431938,0.03977249,0.0006279476,0.000038004368,0.0002773769,0.0030713337,0.02746943,0.0109049445,0.047112003,0.5303425,0.33973214],"study_design_scores_gemma":[0.00006348019,0.00014829653,0.10389826,0.0006274831,0.000048906455,0.00020267858,0.0016220697,0.01526047,0.009483771,0.00445864,0.8638504,0.00033552432],"about_ca_topic_score_codex":0.9247538,"about_ca_topic_score_gemma":0.915331,"teacher_disagreement_score":0.075246215,"about_ca_system_score_codex":0.05194078,"about_ca_system_score_gemma":0.07842109,"threshold_uncertainty_score":0.37685853},"labels":[],"label_agreement":null},{"id":"W2021957535","doi":"10.1243/1748006xjrr51","title":"Interactive enhanced particle swarm optimization: A multi-objective reliability application","year":2007,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Particle swarm optimization; Mathematical optimization; Multi-swarm optimization; Maximization; Reliability (semiconductor); Metaheuristic; Computer science; Position (finance); Convergence (economics); Process (computing); Minification; Mathematics","score_opus":0.0195059979262874,"score_gpt":0.2871447188501895,"score_spread":0.2676387209239021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021957535","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010893401,0.00026317444,0.9856205,0.000106892105,0.00003355032,0.000049546394,0.000015567022,0.00028489504,0.0027325742],"genre_scores_gemma":[0.45988113,0.0005693037,0.53601724,0.00010341562,0.00008297317,0.00020997219,0.00006815567,0.0000988498,0.0029689933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960715,0.00015389387,0.000019768118,0.000046282203,0.00014782493,0.000025042013],"domain_scores_gemma":[0.9994122,0.00035082895,0.00006346266,0.000044435637,0.00011309549,0.000016035565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009952039,0.0008278768,0.00070139795,0.0005172722,0.0002701323,0.0005382572,0.00087653735,0.0010754245,0.0015174868],"category_scores_gemma":[0.0019523686,0.0003410825,0.00063510594,0.00050394837,0.00037238997,0.0006268337,0.000671867,0.00071440137,0.0002642653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010115094,0.00007742535,0.00084719784,0.00016302429,0.000090324174,0.00014425807,0.0001216472,0.8733873,0.005606104,0.009103381,0.0008594714,0.1094988],"study_design_scores_gemma":[0.000010329401,0.000030499035,0.00012467428,0.0000055156374,0.0000077516,0.000022952869,0.000003386323,0.99701023,0.00084176054,0.0010680761,0.000871353,0.0000035567575],"about_ca_topic_score_codex":0.0015422285,"about_ca_topic_score_gemma":0.0013070817,"teacher_disagreement_score":0.0015422285,"about_ca_system_score_codex":0.00033301645,"about_ca_system_score_gemma":0.00039984877,"threshold_uncertainty_score":0.0052632093},"labels":[],"label_agreement":null},{"id":"W2023229815","doi":"10.1016/j.nonrwa.2011.05.017","title":"Solving inverse problems for differential equations by a “generalized collage” method and application to a mean field stochastic model","year":2011,"lang":"en","type":"article","venue":"Nonlinear Analysis Real World Applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Guelph","funders":"","keywords":"Inverse problem; Mathematics; Applied mathematics; Boundary value problem; Differential equation; Computer science; Mathematical optimization; Mathematical analysis","score_opus":0.08131288742530614,"score_gpt":0.3606972001979011,"score_spread":0.27938431277259496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023229815","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070031453,0.00021163504,0.99127156,0.00021199237,0.00005120103,0.00001622898,0.000008445942,0.00003037014,0.00119552],"genre_scores_gemma":[0.26593155,0.001014209,0.72174454,0.00024143948,0.000280198,0.0002578051,0.00010319705,0.00018903383,0.01023808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99936384,0.0003272028,0.000029587258,0.00009418697,0.00014687591,0.000038396847],"domain_scores_gemma":[0.99826556,0.0012302415,0.00015508101,0.00010492506,0.00017250617,0.00007170582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018040256,0.0011602747,0.001360308,0.0008952334,0.00082225195,0.0012639011,0.0012784214,0.002586226,0.001707205],"category_scores_gemma":[0.00443342,0.00072352664,0.001960714,0.000732481,0.0025201256,0.0016020813,0.0030418942,0.0019599996,0.00028122103],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006110722,0.00006683072,0.0005324799,0.00018432715,0.00013009318,0.0002768077,0.00027245446,0.7086454,0.005916296,0.2528035,0.001617103,0.029493496],"study_design_scores_gemma":[0.000010319959,0.000019776402,0.000057445824,0.0000060303983,0.000008466514,0.000043040283,0.00001179883,0.974986,0.00043579433,0.023580538,0.00082498207,0.000015823673],"about_ca_topic_score_codex":0.0038864329,"about_ca_topic_score_gemma":0.0033170243,"teacher_disagreement_score":0.0038864329,"about_ca_system_score_codex":0.00049990206,"about_ca_system_score_gemma":0.0011076703,"threshold_uncertainty_score":0.009540677},"labels":[],"label_agreement":null},{"id":"W2023379762","doi":"10.1002/nav.20099","title":"Properties of the geometric and related processes","year":2005,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Process (computing); Geometric series; Computer science; Series (stratigraphy); Complement (music); Stochastic process; Scheduling (production processes); Geometric modeling; Geometric programming; Mathematics; Mathematical optimization; Statistics; Geometry; Mathematical analysis","score_opus":0.38796704095847817,"score_gpt":0.4246007972902171,"score_spread":0.03663375633173893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023379762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21951601,0.0009753264,0.7348487,0.0014481139,0.00023462085,0.0002766147,0.00044495962,0.000332956,0.041922744],"genre_scores_gemma":[0.9333126,0.0011499521,0.049816493,0.00039549172,0.0005889492,0.00044239085,0.00047916584,0.000119492855,0.013695445],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99738544,0.00068498735,0.00014115481,0.00055491016,0.00095135,0.00028227747],"domain_scores_gemma":[0.9840338,0.0082368525,0.0032723525,0.0011775112,0.0024119727,0.0008675296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004605078,0.001070385,0.001430848,0.0028457835,0.001229643,0.0029735565,0.0017831686,0.001993719,0.01203616],"category_scores_gemma":[0.023144629,0.00046018264,0.0016454362,0.0014900018,0.005116816,0.004434883,0.0020729434,0.0023496912,0.0013295922],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018949897,0.000018253211,0.0004985753,0.00003055424,0.000010964535,0.00007414608,0.000068790854,0.010222358,0.00082128844,0.9841403,0.00045940152,0.0036364915],"study_design_scores_gemma":[0.000034220346,0.0001141664,0.0005299527,0.000026561034,0.000017669348,0.0004027532,0.00006941244,0.1296004,0.0011720676,0.86419314,0.0038053838,0.000034314035],"about_ca_topic_score_codex":0.0008481313,"about_ca_topic_score_gemma":0.00038086143,"teacher_disagreement_score":0.01203616,"about_ca_system_score_codex":0.0015171234,"about_ca_system_score_gemma":0.0013222112,"threshold_uncertainty_score":0.040265024},"labels":[],"label_agreement":null},{"id":"W2023504215","doi":"10.1007/s00170-012-4303-0","title":"Robustness study and reliability growth based on exploratory design of experiments and statistical analysis: a case study using a train door test bench","year":2012,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche","keywords":"Robustness (evolution); Reliability engineering; Test bench; Computer science; Mechatronics; Design of experiments; Multivariate statistics; Reliability (semiconductor); Engineering; Artificial intelligence; Machine learning; Statistics; Mathematics","score_opus":0.07297041722439629,"score_gpt":0.3627122915227605,"score_spread":0.28974187429836423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023504215","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6403299,0.00037689222,0.35535657,0.00033249776,0.000044415807,0.000565154,0.00014532277,0.00043289937,0.0024163064],"genre_scores_gemma":[0.96276146,0.000058189846,0.036666345,0.000024920537,0.000014093739,0.00013242944,0.000036001915,0.00003889885,0.0002676013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98865277,0.007874581,0.00035565434,0.0006972142,0.0020712279,0.0003484763],"domain_scores_gemma":[0.8477824,0.13215062,0.0071398863,0.006425068,0.0059494916,0.00055260496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018297978,0.0011433373,0.0008062269,0.0013970035,0.000517268,0.0012251105,0.0017894146,0.0015977185,0.0010809458],"category_scores_gemma":[0.052323442,0.00042818804,0.0012837425,0.0006021914,0.0013242075,0.001282328,0.00078919745,0.0010168992,0.00013648668],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0044955737,0.0029748802,0.030334577,0.0012785384,0.0007199849,0.0030738094,0.001538028,0.66708845,0.16912906,0.012547564,0.0009467178,0.10587286],"study_design_scores_gemma":[0.00030797467,0.012375367,0.02369743,0.00008516856,0.0005095044,0.0009348502,0.000624017,0.8659779,0.085078776,0.008925653,0.001312563,0.00017087869],"about_ca_topic_score_codex":0.0010187955,"about_ca_topic_score_gemma":0.0008134018,"teacher_disagreement_score":0.018297978,"about_ca_system_score_codex":0.0010771566,"about_ca_system_score_gemma":0.00094109226,"threshold_uncertainty_score":0.09677017},"labels":[],"label_agreement":null},{"id":"W2024514512","doi":"10.1016/j.ijthermalsci.2005.04.002","title":"Application of a sensitivity equation method to turbulent flows with heat transfer","year":2005,"lang":"en","type":"article","venue":"International Journal of Thermal Sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Air Force Office of Scientific Research; Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Turbulence; Sensitivity (control systems); Mechanics; Heat transfer; Churchill–Bernstein equation; Materials science; Thermodynamics; Physics; Reynolds number; Nusselt number","score_opus":0.06954115539484788,"score_gpt":0.36522573416499415,"score_spread":0.29568457877014626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024514512","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00875414,0.00009234317,0.9894986,0.00007512331,0.000055994384,0.00004449606,0.00001898578,0.0001698992,0.0012904502],"genre_scores_gemma":[0.6111954,0.000458973,0.38144842,0.000117612944,0.00017555057,0.00027288334,0.000095758915,0.00033424862,0.005901263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994031,0.0002839152,0.000032277567,0.00007456853,0.00017681868,0.000029296547],"domain_scores_gemma":[0.9974438,0.0020553411,0.0001016172,0.00010870104,0.00023817783,0.00005237914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016250104,0.00082777615,0.0009778575,0.00088513247,0.00057903,0.00085942703,0.00078784686,0.0011501795,0.0018843174],"category_scores_gemma":[0.004449951,0.0008278685,0.0015157625,0.0005208523,0.00090535596,0.0009288644,0.0014896528,0.00142403,0.0002289878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006100299,0.00010621532,0.00046459294,0.00012219469,0.000098221586,0.00011474794,0.000089506175,0.9162423,0.014712732,0.02850513,0.00042815183,0.039055213],"study_design_scores_gemma":[0.0000028619604,0.000011915764,0.000051936844,0.0000023553207,0.0000052062874,0.000009390444,0.0000015178117,0.99720985,0.0008584241,0.0016276346,0.00021367762,0.000005268818],"about_ca_topic_score_codex":0.003160969,"about_ca_topic_score_gemma":0.0015411664,"teacher_disagreement_score":0.003160969,"about_ca_system_score_codex":0.00044623646,"about_ca_system_score_gemma":0.0009877228,"threshold_uncertainty_score":0.008594036},"labels":[],"label_agreement":null},{"id":"W2025159868","doi":"10.1115/imece2007-42071","title":"Stability of SDOF Nonlinear Viscoelastic System Under the Excitation of Wide-Band Noise","year":2007,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Viscoelasticity; Nonlinear system; Excitation; Parametric statistics; Bifurcation; Mathematical analysis; Vibration; Noise (video); Stability (learning theory); Differential equation; Lyapunov exponent; Equations of motion; Mathematics; Control theory (sociology); Physics; Classical mechanics; Acoustics; Computer science","score_opus":0.07621952701150686,"score_gpt":0.3204820923440906,"score_spread":0.24426256533258373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025159868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6865948,0.00031878942,0.3080228,0.00022950681,0.00003403853,0.000015696332,0.00005415976,0.00008294939,0.004647265],"genre_scores_gemma":[0.9978758,0.0000891681,0.001446702,0.000008963219,0.000009631706,0.000007973185,0.000017256705,0.0000034365808,0.000541056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998103,0.000041726926,0.0000092968085,0.000048430997,0.000064262545,0.00002599866],"domain_scores_gemma":[0.9995084,0.00019580865,0.00015752867,0.000025928866,0.00008361875,0.000028736518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037566002,0.00024925082,0.00038981263,0.00024341544,0.000296847,0.00042894302,0.00022600731,0.00038972718,0.00034081677],"category_scores_gemma":[0.0013509272,0.00009233745,0.00025285748,0.00013541357,0.0006283386,0.00037178182,0.00051416643,0.00024521,0.00004538523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020932547,0.000026054093,0.005873943,0.00011471686,0.00009021661,0.0006269424,0.0003268079,0.8640708,0.06360936,0.047807816,0.0002644583,0.016979642],"study_design_scores_gemma":[0.0000036464962,0.00003562275,0.0011402813,0.0000034730992,0.0000062528966,0.000063911066,0.000020355794,0.9934716,0.0014665413,0.0036617094,0.000118726086,0.000007885714],"about_ca_topic_score_codex":0.0016788929,"about_ca_topic_score_gemma":0.0005416922,"teacher_disagreement_score":0.0016788929,"about_ca_system_score_codex":0.00023748579,"about_ca_system_score_gemma":0.00026730463,"threshold_uncertainty_score":0.0033382177},"labels":[],"label_agreement":null},{"id":"W2026298082","doi":"10.1142/s0218539307002490","title":"THE JOINT PREDICTIVE-POSTERIOR METHOD IN THE BAYESIAN ANALYSIS OF STRESS-STRENGTH RELIABILITY","year":2007,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Université de Moncton","keywords":"Reliability (semiconductor); Credibility; Joint (building); Computer science; Bridge (graph theory); Reliability engineering; Bayesian probability; Stress (linguistics); Quality (philosophy); Posterior probability; Engineering; Artificial intelligence; Structural engineering","score_opus":0.04953706583801604,"score_gpt":0.3748501119218459,"score_spread":0.3253130460838299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026298082","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008614393,0.0003557905,0.99796486,0.00010095492,0.000023887551,0.0000071065856,0.00002550934,0.000046975496,0.00061350537],"genre_scores_gemma":[0.3900781,0.0058962507,0.5931637,0.00032494083,0.0009991087,0.00045637428,0.0004940914,0.0002789356,0.008308444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748874,0.0013330742,0.0000693725,0.00027596808,0.0007262024,0.000106764746],"domain_scores_gemma":[0.9939585,0.0050270455,0.00022560864,0.0003657177,0.00034621308,0.00007693264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058455,0.0012071633,0.0013097348,0.0015680969,0.00059469754,0.0015097851,0.0018765285,0.0012866926,0.0020627936],"category_scores_gemma":[0.018226786,0.00084861065,0.0011845495,0.0018462444,0.002106499,0.002524213,0.0016536327,0.0026887308,0.0006341774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006514715,0.000041751027,0.00067515194,0.00020561185,0.00010518214,0.00010903906,0.00012246643,0.5345145,0.0010799109,0.40051252,0.0018219944,0.060746733],"study_design_scores_gemma":[0.0000100874395,0.000028908265,0.00017165198,0.00002980359,0.000030410927,0.000053059706,0.000009105736,0.8602813,0.00040176665,0.13712509,0.0018341255,0.000024656918],"about_ca_topic_score_codex":0.0044213934,"about_ca_topic_score_gemma":0.002912541,"teacher_disagreement_score":0.0058455,"about_ca_system_score_codex":0.0008078137,"about_ca_system_score_gemma":0.0014656243,"threshold_uncertainty_score":0.030914366},"labels":[],"label_agreement":null},{"id":"W2026947217","doi":"10.1016/j.jcp.2009.03.022","title":"Verified predictions of shape sensitivities in wall-bounded turbulent flows by an adaptive finite-element method","year":2009,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Sensitivity (control systems); Turbulence; Estimator; Finite element method; Convergence (economics); Grid; Bounded function; Flow (mathematics); Boundary (topology); Applied mathematics; Mathematics; Adaptive mesh refinement; Mathematical optimization; Computer science; Algorithm; Mathematical analysis; Geometry; Mechanics; Physics","score_opus":0.06811778838996906,"score_gpt":0.34112456278845343,"score_spread":0.27300677439848436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026947217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21778773,0.00007033703,0.77545977,0.00018796402,0.0000870672,0.00007597316,0.000061417304,0.00058248255,0.0056873416],"genre_scores_gemma":[0.9502281,0.000022637967,0.048686814,0.000035596422,0.000009341173,0.00003808584,0.000030933817,0.000062801024,0.00088565826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964607,0.00011596991,0.000020253334,0.000052937095,0.00013609919,0.000028656013],"domain_scores_gemma":[0.9981627,0.0009912362,0.00012384268,0.00020445642,0.00045803972,0.00005976095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009685903,0.000536131,0.00043444554,0.00040348768,0.00040129316,0.00066536776,0.00085044536,0.0011075315,0.0010841974],"category_scores_gemma":[0.0046879034,0.00037689973,0.0004621692,0.00017617576,0.00086612004,0.00060288596,0.0008094153,0.0008183693,0.00019385977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051155068,0.000033025135,0.00063214696,0.000020982809,0.000009253525,0.000046125147,0.000033173816,0.981092,0.0076615023,0.004110176,0.00011815731,0.006192385],"study_design_scores_gemma":[0.0000018065917,0.0000036332158,0.000050238436,0.0000010285671,7.0946265e-7,0.0000021694984,0.0000012278141,0.99894315,0.00075869606,0.00021561616,0.000020124207,0.0000017333855],"about_ca_topic_score_codex":0.0030571728,"about_ca_topic_score_gemma":0.0014281552,"teacher_disagreement_score":0.0030571728,"about_ca_system_score_codex":0.0004000638,"about_ca_system_score_gemma":0.00080282305,"threshold_uncertainty_score":0.0060787797},"labels":[],"label_agreement":null},{"id":"W2027362114","doi":"10.1016/j.jmaa.2010.07.036","title":"Finite element approximations of stochastic optimal control problems constrained by stochastic elliptic PDEs","year":2010,"lang":"en","type":"article","venue":"Journal of Mathematical Analysis and Applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Mathematics; Lagrange multiplier; Polynomial chaos; Optimal control; Applied mathematics; Stochastic control; Finite element method; Partial differential equation; Stochastic partial differential equation; Stochastic differential equation; Mathematical optimization; Mathematical analysis; Monte Carlo method","score_opus":0.024039279610176538,"score_gpt":0.28841090676905645,"score_spread":0.2643716271588799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027362114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05639336,0.00050981843,0.9338752,0.00059833267,0.00021098975,0.000053768552,0.00007934921,0.00012507073,0.008154162],"genre_scores_gemma":[0.8753825,0.00062004174,0.11127211,0.00023787643,0.00014370383,0.00023987706,0.0002797934,0.0001391361,0.011684995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993274,0.00027596834,0.00004224281,0.000071349525,0.00021725013,0.00006569755],"domain_scores_gemma":[0.99722713,0.0018812254,0.0002480913,0.00013701504,0.00037049988,0.00013598872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019113745,0.00080822525,0.0018259629,0.0009314287,0.00047520405,0.0015927538,0.0011345326,0.0030185822,0.0023376667],"category_scores_gemma":[0.006965956,0.0009145906,0.0008688523,0.00060215034,0.0019773685,0.00094916916,0.001826662,0.0015372898,0.000254153],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028161849,0.000021611928,0.00016291469,0.000024645913,0.000013804368,0.000018740166,0.000026638168,0.9808889,0.0004296725,0.016472487,0.00012761603,0.0017846999],"study_design_scores_gemma":[0.000002833533,0.0000034654981,0.000014791577,0.000003715665,0.0000011193878,0.0000022289728,0.0000033140157,0.9984484,0.00006706332,0.0013286654,0.00012251444,0.0000018369963],"about_ca_topic_score_codex":0.0076037785,"about_ca_topic_score_gemma":0.0037938266,"teacher_disagreement_score":0.0076037785,"about_ca_system_score_codex":0.0009840403,"about_ca_system_score_gemma":0.0013779524,"threshold_uncertainty_score":0.015119016},"labels":[],"label_agreement":null},{"id":"W2029783988","doi":"10.1061/(asce)be.1943-5592.0000567","title":"Eurocodes and Their Implications for Bridge Design: Background, Implementation, and Comparison to North American Practice","year":2013,"lang":"en","type":"article","venue":"Journal of Bridge Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Serviceability (structure); Engineering; Construction engineering; Eurocode; Risk analysis (engineering); Bridge (graph theory); Structural system; Building design; Civil engineering; Architectural engineering; Structural engineering","score_opus":0.1373083028886936,"score_gpt":0.38970544424716475,"score_spread":0.25239714135847113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029783988","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105657525,0.09286209,0.037381288,0.13192746,0.0075463452,0.0003973835,0.005439608,0.0006196113,0.6181686],"genre_scores_gemma":[0.7547837,0.07985559,0.06568839,0.02329759,0.0019897013,0.0015042012,0.0051924093,0.00091711496,0.066771284],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9869747,0.005193999,0.0011511045,0.0008520875,0.0049140844,0.0009139664],"domain_scores_gemma":[0.9661095,0.0123777855,0.0029543932,0.0014910623,0.016046347,0.0010209535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018532526,0.0005676851,0.00037765823,0.0067001656,0.0014767178,0.0047958405,0.001735194,0.0018990438,0.006946094],"category_scores_gemma":[0.061163306,0.000292113,0.00045400878,0.014674296,0.003484214,0.004782003,0.0025193323,0.0021107541,0.0010409084],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017149637,0.00012286931,0.019114673,0.00086384005,0.000017711067,0.00019970362,0.0063611385,0.0016863361,0.00025996927,0.4900431,0.092369676,0.38878945],"study_design_scores_gemma":[0.00002116057,0.00008142212,0.065874904,0.003990339,0.000019245874,0.00022452143,0.008701588,0.001713686,0.0003974259,0.022593688,0.89632714,0.0000548405],"about_ca_topic_score_codex":0.079803325,"about_ca_topic_score_gemma":0.04365111,"teacher_disagreement_score":0.079803325,"about_ca_system_score_codex":0.012933926,"about_ca_system_score_gemma":0.01351655,"threshold_uncertainty_score":0.15867764},"labels":[],"label_agreement":null},{"id":"W2029829488","doi":"10.1016/j.cam.2011.01.037","title":"Uncertainty investigations in nonlinear aeroelastic systems","year":2011,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aeroelasticity; Polynomial chaos; Nonlinear system; Mathematics; Monte Carlo method; Uncertainty quantification; Collocation (remote sensing); Applied mathematics; Collocation method; Numerical analysis; Mathematical optimization; Aerodynamics; Mathematical analysis; Computer science; Engineering","score_opus":0.1119599238654512,"score_gpt":0.2953709390830704,"score_spread":0.18341101521761918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029829488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36105832,0.0143307885,0.576937,0.0023536407,0.00029825052,0.000068117944,0.0001781253,0.00010300718,0.0446727],"genre_scores_gemma":[0.99152607,0.0017313747,0.004447921,0.000045612167,0.000099957986,0.000013836355,0.000031007177,0.000017825905,0.0020863544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994585,0.00012678948,0.000037140526,0.00005650096,0.00027057528,0.000050420484],"domain_scores_gemma":[0.998028,0.0013889862,0.0002095727,0.000088988076,0.00023331857,0.00005125388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089030684,0.00048327981,0.00069117325,0.001131973,0.00067991804,0.0013419406,0.00056467927,0.0008553801,0.001213874],"category_scores_gemma":[0.0051644547,0.00031330268,0.00052809843,0.0005827037,0.0015137028,0.0016656844,0.0017489659,0.00085177337,0.00010030379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016425055,0.00005796965,0.0034033526,0.00051669957,0.00013456924,0.00073668,0.000583423,0.7775107,0.020039028,0.15073083,0.0007040261,0.045418426],"study_design_scores_gemma":[0.0000053757935,0.00006185074,0.0021846646,0.00004943875,0.000019475307,0.00013534473,0.00014549131,0.924057,0.0043791556,0.067115344,0.0018170554,0.000029786717],"about_ca_topic_score_codex":0.0020733569,"about_ca_topic_score_gemma":0.0008283952,"teacher_disagreement_score":0.0020733569,"about_ca_system_score_codex":0.00045881385,"about_ca_system_score_gemma":0.00033736904,"threshold_uncertainty_score":0.0047084093},"labels":[],"label_agreement":null},{"id":"W2032469854","doi":"10.1115/1.3148087","title":"A Risk-Informed Approach to Leak-Before-Break Assessment of Pressure Tubes in CANDU Reactors","year":2010,"lang":"en","type":"article","venue":"Journal of Pressure Vessel Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Probabilistic logic; Leak; Reliability engineering; Risk assessment; Computer science; Probabilistic method; Risk analysis (engineering); Engineering","score_opus":0.025874498712231478,"score_gpt":0.3262839600182255,"score_spread":0.30040946130599405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032469854","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03488319,0.000070998416,0.9624904,0.00020637504,0.000009713216,0.00007798974,0.00004708883,0.0001210048,0.0020932963],"genre_scores_gemma":[0.8700906,0.000107977816,0.12782447,0.000041755757,0.00001836732,0.00018531736,0.000059484537,0.000030424551,0.0016415664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795616,0.0010984852,0.00006822305,0.0002183758,0.00056183094,0.0000968972],"domain_scores_gemma":[0.99676704,0.0021623739,0.00044803612,0.00014178538,0.00038182878,0.000098896184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041605304,0.0008749799,0.0010454785,0.0010679405,0.0006792348,0.0015055471,0.0018833849,0.0012987773,0.0014022045],"category_scores_gemma":[0.009027469,0.00082852156,0.000785225,0.0005101862,0.0013929345,0.0012875544,0.0019477375,0.0013406983,0.00012744089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002519403,0.000011640611,0.0003122043,0.000015562884,0.000011027227,0.000025836858,0.00001745965,0.98765326,0.0005489471,0.0061379247,0.00006607823,0.0051748706],"study_design_scores_gemma":[0.0000037058373,0.000013463029,0.00011180723,0.0000032003459,0.0000029965597,0.000006603755,0.000004444347,0.9964412,0.0003922562,0.0029101812,0.00010365202,0.000006573883],"about_ca_topic_score_codex":0.0067973677,"about_ca_topic_score_gemma":0.0036050705,"teacher_disagreement_score":0.9932026,"about_ca_system_score_codex":0.0020427464,"about_ca_system_score_gemma":0.0027696246,"threshold_uncertainty_score":0.022003233},"labels":[],"label_agreement":null},{"id":"W2033235294","doi":"10.1115/1.2035111","title":"Smoothing Monte Carlo Exchange Factors through Constrained Maximum Likelihood Estimation","year":2005,"lang":"en","type":"article","venue":"Journal of Heat Transfer","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Monte Carlo method; Reciprocity (cultural anthropology); Smoothing; Constraint (computer-aided design); Enclosure; Applied mathematics; Mathematical optimization; Set (abstract data type); Mathematics; Statistical physics; Computer science; Statistics; Physics","score_opus":0.06742746193247906,"score_gpt":0.3145169404244042,"score_spread":0.24708947849192514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033235294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061629633,0.00005060557,0.9931838,0.00003180848,0.0000065606087,0.000024819788,0.00001667296,0.00024277592,0.00027996468],"genre_scores_gemma":[0.24749626,0.00014432744,0.7503065,0.000064648935,0.000028673241,0.00029890286,0.00026610005,0.000283906,0.0011107278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855226,0.00075220596,0.0000890823,0.00018714034,0.00034915516,0.00007019396],"domain_scores_gemma":[0.9898207,0.008092653,0.00060209003,0.0007353316,0.00064997986,0.000099234225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004734634,0.0010652179,0.0016214798,0.0014302697,0.00076041225,0.0012374813,0.0014760471,0.0013094816,0.002178728],"category_scores_gemma":[0.022028249,0.0009638503,0.0011117895,0.0010735573,0.0010176188,0.0017978332,0.0012751356,0.0016950405,0.00046636662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009244829,0.00004006928,0.0006848945,0.00006923179,0.000061701176,0.00003538689,0.00006230442,0.9465835,0.001422046,0.014121588,0.0004521458,0.036374617],"study_design_scores_gemma":[0.000008831059,0.000009385708,0.000110807996,0.000006162604,0.0000062842705,0.000008103635,0.0000045427023,0.99072987,0.00060537545,0.008172637,0.00032851181,0.000009413389],"about_ca_topic_score_codex":0.005236911,"about_ca_topic_score_gemma":0.0051098615,"teacher_disagreement_score":0.005236911,"about_ca_system_score_codex":0.0008478008,"about_ca_system_score_gemma":0.0014917352,"threshold_uncertainty_score":0.025039434},"labels":[],"label_agreement":null},{"id":"W2036075241","doi":"10.1016/j.mejo.2008.03.008","title":"A probabilistic design optimization for MEMS tunable capacitors","year":2008,"lang":"en","type":"article","venue":"Microelectronics Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Probabilistic logic; Probabilistic design; Monte Carlo method; Optimal design; Capacitor; Mathematical optimization; Cumulative distribution function; Random variable; Moment (physics); Reliability (semiconductor); Probability distribution; Electronic engineering; Engineering; Computer science; Reliability engineering; Probability density function; Engineering design process; Mathematics; Electrical engineering; Mechanical engineering; Voltage","score_opus":0.12765270971250847,"score_gpt":0.3105986576054431,"score_spread":0.1829459478929346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036075241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013668334,0.0004683465,0.9811369,0.00050062867,0.000033547578,0.00004496879,0.00004298722,0.000098461605,0.0040057246],"genre_scores_gemma":[0.75972736,0.00089300185,0.23089468,0.00030677568,0.00017776534,0.00025097156,0.00009986253,0.00013923811,0.007510301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991535,0.00028649915,0.000026321108,0.00012682818,0.00033129074,0.00007560094],"domain_scores_gemma":[0.9987011,0.00094190415,0.0001246871,0.000054041364,0.000149502,0.000028803594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002093683,0.0008084175,0.0013261518,0.0006532497,0.00046429023,0.0011407133,0.0011576887,0.001554293,0.0026395079],"category_scores_gemma":[0.0055696373,0.0011432066,0.00096402294,0.0008539391,0.00086142623,0.0009783207,0.0010590451,0.0007948379,0.00021878417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021911503,0.000009314404,0.000082828665,0.00003235498,0.000023649814,0.000016513475,0.000008586414,0.98537725,0.0007868096,0.007031556,0.0002729867,0.006336248],"study_design_scores_gemma":[0.0000071944764,0.000013488756,0.00005984301,0.0000032448247,0.000009254177,0.0000094010475,0.0000021191297,0.9965342,0.0001469688,0.0029895452,0.00022118437,0.0000035461067],"about_ca_topic_score_codex":0.0027665708,"about_ca_topic_score_gemma":0.0025727677,"teacher_disagreement_score":0.0027665708,"about_ca_system_score_codex":0.001401734,"about_ca_system_score_gemma":0.001208803,"threshold_uncertainty_score":0.011072636},"labels":[],"label_agreement":null},{"id":"W2038311512","doi":"10.1115/1.4001357","title":"Characteristic Values in the Scaling of Differential Equations in Engineering","year":2010,"lang":"en","type":"article","venue":"Journal of Applied Mechanics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Scaling; Multiphysics; Applied mathematics; Range (aeronautics); Curvature; Simple (philosophy); Fluid dynamics; Mathematics; Computer science; Flow (mathematics); Mathematical optimization; Finite element method; Physics; Mechanics; Geometry","score_opus":0.04170106293496979,"score_gpt":0.2922954370113027,"score_spread":0.2505943740763329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038311512","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007247215,0.0007905381,0.987755,0.00021824283,0.0000673169,0.00002745403,0.000022594442,0.000080745616,0.0037908973],"genre_scores_gemma":[0.55993986,0.002980254,0.43097088,0.00025063477,0.00041712445,0.00034082631,0.00014022485,0.00024366936,0.004716573],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997171,0.0011935909,0.00018109994,0.00033541856,0.0009959016,0.00012307991],"domain_scores_gemma":[0.9888785,0.007431162,0.0011077578,0.0009865342,0.0013954412,0.00020062261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004934514,0.00089453935,0.000858644,0.0025968815,0.00062417216,0.0021699416,0.0011300091,0.00090553757,0.0013982448],"category_scores_gemma":[0.022158468,0.00051303365,0.0009750424,0.0014460586,0.0060155415,0.0045874794,0.00207946,0.0024368544,0.0004102327],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010414127,0.0000133263375,0.00057228556,0.00013617687,0.000011038086,0.000048174417,0.00019881094,0.046202287,0.001390914,0.9345547,0.0005736591,0.016288213],"study_design_scores_gemma":[0.0000059508243,0.00004527755,0.0003723858,0.00007436257,0.000007876009,0.00008353077,0.00006007059,0.316282,0.0013617702,0.67585975,0.005816489,0.000030417075],"about_ca_topic_score_codex":0.00090938347,"about_ca_topic_score_gemma":0.0004936852,"teacher_disagreement_score":0.004934514,"about_ca_system_score_codex":0.001425011,"about_ca_system_score_gemma":0.001042305,"threshold_uncertainty_score":0.026096523},"labels":[],"label_agreement":null},{"id":"W2038568847","doi":"10.1016/s0378-3758(01)00232-4","title":"How are moments and moments of spacings related to distribution functions?","year":2002,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; L-moment; Moment (physics); Random variable; Independent and identically distributed random variables; Method of moments (probability theory); Central moment; Distribution (mathematics); Moment-generating function; Range (aeronautics); Function (biology); Expression (computer science); Mathematical analysis; Distribution function; Applied mathematics; Statistics; Order statistic; Quantum mechanics","score_opus":0.08884896453767013,"score_gpt":0.3340390328909998,"score_spread":0.24519006835332965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038568847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10118225,0.004363699,0.884085,0.0037729568,0.00032826912,0.000029436615,0.0003978361,0.00041753822,0.005423015],"genre_scores_gemma":[0.93315685,0.0037804074,0.05838827,0.0006513393,0.00093712745,0.00009033103,0.0003468333,0.00035364798,0.0022952282],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99573284,0.001692262,0.0002592892,0.0009264182,0.0009287787,0.00046036503],"domain_scores_gemma":[0.90665025,0.069218524,0.012144474,0.006305032,0.004245361,0.0014365042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007182055,0.00070786057,0.001952255,0.0034211383,0.0007497486,0.005258684,0.0028205505,0.0041719438,0.0041021924],"category_scores_gemma":[0.13567543,0.0016308624,0.0010919631,0.003386011,0.005836717,0.021402143,0.0019405698,0.003188872,0.0010351528],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021543883,0.00006446299,0.015042577,0.00025878288,0.00017383906,0.00021042612,0.00062063226,0.08550734,0.0021043206,0.807741,0.0031212005,0.084940106],"study_design_scores_gemma":[0.00002173364,0.000023224913,0.0059471433,0.000052965097,0.000029645122,0.00030449007,0.00020930529,0.07449507,0.0006485284,0.9169529,0.0012213638,0.000093633],"about_ca_topic_score_codex":0.0013713222,"about_ca_topic_score_gemma":0.0006006002,"teacher_disagreement_score":0.007182055,"about_ca_system_score_codex":0.0013439424,"about_ca_system_score_gemma":0.000865993,"threshold_uncertainty_score":0.03798282},"labels":[],"label_agreement":null},{"id":"W2039354945","doi":"10.1002/(sici)1097-0207(20000530)48:3<421::aid-nme885>3.0.co;2-x","title":"Optimum design of truss structures undergoing large deflections subject to a system stability constraint","year":2000,"lang":"en","type":"article","venue":"International Journal for Numerical Methods in Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Truss; Buckling; Constraint (computer-aided design); Structural engineering; Eigenvalues and eigenvectors; Stability (learning theory); Structural system; Displacement (psychology); Engineering; Computer science; Mathematical optimization; Mathematics; Mechanical engineering","score_opus":0.12425420416682047,"score_gpt":0.4418541749500622,"score_spread":0.3175999707832417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039354945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059815124,0.0001545786,0.93706983,0.000084781364,0.0000095851565,0.000066195185,0.000024348366,0.00013202884,0.0026434907],"genre_scores_gemma":[0.6379754,0.0003201818,0.35842165,0.00005807241,0.000018882267,0.0003541727,0.00008342361,0.00007484338,0.0026933942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997683,0.0000718742,0.000009285485,0.000030642674,0.000080926555,0.000038985087],"domain_scores_gemma":[0.9995333,0.00024262597,0.00009504868,0.000024270825,0.000070062386,0.000034705667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006294751,0.00066712464,0.0007587044,0.0005539596,0.00026145615,0.0003545614,0.0004997741,0.000888468,0.0014827754],"category_scores_gemma":[0.0013109198,0.0006861283,0.00049850426,0.00026602173,0.0005909619,0.00043440537,0.00064986694,0.00053764187,0.00027810733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039944145,0.000032475597,0.00028065123,0.000052471543,0.000013810963,0.00003862101,0.000047585723,0.9637678,0.0058131465,0.0042570313,0.0002353018,0.025421204],"study_design_scores_gemma":[0.000026208523,0.00015957582,0.00024311255,0.0000100795505,0.000008618948,0.000027558652,0.000016630418,0.9926772,0.002008939,0.00415657,0.00065949856,0.0000060060916],"about_ca_topic_score_codex":0.0007677144,"about_ca_topic_score_gemma":0.0014352635,"teacher_disagreement_score":0.0014827754,"about_ca_system_score_codex":0.00030593754,"about_ca_system_score_gemma":0.0006998449,"threshold_uncertainty_score":0.0049604177},"labels":[],"label_agreement":null},{"id":"W2039638270","doi":"10.1061/(asce)cp.1943-5487.0000204","title":"Computer Program for Multimodel Reliability and Optimization Analysis","year":2012,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":159,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Killam Trusts","keywords":"Computer science; Reliability (semiconductor); Scripting language; Computer program; Computation; Program analysis; Probabilistic logic; Software; Reliability engineering; Distributed computing; Artificial intelligence; Engineering; Programming language","score_opus":0.04211673820685264,"score_gpt":0.32377346327767986,"score_spread":0.28165672507082723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039638270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021616705,0.000059198424,0.95893556,0.00007623762,0.00004607143,0.00021960886,0.0021944116,0.027066154,0.009241127],"genre_scores_gemma":[0.04200984,0.0001531006,0.9309787,0.00013189125,0.00004781716,0.0024160473,0.0052536945,0.0061563863,0.012852497],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994449,0.00012518212,0.000039559705,0.00009815355,0.00022593656,0.000066286324],"domain_scores_gemma":[0.99872786,0.0005769038,0.000058225993,0.00019178833,0.00039760355,0.00004751733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012030178,0.0011388047,0.00096515066,0.0010772543,0.00065497437,0.0008133577,0.0018710314,0.0007124348,0.06319012],"category_scores_gemma":[0.0025252684,0.00063744205,0.0009692222,0.000981994,0.0003345445,0.0009315135,0.0009855636,0.0021582597,0.016631028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003779,0.00043513865,0.002337832,0.0008057408,0.00029076383,0.00036962717,0.00028805048,0.2712273,0.018382562,0.09229127,0.20136333,0.41183043],"study_design_scores_gemma":[0.00022057662,0.00006916521,0.0008701176,0.0000593086,0.0000443758,0.00019120965,0.000024495546,0.8559145,0.009216213,0.029007737,0.10433629,0.000045942994],"about_ca_topic_score_codex":0.0024003626,"about_ca_topic_score_gemma":0.0022050657,"teacher_disagreement_score":0.06319012,"about_ca_system_score_codex":0.00053941796,"about_ca_system_score_gemma":0.0014917238,"threshold_uncertainty_score":0.21139199},"labels":[],"label_agreement":null},{"id":"W2039749720","doi":"10.1049/ip-cds:20030469","title":"Efficient non-Monte Carlo method for statistical analysis of periodically switched linear circuits in frequency domain","year":2003,"lang":"en","type":"article","venue":"IEE Proceedings - Circuits Devices and Systems","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"PSL; Electronic circuit; Monte Carlo method; Frequency domain; Algorithm; Computer science; Switched capacitor; Network analysis; Electronic engineering; Mathematics; Capacitor; Statistics; Voltage; Electrical engineering; Engineering","score_opus":0.05209852349859644,"score_gpt":0.33341107899506867,"score_spread":0.2813125554964722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039749720","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00083506986,0.000079960584,0.99855953,0.000029618615,0.000013794733,0.000012008514,0.000010240949,0.00009806922,0.0003617146],"genre_scores_gemma":[0.10828536,0.0004408386,0.88616115,0.00015082202,0.00008259676,0.0003715869,0.00014580801,0.00021982742,0.004142056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991429,0.00035922293,0.000026745522,0.000059920298,0.00036838505,0.00004279428],"domain_scores_gemma":[0.9971129,0.0022358678,0.00012304727,0.00017330567,0.00031111625,0.00004381118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017365218,0.00055071106,0.0008728003,0.0011372486,0.0004634795,0.0007356524,0.0012410654,0.00081853283,0.0029278605],"category_scores_gemma":[0.0043553524,0.00054125505,0.00079081074,0.0007359792,0.0009355386,0.0009471154,0.00063734216,0.0013077459,0.00064997975],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000687056,0.00008936918,0.0005141944,0.0001929765,0.000092665345,0.00014940141,0.00009945372,0.6793005,0.0069488417,0.22267318,0.0023179576,0.0875528],"study_design_scores_gemma":[0.00000622243,0.000008915276,0.000056888493,0.000005361129,0.0000032775395,0.000022513239,0.0000021668916,0.98709166,0.000726597,0.010975003,0.0010933774,0.000008019316],"about_ca_topic_score_codex":0.0017442867,"about_ca_topic_score_gemma":0.0022219012,"teacher_disagreement_score":0.0029278605,"about_ca_system_score_codex":0.00091583014,"about_ca_system_score_gemma":0.0012732035,"threshold_uncertainty_score":0.009794712},"labels":[],"label_agreement":null},{"id":"W2039928771","doi":"10.1016/s0378-3758(01)00167-7","title":"Analysis of domain means in complex surveys","year":2002,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Western University","funders":"","keywords":"Mathematics; Statistics; Poisson distribution; Sampling (signal processing); Sampling design; Domain (mathematical analysis); Simple random sample; Cluster analysis; Model selection; Data mining; Econometrics; Computer science","score_opus":0.20040293822362085,"score_gpt":0.3872463047435732,"score_spread":0.18684336651995234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039928771","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02015842,0.0006520673,0.97779524,0.0005215914,0.000022829783,0.00003928374,0.00015606383,0.00013835421,0.0005160947],"genre_scores_gemma":[0.6507364,0.0040646163,0.33697549,0.0004016067,0.00052956556,0.00078863505,0.0014691446,0.0003023829,0.004732196],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99085355,0.0064091487,0.0003383335,0.0011033979,0.0008793653,0.00041616528],"domain_scores_gemma":[0.8596705,0.124944076,0.004553026,0.006650726,0.0030276491,0.001153958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019790996,0.0011975329,0.0034839953,0.0038278205,0.0012752363,0.0038393,0.0033782206,0.002515916,0.0031438384],"category_scores_gemma":[0.0987856,0.0022096636,0.0027137683,0.0035488901,0.0046172775,0.0064918175,0.004330137,0.0031685773,0.00044133095],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013176262,0.00008485485,0.0068292613,0.00027239326,0.00040873603,0.00006972226,0.0004212356,0.46516547,0.0004757886,0.48624814,0.0030296154,0.03686307],"study_design_scores_gemma":[0.000016481314,0.0000333094,0.00088144507,0.00003412504,0.000038434177,0.00003920811,0.00007358619,0.69984806,0.00021956944,0.29787567,0.0009160007,0.00002403599],"about_ca_topic_score_codex":0.003698475,"about_ca_topic_score_gemma":0.0031449012,"teacher_disagreement_score":0.019790996,"about_ca_system_score_codex":0.0020680882,"about_ca_system_score_gemma":0.0022671658,"threshold_uncertainty_score":0.104665995},"labels":[],"label_agreement":null},{"id":"W2040494026","doi":"10.1081/sta-100105705","title":"PROGRESSIVE INTERVAL CENSORING: SOME MATHEMATICAL RESULTS WITH APPLICATIONS TO INFERENCE","year":2001,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Censoring (clinical trials); Inference; Interval estimation; Statistics; Sample (material); Sample size determination; Point estimation; Confidence interval; Mathematics; Computer science; Econometrics; Artificial intelligence","score_opus":0.15568394180839173,"score_gpt":0.49541210194645513,"score_spread":0.3397281601380634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040494026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078187115,0.0028825812,0.991867,0.0009894704,0.00012919467,0.000022195458,0.000075521246,0.00005493009,0.0031971305],"genre_scores_gemma":[0.10599625,0.021277852,0.8570676,0.0024193933,0.0039148503,0.00059195654,0.0005615393,0.0002542991,0.007916266],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9928515,0.0034928408,0.00048470334,0.00087913615,0.0020294061,0.00026252764],"domain_scores_gemma":[0.95463526,0.038119785,0.0018590788,0.0028563824,0.0020983014,0.00043131487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020715067,0.0017537018,0.0015911418,0.0040540723,0.0014349588,0.003548275,0.003217443,0.002831218,0.004196631],"category_scores_gemma":[0.064379774,0.0010008138,0.0027712581,0.0057564927,0.007100061,0.0082324445,0.0036769628,0.0071552577,0.0017195228],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018146782,0.000031028805,0.0005749213,0.00022949673,0.00006909805,0.00021077725,0.00027436373,0.016141117,0.00023147103,0.946176,0.0027415697,0.033302102],"study_design_scores_gemma":[0.000012567249,0.000034607085,0.0002977057,0.00012804946,0.0000378337,0.00026266827,0.000049807495,0.06306295,0.00032811906,0.9247869,0.010963623,0.000035102756],"about_ca_topic_score_codex":0.002045685,"about_ca_topic_score_gemma":0.0012445439,"teacher_disagreement_score":0.020715067,"about_ca_system_score_codex":0.002113719,"about_ca_system_score_gemma":0.0018483275,"threshold_uncertainty_score":0.1095531},"labels":[],"label_agreement":null},{"id":"W2041724233","doi":"10.1006/jsvi.2000.2951","title":"ALMOST-SURE STABILITY OF A GYROPENDULUM SUBJECTED TO WHITE-NOISE RANDOM SUPPORT MOTION","year":2000,"lang":"en","type":"article","venue":"Journal of Sound and Vibration","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Australian Research Council","keywords":"Instability; White noise; Lyapunov exponent; Mathematics; Exponent; Stability (learning theory); Noise (video); Motion (physics); Sign (mathematics); Control theory (sociology); Exponential stability; Mathematical analysis; Statistical physics; Classical mechanics; Physics; Mechanics; Statistics; Nonlinear system; Computer science","score_opus":0.0502554470693555,"score_gpt":0.29788769446019986,"score_spread":0.24763224739084436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041724233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76380676,0.00038901865,0.22824669,0.0008197009,0.00011201167,0.00004616557,0.00016162061,0.00017740804,0.006240697],"genre_scores_gemma":[0.99537706,0.00008848861,0.0016442243,0.000025296382,0.000014579313,0.000018172786,0.000032369317,0.000014393472,0.0027854047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995542,0.00011650962,0.000019014558,0.0001162151,0.000118749565,0.00007521207],"domain_scores_gemma":[0.9984811,0.00083716505,0.00028241082,0.00006005183,0.00021857819,0.00012068906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010200766,0.000840185,0.0012125076,0.0006893208,0.0006164352,0.0014199371,0.0006082846,0.0019210842,0.0018431637],"category_scores_gemma":[0.003704788,0.00033994205,0.0004097217,0.0002773889,0.001891375,0.00086334127,0.0018532949,0.00056432065,0.00022488159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014441826,0.00012884385,0.004502306,0.00039179696,0.00026039116,0.0016686607,0.00059568725,0.81854427,0.085960135,0.06908723,0.00089437753,0.016522128],"study_design_scores_gemma":[0.000020910999,0.00016675748,0.0012698637,0.000011998926,0.00002072754,0.00006477002,0.00006515108,0.9926656,0.00239995,0.0030842947,0.00020885539,0.000021088135],"about_ca_topic_score_codex":0.00381738,"about_ca_topic_score_gemma":0.0011828898,"teacher_disagreement_score":0.00381738,"about_ca_system_score_codex":0.0006044101,"about_ca_system_score_gemma":0.00061944034,"threshold_uncertainty_score":0.007590294},"labels":[],"label_agreement":null},{"id":"W2041801828","doi":"10.4271/2013-01-0329","title":"Estimation of One-Sided Lower Tolerance Limits for a Weibull Distribution Using the Monte Carlo Pivotal Simulation Technique","year":2013,"lang":"en","type":"article","venue":"SAE International Journal of Materials and Manufacturing","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Chrysler (Canada)","funders":"","keywords":"Monte Carlo method; Weibull distribution; Statistical physics; Distribution (mathematics); Computer science; Mathematics; Statistics; Physics; Mathematical analysis","score_opus":0.057187005584087486,"score_gpt":0.3276452871932798,"score_spread":0.2704582816091923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041801828","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01470715,0.00006183511,0.9841651,0.0000477627,0.0000075878925,0.000015911315,0.000018575583,0.00013918449,0.00083696307],"genre_scores_gemma":[0.7009286,0.00020041138,0.29661202,0.00008446945,0.000025318055,0.0001671599,0.00018345675,0.00022485331,0.00157375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771714,0.0011552882,0.00010414008,0.00024155312,0.00060393376,0.00017790298],"domain_scores_gemma":[0.9864268,0.010468391,0.0009632641,0.0009263064,0.0010486931,0.00016649005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070588505,0.00083620223,0.0011030177,0.0019711158,0.0005571839,0.0019148471,0.0013923269,0.0014382647,0.0025003192],"category_scores_gemma":[0.021551225,0.00077664404,0.001263933,0.0012073565,0.0012686398,0.0016621439,0.0017276832,0.0018332204,0.0005170302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013820373,0.000035221237,0.0008394905,0.00006189867,0.000031938485,0.000065006665,0.00010171212,0.93682677,0.0034982425,0.039040353,0.0003134215,0.019047756],"study_design_scores_gemma":[0.0000063400894,0.000024380397,0.0002004977,0.000018673945,0.000006754753,0.00002723469,0.000009095723,0.9905613,0.0014379526,0.007469052,0.00022531342,0.000013443093],"about_ca_topic_score_codex":0.0019245077,"about_ca_topic_score_gemma":0.0014641812,"teacher_disagreement_score":0.0070588505,"about_ca_system_score_codex":0.0009593437,"about_ca_system_score_gemma":0.0014780085,"threshold_uncertainty_score":0.037331164},"labels":[],"label_agreement":null},{"id":"W2042026655","doi":"10.4271/2014-01-0656","title":"Sensitivity and Uncertainty Analysis in Computational Thermal Models","year":2014,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Chrysler (Canada)","funders":"","keywords":"Sensitivity (control systems); Computer science; Thermal; Physics; Electronic engineering; Engineering; Thermodynamics","score_opus":0.03294132315572967,"score_gpt":0.28534207953050056,"score_spread":0.2524007563747709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042026655","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022376856,0.0016809555,0.9598147,0.0011994232,0.00010385139,0.0001076592,0.000531477,0.00034668818,0.013838343],"genre_scores_gemma":[0.903058,0.0021132252,0.08445558,0.00031322447,0.00021552776,0.00059797184,0.0007128072,0.00032056327,0.008213051],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99564326,0.0027596748,0.00014181365,0.0004044415,0.000794179,0.00025660603],"domain_scores_gemma":[0.98013544,0.01789859,0.0007039225,0.00038840977,0.00073541637,0.00013812188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005630631,0.0018132715,0.0017820667,0.0023353514,0.00089686754,0.0027686092,0.0014895202,0.001865211,0.0030531005],"category_scores_gemma":[0.020994278,0.0014725885,0.0022910272,0.0017703385,0.0024070034,0.0021185724,0.002733256,0.0025709195,0.0003398489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009315209,0.000005812528,0.00017729492,0.000028748002,0.000032134052,0.000023804512,0.00001712911,0.98377407,0.000055330842,0.014126208,0.0001918396,0.0015583506],"study_design_scores_gemma":[0.00000219884,0.0000043171995,0.000057360776,0.000011278462,0.000007221954,0.000004998052,0.000006658616,0.9800858,0.00006293892,0.019443737,0.00030730327,0.000006109969],"about_ca_topic_score_codex":0.015418362,"about_ca_topic_score_gemma":0.006059592,"teacher_disagreement_score":0.015418362,"about_ca_system_score_codex":0.0027457725,"about_ca_system_score_gemma":0.0020002534,"threshold_uncertainty_score":0.030657232},"labels":[],"label_agreement":null},{"id":"W2042318447","doi":"10.1021/ie071239s","title":"Reply to “Further Theoretical Results on ‘Relative Gain Array for Norm-Bonded Uncertain Systems'”","year":2007,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Citation; Computer science; Norm (philosophy); Reuse; Social media; Library science; Information retrieval; Operations research; World Wide Web; Mathematics; Political science; Engineering","score_opus":0.24321690136237303,"score_gpt":0.42479263937044087,"score_spread":0.18157573800806784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042318447","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002797008,0.0023787664,0.0029934472,0.94964236,0.04265717,0.000017071328,0.00019910488,0.000064173815,0.0017682101],"genre_scores_gemma":[0.005584605,0.0014479432,0.0016004321,0.94211704,0.042109843,0.000079265374,0.00005701843,0.000049295533,0.006954662],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99657357,0.00094550627,0.0005155885,0.0005628487,0.0010613129,0.000341151],"domain_scores_gemma":[0.98656905,0.008974506,0.0007862894,0.0004741966,0.0028379385,0.00035809667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006170202,0.0015164843,0.0014991016,0.000907897,0.0026591378,0.0024540129,0.0053415634,0.031597428,0.009357778],"category_scores_gemma":[0.03143164,0.0009340851,0.0019858114,0.0011271724,0.0052092494,0.006111686,0.0026028892,0.035778217,0.00816428],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007489004,0.00001637913,0.00015960856,0.00010021777,0.000030487983,0.00020839961,0.00017515835,0.00024584413,0.00024447756,0.013656836,0.9791838,0.00590389],"study_design_scores_gemma":[0.000090280955,0.000054044347,0.00092865917,0.000249883,0.00006119951,0.0003987069,0.00038296383,0.0012511709,0.0012629161,0.042831108,0.9523566,0.00013259714],"about_ca_topic_score_codex":0.005011259,"about_ca_topic_score_gemma":0.005398798,"teacher_disagreement_score":0.031597428,"about_ca_system_score_codex":0.0029659295,"about_ca_system_score_gemma":0.0019774192,"threshold_uncertainty_score":0.032631516},"labels":[],"label_agreement":null},{"id":"W2042983033","doi":"10.1115/detc2007-35517","title":"Reliable Space Pursuing for RBDO With Black-Box Performance Functions","year":2007,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Black box; Computer science; Computation; Probabilistic logic; Mathematical optimization; Reliability (semiconductor); Point (geometry); Nested loop join; Space (punctuation); Optimization problem; Algorithm; Mathematics; Data mining; Artificial intelligence; Power (physics)","score_opus":0.05874408360609289,"score_gpt":0.3026771026564616,"score_spread":0.24393301905036868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042983033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007490545,0.00012815019,0.99041545,0.00005017213,0.0000055620744,0.000035167508,0.0000069525995,0.000096594835,0.0017714358],"genre_scores_gemma":[0.46447405,0.00029621331,0.5328867,0.00006940216,0.000020178928,0.000313852,0.0000486652,0.00012813028,0.0017628579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989379,0.000510903,0.000037683392,0.00011487575,0.0003466216,0.0000520657],"domain_scores_gemma":[0.99746084,0.0017555417,0.00028021363,0.00023466218,0.0002229148,0.00004576674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036978652,0.001030352,0.000882886,0.0007400351,0.000410213,0.00079735427,0.0006487401,0.0008253999,0.0018982332],"category_scores_gemma":[0.006612667,0.00046743115,0.00078313873,0.00036025554,0.0016061142,0.0010733488,0.0013091754,0.0012608537,0.0003243709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007482233,0.000033430526,0.00039276737,0.00013535826,0.00002564415,0.00005564328,0.00011474271,0.89527905,0.0047526867,0.066208765,0.00026316167,0.032663967],"study_design_scores_gemma":[0.000013524405,0.00010609826,0.000082033854,0.000019020208,0.000006910621,0.000023897175,0.000010683779,0.9830836,0.0017280296,0.013731176,0.0011851387,0.0000098892515],"about_ca_topic_score_codex":0.00085861713,"about_ca_topic_score_gemma":0.00066962035,"teacher_disagreement_score":0.0036978652,"about_ca_system_score_codex":0.00055474316,"about_ca_system_score_gemma":0.0011768086,"threshold_uncertainty_score":0.019556463},"labels":[],"label_agreement":null},{"id":"W2043625782","doi":"10.3390/e16073832","title":"Variational Bayes for Regime-Switching Log-Normal Models","year":2014,"lang":"en","type":"article","venue":"Entropy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bayes' theorem; Context (archaeology); Divergence (linguistics); Projection (relational algebra); Computer science; Point (geometry); Applied mathematics; Algorithm; Kullback–Leibler divergence; Information geometry; Mathematics; Mathematical optimization; Geometry; Bayesian probability; Artificial intelligence","score_opus":0.06667647440428597,"score_gpt":0.30559038266656374,"score_spread":0.23891390826227776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043625782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010130416,0.0008050621,0.9847386,0.0008541415,0.000037294663,0.000027167143,0.00014084244,0.000079823396,0.003186581],"genre_scores_gemma":[0.70664084,0.0028289699,0.27506053,0.00047687715,0.00028364107,0.0004162738,0.00086387387,0.00024208435,0.0131869605],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974552,0.0015237167,0.00007814344,0.0003035593,0.00049113075,0.00014835471],"domain_scores_gemma":[0.99377316,0.0052344566,0.00030726564,0.000187379,0.00032104267,0.00017670309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054912427,0.0009378479,0.0015007742,0.0010245455,0.00057859224,0.0018828014,0.0018043116,0.0015144431,0.0027539204],"category_scores_gemma":[0.012671325,0.00075515185,0.0010859676,0.001018109,0.002744038,0.0024799684,0.0022170604,0.002688232,0.00031997293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003176173,0.00001760018,0.0003932168,0.00007943921,0.000045341512,0.00005714599,0.0000980443,0.35408083,0.00029899838,0.6320167,0.0011666389,0.0117142955],"study_design_scores_gemma":[0.0000057058596,0.0000065905683,0.00008306606,0.000010349136,0.0000036573788,0.000010486197,0.000008320682,0.656677,0.000052908774,0.34259042,0.00054329797,0.000008293731],"about_ca_topic_score_codex":0.0070351474,"about_ca_topic_score_gemma":0.0046785264,"teacher_disagreement_score":0.0070351474,"about_ca_system_score_codex":0.0028345224,"about_ca_system_score_gemma":0.0021733618,"threshold_uncertainty_score":0.029040813},"labels":[],"label_agreement":null},{"id":"W2044504313","doi":"10.1139/l08-146","title":"Risk-based framework for accommodating uncertainty in highway geometric design","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"A priori and a posteriori; Reliability (semiconductor); Engineering design process; Reliability engineering; Calibration; Probabilistic design; Geometric design; Computer science; Set (abstract data type); Design process; Process (computing); Engineering; Mathematics; Statistics; Transport engineering; Work in process; Power (physics)","score_opus":0.05903583557614908,"score_gpt":0.28692780104070004,"score_spread":0.22789196546455096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044504313","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018975592,0.00012781161,0.9964361,0.00013117278,0.000011031155,0.000017081442,0.000026086662,0.00004791555,0.0013051855],"genre_scores_gemma":[0.48540697,0.0012675815,0.5070032,0.00018343383,0.00017308023,0.00044862545,0.00021331238,0.0001268576,0.005176976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99485755,0.0026039437,0.00017376685,0.00051606784,0.0016137006,0.00023500319],"domain_scores_gemma":[0.9939824,0.0038376322,0.00069758634,0.0005036841,0.0008539868,0.00012478701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010157449,0.001607948,0.0012616208,0.0020866075,0.0005951571,0.0021767933,0.0028822098,0.0018285991,0.0022805247],"category_scores_gemma":[0.015220321,0.0010254781,0.001488719,0.0012772602,0.0025096806,0.0028837917,0.0020167967,0.0021232914,0.00033664182],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070675983,0.000010454781,0.0001188997,0.000021591733,0.000014804937,0.000029418936,0.000036628804,0.8728482,0.0001495297,0.12122225,0.00023962393,0.0053016166],"study_design_scores_gemma":[0.000006213277,0.000036855887,0.000086902866,0.000018886813,0.000012296367,0.000025920574,0.000013832851,0.85770684,0.00018233915,0.1404675,0.0014238782,0.00001844098],"about_ca_topic_score_codex":0.003917368,"about_ca_topic_score_gemma":0.0029681611,"teacher_disagreement_score":0.010157449,"about_ca_system_score_codex":0.002934603,"about_ca_system_score_gemma":0.0024437157,"threshold_uncertainty_score":0.053718388},"labels":[],"label_agreement":null},{"id":"W2044751504","doi":"10.4271/2014-01-0684","title":"Optimization of HVAC Panel Aiming Studies using Parametric Modeling and Automated Simulation","year":2014,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Chrysler (Canada)","funders":"","keywords":"HVAC; Computer science; Parametric statistics; Parametric model; Engineering; Mechanical engineering; Mathematics","score_opus":0.1268244835040641,"score_gpt":0.3564293632249509,"score_spread":0.22960487972088678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044751504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32424787,0.00052220403,0.6359019,0.00028008112,0.000051240197,0.00023613598,0.0002570757,0.0006027977,0.03790072],"genre_scores_gemma":[0.9815413,0.0000871127,0.016249856,0.000017382326,0.000008048332,0.00008296235,0.000067320805,0.000055159424,0.0018908264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996735,0.0001408134,0.000008447088,0.00003670448,0.000090546746,0.000049985876],"domain_scores_gemma":[0.9991123,0.00056529563,0.00008334657,0.0000655989,0.00013676588,0.0000367311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008981049,0.0008255954,0.0010711005,0.0007304376,0.00085500407,0.0013276606,0.0005633031,0.0011375612,0.0038856762],"category_scores_gemma":[0.0022166797,0.0007581157,0.00081511895,0.00061616296,0.00043596685,0.0009314052,0.00059725996,0.0007569852,0.0003824716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001872044,0.000014373323,0.000093349845,0.000012543715,0.0000048139186,0.000011851429,0.0000053113176,0.996651,0.0006033655,0.0003568046,0.00006226452,0.002165651],"study_design_scores_gemma":[0.0000038113876,0.000029513374,0.00015252075,0.0000026898629,0.000004111914,0.0000046492355,0.000008053256,0.99891245,0.00044564356,0.00032063836,0.00011250444,0.00000336502],"about_ca_topic_score_codex":0.0049533956,"about_ca_topic_score_gemma":0.004134296,"teacher_disagreement_score":0.0049533956,"about_ca_system_score_codex":0.0009986887,"about_ca_system_score_gemma":0.0010655762,"threshold_uncertainty_score":0.012998939},"labels":[],"label_agreement":null},{"id":"W2045162452","doi":"10.1155/2015/280940","title":"Reliability-Based Robust Design Optimization of Structures Considering Uncertainty in Design Variables","year":2015,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sobol sequence; Robustness (evolution); Weighting; Reliability (semiconductor); Mathematical optimization; Sensitivity (control systems); Variance (accounting); Engineering design process; Reliability engineering; Computer science; Engineering; Mathematics","score_opus":0.14073431811240197,"score_gpt":0.29649670705087877,"score_spread":0.1557623889384768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045162452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065440703,0.00022721806,0.9916338,0.000044252778,0.000008800425,0.000014695975,0.000015987845,0.00006515087,0.0014460307],"genre_scores_gemma":[0.7734598,0.000806098,0.2223296,0.00007428683,0.000055538967,0.00030475,0.00014811747,0.00012988265,0.002691937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992021,0.00032691436,0.000025057852,0.00013751742,0.00024607277,0.00006246778],"domain_scores_gemma":[0.9989197,0.0007541064,0.00012473772,0.00005378857,0.00013233218,0.00001530345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016041961,0.0013375933,0.0012539721,0.00087734876,0.00027288028,0.0008183479,0.0006922332,0.0009030737,0.001120166],"category_scores_gemma":[0.0030422283,0.00059610145,0.0009906791,0.0005572296,0.0007942636,0.0006555011,0.0008257232,0.0007428748,0.0002552263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019410145,0.0000071168533,0.00009251545,0.00006235622,0.000021328286,0.000024535508,0.000019067858,0.9797157,0.0023811448,0.006050436,0.00009523362,0.011511077],"study_design_scores_gemma":[0.0000032072562,0.000027665119,0.00005605337,0.0000054026145,0.000007901042,0.000010214348,0.000003196282,0.99557364,0.0008377574,0.0031822019,0.00028883413,0.000003957548],"about_ca_topic_score_codex":0.0012123568,"about_ca_topic_score_gemma":0.00080512173,"teacher_disagreement_score":0.0016041961,"about_ca_system_score_codex":0.0006108767,"about_ca_system_score_gemma":0.0009783431,"threshold_uncertainty_score":0.008483946},"labels":[],"label_agreement":null},{"id":"W2045367310","doi":"10.5402/2011/189735","title":"Comparison of Two Approaches for Detection and Estimation of Radioactive Sources","year":2011,"lang":"en","type":"article","venue":"ISRN Applied Mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Bayesian probability; Reversible-jump Markov chain Monte Carlo; Sampling (signal processing); Monte Carlo method; Sample (material); Data mining; Statistics; Mathematics; Artificial intelligence; Physics","score_opus":0.26565745087459725,"score_gpt":0.3599585264593424,"score_spread":0.09430107558474515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045367310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005171662,0.0004736088,0.99346364,0.00006877714,0.000022883438,0.000052656076,0.000019209016,0.00016537296,0.0005621948],"genre_scores_gemma":[0.1504781,0.0010741445,0.84630966,0.00011734435,0.000105426014,0.00028808333,0.00024605638,0.00015248034,0.0012287161],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9900742,0.0039058253,0.0005854295,0.001219878,0.003930517,0.00028415336],"domain_scores_gemma":[0.9707926,0.023361702,0.0012496442,0.0014797883,0.0028580467,0.0002583526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009613823,0.0016259714,0.0024807716,0.0046378467,0.00061030284,0.0017171116,0.0031254217,0.002481808,0.001627783],"category_scores_gemma":[0.041125633,0.0009463225,0.0018159023,0.0016165214,0.0016680101,0.0026789466,0.0032535426,0.001653277,0.0006626447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007533418,0.0002812281,0.0041838475,0.0006871571,0.0004900004,0.00012369188,0.00039843933,0.42120695,0.0065535484,0.031136092,0.00061988604,0.5335658],"study_design_scores_gemma":[0.00010812863,0.00028595835,0.0025453344,0.00006821766,0.00010376044,0.00021716487,0.00008245078,0.9732271,0.0062588146,0.015270046,0.0017433071,0.0000897559],"about_ca_topic_score_codex":0.0045554475,"about_ca_topic_score_gemma":0.0035941147,"teacher_disagreement_score":0.009613823,"about_ca_system_score_codex":0.0014408181,"about_ca_system_score_gemma":0.0018310229,"threshold_uncertainty_score":0.050843358},"labels":[],"label_agreement":null},{"id":"W2046054542","doi":"10.1115/omae2008-57197","title":"Hierarchical Bayes Analysis of Rare Events Using High-Dispersion Poisson Mixtures","year":2008,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Poisson distribution; Count data; Bayes' theorem; Statistics; Event (particle physics); Mathematics; Posterior probability; Dispersion (optics); Applied mathematics; Statistical physics; Computer science; Bayesian probability; Physics","score_opus":0.08616200276187927,"score_gpt":0.32985798240274133,"score_spread":0.24369597964086206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046054542","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0134739,0.00021132122,0.9854155,0.0001206601,0.000015485759,0.00004225917,0.00006660701,0.00011146545,0.00054274854],"genre_scores_gemma":[0.60767025,0.0009499235,0.3854187,0.00015685408,0.00024023438,0.00034627388,0.00068255083,0.000119300865,0.004415937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99343216,0.0030842372,0.00035119077,0.0010542786,0.0016755838,0.00040255723],"domain_scores_gemma":[0.96873605,0.026183952,0.0021273263,0.0010077588,0.0016163978,0.00032850844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013142542,0.0010077126,0.0019406151,0.0032213908,0.0011986523,0.0023957756,0.0022133852,0.0013092707,0.002614614],"category_scores_gemma":[0.0387762,0.0011528494,0.0016307373,0.0016641571,0.0020559838,0.0028551081,0.0016449544,0.0019673468,0.00043457912],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002034312,0.00007706693,0.00522965,0.00015574082,0.00020587198,0.0002777258,0.00047761813,0.7740785,0.0011352347,0.15024796,0.0009415691,0.06696969],"study_design_scores_gemma":[0.000013933254,0.000014209771,0.00063808303,0.000015704185,0.00002096182,0.000027817452,0.000021360442,0.95423335,0.00023181488,0.044442385,0.00032260024,0.000017700559],"about_ca_topic_score_codex":0.013611563,"about_ca_topic_score_gemma":0.009840912,"teacher_disagreement_score":0.013611563,"about_ca_system_score_codex":0.0022358852,"about_ca_system_score_gemma":0.0021884034,"threshold_uncertainty_score":0.069505215},"labels":[],"label_agreement":null},{"id":"W2047001853","doi":"10.1139/l02-087","title":"Load factor calibration for the proposed 2005 edition of the National Building Code of Canada: Statistics of loads and load effects","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Wind engineering; Building code; Structural load; Snow; Load factor; Calibration; Structural engineering; Statistics; Occupancy; Engineering; Meteorology; Civil engineering; Mathematics; Geography","score_opus":0.027443978412715852,"score_gpt":0.24691992933298396,"score_spread":0.21947595092026811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047001853","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08380768,0.0036683918,0.15327859,0.004479662,0.002462486,0.0030084753,0.47488576,0.00712688,0.2672822],"genre_scores_gemma":[0.31872633,0.0042022276,0.15061878,0.0014811446,0.0002309163,0.0021169013,0.38560653,0.0022179394,0.13479923],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.98634595,0.00045415867,0.0004999775,0.00047644784,0.011397862,0.00082561147],"domain_scores_gemma":[0.95041823,0.0012099289,0.0012348174,0.0013952086,0.04518956,0.00055230194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029756946,0.0011072793,0.0006418888,0.0056911632,0.0023318245,0.0020563467,0.0021522944,0.0006604896,0.0103093125],"category_scores_gemma":[0.016551245,0.0005178134,0.0007706078,0.011880254,0.000763806,0.0011340788,0.00092897547,0.0018044156,0.0034894561],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027686273,0.00019978336,0.07571494,0.0006966331,0.00007390731,0.00017423886,0.0011535101,0.032623276,0.0035440724,0.037818264,0.6117049,0.23601958],"study_design_scores_gemma":[0.0000335079,0.00010048972,0.20689376,0.00033326104,0.000050515293,0.00015767573,0.0008148841,0.016691383,0.0045637307,0.0035662616,0.76652247,0.0002720223],"about_ca_topic_score_codex":0.97113466,"about_ca_topic_score_gemma":0.9691667,"teacher_disagreement_score":0.046789885,"about_ca_system_score_codex":0.046789885,"about_ca_system_score_gemma":0.087732896,"threshold_uncertainty_score":0.33948594},"labels":[],"label_agreement":null},{"id":"W2047048918","doi":"10.1007/s00158-010-0532-8","title":"Aircraft wing box optimization considering uncertainty in surrogate models","year":2010,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Surrogate model; Conceptual design; Engineering design process; Multidisciplinary design optimization; Finite element method; Computational fluid dynamics; Computer science; Aerospace; Design process; Mathematical optimization; Process (computing); Probabilistic design; Wing; Reliability (semiconductor); Uncertainty quantification; Engineering; Aerospace engineering; Mechanical engineering; Work in process; Mathematics; Structural engineering","score_opus":0.04370477696520633,"score_gpt":0.3069621060738855,"score_spread":0.26325732910867916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047048918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06667732,0.00042018466,0.9278802,0.00032279626,0.000047015856,0.000034079567,0.00011608671,0.00008926978,0.0044130385],"genre_scores_gemma":[0.93026316,0.00036178282,0.067112,0.000058184065,0.000042042833,0.00010511706,0.00018383285,0.000069891226,0.0018040017],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855,0.0009049525,0.000043993652,0.000100910904,0.0003121922,0.000087819346],"domain_scores_gemma":[0.9961777,0.0029903085,0.00028353347,0.00021012349,0.00026125062,0.0000769914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035581768,0.0008202682,0.0017785532,0.00085358514,0.0003924851,0.0015333433,0.00082605536,0.0021526918,0.0009312198],"category_scores_gemma":[0.009709705,0.0010843606,0.001012413,0.00087460387,0.0011265572,0.0018429435,0.0013555662,0.0012159507,0.00014651606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012668697,0.000004712393,0.000047660753,0.000008300582,0.0000064631,0.000009155586,0.00000367337,0.9959299,0.000118944736,0.0030193664,0.000030490632,0.0008086939],"study_design_scores_gemma":[0.0000014226403,0.0000053179174,0.000018902821,0.0000021667347,0.0000013627072,0.000002301377,8.8231866e-7,0.99835676,0.000059807637,0.0015168015,0.000032986576,0.0000012450522],"about_ca_topic_score_codex":0.0013914467,"about_ca_topic_score_gemma":0.0010021247,"teacher_disagreement_score":0.0035581768,"about_ca_system_score_codex":0.0007497658,"about_ca_system_score_gemma":0.000723619,"threshold_uncertainty_score":0.018817663},"labels":[],"label_agreement":null},{"id":"W2047455044","doi":"10.1155/2013/850148","title":"Large-Scale CFD Parallel Computing Dealing with Massive Mesh","year":2013,"lang":"en","type":"article","venue":"Journal of Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Science and Technology Facilities Council","keywords":"Computer science; Computational fluid dynamics; Porting; Parallel computing; Supercomputer; Software; Computational science; Code (set theory); Mesh generation; Operating system; Finite element method; Programming language; Set (abstract data type); Engineering","score_opus":0.027992680237307804,"score_gpt":0.2697488894432583,"score_spread":0.2417562092059505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047455044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030541647,0.0007960057,0.9420491,0.00070994423,0.00034908621,0.0001697097,0.00020472716,0.0018333894,0.023346357],"genre_scores_gemma":[0.43099806,0.0013829916,0.55663705,0.00025103678,0.0002104337,0.00048092674,0.0008719207,0.0005659025,0.008601648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991291,0.00018028659,0.000047811714,0.000115979485,0.00044550648,0.00008141178],"domain_scores_gemma":[0.9984565,0.0005910585,0.000070550144,0.00038585457,0.00042964434,0.00006635307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008879957,0.0005574668,0.00079446234,0.00058106426,0.0012425109,0.0012063087,0.0011326903,0.00089996756,0.002920505],"category_scores_gemma":[0.004343287,0.0004701844,0.0007757018,0.0014600314,0.0009178109,0.0014752967,0.0014307145,0.0013431975,0.00076269277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009308691,0.00008775517,0.0026243578,0.00037040137,0.00006621228,0.0005645396,0.00035663773,0.754307,0.02675004,0.10045789,0.011867914,0.102454245],"study_design_scores_gemma":[0.000019589155,0.000015011063,0.0004850511,0.00001903336,0.000011054993,0.00008785263,0.000033594068,0.9540063,0.007457599,0.020108413,0.01774074,0.000015809801],"about_ca_topic_score_codex":0.004639379,"about_ca_topic_score_gemma":0.002506945,"teacher_disagreement_score":0.004639379,"about_ca_system_score_codex":0.0007463778,"about_ca_system_score_gemma":0.0014919143,"threshold_uncertainty_score":0.009770095},"labels":[],"label_agreement":null},{"id":"W2050368869","doi":"10.1016/j.pnucene.2014.07.043","title":"Efficient functional reliability estimation for a passive residual heat removal system with subset simulation based on importance sampling","year":2014,"lang":"en","type":"article","venue":"Progress in Nuclear Energy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Residual; Monte Carlo method; Markov chain Monte Carlo; Reliability (semiconductor); Sampling (signal processing); Importance sampling; Markov chain; Reliability engineering; Mathematical optimization; Algorithm; Mathematics; Statistics; Machine learning","score_opus":0.044032337252686966,"score_gpt":0.2994875957090929,"score_spread":0.25545525845640593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050368869","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06690838,0.00008267758,0.932061,0.000059783102,0.000010501626,0.000029724784,0.000026371643,0.00019707956,0.0006245833],"genre_scores_gemma":[0.93738544,0.00004863972,0.061613727,0.000022651906,0.00001071528,0.00008452715,0.00009121522,0.00004397825,0.0006991303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963,0.00015132717,0.000017518927,0.000059595743,0.00010540658,0.000036144058],"domain_scores_gemma":[0.9981699,0.0013259433,0.00010761163,0.00011062353,0.00024370704,0.000042219883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011408064,0.00071785424,0.0011910774,0.00047203884,0.00034176715,0.0005260497,0.0008330501,0.0005815465,0.0007695393],"category_scores_gemma":[0.0027169718,0.0004892794,0.00071401475,0.00026895193,0.0004966818,0.0007194096,0.00067814393,0.0006281419,0.000090409194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008089696,0.000022000846,0.00047844579,0.000026163454,0.000019665275,0.000015792966,0.000017962046,0.9867754,0.00207472,0.0012799654,0.00007479113,0.00913425],"study_design_scores_gemma":[0.0000010768438,0.0000067997316,0.000041414758,3.888779e-7,0.0000017545907,0.0000014873078,6.2643284e-7,0.99957854,0.0001685797,0.0001871873,0.000011481317,7.11791e-7],"about_ca_topic_score_codex":0.0045812037,"about_ca_topic_score_gemma":0.0028908744,"teacher_disagreement_score":0.0045812037,"about_ca_system_score_codex":0.0005483418,"about_ca_system_score_gemma":0.00087982457,"threshold_uncertainty_score":0.00910908},"labels":[],"label_agreement":null},{"id":"W2050416191","doi":"10.1080/16864360.2014.846097","title":"A Virtual Prognostic Tool for Nuclear Power Electronics Reliability","year":2013,"lang":"en","type":"article","venue":"Computer-Aided Design and Applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Reliability engineering; Nuclear power; Electronics; Power electronics; Computer science; Systems engineering; Power (physics); Engineering; Electrical engineering; Physics; Nuclear physics","score_opus":0.03419845076493652,"score_gpt":0.27678584043198634,"score_spread":0.24258738966704982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050416191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012425226,0.0006569723,0.9521185,0.00029679234,0.0003029698,0.00007191491,0.0008937349,0.023925927,0.009307951],"genre_scores_gemma":[0.6177665,0.0012350958,0.3606604,0.00024969433,0.00023996571,0.00031642598,0.0018093562,0.002090412,0.01563218],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997198,0.000054778026,0.000019323088,0.00003349695,0.00015341223,0.000019246176],"domain_scores_gemma":[0.99918145,0.00038898436,0.00008010712,0.000114756134,0.00019559755,0.000039053382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007337221,0.0008709639,0.0006227971,0.0015140256,0.00037464596,0.0011244847,0.0010433423,0.00084868376,0.022809215],"category_scores_gemma":[0.0035417697,0.0003494621,0.0005494098,0.00087748124,0.0003679214,0.0014287259,0.0011610382,0.000723579,0.0035603347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035486126,0.00008713026,0.0023127068,0.00040892826,0.000068256406,0.00041292218,0.00022784229,0.38647118,0.011429539,0.026792372,0.03204922,0.5393851],"study_design_scores_gemma":[0.000043455537,0.00009508295,0.00062618504,0.00007049636,0.000043705568,0.00025219077,0.000034101842,0.9503492,0.0039774496,0.01714427,0.027326522,0.000037289745],"about_ca_topic_score_codex":0.0010247452,"about_ca_topic_score_gemma":0.0010410919,"teacher_disagreement_score":0.022809215,"about_ca_system_score_codex":0.0003429186,"about_ca_system_score_gemma":0.0006674458,"threshold_uncertainty_score":0.076304376},"labels":[],"label_agreement":null},{"id":"W2050552555","doi":"10.1016/j.cma.2014.02.023","title":"Anchored ANOVA Petrov–Galerkin projection schemes for parabolic stochastic partial differential equations","year":2014,"lang":"en","type":"article","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Polynomial chaos; Projection (relational algebra); Galerkin method; Applied mathematics; Discretization; Partial differential equation; Stochastic partial differential equation; Stochastic differential equation; Petrov–Galerkin method; Stochastic process; Mathematical optimization; Mathematical analysis; Algorithm; Monte Carlo method; Finite element method","score_opus":0.08587995127026103,"score_gpt":0.3619799532534061,"score_spread":0.27610000198314505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050552555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027217764,0.00034285395,0.968251,0.00035381693,0.00011465861,0.000048387636,0.0000604722,0.00009626932,0.0035148195],"genre_scores_gemma":[0.70321923,0.0009827224,0.2718954,0.00020075803,0.00021503057,0.00043027135,0.00029644178,0.00021911091,0.02254105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993229,0.00033896623,0.000033994147,0.00006723284,0.00018700276,0.00004993934],"domain_scores_gemma":[0.9984946,0.0007822898,0.00016778806,0.00011536855,0.00029185083,0.00014804576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001633166,0.0012427717,0.0013585938,0.0007373853,0.0005471157,0.0015774837,0.0013067699,0.0018100044,0.0021199863],"category_scores_gemma":[0.0049419,0.00064462,0.0010180702,0.0007273268,0.002088174,0.0014411549,0.004301677,0.002885638,0.00036743784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016147573,0.0001109997,0.00055884436,0.00016648686,0.00006946117,0.00008995634,0.00021277166,0.5796078,0.005709603,0.3804397,0.0010913775,0.031781513],"study_design_scores_gemma":[0.000009616073,0.000021470041,0.000055893466,0.0000078869225,0.0000053821755,0.000008038046,0.000009227441,0.96227574,0.00036969586,0.03655296,0.00067454414,0.000009604487],"about_ca_topic_score_codex":0.003336593,"about_ca_topic_score_gemma":0.001951081,"teacher_disagreement_score":0.003336593,"about_ca_system_score_codex":0.0010301581,"about_ca_system_score_gemma":0.0018127357,"threshold_uncertainty_score":0.00863713},"labels":[],"label_agreement":null},{"id":"W2052340926","doi":"10.1007/s12206-014-0127-1","title":"Multidisciplinary wing design optimization considering global sensitivity and uncertainty of approximation models","year":2014,"lang":"en","type":"article","venue":"Journal of Mechanical Science and Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Canada Research Chairs; Toronto Metropolitan University","funders":"National Research Foundation of Korea; Konkuk University","keywords":"Multidisciplinary design optimization; Sensitivity (control systems); Mathematical optimization; Curse of dimensionality; Engineering design process; Uncertainty quantification; Computer science; Uncertainty analysis; Surrogate model; Reliability (semiconductor); Optimization problem; Multi-objective optimization; Multidisciplinary approach; Mathematics; Engineering; Simulation; Artificial intelligence; Machine learning","score_opus":0.06403059327571445,"score_gpt":0.3108292826997445,"score_spread":0.24679868942403008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052340926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12174478,0.00045363512,0.8735401,0.0004161921,0.0000528921,0.000036049263,0.000063889376,0.000045115074,0.003647328],"genre_scores_gemma":[0.97442853,0.00016830159,0.024209712,0.000040058745,0.000028001245,0.00003990927,0.00004496518,0.000023652361,0.0010168657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990108,0.000493516,0.000032222375,0.00016264732,0.00019104683,0.00010969264],"domain_scores_gemma":[0.9969344,0.0022489724,0.00031649435,0.00014544092,0.00024772677,0.00010705952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031329573,0.0011564409,0.0019883902,0.0012773138,0.000551647,0.0016728897,0.0010868168,0.0022815394,0.00080675905],"category_scores_gemma":[0.007829316,0.001115572,0.0015945839,0.00072148716,0.0014143405,0.0018967863,0.0024309484,0.0013284101,0.000075256314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014437515,0.000006480916,0.00013594472,0.000011196781,0.0000174391,0.000016510614,0.000006332084,0.9966877,0.00024177978,0.0019275006,0.00002211468,0.0009126193],"study_design_scores_gemma":[0.0000012187967,0.000009262964,0.000051235726,0.000001991728,0.0000037998093,0.0000025595177,0.0000024013623,0.99891984,0.00007279844,0.0009124976,0.000020423417,0.0000020230677],"about_ca_topic_score_codex":0.0031850487,"about_ca_topic_score_gemma":0.0015974256,"teacher_disagreement_score":0.0031850487,"about_ca_system_score_codex":0.0010672733,"about_ca_system_score_gemma":0.00087789353,"threshold_uncertainty_score":0.01656884},"labels":[],"label_agreement":null},{"id":"W2053161742","doi":"10.1016/s0167-6105(01)00076-9","title":"Probabilistic assessment of wind-sensitive structures with uncertain parameters","year":2001,"lang":"en","type":"article","venue":"Journal of Wind Engineering and Industrial Aerodynamics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Serviceability (structure); Mathematics; Probabilistic logic; Statistics; Conditional expectation; Reliability (semiconductor); Limit (mathematics); Structural engineering; Engineering; Mathematical analysis; Physics","score_opus":0.06655499104912159,"score_gpt":0.298791739490958,"score_spread":0.2322367484418364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053161742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31798214,0.0004588243,0.67763335,0.00026629056,0.000026274905,0.000086755615,0.00017848404,0.00017174354,0.0031961573],"genre_scores_gemma":[0.98906696,0.00012193072,0.01024337,0.000013259173,0.00001471362,0.000024386049,0.00007583915,0.000015003905,0.00042448234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982443,0.0006041791,0.00008890555,0.0001688385,0.000773413,0.00012037654],"domain_scores_gemma":[0.9928444,0.00523009,0.0008397296,0.00038728997,0.0005478117,0.00015071216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044712257,0.0010822569,0.0013167829,0.0018394758,0.00054642203,0.0014266431,0.0014239853,0.0021412666,0.001307908],"category_scores_gemma":[0.014225836,0.0015671611,0.001190855,0.001069867,0.0013706518,0.0022663914,0.0017520706,0.00079178426,0.00017904384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043674514,0.0000071335244,0.0005841755,0.000014163739,0.000016996359,0.00005102169,0.000012093049,0.9946472,0.0006958813,0.0017442745,0.00003725255,0.0021460673],"study_design_scores_gemma":[0.0000044795006,0.00002534078,0.00085094885,0.000003656881,0.000013396703,0.000031215448,0.00000775707,0.99514717,0.000392522,0.003458883,0.000054214906,0.000010422662],"about_ca_topic_score_codex":0.0014666148,"about_ca_topic_score_gemma":0.0020631833,"teacher_disagreement_score":0.0044712257,"about_ca_system_score_codex":0.0007985393,"about_ca_system_score_gemma":0.00044757265,"threshold_uncertainty_score":0.023646414},"labels":[],"label_agreement":null},{"id":"W2054148923","doi":"10.1023/b:opte.0000048536.47956.62","title":"A Coupled-Adjoint Sensitivity Analysis Method for High-Fidelity Aero-Structural Design","year":2004,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":256,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Air Force Office of Scientific Research; Fundação para a Ciência e a Tecnologia","keywords":"Sensitivity (control systems); Aerodynamics; Parameterized complexity; Mathematical optimization; Computer science; Mathematics; Applied mathematics; Topology (electrical circuits); Algorithm; Aerospace engineering; Engineering","score_opus":0.04790198100555463,"score_gpt":0.309288998847244,"score_spread":0.26138701784168933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054148923","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017465553,0.000033080025,0.9970336,0.00002509073,0.000014759846,0.000019841269,0.000015878146,0.00017709004,0.00093408977],"genre_scores_gemma":[0.32126647,0.0001535097,0.67317635,0.00015406615,0.00007389024,0.00038543108,0.00012641835,0.00036633655,0.004297572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993259,0.00027188877,0.000023568537,0.000080870144,0.0002632732,0.000034549343],"domain_scores_gemma":[0.9988028,0.000770967,0.000074800075,0.000089140005,0.0002172761,0.000045014833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015216259,0.0009100179,0.0010668996,0.0006920993,0.00053884136,0.0008697066,0.0013933723,0.0015691201,0.0040555317],"category_scores_gemma":[0.0030159685,0.000908381,0.001235019,0.00045396204,0.0008298924,0.00091347797,0.0019395356,0.0014143129,0.0006719547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003973238,0.0000534576,0.00015186174,0.00008429075,0.000054849716,0.000042405314,0.000036384823,0.9564904,0.0063951085,0.009959289,0.00044808656,0.02624408],"study_design_scores_gemma":[0.000003893778,0.000008395125,0.000023715824,0.0000027851204,0.0000044757653,0.0000061764554,0.0000011991364,0.9980034,0.00041897123,0.0012247964,0.0002981275,0.000004113359],"about_ca_topic_score_codex":0.0027231886,"about_ca_topic_score_gemma":0.0024708633,"teacher_disagreement_score":0.0040555317,"about_ca_system_score_codex":0.0004895075,"about_ca_system_score_gemma":0.0011179046,"threshold_uncertainty_score":0.01356709},"labels":[],"label_agreement":null},{"id":"W2054243597","doi":"10.1016/s0309-1708(03)00006-x","title":"Mean-value second-order uncertainty analysis method: application to water quality modelling","year":2003,"lang":"en","type":"article","venue":"Advances in Water Resources","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Monte Carlo method; Moment (physics); Value (mathematics); Applied mathematics; Mathematics; Second moment of area; Mathematical optimization; Point (geometry); Function (biology); Statistics; Physics","score_opus":0.0523356917190367,"score_gpt":0.37399747243024434,"score_spread":0.32166178071120766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054243597","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003354405,0.00008473451,0.99590516,0.000046828944,0.000016341952,0.000009108158,0.000021798072,0.00014692085,0.00041471297],"genre_scores_gemma":[0.42976928,0.00044097923,0.56500286,0.00014423806,0.00009020774,0.00023529616,0.00020939812,0.00032481973,0.0037829534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992305,0.0002733254,0.000041725998,0.000100048805,0.00031414797,0.000040317726],"domain_scores_gemma":[0.9969086,0.0022140585,0.00018221233,0.00013008698,0.0005160224,0.000049031918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025737516,0.00079822016,0.00173827,0.0009915767,0.0006794902,0.0014660138,0.0014551835,0.0015532958,0.0015192804],"category_scores_gemma":[0.0065958556,0.00074558234,0.0014508906,0.0011275816,0.0007391805,0.0013290555,0.0010343691,0.0016707025,0.00031259167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007386047,0.00004153855,0.00031865804,0.00008239047,0.00008312549,0.000038595277,0.00004415317,0.928865,0.0022048864,0.014578826,0.00056221255,0.053106748],"study_design_scores_gemma":[0.0000035637552,0.000007533355,0.000044369273,0.0000024248666,0.0000058210812,0.0000075726184,0.0000013575792,0.9962907,0.00038815278,0.0030486821,0.00019252833,0.0000073065444],"about_ca_topic_score_codex":0.0054467223,"about_ca_topic_score_gemma":0.0043684174,"teacher_disagreement_score":0.0054467223,"about_ca_system_score_codex":0.00092087634,"about_ca_system_score_gemma":0.0018490058,"threshold_uncertainty_score":0.0136114955},"labels":[],"label_agreement":null},{"id":"W2054652040","doi":"10.1016/j.probengmech.2014.11.001","title":"Stochastic stability of SDOF linear viscoelastic system under wideband noise excitation","year":2014,"lang":"en","type":"article","venue":"Probabilistic Engineering Mechanics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Lakehead University","funders":"","keywords":"Mathematics; Lyapunov exponent; White noise; Moment (physics); Parametric statistics; Stability (learning theory); Noise (video); Viscoelasticity; Applied mathematics; Control theory (sociology); Stochastic process; Mathematical analysis; Nonlinear system; Statistical physics; Physics; Computer science; Classical mechanics; Statistics","score_opus":0.04752599359812786,"score_gpt":0.2704005447298213,"score_spread":0.2228745511316934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054652040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4015223,0.0005171872,0.5836942,0.0008001034,0.0000815754,0.00004966226,0.00016170702,0.0001515377,0.013021679],"genre_scores_gemma":[0.995301,0.000109547014,0.0019591625,0.000026152793,0.000014791512,0.000018901465,0.00003457127,0.000009323313,0.002526418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961156,0.00008528174,0.00001791948,0.00011100677,0.00011794599,0.000056288925],"domain_scores_gemma":[0.99859864,0.00062693644,0.00036141893,0.00006264398,0.00029258328,0.000057797803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007835761,0.0004181225,0.0006269039,0.00047587248,0.00040304518,0.0008973086,0.00044734892,0.00085756724,0.001321184],"category_scores_gemma":[0.0030889958,0.00024081983,0.00038503503,0.0002775423,0.0011417798,0.0006700726,0.0011310644,0.0005440029,0.000116723066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014320831,0.00003171985,0.0032142366,0.00012187789,0.00006616334,0.00029810224,0.00020444136,0.9205726,0.010844941,0.056351986,0.00043447185,0.0077163344],"study_design_scores_gemma":[0.0000028282213,0.00002098262,0.00062818656,0.0000060507327,0.0000051562038,0.00002979261,0.000015319212,0.99491864,0.0003609304,0.0039250185,0.00008028102,0.0000067961287],"about_ca_topic_score_codex":0.003506838,"about_ca_topic_score_gemma":0.0016137763,"teacher_disagreement_score":0.003506838,"about_ca_system_score_codex":0.00056188216,"about_ca_system_score_gemma":0.0005672903,"threshold_uncertainty_score":0.0069728494},"labels":[],"label_agreement":null},{"id":"W2055465780","doi":"10.1139/l07-002","title":"Reliability-based optimal design software for earthquake engineering applications","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"OpenSees; Finite element method; Maintainability; Computer science; Reliability (semiconductor); Flexibility (engineering); Software; Search-based software engineering; Reliability engineering; Nonlinear system; Engineering; Structural engineering; Software design; Software development","score_opus":0.041868673246201894,"score_gpt":0.2670578918954827,"score_spread":0.2251892186492808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055465780","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010378715,0.00004252883,0.9910778,0.000039115108,0.000012766354,0.00003721993,0.00015660867,0.0060090004,0.0015871031],"genre_scores_gemma":[0.057524372,0.000270031,0.93490833,0.00006651118,0.00003086577,0.00062801613,0.0012572217,0.0025450462,0.0027696702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990387,0.0002434119,0.00010385553,0.00009357571,0.00046998134,0.000050489747],"domain_scores_gemma":[0.99788755,0.0011802104,0.00016739126,0.00023960113,0.00048891775,0.00003637178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021507987,0.0012555143,0.0006599614,0.0010940838,0.00038181624,0.00092031626,0.001619305,0.00085289,0.0151657],"category_scores_gemma":[0.0063893623,0.00088027073,0.0010594426,0.0007480237,0.00051401847,0.00079210504,0.0012461084,0.001264287,0.004004243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015089058,0.000099463025,0.0009916254,0.00056016684,0.00010059707,0.00012273215,0.0002008367,0.67982155,0.008722805,0.05985452,0.015762802,0.23361199],"study_design_scores_gemma":[0.00006763893,0.00003936546,0.00019365182,0.000048650436,0.000028135319,0.0000763787,0.000017500866,0.9424338,0.004435388,0.026112493,0.026527917,0.000019130017],"about_ca_topic_score_codex":0.001986474,"about_ca_topic_score_gemma":0.0022987833,"teacher_disagreement_score":0.0151657,"about_ca_system_score_codex":0.0006274042,"about_ca_system_score_gemma":0.00141207,"threshold_uncertainty_score":0.05073428},"labels":[],"label_agreement":null},{"id":"W2056145867","doi":"10.1121/1.1538246","title":"Reduced models for the medium-frequency dynamics of stochastic systems","year":2003,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Office of Naval Research","keywords":"Statistical energy analysis; Frequency domain; Reduction (mathematics); Computer science; Range (aeronautics); Energy operator; Coupling (piping); Probabilistic logic; Frequency band; Vibration; Operator (biology); Polynomial chaos; Energy (signal processing); Algorithm; Statistical physics; Mathematics; Acoustics; Physics; Telecommunications; Statistics; Artificial intelligence","score_opus":0.05371994048544541,"score_gpt":0.3015524605492556,"score_spread":0.2478325200638102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056145867","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01366316,0.00022301209,0.9828151,0.00021643312,0.000029445751,0.000023752886,0.000094166935,0.00008279462,0.0028520757],"genre_scores_gemma":[0.8374201,0.0012359333,0.14681002,0.00012962657,0.00016092091,0.00038011034,0.0004308258,0.00009697709,0.013335497],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995245,0.00016194524,0.000017185444,0.00007648665,0.0001803134,0.00003965106],"domain_scores_gemma":[0.9994349,0.00031477638,0.00008433446,0.000058024725,0.00008251657,0.00002540915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007479054,0.00060815853,0.0007346859,0.0005151439,0.0002811827,0.0008789708,0.0010425866,0.00074435835,0.0022680103],"category_scores_gemma":[0.0019908538,0.0002815714,0.0009513266,0.00031430367,0.00075452577,0.0008926399,0.0008452212,0.0014690441,0.0005315808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021534357,0.00001890534,0.00021655567,0.00005004353,0.000026738031,0.000055329045,0.000071324816,0.8302897,0.0021375222,0.1601358,0.00046540765,0.006511172],"study_design_scores_gemma":[0.000002905766,0.000008307818,0.0000493583,0.000002676136,0.0000036292263,0.000008497948,0.000005762065,0.9762429,0.00011390826,0.023022817,0.00053495803,0.000004303053],"about_ca_topic_score_codex":0.002542777,"about_ca_topic_score_gemma":0.0025204483,"teacher_disagreement_score":0.002542777,"about_ca_system_score_codex":0.0006697266,"about_ca_system_score_gemma":0.00068818784,"threshold_uncertainty_score":0.007587254},"labels":[],"label_agreement":null},{"id":"W2056744232","doi":"10.1061/(asce)0887-3801(2007)21:3(151)","title":"Methods and Object-Oriented Software for FE Reliability and Sensitivity Analysis with Application to a Bridge Structure","year":2007,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Norges Forskningsråd; National Science Foundation","keywords":"Maintainability; OpenSees; Software; Finite element method; Computer science; Reliability engineering; Reliability (semiconductor); Object-oriented programming; Sensitivity (control systems); Software sizing; Extensibility; Software reliability testing; Software quality; Software development; Software construction; Engineering; Structural engineering; Programming language","score_opus":0.02064872989765273,"score_gpt":0.3389648574368721,"score_spread":0.3183161275392194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056744232","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016939113,0.000071964176,0.9951243,0.000032793403,0.000023162573,0.000037571263,0.00008918508,0.0033432958,0.0011083005],"genre_scores_gemma":[0.006294335,0.00025851044,0.9894974,0.00005674336,0.00003746418,0.00046746802,0.00028554388,0.0013238979,0.0017786389],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858814,0.0004038922,0.00018285713,0.00014544146,0.0006211228,0.00005851911],"domain_scores_gemma":[0.9962269,0.0026370408,0.00024317815,0.00043872662,0.0003995497,0.000054628086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029420252,0.0018976678,0.00096972374,0.0024540047,0.0007489973,0.0013785639,0.002525272,0.0013416467,0.019394588],"category_scores_gemma":[0.0076140515,0.0012204947,0.001848573,0.0019936555,0.0010191338,0.001314347,0.0015832047,0.0025624218,0.007481749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009431293,0.00022862057,0.00088363024,0.0016977038,0.000321226,0.00045335482,0.00054399314,0.15630814,0.011513737,0.30087453,0.030163324,0.4969175],"study_design_scores_gemma":[0.00014130441,0.00008426815,0.0005847356,0.00039766871,0.00008325122,0.00068742875,0.00007355574,0.605605,0.009350199,0.17465444,0.20823807,0.00010011377],"about_ca_topic_score_codex":0.001408733,"about_ca_topic_score_gemma":0.0017590609,"teacher_disagreement_score":0.019394588,"about_ca_system_score_codex":0.00049100595,"about_ca_system_score_gemma":0.001058393,"threshold_uncertainty_score":0.064881325},"labels":[],"label_agreement":null},{"id":"W2059186684","doi":"10.1016/j.spl.2007.11.023","title":"Nonparametric confidence intervals for quantile intervals and quantile differences based on record statistics","year":2008,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Quantile; CDF-based nonparametric confidence interval; Confidence interval; Statistics; Mathematics; Nonparametric statistics; Robust confidence intervals; Confidence distribution; Coverage probability; Quantile function; Confidence and prediction bands; Econometrics; Cumulative distribution function; Probability density function","score_opus":0.13032496200195015,"score_gpt":0.3404191639539342,"score_spread":0.21009420195198403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059186684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063980636,0.00049691834,0.99155575,0.00009655179,0.00004663234,0.000023569799,0.00018023906,0.00022955541,0.0009727115],"genre_scores_gemma":[0.5539134,0.0019682997,0.43708754,0.0002638863,0.00056819065,0.00050600374,0.0016281652,0.00048753552,0.0035769832],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98558944,0.006228703,0.00087720045,0.0023475997,0.0043425504,0.00061443436],"domain_scores_gemma":[0.7926657,0.16903986,0.012385034,0.016912196,0.007948867,0.0010483387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027924815,0.0010850794,0.0022558265,0.0040971832,0.0005769108,0.004520726,0.004913951,0.0031145776,0.0048185894],"category_scores_gemma":[0.2342561,0.00071065885,0.0017917184,0.004558465,0.0034741755,0.0071796654,0.0030088068,0.0040369844,0.00083614566],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005753021,0.0000637872,0.004675126,0.00041520683,0.00024100512,0.00017084868,0.000466438,0.14079484,0.0015775061,0.7452746,0.0022296445,0.10351572],"study_design_scores_gemma":[0.000078827514,0.00017837017,0.0042728917,0.0001678995,0.00014535202,0.00031467216,0.00009931053,0.56837803,0.0025187319,0.41961235,0.004120439,0.00011315646],"about_ca_topic_score_codex":0.0011998615,"about_ca_topic_score_gemma":0.0005349778,"teacher_disagreement_score":0.027924815,"about_ca_system_score_codex":0.0013318533,"about_ca_system_score_gemma":0.0010690535,"threshold_uncertainty_score":0.14768231},"labels":[],"label_agreement":null},{"id":"W2059186931","doi":"10.1016/j.jspi.2010.09.017","title":"Assessing large sample bias in misspecified model scenarios with reference to exposure model misspecification in errors-in-variable regression: A new computational approach","year":2010,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Statistics; Mathematics; Logistic regression; Regression analysis; Monte Carlo method; Variable (mathematics); Regression; Errors-in-variables models; Sample size determination; Regression dilution; Observational error; Sample (material); Econometrics; Polynomial regression","score_opus":0.25768704082430427,"score_gpt":0.4031014321717543,"score_spread":0.14541439134745004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059186931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04569197,0.00017477282,0.95272434,0.00044252368,0.000035273864,0.00004566767,0.00004736895,0.00010944766,0.0007286616],"genre_scores_gemma":[0.73922807,0.00031463645,0.2585823,0.00026938424,0.00012622042,0.00023490551,0.00016123848,0.00012414024,0.0009590669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98857903,0.008942726,0.0004177641,0.0007303195,0.0010906819,0.00023945625],"domain_scores_gemma":[0.7129441,0.273684,0.0049272035,0.0057952655,0.0021885268,0.00046089586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033417545,0.0008690407,0.0020551202,0.0017441307,0.000515531,0.0022809652,0.0026887767,0.0021492243,0.0019505955],"category_scores_gemma":[0.18740043,0.0009354085,0.0016809057,0.0013600314,0.0019814954,0.0032457104,0.0030468933,0.0021752496,0.000103270766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019193953,0.00008654305,0.0077236975,0.00010695806,0.0003076219,0.00024473094,0.00018680326,0.87769467,0.0005404775,0.08071376,0.00050406175,0.031698734],"study_design_scores_gemma":[0.000023864795,0.000042810057,0.00078238867,0.000011038573,0.00004812213,0.000048721242,0.000024187913,0.9595252,0.00026516413,0.039075613,0.00014160833,0.000011265278],"about_ca_topic_score_codex":0.0030770793,"about_ca_topic_score_gemma":0.0027522787,"teacher_disagreement_score":0.033417545,"about_ca_system_score_codex":0.0010672996,"about_ca_system_score_gemma":0.002182842,"threshold_uncertainty_score":0.17673093},"labels":[],"label_agreement":null},{"id":"W2060860046","doi":"10.1520/jai19053","title":"Fuzzy Probabilistic Assessment of Aging Aircraft Structures Subjected to Multiple Site Fatigue Damage","year":2004,"lang":"en","type":"article","venue":"Journal of ASTM International","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Martec (Canada)","funders":"","keywords":"Probabilistic logic; Fuzzy logic; Structural engineering; Materials science; Reliability engineering; Forensic engineering; Computer science; Artificial intelligence; Engineering","score_opus":0.060627435067126735,"score_gpt":0.3707103937024748,"score_spread":0.31008295863534807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060860046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19003399,0.00017553334,0.8080779,0.00008658667,0.00000720308,0.000037936377,0.000038166956,0.0001246218,0.0014180116],"genre_scores_gemma":[0.97016555,0.00006767525,0.029292028,0.000007981348,0.0000055605824,0.000023653072,0.000026102933,0.0000073559395,0.00040423134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951816,0.00014933421,0.00002162277,0.000069202106,0.00020834286,0.00003338197],"domain_scores_gemma":[0.9989932,0.0006209233,0.00013765202,0.00004281824,0.00017149017,0.000033886394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013917092,0.0005570786,0.00052198296,0.0011033593,0.00030409507,0.000675695,0.0006170371,0.00073035026,0.0006796625],"category_scores_gemma":[0.0029057069,0.00030164645,0.00059308176,0.00035944025,0.0005463715,0.00056001806,0.00048879505,0.00033252372,0.00007562213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045896617,0.000012820061,0.00053421233,0.000023851146,0.000014239385,0.00008767709,0.00003663475,0.9779465,0.0058589354,0.0023599244,0.00006037238,0.013018895],"study_design_scores_gemma":[0.0000018865921,0.000025283982,0.00029134713,0.000002777274,0.0000051625107,0.000016296224,0.000007260553,0.9972525,0.001013803,0.0013238506,0.000054610427,0.0000051504967],"about_ca_topic_score_codex":0.002838254,"about_ca_topic_score_gemma":0.0018553088,"teacher_disagreement_score":0.002838254,"about_ca_system_score_codex":0.0007111768,"about_ca_system_score_gemma":0.0003922098,"threshold_uncertainty_score":0.0073601604},"labels":[],"label_agreement":null},{"id":"W2061169144","doi":"10.1007/s00158-007-0200-9","title":"Aero-structural optimization using adjoint coupled post-optimality sensitivities","year":2007,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sensitivity (control systems); Aerodynamics; Multidisciplinary design optimization; Mathematical optimization; Solver; Subspace topology; Computer science; Engineering design process; Aeroelasticity; Optimization problem; Mathematics; Multidisciplinary approach; Engineering; Aerospace engineering","score_opus":0.05318500039116179,"score_gpt":0.3390740008225449,"score_spread":0.2858890004313831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061169144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019389879,0.0000820684,0.9739102,0.000121289,0.000055950204,0.000037563546,0.000045408182,0.00018309396,0.006174591],"genre_scores_gemma":[0.8296044,0.00014584661,0.16416018,0.00011602284,0.00005471241,0.00017733821,0.000110055946,0.00022288339,0.005408585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992962,0.00026867498,0.00003205342,0.00007474452,0.0002623846,0.00006590747],"domain_scores_gemma":[0.9986525,0.00084230164,0.00011724825,0.00011267796,0.00022643102,0.000048808382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001821347,0.0010118531,0.0013729093,0.0008814327,0.00061484013,0.0017209395,0.00084493216,0.0017152971,0.0030954988],"category_scores_gemma":[0.0049571926,0.001392945,0.0011189241,0.00069419364,0.0012442097,0.001910085,0.0023375088,0.0013629133,0.00036210247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001640093,0.000013168568,0.000088289984,0.000014339621,0.000013337423,0.000012190531,0.000009709245,0.99046785,0.0006322013,0.005752755,0.000071838076,0.0029078084],"study_design_scores_gemma":[0.000003932343,0.0000084969415,0.0000439724,0.000002593799,0.000003624967,0.0000031241957,0.0000019471302,0.99656963,0.00043392004,0.0028184613,0.00010679665,0.0000035025957],"about_ca_topic_score_codex":0.0025752576,"about_ca_topic_score_gemma":0.0028365636,"teacher_disagreement_score":0.0030954988,"about_ca_system_score_codex":0.0007677056,"about_ca_system_score_gemma":0.0012463458,"threshold_uncertainty_score":0.010355532},"labels":[],"label_agreement":null},{"id":"W2061640659","doi":"10.1177/0731684405045017","title":"Buckling of Composite Beam-columns with Stochastic Properties","year":2005,"lang":"en","type":"article","venue":"Journal of Reinforced Plastics and Composites","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Center for Theoretical Sciences","keywords":"Buckling; Structural engineering; Flange; Materials science; Context (archaeology); Beam (structure); Composite number; Probabilistic analysis of algorithms; Composite material; Probabilistic logic; Engineering; Computer science","score_opus":0.033131889456801615,"score_gpt":0.24912993261863092,"score_spread":0.2159980431618293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061640659","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81261945,0.00019036232,0.18386595,0.00005062035,0.000009336521,0.000019301584,0.00008668132,0.000119070835,0.003039215],"genre_scores_gemma":[0.9944602,0.00008411411,0.0040995236,0.000012980641,0.000005344474,0.000012850719,0.000058103116,0.000012276985,0.00125466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998066,0.000036699494,0.000005657815,0.000029631718,0.00009736811,0.000024097584],"domain_scores_gemma":[0.9996443,0.00015410827,0.00009947882,0.000022261976,0.000058259637,0.00002154006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026521564,0.0002562616,0.000229856,0.00039519073,0.00013152287,0.0003051746,0.0001982802,0.00021355906,0.00046984633],"category_scores_gemma":[0.00053010945,0.00021611915,0.00030756093,0.00017457879,0.00039595904,0.00022701675,0.00024544692,0.00019986478,0.00009267079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081801416,0.000033503173,0.0025644328,0.000046798425,0.000037905098,0.0002192981,0.0000385965,0.89409316,0.092933536,0.004233056,0.00010210453,0.0056158807],"study_design_scores_gemma":[0.0000012090204,0.000021279837,0.0014279648,0.0000011728347,0.0000022857241,0.000020547499,0.0000046929003,0.99483216,0.0031490275,0.00049272797,0.00004262985,0.0000042312245],"about_ca_topic_score_codex":0.0020124584,"about_ca_topic_score_gemma":0.0024880157,"teacher_disagreement_score":0.0020124584,"about_ca_system_score_codex":0.00037006685,"about_ca_system_score_gemma":0.00026561646,"threshold_uncertainty_score":0.0040014386},"labels":[],"label_agreement":null},{"id":"W2061807375","doi":"10.1139/t07-061","title":"Reliability approach for the side resistance of piles by means of the total stress analysis (α Method)","year":2007,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pile; Reliability (semiconductor); Geotechnical engineering; Stress (linguistics); Shear strength (soil); Clay soil; Resistance Factors; Soil water; Structural engineering; Engineering; Geology; Soil science","score_opus":0.0405240234410911,"score_gpt":0.30834265262734095,"score_spread":0.2678186291862499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061807375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009447186,0.00030497572,0.98926675,0.00003155939,0.000012865012,0.000016364866,0.00001802561,0.0000731761,0.0008291334],"genre_scores_gemma":[0.8016895,0.0009884086,0.19412893,0.0000363223,0.00010370219,0.00015234675,0.00010530975,0.00007926109,0.0027162852],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915814,0.00028666828,0.00003974967,0.0001250231,0.00034450294,0.00004593153],"domain_scores_gemma":[0.998256,0.0008393472,0.00024437465,0.00015195941,0.00047139855,0.00003680828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013690556,0.0009477395,0.00055586075,0.0021957753,0.0002605603,0.00053429854,0.00075846544,0.00043777924,0.001169528],"category_scores_gemma":[0.0036098936,0.0002877706,0.00094551546,0.00043906606,0.00055244286,0.00062184694,0.00043831274,0.00069006125,0.0003776885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110775305,0.00005977407,0.005178237,0.00040609535,0.00019037457,0.00026911972,0.00038373919,0.72333956,0.037065346,0.059329357,0.0013194012,0.17234822],"study_design_scores_gemma":[0.0000049262762,0.00009895833,0.0014021778,0.0000288379,0.000033236687,0.00017640657,0.000026206471,0.97998065,0.0037156956,0.013249908,0.0012585118,0.000024450004],"about_ca_topic_score_codex":0.0015404277,"about_ca_topic_score_gemma":0.0008181292,"teacher_disagreement_score":0.0021957753,"about_ca_system_score_codex":0.0003719814,"about_ca_system_score_gemma":0.00054266525,"threshold_uncertainty_score":0.007240355},"labels":[],"label_agreement":null},{"id":"W2062186398","doi":"10.2514/1.11485","title":"Development of a Fuzzy Probabilistic Methodology for Multiple-Site Fatigue Damage","year":2004,"lang":"en","type":"article","venue":"Journal of Aircraft","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Martec (Canada)","funders":"","keywords":"Probabilistic logic; Fuzzy logic; Structural engineering; Computer science; Engineering; Reliability engineering; Artificial intelligence","score_opus":0.25949212954547607,"score_gpt":0.3945729695774596,"score_spread":0.1350808400319835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062186398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003420161,0.000031776504,0.9992675,0.000013948481,0.000003924213,0.000016323453,0.000006458209,0.000018607829,0.00029948688],"genre_scores_gemma":[0.0777981,0.00028779428,0.9200083,0.000036125333,0.000041381496,0.0003711669,0.00005481232,0.0000405207,0.0013617569],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922705,0.0002201065,0.00004631546,0.00014063969,0.00032073812,0.000045129702],"domain_scores_gemma":[0.9989411,0.0005692562,0.00012774918,0.000072865485,0.00025547,0.000033588596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021437765,0.0010355834,0.0010140283,0.00159239,0.0006586871,0.0010849481,0.002200244,0.0012322459,0.0030482912],"category_scores_gemma":[0.0031102838,0.00059682946,0.0015852412,0.0010942406,0.00095511886,0.0013131071,0.0012408332,0.0013064444,0.0006118116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012915498,0.00004169512,0.00031731607,0.00018123783,0.000051015282,0.00015441274,0.00012816131,0.77855533,0.005836603,0.13836288,0.0005607773,0.07579767],"study_design_scores_gemma":[0.0000037755901,0.000035008976,0.000067172026,0.000020340321,0.0000104790115,0.000057792477,0.000012476706,0.9709557,0.0006697392,0.026568098,0.0015855237,0.000013819729],"about_ca_topic_score_codex":0.0029278093,"about_ca_topic_score_gemma":0.0026506481,"teacher_disagreement_score":0.0030482912,"about_ca_system_score_codex":0.0010875025,"about_ca_system_score_gemma":0.0016744942,"threshold_uncertainty_score":0.011337459},"labels":[],"label_agreement":null},{"id":"W2062606777","doi":"10.1115/1.1876493","title":"A Convex Approach Solving Simultaneous Mechanical Structure and Control System Design Problems With Multiple Closed-loop Performance Specifications","year":2004,"lang":"en","type":"article","venue":"Journal of Dynamic Systems Measurement and Control","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Convex optimization; Controller (irrigation); Control theory (sociology); Set (abstract data type); Loop (graph theory); Convex combination; Matrix (chemical analysis); Mathematical optimization; Computer science; Regular polygon; Mathematics; Control (management)","score_opus":0.05624435453098967,"score_gpt":0.22736897853975097,"score_spread":0.1711246240087613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062606777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003989985,0.000023475995,0.9949616,0.00002914936,0.0000049667287,0.000027452057,0.000007466538,0.00004641054,0.00090950954],"genre_scores_gemma":[0.42496857,0.00012914513,0.5708162,0.0000951956,0.000041067316,0.000441346,0.00015407127,0.000110362496,0.0032439665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976471,0.00076788734,0.00011056464,0.0003238195,0.0009715257,0.00017907997],"domain_scores_gemma":[0.99769235,0.0013214861,0.0001823735,0.0001639904,0.0005778582,0.00006197471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029691898,0.0018404435,0.0013768347,0.0006755299,0.00053596235,0.0012389543,0.0013491471,0.0013011124,0.0029301278],"category_scores_gemma":[0.0044571497,0.0010926551,0.0014258655,0.00058904174,0.0011429747,0.0013142688,0.001734592,0.0017159178,0.00038835345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073021074,0.000062143336,0.00037026053,0.00014620973,0.00006083285,0.00011424536,0.00011253842,0.9277187,0.0070859212,0.022370143,0.00045125472,0.041434802],"study_design_scores_gemma":[0.000015958698,0.000085758315,0.00007416351,0.000007361918,0.000010381215,0.000017986638,0.000017414204,0.9939995,0.0021396421,0.0031162768,0.00050863845,0.000006803378],"about_ca_topic_score_codex":0.0034100667,"about_ca_topic_score_gemma":0.0025761714,"teacher_disagreement_score":0.0034100667,"about_ca_system_score_codex":0.0008863769,"about_ca_system_score_gemma":0.002193832,"threshold_uncertainty_score":0.015702784},"labels":[],"label_agreement":null},{"id":"W2062754865","doi":"10.1007/s00158-003-0291-x","title":"Stable relaxations of stochastic stress-constrained weight minimization problems","year":2003,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Vetenskapsrådet","keywords":"Mathematics; Mathematical optimization; Relaxation (psychology); A priori and a posteriori; Constraint (computer-aided design); Applied mathematics","score_opus":0.033331874138699244,"score_gpt":0.28818891473729585,"score_spread":0.2548570405985966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062754865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04072195,0.0003216619,0.9511475,0.0005587263,0.00005772517,0.00004875269,0.00010985153,0.000054984066,0.006978893],"genre_scores_gemma":[0.8080186,0.0010129078,0.17627595,0.0003715042,0.00024855274,0.00048437808,0.00063336844,0.00028420586,0.012670438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991573,0.0003997833,0.00003883661,0.000113995724,0.00019887408,0.00009123085],"domain_scores_gemma":[0.9976447,0.0015545218,0.00029399546,0.00010141798,0.00027868027,0.00012665176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026304575,0.0010277114,0.0010539447,0.0010437707,0.0004705334,0.0015011502,0.0012769324,0.0016284395,0.0032104175],"category_scores_gemma":[0.008667509,0.0009005878,0.0007385418,0.0009767602,0.0012510975,0.001490275,0.001750306,0.0013426853,0.00027699617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000664606,0.000046105946,0.00023062604,0.00008384582,0.000041067473,0.00004842176,0.00008092632,0.86237544,0.0013530256,0.121092826,0.0012176615,0.013363661],"study_design_scores_gemma":[0.000011220335,0.000024545521,0.000068058514,0.000011616675,0.0000050611116,0.00000933208,0.000013869861,0.95968384,0.00019091195,0.039533157,0.00044357445,0.000004752194],"about_ca_topic_score_codex":0.002045979,"about_ca_topic_score_gemma":0.001996651,"teacher_disagreement_score":0.0032104175,"about_ca_system_score_codex":0.00091828767,"about_ca_system_score_gemma":0.0011349926,"threshold_uncertainty_score":0.013911366},"labels":[],"label_agreement":null},{"id":"W2062756108","doi":"10.1016/s0266-8920(03)00030-4","title":"On finite element analysis of beams with random material properties","year":2003,"lang":"en","type":"article","venue":"Probabilistic Engineering Mechanics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Finite element method; Stiffness matrix; Mathematics; Beam (structure); Mathematical analysis; Random variable; Random function; Stiffness; Matrix (chemical analysis); Taylor series; Material properties; Probability density function; Boundary value problem; Structural engineering; Physics; Materials science; Engineering","score_opus":0.03719129322193755,"score_gpt":0.24431151407309645,"score_spread":0.2071202208511589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062756108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043644677,0.00042422084,0.99275136,0.00018621239,0.0000449383,0.000023252778,0.00002716418,0.00006878344,0.0021095672],"genre_scores_gemma":[0.32806578,0.0043748002,0.65167344,0.0006053136,0.00046125692,0.0006233192,0.00049897935,0.0004912729,0.01320586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894196,0.00046602736,0.000050742714,0.00008987136,0.00040004682,0.00005141474],"domain_scores_gemma":[0.99511874,0.0040747793,0.00018228301,0.00026199236,0.00029395107,0.0000682562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026388967,0.0011193297,0.0018431102,0.0015695647,0.00048416283,0.0010059462,0.0020086886,0.0026269197,0.002837957],"category_scores_gemma":[0.008875825,0.0011759105,0.0013302035,0.001095836,0.0022269937,0.0019352474,0.0018617867,0.0015163594,0.00069327437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051309104,0.00007582827,0.00024703078,0.00015368799,0.00005844029,0.000051305087,0.00009178933,0.87901473,0.0034325689,0.089456916,0.0010151648,0.026351295],"study_design_scores_gemma":[0.000005122134,0.00000891909,0.00006039699,0.000013830542,0.000005533464,0.000009106286,0.000005829962,0.9668937,0.0003999083,0.03173139,0.00085980474,0.0000064102196],"about_ca_topic_score_codex":0.0018443748,"about_ca_topic_score_gemma":0.0013271195,"teacher_disagreement_score":0.002837957,"about_ca_system_score_codex":0.000599154,"about_ca_system_score_gemma":0.0005654449,"threshold_uncertainty_score":0.013955951},"labels":[],"label_agreement":null},{"id":"W2062771488","doi":"10.1115/1.4007288","title":"Comparison of Elastic and Plastic Reference Volume Approaches","year":2012,"lang":"en","type":"article","venue":"Journal of Pressure Vessel Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Volume (thermodynamics); Limit (mathematics); Limit load; Tangent; Mathematics; Finite element method; Nonlinear system; Multiplier (economics); Mathematical analysis; Structural engineering; Geometry; Thermodynamics; Engineering; Physics","score_opus":0.16631095801585094,"score_gpt":0.352191390422918,"score_spread":0.1858804324070671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062771488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010135087,0.0005047604,0.98466575,0.0000535623,0.000022120299,0.000027188511,0.000018504457,0.0002578369,0.0043151393],"genre_scores_gemma":[0.4254513,0.0013117818,0.5648198,0.000085514825,0.0000712421,0.00022747608,0.00018536988,0.00031496683,0.007532589],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985361,0.00037073405,0.00006925138,0.00014766047,0.00081455114,0.00006162153],"domain_scores_gemma":[0.99811554,0.0008684236,0.00026222007,0.00023316598,0.00048503318,0.00003560886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014906916,0.00065308635,0.000465359,0.001827054,0.00025522802,0.0009302124,0.0019338874,0.00069021614,0.0022638135],"category_scores_gemma":[0.005066561,0.00036094757,0.00046028814,0.0007880042,0.0005861117,0.0013958432,0.001232321,0.0005523768,0.0005616741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022511462,0.000062713516,0.0015445498,0.0003259157,0.00005424406,0.00008595071,0.00021463778,0.4085136,0.011760454,0.0597692,0.0011087027,0.516335],"study_design_scores_gemma":[0.000020988991,0.00013649979,0.0010665838,0.000047849444,0.000019460225,0.00012338586,0.000053336455,0.9614555,0.016777646,0.013239909,0.007022833,0.000036030448],"about_ca_topic_score_codex":0.0009049558,"about_ca_topic_score_gemma":0.00091129116,"teacher_disagreement_score":0.0022638135,"about_ca_system_score_codex":0.00040140623,"about_ca_system_score_gemma":0.0006016888,"threshold_uncertainty_score":0.007883668},"labels":[],"label_agreement":null},{"id":"W2062916228","doi":"10.1016/j.ress.2010.08.012","title":"A physics informed emulator for laser-driven radiating shock simulations","year":2011,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Context (archaeology); Shock (circulatory); Physics; Sensitivity (control systems); Computer science; Algorithm; Reduction (mathematics); Mathematics; Geometry","score_opus":0.08082633138963197,"score_gpt":0.2962522364865006,"score_spread":0.21542590509686865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062916228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04562563,0.00011638314,0.9192488,0.00021058052,0.000118511656,0.00021214732,0.0007361437,0.0168799,0.01685184],"genre_scores_gemma":[0.6015182,0.0001959734,0.38075396,0.0004334302,0.00006597878,0.0008533814,0.0012316734,0.004945755,0.010001562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972624,0.00007890418,0.000015272632,0.000027537388,0.00011869267,0.00003339586],"domain_scores_gemma":[0.9990514,0.00045818987,0.00007919896,0.00012604703,0.00021693922,0.00006813905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010109941,0.0006935235,0.0007054194,0.00054725807,0.0005523494,0.0009082798,0.0025202853,0.0015760147,0.017373173],"category_scores_gemma":[0.0032889487,0.0005008777,0.00048070372,0.00040965437,0.00038378392,0.00081881246,0.0012849224,0.0012859423,0.00238734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002261868,0.00016477959,0.00086811295,0.00008169092,0.00006846773,0.00013389155,0.00007371971,0.95332575,0.005292774,0.012714427,0.0036212604,0.023428854],"study_design_scores_gemma":[0.000022634404,0.000014307464,0.00004834501,0.000004396915,0.0000043150544,0.000009589595,0.0000044765266,0.9959292,0.0016344157,0.001113508,0.0012096852,0.0000052338596],"about_ca_topic_score_codex":0.0019507897,"about_ca_topic_score_gemma":0.0019068371,"teacher_disagreement_score":0.017373173,"about_ca_system_score_codex":0.0006635127,"about_ca_system_score_gemma":0.0010089735,"threshold_uncertainty_score":0.05811906},"labels":[],"label_agreement":null},{"id":"W2063287639","doi":"10.1115/omae2004-51430","title":"Optimal Design and Portfolio Risk Management for Groups of Structures","year":2004,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Portfolio; Optimal design; Parametric statistics; Dependency (UML); Risk management; Mathematical optimization; Naval architecture; Reliability engineering; Risk analysis (engineering); Engineering; Mathematics; Artificial intelligence; Machine learning; Statistics; Economics","score_opus":0.05346384093401048,"score_gpt":0.30636701715875975,"score_spread":0.2529031762247493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063287639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043162435,0.0005749004,0.95261115,0.0001937911,0.0000250933,0.00008215145,0.000042970067,0.00005298424,0.0032545384],"genre_scores_gemma":[0.79495126,0.0008145859,0.19953743,0.00008094636,0.00007371937,0.00043673243,0.00015864163,0.000058698584,0.0038879476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974251,0.0012408283,0.00009606716,0.00040697437,0.0005448199,0.00028627066],"domain_scores_gemma":[0.99783665,0.001222249,0.00040072695,0.00015339204,0.0002302204,0.0001568521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036082696,0.0015051399,0.0017470222,0.0012441605,0.0003983437,0.0015169652,0.0011349801,0.0016425819,0.0021175928],"category_scores_gemma":[0.00558523,0.00093146693,0.0012929561,0.000828687,0.0014714572,0.0013518906,0.0017293107,0.0011056382,0.00032207058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039443996,0.00002432497,0.0003730615,0.00003399925,0.000053027812,0.00004138156,0.000030476498,0.97503984,0.0005706208,0.011622128,0.00015483236,0.012016755],"study_design_scores_gemma":[0.000025005585,0.00017519815,0.00023647927,0.000013554623,0.00003097372,0.00003111212,0.000022546197,0.9695637,0.00042015765,0.028659139,0.0008118904,0.000010243538],"about_ca_topic_score_codex":0.0010218453,"about_ca_topic_score_gemma":0.0007000417,"teacher_disagreement_score":0.0036082696,"about_ca_system_score_codex":0.0011242577,"about_ca_system_score_gemma":0.0011274982,"threshold_uncertainty_score":0.019082606},"labels":[],"label_agreement":null},{"id":"W2063436803","doi":"10.1061/(asce)0733-9445(2004)130:1(138)","title":"Impact of New ACI 318 Flexural Resistance Factor on Bond Failures","year":2003,"lang":"en","type":"article","venue":"Journal of Structural Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Buckland & Taylor (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flexural strength; Structural engineering; Reinforcement; Materials science; Bar (unit); Consistency (knowledge bases); Composite material; Engineering; Mathematics; Physics; Geometry","score_opus":0.05730541394367864,"score_gpt":0.3374473763092884,"score_spread":0.28014196236560973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063436803","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9879127,0.00008684283,0.008469402,0.000025604995,0.0000062294116,0.000017533843,0.00013434827,0.0001875984,0.0031595689],"genre_scores_gemma":[0.99667823,0.000018407243,0.0029050938,0.000006947295,8.752767e-7,0.000013531527,0.000083349456,0.000024282946,0.0002694066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928075,0.00025227573,0.000030412342,0.00011159055,0.00024640025,0.0000786485],"domain_scores_gemma":[0.99659616,0.002026572,0.00039133686,0.00047890833,0.0003968083,0.00011018127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015309484,0.00064523524,0.00042770422,0.0005854507,0.00028248213,0.00051457045,0.00067414425,0.0006438166,0.0014620341],"category_scores_gemma":[0.0047097825,0.0003266394,0.0004504916,0.0003263872,0.00032992807,0.00046632037,0.0003176193,0.00044458144,0.0001994163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006628255,0.00009721894,0.019914972,0.000041430543,0.000051934625,0.0001513567,0.000045276935,0.9510864,0.013729387,0.00080462015,0.00022214494,0.013192466],"study_design_scores_gemma":[0.000037384663,0.00071619166,0.010964098,0.000012723677,0.000051325685,0.00008782603,0.000036237914,0.96857554,0.018577864,0.00031501302,0.00060013455,0.000025559124],"about_ca_topic_score_codex":0.0030784474,"about_ca_topic_score_gemma":0.0030328785,"teacher_disagreement_score":0.0030784474,"about_ca_system_score_codex":0.00088939833,"about_ca_system_score_gemma":0.000349978,"threshold_uncertainty_score":0.008096516},"labels":[],"label_agreement":null},{"id":"W2063449307","doi":"10.1115/fedsm-icnmm2010-30041","title":"Application of Bayesian Inference to the Flutter Margin Method: New Developments","year":2010,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Carleton University","funders":"","keywords":"Flutter; Probability density function; Markov chain Monte Carlo; Monte Carlo method; Control theory (sociology); Margin (machine learning); Mathematics; Computer science; Engineering; Statistics; Aerodynamics; Artificial intelligence; Machine learning","score_opus":0.057217685090783045,"score_gpt":0.3765357071202513,"score_spread":0.31931802202946824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063449307","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007051899,0.0003480906,0.9980154,0.00013766924,0.000020206031,0.000008047958,0.00001197154,0.000050540737,0.0007028255],"genre_scores_gemma":[0.10215138,0.0024913643,0.89105886,0.0002428463,0.00040862514,0.00013358757,0.00010419956,0.0001726883,0.0032364503],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99701786,0.0015106876,0.00012255693,0.00036755003,0.00091812044,0.00006320427],"domain_scores_gemma":[0.9907286,0.0071769776,0.00032292024,0.00075794733,0.00091295206,0.00010069137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071482453,0.0009059458,0.0013605348,0.0014585159,0.0004960229,0.0013316015,0.0016889691,0.0013260178,0.0023739494],"category_scores_gemma":[0.01996831,0.00082429714,0.0011534446,0.0011093768,0.0013727458,0.0021419504,0.0016751854,0.0025103837,0.000784531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010246757,0.00011397968,0.0015968777,0.00028814017,0.0003021047,0.00015644165,0.00030036125,0.3139433,0.003691511,0.2190667,0.0025167556,0.45792136],"study_design_scores_gemma":[0.000016737227,0.000024421177,0.00043349437,0.000051762145,0.000021572834,0.000062444975,0.000012640165,0.92641324,0.00068888394,0.06628506,0.005955559,0.000034152345],"about_ca_topic_score_codex":0.0058354307,"about_ca_topic_score_gemma":0.0053366227,"teacher_disagreement_score":0.0071482453,"about_ca_system_score_codex":0.0006945523,"about_ca_system_score_gemma":0.0014087349,"threshold_uncertainty_score":0.037804008},"labels":[],"label_agreement":null},{"id":"W2063777739","doi":"10.1111/j.1934-6093.2001.tb00066.x","title":"Discrete‐Time Risk‐Sensitive Filters with Non‐Gaussian Initial Conditions and Their Ergodic Properties","year":2001,"lang":"en","type":"article","venue":"Asian Journal of Control","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematics; Gaussian; Control theory (sociology); Applied mathematics; Filter (signal processing); Riccati equation; Stability theory; Covariance; Initialization; Mathematical analysis; Statistics; Computer science; Nonlinear system","score_opus":0.020999885189534665,"score_gpt":0.26196317628069277,"score_spread":0.2409632910911581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063777739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06558794,0.00019267341,0.9307941,0.00018583373,0.000023276241,0.00002898198,0.000035611203,0.0001047816,0.0030467883],"genre_scores_gemma":[0.96259177,0.00031670224,0.03166683,0.00007941543,0.000031216252,0.00010493531,0.00008374618,0.000042460175,0.005082825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99871755,0.00028961862,0.00007036209,0.00026142958,0.00046192863,0.00019915999],"domain_scores_gemma":[0.9912793,0.004762173,0.001459913,0.000531947,0.0017078386,0.0002589209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030334913,0.0008353418,0.0011736847,0.00092670135,0.00064509845,0.0015707988,0.0007855941,0.0013942968,0.0019389245],"category_scores_gemma":[0.015054558,0.00058319565,0.0010773189,0.00048256214,0.002287836,0.0018361764,0.0010772655,0.0013708547,0.0003975787],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010830674,0.000044566485,0.00154084,0.000092286944,0.00009140944,0.00023271271,0.00023290697,0.759762,0.008261799,0.21702224,0.0003622416,0.012248722],"study_design_scores_gemma":[0.00000823502,0.000040637915,0.00031339738,0.00001187896,0.000015307753,0.00003726948,0.000016601629,0.9793549,0.0023941197,0.017543934,0.00024745998,0.0000161799],"about_ca_topic_score_codex":0.0042759767,"about_ca_topic_score_gemma":0.0014863174,"teacher_disagreement_score":0.0042759767,"about_ca_system_score_codex":0.001732835,"about_ca_system_score_gemma":0.0013630878,"threshold_uncertainty_score":0.016042829},"labels":[],"label_agreement":null},{"id":"W2063927119","doi":"10.1023/b:opte.0000013632.20417.13","title":"Simultaneous Solution Strategies for Inclusion of Input Saturation in the Optimal Design of Dynamically Operable Plants","year":2004,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Mathematical optimization; Bilinear interpolation; Saturation (graph theory); Computer science; Complementarity (molecular biology); Optimization problem; Mathematics","score_opus":0.03238951122103703,"score_gpt":0.27441901317422435,"score_spread":0.24202950195318731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063927119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03221655,0.000143365,0.9624395,0.0001106325,0.0000250192,0.000055330995,0.000016132257,0.00011810826,0.004875287],"genre_scores_gemma":[0.94537616,0.00013777275,0.052069385,0.000060656865,0.00002189791,0.00020424406,0.000022109261,0.000044850334,0.0020628911],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994236,0.00021038599,0.0000339292,0.000079503734,0.00016511211,0.00008745687],"domain_scores_gemma":[0.99855846,0.0009759333,0.00015572697,0.000058017482,0.0002012355,0.000050597373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001492143,0.0014795003,0.0010214113,0.0005508694,0.0005175568,0.0010956033,0.00105671,0.0010859122,0.0020527293],"category_scores_gemma":[0.0035408037,0.0011261936,0.0006712407,0.0005012259,0.0009119983,0.0014612991,0.0020294935,0.0010652661,0.00029413207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023865975,0.00010623966,0.00023978882,0.00021972238,0.00007828647,0.00015019561,0.0002402386,0.9021296,0.012627882,0.03936929,0.0004565219,0.044143558],"study_design_scores_gemma":[0.000034509707,0.00017220002,0.00006979674,0.0000138713085,0.000022038595,0.000017494822,0.000024143566,0.9869266,0.002567555,0.009678002,0.00046508928,0.0000086323225],"about_ca_topic_score_codex":0.0012529775,"about_ca_topic_score_gemma":0.0017514962,"teacher_disagreement_score":0.0020527293,"about_ca_system_score_codex":0.0004969195,"about_ca_system_score_gemma":0.00077843346,"threshold_uncertainty_score":0.007891297},"labels":[],"label_agreement":null},{"id":"W2064274170","doi":"10.1111/j.1539-6924.2009.01352.x","title":"The Development of Posterior Probability Models in Risk-Based Integrity Modeling","year":2010,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Posterior probability; Markov chain Monte Carlo; Algorithm; Prior probability; Laplace transform; Markov chain; Computer science; Reliability (semiconductor); Conjugate prior; Bayesian probability; Mathematical optimization; Laplace's method; Importance sampling; Monte Carlo method; Applied mathematics; Mathematics; Statistics; Machine learning; Artificial intelligence","score_opus":0.08800774746663248,"score_gpt":0.3263872329337209,"score_spread":0.23837948546708843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064274170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010792611,0.00008988196,0.998355,0.000036371956,0.0000057885045,0.000010203759,0.000012890865,0.00005001621,0.00036064704],"genre_scores_gemma":[0.33016205,0.001711331,0.6612929,0.00014630942,0.00017731203,0.0004975391,0.00048748002,0.0002752734,0.005249814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973296,0.0011169916,0.00012350304,0.00043681596,0.0008647608,0.00012825249],"domain_scores_gemma":[0.99278814,0.0054911654,0.0005006245,0.00030645708,0.0008128738,0.00010085605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005272536,0.0013069784,0.0015815067,0.001738218,0.00061287166,0.0018938682,0.002294295,0.0016044604,0.0020131099],"category_scores_gemma":[0.015323728,0.0012928052,0.001525474,0.0012413705,0.0017429861,0.0032094258,0.0017369053,0.0028780263,0.0005327193],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001486542,0.000014458451,0.0003138972,0.000042401833,0.000021967591,0.000038200447,0.00005810109,0.93914473,0.00049343304,0.04463967,0.00024896782,0.0149693],"study_design_scores_gemma":[0.0000015212454,0.0000063318525,0.000040801297,0.000006188857,0.000004592497,0.00001085059,0.000002671937,0.9902362,0.0002331103,0.009156496,0.0002960943,0.0000051653797],"about_ca_topic_score_codex":0.0049707703,"about_ca_topic_score_gemma":0.0029255687,"teacher_disagreement_score":0.005272536,"about_ca_system_score_codex":0.0013517336,"about_ca_system_score_gemma":0.0014784907,"threshold_uncertainty_score":0.027884185},"labels":[],"label_agreement":null},{"id":"W2064559660","doi":"10.1139/t05-044","title":"Reliability of bored pile foundations considering bias in failure criteria","year":2005,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Pile; Reliability engineering; Failure mode and effects analysis; Failure causes; Resistance Factors; Structural engineering; Geotechnical engineering; Engineering","score_opus":0.10481874543457707,"score_gpt":0.33760848244189656,"score_spread":0.23278973700731947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064559660","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9756114,0.00008532575,0.0237521,0.000015257283,0.0000022641862,0.0000066213056,0.00006314501,0.000083145234,0.00038082586],"genre_scores_gemma":[0.99865746,0.000022930704,0.0011812228,0.0000014297669,8.996477e-7,0.0000034341467,0.000056146782,0.0000046184705,0.00007173364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921024,0.00020168281,0.000036437224,0.00010801163,0.0003613407,0.00008223825],"domain_scores_gemma":[0.99619424,0.0020808883,0.000514821,0.00032343983,0.0008233852,0.00006334019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016247907,0.00032000008,0.00039431933,0.0008096629,0.00016251001,0.00031863837,0.00038902767,0.000395893,0.0004314756],"category_scores_gemma":[0.008558161,0.00023290701,0.0003470919,0.0004915646,0.00053504127,0.00048356756,0.0003425179,0.00020612156,0.000116448246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004696274,0.000031850108,0.040750522,0.00008165898,0.000055830053,0.0003602201,0.00011780299,0.9071834,0.028899327,0.0008374649,0.0001746255,0.021037728],"study_design_scores_gemma":[0.000026399006,0.0005280577,0.07382512,0.000021712269,0.00008318299,0.0004450247,0.00008285932,0.8992461,0.023180034,0.0021329925,0.00035856402,0.00006999194],"about_ca_topic_score_codex":0.004438613,"about_ca_topic_score_gemma":0.0033101707,"teacher_disagreement_score":0.004438613,"about_ca_system_score_codex":0.0005825432,"about_ca_system_score_gemma":0.00041488724,"threshold_uncertainty_score":0.008825541},"labels":[],"label_agreement":null},{"id":"W2064684680","doi":"10.1186/1754-0410-3-2","title":"Universal nonlinear filtering using Feynman path integrals II: the continuous-continuous model with additive noise","year":2009,"lang":"en","type":"article","venue":"PMC Physics A","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Path integral formulation; Feynman diagram; Mathematics; Functional integration; Corollary; Applied mathematics; Path (computing); Noise (video); Gaussian noise; Computation; Gaussian; Mathematical analysis; Nonlinear system; Integral equation; Algorithm; Pure mathematics; Mathematical physics; Computer science; Physics; Quantum mechanics; Artificial intelligence","score_opus":0.05762377472086837,"score_gpt":0.29360703916489894,"score_spread":0.23598326444403056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064684680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02472256,0.0004708092,0.9719193,0.00026912589,0.0000369563,0.000009594052,0.000028057604,0.0000819755,0.0024616043],"genre_scores_gemma":[0.9236362,0.001035789,0.06790453,0.00016213083,0.00014484568,0.000047323858,0.00009364219,0.000110844565,0.0068646953],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994947,0.00014340355,0.00001881691,0.00009516709,0.00017545455,0.00007243066],"domain_scores_gemma":[0.9987702,0.0007130043,0.0001709738,0.00014258079,0.00012519941,0.00007799777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014657358,0.00074516603,0.001160989,0.00078801054,0.00043155314,0.0017908809,0.0011809376,0.001579998,0.0013014713],"category_scores_gemma":[0.004305835,0.0005049038,0.000846081,0.00071497995,0.0020361692,0.0032351464,0.0013091572,0.001454279,0.00020194161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004513101,0.000028987493,0.00044125953,0.00006968947,0.000045164506,0.00010709648,0.00010966193,0.2802524,0.0020398644,0.703607,0.0007154669,0.012538097],"study_design_scores_gemma":[0.0000035142814,0.000007315874,0.000117765114,0.000007052617,0.000006473932,0.000027430358,0.0000075823173,0.84034544,0.00028110418,0.1589185,0.00026576186,0.000012094043],"about_ca_topic_score_codex":0.003161636,"about_ca_topic_score_gemma":0.001609086,"teacher_disagreement_score":0.003161636,"about_ca_system_score_codex":0.0010562943,"about_ca_system_score_gemma":0.0009553032,"threshold_uncertainty_score":0.0077516437},"labels":[],"label_agreement":null},{"id":"W2064759387","doi":"10.1115/1.2337311","title":"Failure Surface Frontier for Reliability Assessment on Expensive Performance Function","year":2006,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Reliability (semiconductor); Discriminative model; Limit (mathematics); Surface (topology); Reliability engineering; Sampling (signal processing); Limit state design; Quadratic equation; Mathematical optimization; Point (geometry); Function (biology); Computer science; Set (abstract data type); Mathematics; Algorithm; Engineering; Machine learning; Structural engineering; Power (physics)","score_opus":0.07065850073236699,"score_gpt":0.3206035804123795,"score_spread":0.24994507968001253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064759387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018686712,0.00027010427,0.9796674,0.00006537359,0.000007493336,0.000025831465,0.000056279932,0.00020393245,0.0010167903],"genre_scores_gemma":[0.76048124,0.00054205983,0.23643011,0.00006041958,0.000053085278,0.00027498536,0.0005490112,0.00012565672,0.0014834256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985746,0.0005103856,0.000058819383,0.00020354526,0.0005347834,0.00011790041],"domain_scores_gemma":[0.9955213,0.00315935,0.00028675012,0.0003401383,0.0005858456,0.0001065875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029228297,0.0012544857,0.0017210526,0.0030068264,0.00043891853,0.0012797957,0.0011384398,0.00095351035,0.001874503],"category_scores_gemma":[0.011479982,0.000359331,0.0009646143,0.0012182031,0.0013596151,0.0023379447,0.0014089275,0.0012570358,0.00045463935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009936338,0.000033943015,0.0014340642,0.00012248929,0.00003676393,0.000069965165,0.000091684524,0.8870495,0.0024877486,0.037253473,0.00072650297,0.07059445],"study_design_scores_gemma":[0.0000033211259,0.000028790659,0.00032572716,0.000008960064,0.0000042778597,0.0000206255,0.000010932256,0.9861231,0.0005004851,0.012613486,0.0003521044,0.000008220195],"about_ca_topic_score_codex":0.001992375,"about_ca_topic_score_gemma":0.00059276726,"teacher_disagreement_score":0.0030068264,"about_ca_system_score_codex":0.0010797004,"about_ca_system_score_gemma":0.00065616175,"threshold_uncertainty_score":0.01545763},"labels":[],"label_agreement":null},{"id":"W2065326851","doi":"10.1016/j.compstruc.2006.02.014","title":"Shape sensitivities in the reliability analysis of nonlinear frame structures","year":2006,"lang":"en","type":"article","venue":"Computers & Structures","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonlinear system; Reliability (semiconductor); Structural engineering; Frame (networking); Frame analysis; Computer science; Mathematics; Reliability engineering; Engineering; Physics; Psychology; Social psychology; Telecommunications","score_opus":0.03239936015393084,"score_gpt":0.2981321854297049,"score_spread":0.265732825275774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065326851","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17694505,0.0019217269,0.8103669,0.00063045556,0.00006196528,0.000034476212,0.00006019114,0.00019086011,0.0097883],"genre_scores_gemma":[0.97713965,0.0008451983,0.018825756,0.000075420685,0.00006463168,0.000022274357,0.00003320994,0.000072484,0.0029213296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943215,0.00024126898,0.000012859245,0.00004766875,0.0002245137,0.00004153482],"domain_scores_gemma":[0.99724436,0.0019918017,0.0002567154,0.00013923462,0.0003120595,0.000055948116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016653126,0.0005652652,0.00062982703,0.001202835,0.0003792513,0.0009571603,0.00054053863,0.000912226,0.0007818469],"category_scores_gemma":[0.008271719,0.0006375494,0.000563403,0.0006388358,0.0012165606,0.0012825552,0.00088734354,0.0008311285,0.0002019418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004135161,0.000012197499,0.00066287885,0.000038131235,0.000019553021,0.00008106049,0.00007970116,0.9376802,0.0043593612,0.03946046,0.00024340872,0.017321657],"study_design_scores_gemma":[0.0000021950743,0.000016681332,0.00056944427,0.000010338904,0.0000074484597,0.00003811344,0.000014212157,0.9674329,0.0010630648,0.030605415,0.0002281141,0.0000120670165],"about_ca_topic_score_codex":0.001812206,"about_ca_topic_score_gemma":0.0014478327,"teacher_disagreement_score":0.001812206,"about_ca_system_score_codex":0.000755108,"about_ca_system_score_gemma":0.00035658648,"threshold_uncertainty_score":0.008807123},"labels":[],"label_agreement":null},{"id":"W2066180388","doi":"10.1080/01621459.2013.794730","title":"Parameter Estimation of Partial Differential Equation Models","year":2013,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":135,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Western University","funders":"National Institute of Environmental Health Sciences; National Science Foundation; National Institutes of Health; National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; King Abdullah University of Science and Technology","keywords":"Mathematics; Applied mathematics; Estimation; First-order partial differential equation; Partial differential equation; Statistics; Mathematical analysis; Economics","score_opus":0.06108375704515718,"score_gpt":0.32261127117850397,"score_spread":0.2615275141333468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066180388","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057763373,0.00015231436,0.9932595,0.000098798955,0.000010351746,0.000024117564,0.00006614129,0.00014148843,0.00047096738],"genre_scores_gemma":[0.56477225,0.0012589007,0.42908964,0.00015900767,0.000100936,0.00047720366,0.0009414754,0.0002497673,0.0029508586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982589,0.00080302544,0.00011018758,0.0003698942,0.0003650093,0.00009307865],"domain_scores_gemma":[0.9930581,0.005335149,0.0005710562,0.0003864188,0.0005480514,0.00010111928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030147415,0.0010920771,0.0014713829,0.0012165768,0.00050148106,0.0013910844,0.0020718302,0.0017227492,0.0015334067],"category_scores_gemma":[0.016919529,0.00092775305,0.0016180545,0.0011810361,0.0010113693,0.0021865645,0.0018860006,0.0022807992,0.0004009257],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002847309,0.000032584936,0.0018211955,0.00010524717,0.00006477761,0.0000681028,0.0000733282,0.940668,0.0008767658,0.02654181,0.0006890663,0.02903068],"study_design_scores_gemma":[0.0000038801395,0.0000055422806,0.00017178948,0.000008919977,0.0000065599397,0.000013580538,0.0000064415917,0.98772323,0.0002449352,0.011358913,0.00044681263,0.000009424285],"about_ca_topic_score_codex":0.0074901213,"about_ca_topic_score_gemma":0.004173963,"teacher_disagreement_score":0.0074901213,"about_ca_system_score_codex":0.0009401499,"about_ca_system_score_gemma":0.0016290158,"threshold_uncertainty_score":0.015943706},"labels":[],"label_agreement":null},{"id":"W2067781235","doi":"10.1007/s12206-013-0517-9","title":"Minimum weight design of beams against failure under uncertain loading by convex analysis","year":2013,"lang":"en","type":"article","venue":"Journal of Mechanical Science and Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Inyuvesi Yakwazulu-Natali; Ryerson University","keywords":"Minimum weight; Regular polygon; Structural engineering; Mathematics; Convex analysis; Materials science; Mathematical optimization; Convex optimization; Engineering; Geometry","score_opus":0.0348733296879919,"score_gpt":0.28903369037301774,"score_spread":0.25416036068502584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067781235","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01915576,0.00012038783,0.9781511,0.0001736888,0.00002808244,0.00003981705,0.00005196892,0.00009633976,0.0021827912],"genre_scores_gemma":[0.8662164,0.000463554,0.1262322,0.000138917,0.00007330661,0.00044623084,0.00020778993,0.00020738367,0.0060141636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889874,0.0004721454,0.000039073657,0.00014334731,0.00029724182,0.00014944191],"domain_scores_gemma":[0.9977354,0.0012343824,0.00036130767,0.000119972356,0.00042806458,0.00012079045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023837495,0.0021625678,0.0023622506,0.001253002,0.0005101891,0.0013635183,0.0020189427,0.0023544622,0.002361618],"category_scores_gemma":[0.0073246188,0.0014637023,0.0008350266,0.00082619104,0.0016499001,0.0016304556,0.0022387817,0.00128835,0.0004594548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052946398,0.000012794797,0.00009694854,0.000043843567,0.000021267055,0.00002251924,0.000030815136,0.98337513,0.0020532846,0.0067465305,0.00027506045,0.007268808],"study_design_scores_gemma":[0.000007158478,0.000034338093,0.000048033977,0.00000777743,0.000005635895,0.000004680076,0.00000850216,0.9941328,0.0003758765,0.0052186903,0.00015135585,0.000005196468],"about_ca_topic_score_codex":0.0032572914,"about_ca_topic_score_gemma":0.0016110122,"teacher_disagreement_score":0.0032572914,"about_ca_system_score_codex":0.00089104736,"about_ca_system_score_gemma":0.0012626318,"threshold_uncertainty_score":0.012606621},"labels":[],"label_agreement":null},{"id":"W2068998363","doi":"10.1142/s0218539312500015","title":"PROBABILITY-BASED DESIGN OPTIMIZATION OF DYNAMIC SYSTEMS","year":2012,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Probability distribution; Computer science; Probabilistic logic; Mathematical optimization; Random variable; Monte Carlo method; Design matrix; Probabilistic design; Algorithm; Mathematics; Engineering design process; Artificial intelligence; Machine learning; Statistics","score_opus":0.10571695914307264,"score_gpt":0.34788662566368195,"score_spread":0.2421696665206093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068998363","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035471886,0.00020953604,0.9919859,0.00009793653,0.000023357974,0.000037398415,0.000019481897,0.00009826303,0.0039809225],"genre_scores_gemma":[0.6140391,0.0013726854,0.3708246,0.00015562955,0.00009915423,0.00084569136,0.00021228324,0.00023069249,0.012220183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856925,0.0005923657,0.00005735995,0.00023155732,0.0004578325,0.00009159278],"domain_scores_gemma":[0.9983405,0.0010976786,0.00014846429,0.000085178006,0.00028701476,0.00004122531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029511955,0.0011996409,0.0011175829,0.0009725647,0.00050377956,0.0013844814,0.0008359621,0.0008927355,0.0027522368],"category_scores_gemma":[0.004931717,0.0007526575,0.0007725106,0.0007254605,0.0013833717,0.0010492491,0.0012565019,0.0010892422,0.0005033785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011411923,0.000010055381,0.00006590938,0.00003732452,0.000009036945,0.000010482539,0.000012565379,0.9781829,0.00036497315,0.012811408,0.00015496336,0.008329009],"study_design_scores_gemma":[0.0000058910396,0.000025138972,0.00004090405,0.0000064518517,0.0000044094336,0.0000044254,0.000004722311,0.9896097,0.0001972602,0.009389311,0.00070754596,0.0000041933504],"about_ca_topic_score_codex":0.0026265802,"about_ca_topic_score_gemma":0.0017596103,"teacher_disagreement_score":0.0029511955,"about_ca_system_score_codex":0.0014008319,"about_ca_system_score_gemma":0.0017579828,"threshold_uncertainty_score":0.015607595},"labels":[],"label_agreement":null},{"id":"W2069262358","doi":"10.1115/imece2006-13958","title":"Combined Effects of Stress Concentration and Fatigue on the Reliability of Notched Laminates","year":2006,"lang":"en","type":"article","venue":"Design Engineering and Computers and Information in Engineering, Parts A and B","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Composite laminates; Materials science; Composite material; Stiffness; Composite number; Stress concentration; Stress (linguistics); Structural engineering; Aerospace; Reliability (semiconductor); Fatigue limit; Fracture mechanics; Engineering","score_opus":0.013986569856614704,"score_gpt":0.21377442005862868,"score_spread":0.19978785020201398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069262358","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9694091,0.0005821082,0.02926885,0.00004252741,0.0000060613024,0.0000062145173,0.000020907075,0.000036520494,0.000627779],"genre_scores_gemma":[0.9988042,0.00009881962,0.0009936225,0.00000502828,0.000004598544,0.000003255046,0.000007928284,0.000002564934,0.00007991369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933845,0.00022477758,0.00004700275,0.0000880496,0.00021404677,0.00008758947],"domain_scores_gemma":[0.99413234,0.003848421,0.001001879,0.00019988968,0.0007134444,0.00010408147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010184693,0.0003968155,0.00048503492,0.0005867041,0.0001763023,0.00034093778,0.00020413443,0.00049358734,0.000292091],"category_scores_gemma":[0.003912526,0.00029201803,0.00033521108,0.00019202287,0.0005012772,0.00032468032,0.00026880146,0.00023844824,0.00007686391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005922089,0.00009042658,0.016222687,0.00015369894,0.00015778739,0.00060870644,0.00013665797,0.82938737,0.12210414,0.0010172889,0.000105033,0.02942405],"study_design_scores_gemma":[0.000017367993,0.0012161048,0.017869866,0.000019714591,0.00015245016,0.00048042205,0.000059716916,0.93077606,0.048207488,0.0009415486,0.0002244031,0.000034849945],"about_ca_topic_score_codex":0.0006168998,"about_ca_topic_score_gemma":0.00079439115,"teacher_disagreement_score":0.0010184693,"about_ca_system_score_codex":0.00029436324,"about_ca_system_score_gemma":0.00015644486,"threshold_uncertainty_score":0.0053862333},"labels":[],"label_agreement":null},{"id":"W2069665385","doi":"10.1016/s0307-904x(01)00082-8","title":"Stochastic modeling of heat exchanger response to data uncertainties","year":2002,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heat exchanger; NTU method; Inlet; Mechanics; Heat transfer; Concentric tube heat exchanger; Thermodynamics; Steady state (chemistry); Tube (container); Shell and tube heat exchanger; Materials science; Environmental science; Plate heat exchanger; Micro heat exchanger; Chemistry; Engineering; Physics; Mechanical engineering","score_opus":0.30814194432341685,"score_gpt":0.33905006712671576,"score_spread":0.03090812280329891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069665385","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12052742,0.00030436978,0.87497485,0.00071749795,0.0001018175,0.000046873876,0.00027543053,0.00032417395,0.002727565],"genre_scores_gemma":[0.99020493,0.00020368471,0.007331808,0.00005583229,0.000049486134,0.00004994661,0.0001623444,0.000054376953,0.0018877072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984371,0.000619076,0.00007939883,0.0002805716,0.0003582455,0.0002255439],"domain_scores_gemma":[0.993807,0.004421028,0.0007177183,0.00034048254,0.0005894811,0.00012429043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003438942,0.00075078284,0.0011867948,0.0006621311,0.00038485572,0.0014505369,0.0016265993,0.0018507614,0.0014709226],"category_scores_gemma":[0.013186486,0.0010638325,0.0008518196,0.00079177856,0.0012358521,0.0016281644,0.001091958,0.0012705233,0.00023401978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017160819,0.0000047296467,0.00011569438,0.000009262928,0.000008569611,0.000012802971,0.000010664392,0.994624,0.00028259255,0.0042917067,0.000047679994,0.00057517877],"study_design_scores_gemma":[0.000001423741,0.0000033650124,0.000060082097,7.296683e-7,0.0000017008792,0.0000027059837,0.0000012518661,0.99848723,0.000093987124,0.0013183423,0.00002686743,0.0000022410384],"about_ca_topic_score_codex":0.0062172813,"about_ca_topic_score_gemma":0.003270821,"teacher_disagreement_score":0.0062172813,"about_ca_system_score_codex":0.0012730779,"about_ca_system_score_gemma":0.0009190325,"threshold_uncertainty_score":0.018187046},"labels":[],"label_agreement":null},{"id":"W2070072742","doi":"10.1177/0954406212448341","title":"An efficient method for system reliability analysis of planar mechanisms","year":2012,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Reliability (semiconductor); Monte Carlo method; Probabilistic analysis of algorithms; Minification; Probabilistic logic; Random variable; Mathematical optimization; Algorithm; Mathematics; Statistics","score_opus":0.03845619002336023,"score_gpt":0.3151122879092141,"score_spread":0.2766560978858539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070072742","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005704808,0.000050454026,0.99892235,0.000009053607,0.000006639648,0.000015596157,0.00001156462,0.00007905034,0.00033484522],"genre_scores_gemma":[0.12061677,0.00041260244,0.87456644,0.00005783325,0.00006499448,0.0005398634,0.00019872004,0.00023399542,0.0033088028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909043,0.0002575382,0.000034013734,0.00010152863,0.00047219836,0.0000441664],"domain_scores_gemma":[0.998599,0.0007536806,0.00012771128,0.00020852976,0.00028952205,0.000021553253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017168174,0.00096287695,0.0008553528,0.0018480925,0.0004313993,0.00049336225,0.0009746737,0.0007001868,0.0032506676],"category_scores_gemma":[0.004507584,0.0004616173,0.001252128,0.0009115604,0.00064982916,0.0009209421,0.0008645345,0.0012347166,0.00085972145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004006293,0.000043798183,0.00043859065,0.00020455792,0.0000932065,0.00011454193,0.00010042212,0.7401847,0.018765368,0.110157795,0.0016329011,0.12822407],"study_design_scores_gemma":[0.00000645271,0.000024574185,0.00013889468,0.000010353595,0.000011841871,0.00006188039,0.000005702888,0.98325735,0.001530082,0.012777994,0.0021634342,0.000011420975],"about_ca_topic_score_codex":0.0010636335,"about_ca_topic_score_gemma":0.0008961068,"teacher_disagreement_score":0.0032506676,"about_ca_system_score_codex":0.00051027274,"about_ca_system_score_gemma":0.0010184417,"threshold_uncertainty_score":0.01087451},"labels":[],"label_agreement":null},{"id":"W2070312675","doi":"10.1109/embc.2013.6610688","title":"Subspace method decomposition and identification of the parallel-cascade model of ankle joint stiffness: Theory and simulation","year":2013,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Subspace topology; Cascade; Computer science; Nonlinear system; Monte Carlo method; Stiffness matrix; Noise (video); Stiffness; Algorithm; Replicate; Matrix decomposition; Representation (politics); System identification; Control theory (sociology); Applied mathematics; Mathematics; Data modeling; Artificial intelligence; Engineering; Structural engineering; Statistics","score_opus":0.07375303476207182,"score_gpt":0.35471820657132874,"score_spread":0.28096517180925695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070312675","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016252788,0.00009120731,0.98161584,0.000060508813,0.000009813886,0.000026952603,0.000024364097,0.0000993269,0.001819228],"genre_scores_gemma":[0.77423054,0.0004443503,0.22038354,0.000037025886,0.000023828228,0.00026354074,0.000104503015,0.000069928814,0.0044427398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979657,0.000061850784,0.0000109405955,0.000034611534,0.000076721764,0.000019241086],"domain_scores_gemma":[0.9994936,0.00028896212,0.000059462043,0.000038152622,0.00010082109,0.00001902808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070547353,0.0004066421,0.0005731182,0.00037691786,0.00031544833,0.00039255392,0.000655429,0.0006387106,0.0023667093],"category_scores_gemma":[0.0015430526,0.0003098356,0.00058213965,0.00031877632,0.00053075637,0.0007708193,0.0006021087,0.0006380849,0.00034919567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020841835,0.000017102057,0.0003040602,0.00003024189,0.000011447512,0.000028516793,0.000031825068,0.9798608,0.0013190535,0.008488119,0.0001555767,0.009732451],"study_design_scores_gemma":[0.0000012588027,0.0000051723055,0.0000383945,0.000001287647,9.591803e-7,0.000005825577,0.000001668979,0.99865305,0.00013383705,0.0010630366,0.00009346835,0.0000019588451],"about_ca_topic_score_codex":0.0061721094,"about_ca_topic_score_gemma":0.0035997252,"teacher_disagreement_score":0.0061721094,"about_ca_system_score_codex":0.00041741185,"about_ca_system_score_gemma":0.000715778,"threshold_uncertainty_score":0.012272418},"labels":[],"label_agreement":null},{"id":"W2070516785","doi":"10.1115/icone16-48871","title":"Bayesian Prediction for the Gumbel Distribution Applied to Feeder Pipe Thicknesses","year":2008,"lang":"en","type":"article","venue":"Volume 1: Plant Operations, Maintenance, Installations and Life Cycle; Component Reliability and Materials Issues; Advanced Applications of Nuclear Technology; Codes, Standards, Licensing and Regulatory Issues","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"Gumbel distribution; Bayesian probability; Markov chain Monte Carlo; Margin (machine learning); Credible interval; Interval (graph theory); Statistics; Computer science; Confidence interval; Markov chain; Mathematics; Posterior probability; Prior probability; Measure (data warehouse); Algorithm; Data mining; Machine learning; Extreme value theory","score_opus":0.01615797882934711,"score_gpt":0.2696652697577917,"score_spread":0.2535072909284446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070516785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063598,0.00013734246,0.992431,0.000099658115,0.00001424764,0.000018555013,0.00004003948,0.00012029182,0.00077909784],"genre_scores_gemma":[0.5055393,0.0010234369,0.4886273,0.00022353871,0.00021634978,0.0004235111,0.0005747202,0.00018245072,0.0031893174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99607444,0.0018601896,0.00016883419,0.0007256906,0.00094136543,0.0002294805],"domain_scores_gemma":[0.96800643,0.02687686,0.0017436645,0.0011677876,0.0019708916,0.00023445298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012210876,0.0010943906,0.0016686122,0.0015779455,0.00065692636,0.0018874982,0.0026978757,0.0016744973,0.0029599185],"category_scores_gemma":[0.054388966,0.0010376722,0.001436737,0.0010267822,0.0018843685,0.0034743107,0.0014276671,0.0028804592,0.00076725037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008307645,0.000024194085,0.0013099554,0.00007212543,0.000046487316,0.000065313194,0.00012423212,0.86869067,0.0006226356,0.09614721,0.0006373092,0.032176808],"study_design_scores_gemma":[0.000007709409,0.000018878285,0.00028586335,0.000022329712,0.000008884874,0.000021247502,0.0000068846734,0.9623755,0.00032073792,0.036553044,0.00036088165,0.000017924453],"about_ca_topic_score_codex":0.004422181,"about_ca_topic_score_gemma":0.0026983544,"teacher_disagreement_score":0.012210876,"about_ca_system_score_codex":0.0018516558,"about_ca_system_score_gemma":0.0012204342,"threshold_uncertainty_score":0.06457806},"labels":[],"label_agreement":null},{"id":"W2070594276","doi":"10.1007/s00477-014-0954-8","title":"A PCM-based stochastic hydrological model for uncertainty quantification in watershed systems","year":2014,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Regina","funders":"National Natural Science Foundation of China","keywords":"Polynomial chaos; Monte Carlo method; Uncertainty quantification; Hydrological modelling; Collocation (remote sensing); Uncertainty analysis; Histogram; Computer science; Applied mathematics; Probabilistic logic; Mathematical optimization; Gaussian; Mathematics; Statistics; Simulation","score_opus":0.13700470137619514,"score_gpt":0.3962541908992768,"score_spread":0.2592494895230817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070594276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053242985,0.00022125321,0.94043744,0.00025889828,0.00009621507,0.00005569176,0.00054070813,0.0007401007,0.004406768],"genre_scores_gemma":[0.9222551,0.0002476707,0.074293725,0.000114119204,0.00007721124,0.00015007626,0.0004932858,0.00012052161,0.002248386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997017,0.00010660213,0.000018997604,0.0000552822,0.00008910493,0.000028364555],"domain_scores_gemma":[0.999355,0.00032249236,0.000051324037,0.000068705485,0.00015965883,0.000042801938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006818163,0.00042652772,0.00082493323,0.0004114088,0.00041930663,0.0009317811,0.0012669777,0.0011675279,0.0015617239],"category_scores_gemma":[0.0023208847,0.00029419435,0.0005731931,0.0007700062,0.00042329985,0.0010073209,0.00094434636,0.0008578112,0.00027538964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012726931,0.000017044375,0.00025244773,0.000011432112,0.000008535931,0.000020144393,0.000007162764,0.9918058,0.0004197593,0.0020546457,0.0001997365,0.0051905904],"study_design_scores_gemma":[0.0000024567942,0.0000027875283,0.000038440736,7.0864735e-7,0.0000014360616,0.0000026472073,8.7383006e-7,0.99913305,0.0000616559,0.0006570504,0.0000977318,0.0000012176428],"about_ca_topic_score_codex":0.009219644,"about_ca_topic_score_gemma":0.005701018,"teacher_disagreement_score":0.009219644,"about_ca_system_score_codex":0.000637777,"about_ca_system_score_gemma":0.0011515432,"threshold_uncertainty_score":0.018331945},"labels":[],"label_agreement":null},{"id":"W2070748799","doi":"10.1016/j.strusafe.2013.03.001","title":"Structural reliability analysis based on the concepts of entropy, fractional moment and dimensional reduction method","year":2013,"lang":"en","type":"article","venue":"Structural Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":295,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University Network of Excellence in Nuclear Engineering; China Scholarship Council","keywords":"Principle of maximum entropy; Multiplicative function; Mathematics; Random variable; Applied mathematics; Monte Carlo method; Maximum entropy probability distribution; Entropy (arrow of time); Moment-generating function; Reliability (semiconductor); Mathematical optimization; Algorithm; Statistical physics; Mathematical analysis; Statistics","score_opus":0.03319647537614264,"score_gpt":0.335505857993735,"score_spread":0.3023093826175924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070748799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011392146,0.0004158927,0.98562336,0.00010745999,0.00003299479,0.000010924174,0.00002036056,0.00005165569,0.0023452425],"genre_scores_gemma":[0.8761767,0.0010821287,0.1192768,0.000069881135,0.00022175502,0.0001153876,0.00009030243,0.000080505604,0.002886545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945134,0.00018341988,0.000018464129,0.000044656124,0.00026930062,0.000032809876],"domain_scores_gemma":[0.9992872,0.00042244294,0.00007622079,0.00006641898,0.00012707229,0.000020666537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001001793,0.000513263,0.00081706926,0.001633883,0.0003637603,0.00066474243,0.00057753274,0.00038064815,0.0007818744],"category_scores_gemma":[0.0021344086,0.00022931946,0.000934386,0.0006034045,0.001069326,0.001439289,0.00073581893,0.00081589026,0.00010967935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000511673,0.000032041888,0.0006842839,0.000098587116,0.000082298255,0.00005360922,0.00009479864,0.5315624,0.008022644,0.41073722,0.0006707211,0.047910307],"study_design_scores_gemma":[0.0000027793064,0.000021350537,0.00036664636,0.000007368437,0.000013459556,0.000026779293,0.00000660999,0.92323893,0.00093713176,0.07476612,0.0005984448,0.00001440572],"about_ca_topic_score_codex":0.0007071735,"about_ca_topic_score_gemma":0.0005241892,"teacher_disagreement_score":0.001633883,"about_ca_system_score_codex":0.00057093083,"about_ca_system_score_gemma":0.00062186737,"threshold_uncertainty_score":0.0052980185},"labels":[],"label_agreement":null},{"id":"W2072242021","doi":"10.1016/j.jhydrol.2008.09.004","title":"Cross entropy quantile function estimation from censored samples using partial probability weighted moments","year":2008,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quantile; Quantile function; Mathematics; Statistics; Principle of maximum entropy; Entropy (arrow of time); Random variable; Extreme value theory; Applied mathematics; Moment-generating function","score_opus":0.12056414162146077,"score_gpt":0.34508029273853236,"score_spread":0.22451615111707157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072242021","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04053983,0.000119657125,0.95866597,0.000044076613,0.000009371403,0.000010428289,0.00004579926,0.0002314046,0.00033339334],"genre_scores_gemma":[0.8517641,0.0002250348,0.14629011,0.00005020411,0.000056825542,0.000058263002,0.00040875096,0.00012522089,0.0010215237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99894685,0.0004928762,0.00005839109,0.00016800419,0.00025373755,0.000080146296],"domain_scores_gemma":[0.9931458,0.0052562784,0.00050852227,0.00061007834,0.00038612657,0.00009323194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003917489,0.00054152956,0.0011483551,0.0014656215,0.00028551684,0.0012179444,0.0009993035,0.0009508319,0.001428309],"category_scores_gemma":[0.015859213,0.00075291714,0.0009482892,0.0010307295,0.000719485,0.002023238,0.0014022792,0.0009067385,0.00025144196],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024545248,0.000060783826,0.0064956057,0.00009541286,0.0002097691,0.00014532957,0.000088538174,0.8705239,0.0038966907,0.03365342,0.0005342984,0.08405083],"study_design_scores_gemma":[0.000004240827,0.000009788878,0.0011572682,0.000004691549,0.0000089799105,0.000025330972,0.0000042063593,0.9907873,0.0007885173,0.0071047954,0.00009646592,0.000008432482],"about_ca_topic_score_codex":0.0011911194,"about_ca_topic_score_gemma":0.0011308882,"teacher_disagreement_score":0.003917489,"about_ca_system_score_codex":0.0006637416,"about_ca_system_score_gemma":0.00050403294,"threshold_uncertainty_score":0.020717919},"labels":[],"label_agreement":null},{"id":"W2072532819","doi":"10.4271/2012-01-0808","title":"Parametric Importance Analysis and Design Optimization of a Torque Converter Model Using Sensitivity Information","year":2012,"lang":"en","type":"article","venue":"SAE International Journal of Passenger Cars - Mechanical Systems","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Permission; Sensitivity (control systems); Parametric statistics; Computer science; Torque; Parametric model; Control theory (sociology); Engineering; Mathematics; Electronic engineering; Control (management); Statistics; Physics; Political science; Artificial intelligence; Law","score_opus":0.08382338899500116,"score_gpt":0.32571202584849734,"score_spread":0.24188863685349618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072532819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04821868,0.00019236605,0.9366327,0.00016587514,0.00002037599,0.00011274277,0.0001310637,0.00048682836,0.014039395],"genre_scores_gemma":[0.92462873,0.00020739128,0.070912704,0.000034213346,0.000014754394,0.00021949301,0.00015773931,0.00010651812,0.0037184416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966085,0.000095011965,0.000012365057,0.00004122468,0.0001571501,0.00003334264],"domain_scores_gemma":[0.9994491,0.00035679142,0.000052843043,0.00004519586,0.00008577256,0.000010281694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006768074,0.00089222786,0.00062961306,0.0006016765,0.00036438383,0.0007699203,0.00051097386,0.000576184,0.0029251794],"category_scores_gemma":[0.0019508408,0.00042270226,0.001093866,0.00028605715,0.00051673455,0.00043724812,0.00055671256,0.00079862523,0.00033962654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017799955,0.000015689722,0.00012697486,0.000046756944,0.000010509699,0.000026488236,0.00002616726,0.986362,0.0036635068,0.0022833135,0.0001118157,0.007309053],"study_design_scores_gemma":[0.0000020282034,0.000015657752,0.00006761365,0.0000028326751,0.000006052955,0.00000688917,0.0000035226326,0.99757797,0.0013965432,0.00068148214,0.00023648283,0.0000029770413],"about_ca_topic_score_codex":0.0036196436,"about_ca_topic_score_gemma":0.0025552937,"teacher_disagreement_score":0.0036196436,"about_ca_system_score_codex":0.0007811185,"about_ca_system_score_gemma":0.0011385822,"threshold_uncertainty_score":0.009785712},"labels":[],"label_agreement":null},{"id":"W2073205395","doi":"10.4028/www.scientific.net/amr.123-125.575","title":"Optimal Design of the Aluminum Electrical Railcar under Uncertainty of Material Property","year":2010,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Aluminium; Reliability (semiconductor); Property (philosophy); Optimal design; Structural engineering; Yield (engineering); Finite element method; Stress (linguistics); Mechanical engineering; Materials science; Engineering; Computer science; Composite material","score_opus":0.13988880716811922,"score_gpt":0.39805569011355457,"score_spread":0.2581668829454353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073205395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17197277,0.00058301486,0.81939274,0.0002614019,0.00003661874,0.00006950024,0.000087961074,0.00022025833,0.0073757437],"genre_scores_gemma":[0.9689118,0.00018380681,0.029520612,0.00002639149,0.000011292427,0.00006366591,0.000058186586,0.000028518349,0.0011957441],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994568,0.00016076438,0.000017491688,0.0001170455,0.00015191267,0.00009594436],"domain_scores_gemma":[0.99937445,0.00030424207,0.00013720249,0.00003413527,0.00012236813,0.00002761784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010304704,0.0007976077,0.0009742438,0.00054854614,0.00024578228,0.0007264141,0.00046741584,0.00084553624,0.0012234115],"category_scores_gemma":[0.0016464862,0.00064501207,0.000621439,0.0003162499,0.0006911212,0.00073761307,0.00061704684,0.0003824842,0.0001506882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004228445,0.00000971682,0.00023014676,0.000038704122,0.000013639119,0.00004067259,0.000012173718,0.99068964,0.0030754574,0.0019126375,0.00008524068,0.0038496929],"study_design_scores_gemma":[0.00001451639,0.00010195052,0.00033693766,0.000005025484,0.000015911935,0.000018173874,0.000015031007,0.9959437,0.0014220192,0.0017852105,0.00033349256,0.000008008888],"about_ca_topic_score_codex":0.0021867715,"about_ca_topic_score_gemma":0.0014919016,"teacher_disagreement_score":0.0021867715,"about_ca_system_score_codex":0.00062471494,"about_ca_system_score_gemma":0.0012775729,"threshold_uncertainty_score":0.0054497123},"labels":[],"label_agreement":null},{"id":"W2073407183","doi":"10.1115/1.4026270","title":"Computationally Efficient Reliability Analysis of Mechanisms Based on a Multiplicative Dimensional Reduction Method","year":2013,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Multiplicative function; Monte Carlo method; Principle of maximum entropy; Reliability (semiconductor); Trajectory; Reduction (mathematics); Computer science; Mathematical optimization; Algorithm; Probability distribution; Computation; Mathematics; Applied mathematics; Statistics","score_opus":0.070853009352649,"score_gpt":0.34968046264024333,"score_spread":0.27882745328759434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073407183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054617887,0.00007488594,0.9935168,0.000040063795,0.000008553494,0.000018771452,0.000013497045,0.00007129091,0.0007943488],"genre_scores_gemma":[0.47956657,0.0004132461,0.5168145,0.0000717226,0.000068952,0.00036993364,0.00011440621,0.00010867097,0.0024720654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993856,0.0002380857,0.000023630331,0.00004981456,0.00026867088,0.00003424743],"domain_scores_gemma":[0.998973,0.0006420083,0.00009807409,0.0001304,0.0001377811,0.000018698891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013784998,0.0005957315,0.00078060734,0.0010525164,0.0002927213,0.0005848157,0.00084308087,0.0005376286,0.0014458143],"category_scores_gemma":[0.0026545173,0.0004154973,0.0011643232,0.00047373035,0.00059659965,0.0008104858,0.00083820865,0.0009333294,0.00026470004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026169431,0.000019788306,0.00025036134,0.000059465743,0.000029598194,0.000055215238,0.00003386557,0.93541175,0.0061094826,0.033379953,0.00029333166,0.024331074],"study_design_scores_gemma":[0.0000019375777,0.000010551405,0.00005632669,0.0000019977872,0.0000027522071,0.000013161801,0.0000017475484,0.99646807,0.0003777663,0.002911713,0.00015065384,0.00000329984],"about_ca_topic_score_codex":0.00071207195,"about_ca_topic_score_gemma":0.00060508295,"teacher_disagreement_score":0.0014458143,"about_ca_system_score_codex":0.00041847245,"about_ca_system_score_gemma":0.0006300332,"threshold_uncertainty_score":0.0072903037},"labels":[],"label_agreement":null},{"id":"W2074211846","doi":"10.1016/j.csda.2009.03.006","title":"A note on interval estimation of using lower record data from the generalized exponential distribution","year":2009,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Exponential distribution; Exponential family; Statistics; Applied mathematics; Gamma distribution; Interval estimation; Exponential function; Phase-type distribution; Interval (graph theory); Confidence interval; Combinatorics; Mathematical analysis","score_opus":0.1983595243107808,"score_gpt":0.4122361611757882,"score_spread":0.2138766368650074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074211846","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00092231034,0.0016565621,0.98534805,0.0069531193,0.0025540872,0.000051950927,0.00018626303,0.00037742572,0.0019500923],"genre_scores_gemma":[0.03379646,0.0031339277,0.9453226,0.0054694708,0.0065649236,0.00026914684,0.0002897366,0.00046194327,0.004691845],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98068744,0.011583185,0.0014720258,0.0020130237,0.0039222757,0.0003222125],"domain_scores_gemma":[0.79689366,0.16177382,0.0025436226,0.026519155,0.011335082,0.0009346383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033093818,0.0014995579,0.002312875,0.001408572,0.0011309739,0.0031942618,0.0058559095,0.005059893,0.005574306],"category_scores_gemma":[0.185286,0.0006614953,0.0023041416,0.0029831694,0.004339235,0.008791848,0.0035290145,0.013861915,0.002411987],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013869592,0.00018632693,0.0037219287,0.0010044704,0.00052616186,0.001001288,0.0007413934,0.03134284,0.008879386,0.42674062,0.08746228,0.43700632],"study_design_scores_gemma":[0.00021928127,0.0004287515,0.004785254,0.0006692393,0.0002917279,0.00082614063,0.0002551291,0.25368124,0.013075912,0.59117377,0.13410364,0.00048989407],"about_ca_topic_score_codex":0.007810325,"about_ca_topic_score_gemma":0.0064350744,"teacher_disagreement_score":0.033093818,"about_ca_system_score_codex":0.0010144734,"about_ca_system_score_gemma":0.0017680463,"threshold_uncertainty_score":0.17501897},"labels":[],"label_agreement":null},{"id":"W2074320244","doi":"10.1080/00401706.2015.1006338","title":"Regularized Semiparametric Estimation for Ordinary Differential Equations","year":2015,"lang":"en","type":"article","venue":"Technometrics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Cancer Institute; Directorate for Biological Sciences","keywords":"Ode; Ordinary differential equation; Applied mathematics; Constant (computer programming); Computer science; Mathematics; Differential equation; Mathematical optimization; Mathematical analysis","score_opus":0.29845008793609856,"score_gpt":0.39662496576583606,"score_spread":0.0981748778297375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074320244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051120543,0.00019546709,0.99416226,0.00010730956,0.000014277409,0.00003115836,0.000073473304,0.000104166225,0.00019980936],"genre_scores_gemma":[0.57784617,0.0012815661,0.41476414,0.00024413549,0.00022669893,0.00061685813,0.0012761859,0.00015705897,0.003587124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954052,0.0030012385,0.00021303886,0.00079453713,0.00041560695,0.00017048944],"domain_scores_gemma":[0.97371495,0.021294454,0.0022365814,0.0012767926,0.0012575265,0.00021967811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008970702,0.0012425936,0.0022662533,0.0014583209,0.0004155544,0.0014076547,0.0016475808,0.0013697481,0.0015261372],"category_scores_gemma":[0.038320903,0.0009464289,0.0018667547,0.0011553586,0.0016391957,0.0016664237,0.001774822,0.0025790378,0.00041688993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006975547,0.0000408545,0.0020791502,0.00015067296,0.00022226943,0.00008716382,0.000090670525,0.9299981,0.00071368925,0.042916838,0.00062261196,0.023008278],"study_design_scores_gemma":[0.000005671336,0.000015198733,0.0002569617,0.000010045531,0.000010858163,0.000010716033,0.000006509938,0.9859585,0.00015715172,0.013250658,0.00030669232,0.000011003823],"about_ca_topic_score_codex":0.006305496,"about_ca_topic_score_gemma":0.0039634616,"teacher_disagreement_score":0.008970702,"about_ca_system_score_codex":0.0014277997,"about_ca_system_score_gemma":0.001678021,"threshold_uncertainty_score":0.047442198},"labels":[],"label_agreement":null},{"id":"W2075407357","doi":"10.1002/j.2334-5837.2003.tb02702.x","title":"1.8.4 Probabilistic Risk/Reliability Analysis (PRA) Using a Simulation Approach","year":2003,"lang":"en","type":"article","venue":"INCOSE International Symposium","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Golder Associates (Canada)","funders":"","keywords":"Probabilistic logic; Fault tree analysis; Computer science; Complex system; Reliability (semiconductor); Probabilistic risk assessment; Monte Carlo method; Reliability engineering; Component (thermodynamics); Function (biology); Nonlinear system; Power (physics); Artificial intelligence; Engineering; Mathematics","score_opus":0.07108382634239345,"score_gpt":0.3518030249817037,"score_spread":0.2807191986393102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075407357","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034498014,0.00016172402,0.99028647,0.0001670368,0.000019871299,0.000026927848,0.000024333012,0.00023899163,0.005624859],"genre_scores_gemma":[0.3789666,0.0010160088,0.6097484,0.0001348359,0.0000927595,0.00028258865,0.0001394545,0.00017991298,0.009439266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871874,0.0006467763,0.00006347938,0.00010481124,0.00041413857,0.000052053834],"domain_scores_gemma":[0.9981968,0.0011030758,0.00017667499,0.00024019917,0.0002457087,0.00003757322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019220931,0.00073001906,0.0006178165,0.0009661617,0.0003378274,0.0013803954,0.0010550251,0.00092601374,0.004995871],"category_scores_gemma":[0.0037687244,0.0005078016,0.0013183401,0.0005571268,0.0010027388,0.0017410334,0.0009291906,0.0011098747,0.00083357136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000246754,0.000028610675,0.0005487595,0.000053276704,0.000045421722,0.000051160172,0.00004239997,0.7859632,0.0021620549,0.18895711,0.00068669615,0.021436695],"study_design_scores_gemma":[0.0000052941896,0.000017114482,0.0000836201,0.00001472215,0.0000102682425,0.000033710974,0.000005194203,0.9522544,0.00070077187,0.043889597,0.0029771286,0.000008262805],"about_ca_topic_score_codex":0.0022926694,"about_ca_topic_score_gemma":0.0016992181,"teacher_disagreement_score":0.004995871,"about_ca_system_score_codex":0.0008793486,"about_ca_system_score_gemma":0.0010450587,"threshold_uncertainty_score":0.016712904},"labels":[],"label_agreement":null},{"id":"W2075882712","doi":"10.1115/detc2005-84495","title":"Reliability Assessment Based on the Concept of Failure Surface Frontier","year":2005,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Reliability (semiconductor); Discriminative model; Limit (mathematics); Reliability engineering; Surface (topology); Quadratic equation; Point (geometry); Limit state design; Computer science; Set (abstract data type); Sampling (signal processing); Mathematical optimization; Mathematics; Engineering; Machine learning; Structural engineering; Power (physics)","score_opus":0.046749246586792165,"score_gpt":0.3207530085649596,"score_spread":0.27400376197816745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075882712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021470869,0.00050470186,0.97656643,0.0000966166,0.000010180606,0.000021249227,0.000039270715,0.00011780806,0.0011729293],"genre_scores_gemma":[0.8626533,0.00089553476,0.1349983,0.000056082965,0.00007416675,0.00013302712,0.0002231036,0.000075059615,0.0008914643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984133,0.00055861106,0.00006337113,0.00022169741,0.0006375029,0.00010558214],"domain_scores_gemma":[0.9952899,0.0033696562,0.00030097825,0.00025329908,0.00068667985,0.000099393896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029188388,0.0008258629,0.0014765132,0.0028699574,0.00043843556,0.0013895981,0.0009281363,0.0008960215,0.00095662335],"category_scores_gemma":[0.0098235365,0.00029649484,0.0007414494,0.0010905598,0.001818407,0.0033708871,0.0013835608,0.001014979,0.00021632799],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097591736,0.000029614952,0.0022284298,0.0001504937,0.000052855823,0.0000896493,0.00018377096,0.8072429,0.0044675767,0.09912202,0.00079122384,0.08554389],"study_design_scores_gemma":[0.000005250106,0.000047691537,0.0006871634,0.00001573039,0.000008436075,0.000046946017,0.000022055992,0.9597021,0.00091810006,0.03782085,0.00070712337,0.000018592822],"about_ca_topic_score_codex":0.0010495236,"about_ca_topic_score_gemma":0.00033622005,"teacher_disagreement_score":0.0029188388,"about_ca_system_score_codex":0.0009099516,"about_ca_system_score_gemma":0.00048930576,"threshold_uncertainty_score":0.0154364705},"labels":[],"label_agreement":null},{"id":"W2076215557","doi":"10.1115/1.3013295","title":"Dependability-Based Design Optimization of Degrading Engineering Systems","year":2008,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Dependability; Reliability engineering; Rework; Reliability (semiconductor); Computer science; Monte Carlo method; Function (biology); Probabilistic design; Mathematical optimization; Engineering design process; Engineering","score_opus":0.17468200259832767,"score_gpt":0.3028883141338934,"score_spread":0.12820631153556572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076215557","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020875018,0.00015966677,0.97561044,0.00009157156,0.000012194971,0.000056214685,0.000015702946,0.00009965407,0.0030795764],"genre_scores_gemma":[0.79358757,0.00032994605,0.20216583,0.00008224631,0.000028799506,0.00026050277,0.00006111158,0.00010471538,0.003379342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902785,0.00035632454,0.000035284374,0.00013564338,0.00035522538,0.00008963864],"domain_scores_gemma":[0.99865746,0.00079626695,0.00022402068,0.000073397925,0.00021308995,0.000035647976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017336863,0.0013031376,0.00089531747,0.0007930536,0.0003812621,0.00084012613,0.0008617042,0.0007931023,0.0013938121],"category_scores_gemma":[0.004135851,0.0006373585,0.0006169854,0.00043161464,0.0007862969,0.0007996092,0.0008016717,0.0008781797,0.00019353066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012711576,0.000015154884,0.00011365611,0.000032686636,0.00001003084,0.00001822562,0.000020469392,0.9859542,0.001554047,0.0036081555,0.000063511834,0.008597021],"study_design_scores_gemma":[0.000004133893,0.000039914878,0.000079324585,0.000004901041,0.0000060159664,0.000010042665,0.0000056989757,0.99641293,0.00076507457,0.0023471103,0.00032120242,0.000003782791],"about_ca_topic_score_codex":0.0014258362,"about_ca_topic_score_gemma":0.0011731616,"teacher_disagreement_score":0.0017336863,"about_ca_system_score_codex":0.0010816606,"about_ca_system_score_gemma":0.00096306915,"threshold_uncertainty_score":0.009168744},"labels":[],"label_agreement":null},{"id":"W2077340077","doi":"10.3141/2165-06","title":"Investigating Regression to the Mean in Before-and-After Speed Data Analysis","year":2010,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Confidence interval; Computer science; Regression toward the mean; Regression analysis; Statistics; Speedup; Magnitude (astronomy); Observational error; Regression; Standard deviation; Linear regression; Mathematics","score_opus":0.2174737224091621,"score_gpt":0.4514974621668637,"score_spread":0.2340237397577016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077340077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058897246,0.0005957716,0.93734795,0.00072603096,0.00007435864,0.000106928586,0.000107118736,0.00028600512,0.0018584895],"genre_scores_gemma":[0.7095183,0.00066746154,0.2868403,0.00048551196,0.00011897184,0.00035040674,0.0002880232,0.00023699758,0.0014940759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9660434,0.023702588,0.0011847927,0.0043014395,0.004126451,0.0006412712],"domain_scores_gemma":[0.69964826,0.2573462,0.018136386,0.015562119,0.008854289,0.00045263127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05486011,0.0009597212,0.002012036,0.0017118974,0.0006810014,0.0020259966,0.002010364,0.0021355322,0.0017598531],"category_scores_gemma":[0.26292148,0.0007020359,0.0026563825,0.0029779163,0.0034334522,0.00389498,0.001938455,0.0034097654,0.00031706292],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003869015,0.0002528099,0.06547452,0.00087204063,0.0011638002,0.0005166492,0.0019491299,0.45646068,0.004115386,0.3339321,0.0020383706,0.13283756],"study_design_scores_gemma":[0.000060329527,0.0004524689,0.017851101,0.00018176064,0.00025066992,0.00021717916,0.0004029301,0.79504853,0.0057419706,0.17421213,0.0054648193,0.000116085546],"about_ca_topic_score_codex":0.0054952675,"about_ca_topic_score_gemma":0.003016079,"teacher_disagreement_score":0.05486011,"about_ca_system_score_codex":0.0016268146,"about_ca_system_score_gemma":0.0017882941,"threshold_uncertainty_score":0.2901315},"labels":[],"label_agreement":null},{"id":"W2079621839","doi":"10.1139/l09-082","title":"Simple iteration method for structural static reanalysis","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Program for New Century Excellent Talents in University; Jilin University","keywords":"Simple (philosophy); Iterative method; Stiffness matrix; Acceleration; Finite element method; Computer science; Relaxation (psychology); Applied mathematics; Mathematical optimization; Matrix (chemical analysis); Direct stiffness method; Factorization; Algorithm; Dynamic relaxation; Stiffness; Mathematics; Structural engineering; Engineering","score_opus":0.040417634000109816,"score_gpt":0.3183501492450921,"score_spread":0.27793251524498225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079621839","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006377938,0.00008817647,0.9970745,0.000027848975,0.000042795426,0.00003357844,0.000032729036,0.0002658218,0.0017968377],"genre_scores_gemma":[0.03777611,0.00024786842,0.9553302,0.0000529102,0.00006175832,0.00036795234,0.0001483623,0.00030865968,0.0057062204],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99945396,0.00014385629,0.000028322229,0.000070510265,0.0002814725,0.00002194056],"domain_scores_gemma":[0.9995797,0.00015355158,0.00003696551,0.000059493374,0.00015227223,0.000018047123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006509033,0.0006497019,0.0007127289,0.0009300076,0.00054048456,0.0005696486,0.0011688253,0.00075220363,0.007434341],"category_scores_gemma":[0.0015515168,0.00040579075,0.0008647495,0.0007505929,0.00054547883,0.00079733797,0.0010064993,0.0011033823,0.00288151],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010642796,0.000086900836,0.00087961357,0.00060236343,0.0001338078,0.00023722107,0.0003242477,0.34539035,0.036545664,0.16521284,0.0124893105,0.4379913],"study_design_scores_gemma":[0.000026726491,0.000049263792,0.00020880371,0.00003062431,0.000019327244,0.00015018068,0.000018870654,0.93863446,0.004671855,0.02633384,0.029818807,0.000037205704],"about_ca_topic_score_codex":0.0017519823,"about_ca_topic_score_gemma":0.0022896281,"teacher_disagreement_score":0.007434341,"about_ca_system_score_codex":0.00037649446,"about_ca_system_score_gemma":0.0011299558,"threshold_uncertainty_score":0.024870336},"labels":[],"label_agreement":null},{"id":"W2080665158","doi":"10.1002/qre.783","title":"Set theoretic formulation of performance reliability of multiple response time‐variant systems due to degradations in system components","year":2006,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Warranty; Monte Carlo method; Component (thermodynamics); Computer science; Quality (philosophy); Set (abstract data type); Degradation (telecommunications); Limit (mathematics); Importance sampling; Function (biology); Mathematical optimization; Power (physics); Engineering; Mathematics; Statistics","score_opus":0.044557318347171776,"score_gpt":0.2992332284278251,"score_spread":0.2546759100806534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080665158","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011555998,0.0002099584,0.98520005,0.00017228314,0.000026946092,0.000029975405,0.000031058928,0.000041857693,0.0027319281],"genre_scores_gemma":[0.88563687,0.0005636339,0.10567819,0.000119797616,0.00013471828,0.0003958888,0.00012411825,0.00009366756,0.0072531807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99839157,0.00066002633,0.000049472575,0.00016958866,0.0006243863,0.000104962026],"domain_scores_gemma":[0.9972281,0.001858368,0.00029772095,0.00011752221,0.00041451977,0.00008382338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037522577,0.0011502272,0.0009882555,0.0011312357,0.0003089083,0.00160213,0.0014464146,0.0010390106,0.00199574],"category_scores_gemma":[0.005699075,0.0006485722,0.0010697483,0.00048084554,0.0015910865,0.0011311964,0.0010743871,0.001362363,0.00025196586],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002640491,0.00001652839,0.00025509315,0.00006657766,0.000038035872,0.00008300909,0.00008555624,0.8966561,0.0016573139,0.09556836,0.0003079474,0.0052389987],"study_design_scores_gemma":[0.000002956141,0.000013500701,0.000072282695,0.0000057784946,0.0000053300378,0.000013577955,0.000006736437,0.9878264,0.00026178118,0.011584633,0.00020217101,0.0000049889754],"about_ca_topic_score_codex":0.0020910446,"about_ca_topic_score_gemma":0.0008923647,"teacher_disagreement_score":0.0037522577,"about_ca_system_score_codex":0.0021428086,"about_ca_system_score_gemma":0.0008170257,"threshold_uncertainty_score":0.019844115},"labels":[],"label_agreement":null},{"id":"W2081434932","doi":"10.1007/s13369-011-0106-0","title":"Shrinkage Estimation Using Ranked Set Samples","year":2011,"lang":"en","type":"article","venue":"Arabian Journal for Science and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Estimator; Shrinkage; RSS; Statistics; Monte Carlo method; Sample (material); Mathematics; Set (abstract data type); Estimation; Shrinkage estimator; Population; Sampling (signal processing); Estimation theory; Computer science; Econometrics; Algorithm; Engineering; Bias of an estimator","score_opus":0.23461272134723063,"score_gpt":0.3513971008261586,"score_spread":0.11678437947892797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081434932","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009884574,0.00022086239,0.9887673,0.00009363593,0.000030596006,0.000051073534,0.000068902766,0.00035010403,0.00053288153],"genre_scores_gemma":[0.36643174,0.0006322714,0.6228964,0.0002836023,0.000282087,0.00044619525,0.0017633223,0.00033323353,0.0069311126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99519205,0.0026880421,0.00023100319,0.0005605033,0.0010803884,0.00024802177],"domain_scores_gemma":[0.9826649,0.012329104,0.0007341106,0.001921092,0.002113173,0.00023770724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008764846,0.0013328893,0.0027492284,0.0016462331,0.0009526863,0.0018112857,0.0023156109,0.0022691544,0.0032489686],"category_scores_gemma":[0.02649678,0.0014283756,0.0020834953,0.001177501,0.0011148417,0.0025081914,0.0021071115,0.002426622,0.001869721],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010581953,0.0003272262,0.0030514423,0.0006282312,0.00056135183,0.00029607172,0.00024127054,0.47646117,0.013713406,0.03711728,0.0073290337,0.45921525],"study_design_scores_gemma":[0.000040527488,0.00009497454,0.00045407924,0.00002661,0.00005455094,0.00007073851,0.000019084588,0.98288304,0.0029744264,0.012518402,0.0008386391,0.000024980574],"about_ca_topic_score_codex":0.0020120775,"about_ca_topic_score_gemma":0.0031871551,"teacher_disagreement_score":0.008764846,"about_ca_system_score_codex":0.0006449795,"about_ca_system_score_gemma":0.001510955,"threshold_uncertainty_score":0.04635352},"labels":[],"label_agreement":null},{"id":"W2081682068","doi":"10.1139/t99-096","title":"Reliability evaluation of shallow foundation bearing capacity on<i>c</i>' ϕ' soils","year":2000,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":184,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cohesion (chemistry); Bearing capacity; Probabilistic logic; Shallow foundation; Geotechnical engineering; Reliability (semiconductor); Friction angle; Foundation (evidence); Scale (ratio); Engineering; Geology; Reliability engineering; Mathematics; Statistics; Geography","score_opus":0.11727030318036497,"score_gpt":0.3242191204485232,"score_spread":0.20694881726815822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081682068","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9705383,0.00008217564,0.028725337,0.000015458674,0.0000029313023,0.000008855717,0.00011414726,0.000045772544,0.00046702466],"genre_scores_gemma":[0.99920976,0.000023881705,0.00066985434,6.987325e-7,9.818202e-7,0.0000019560157,0.000039222334,0.0000017101919,0.00005198475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953735,0.00011788146,0.000023298286,0.00007355989,0.00018635084,0.00006154969],"domain_scores_gemma":[0.99771357,0.0012881331,0.00033446713,0.0001411291,0.00044826724,0.00007447371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095011876,0.00036685227,0.00035945434,0.0010916688,0.00015046191,0.00027499345,0.0003936037,0.000309238,0.00046885406],"category_scores_gemma":[0.0037730162,0.00012508607,0.00030331474,0.00070895423,0.000446928,0.00031417518,0.0003119061,0.00014880726,0.00010221902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007022666,0.00004581406,0.09186446,0.00019701065,0.00010009327,0.00038307987,0.0002238465,0.8121114,0.043448284,0.0020847416,0.00025808768,0.04858096],"study_design_scores_gemma":[0.00001027139,0.00057657965,0.0757571,0.000020472131,0.000056205314,0.00017174978,0.00018160712,0.90065455,0.020905603,0.0013564633,0.0002658061,0.00004364569],"about_ca_topic_score_codex":0.0060170186,"about_ca_topic_score_gemma":0.0044912747,"teacher_disagreement_score":0.0060170186,"about_ca_system_score_codex":0.00047073577,"about_ca_system_score_gemma":0.0002824754,"threshold_uncertainty_score":0.011964023},"labels":[],"label_agreement":null},{"id":"W2081724959","doi":"10.1115/1.1498845","title":"Limit Loads for Layered Structures Using Extended Variational Principles and Repeated Elastic Finite Element Analysis","year":2002,"lang":"en","type":"article","venue":"Journal of Pressure Vessel Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Finite element method; Limit load; Limit (mathematics); Finite element limit analysis; Discretization; Limit state design; Limit analysis; Convergence (economics); Upper and lower bounds; Mixed finite element method; Mathematics; Mathematical analysis; Flow (mathematics); Structural engineering; Applied mathematics; Geometry; Engineering","score_opus":0.09624071180118433,"score_gpt":0.3206034404360283,"score_spread":0.22436272863484397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081724959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013720634,0.000056889134,0.9850198,0.000025056786,0.000007035842,0.000023162691,0.000011213197,0.00007548483,0.0010608011],"genre_scores_gemma":[0.44823608,0.00021433746,0.5468539,0.00004592165,0.000020135707,0.00033108678,0.000105489475,0.0001712286,0.004021732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964356,0.00012787402,0.000016548734,0.000033519835,0.00014859045,0.000029814777],"domain_scores_gemma":[0.9993013,0.00039090356,0.00009372563,0.0000670867,0.00012228872,0.00002456717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012087086,0.00082371064,0.0006678053,0.0009685128,0.0003573944,0.0006657039,0.0013294086,0.0009083624,0.0013862356],"category_scores_gemma":[0.0026933914,0.0004851352,0.0007612217,0.00039617295,0.0008266739,0.0010819492,0.0012317859,0.00077133474,0.00032488466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023372275,0.000034330093,0.0004752448,0.000053350996,0.000026590802,0.0000904757,0.000084378,0.91627294,0.008978819,0.049243573,0.00020646419,0.024510602],"study_design_scores_gemma":[0.0000010965331,0.0000048098286,0.00002889133,0.000002959307,9.778835e-7,0.0000056104413,0.000002682386,0.995284,0.00048097465,0.0040619234,0.00012324903,0.0000028940594],"about_ca_topic_score_codex":0.0021234166,"about_ca_topic_score_gemma":0.002379021,"teacher_disagreement_score":0.0021234166,"about_ca_system_score_codex":0.0005563306,"about_ca_system_score_gemma":0.00077666185,"threshold_uncertainty_score":0.0063923597},"labels":[],"label_agreement":null},{"id":"W2082795641","doi":"10.1002/cta.173","title":"Probabilistic design of systems with general distributions of parameters","year":2001,"lang":"en","type":"article","venue":"International Journal of Circuit Theory and Applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Monte Carlo method; Probability density function; Bounded function; Sensitivity (control systems); Probabilistic logic; Random variable; Cumulative distribution function; Stability (learning theory); Domain (mathematical analysis); Function (biology); Mathematical optimization; Mathematics; Computer science; Applied mathematics; Control theory (sociology); Engineering; Statistics; Mathematical analysis","score_opus":0.06427232952823189,"score_gpt":0.3138756535099724,"score_spread":0.24960332398174048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082795641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010420551,0.00010587734,0.9886707,0.000037400012,0.000006148224,0.000020950334,0.000011757587,0.00007246877,0.000654117],"genre_scores_gemma":[0.84655464,0.00030456536,0.15110326,0.000051539417,0.00003127807,0.0002073146,0.000057863825,0.000046968635,0.001642656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99795604,0.00069581927,0.00008552901,0.0003416133,0.00078622217,0.00013477229],"domain_scores_gemma":[0.9975878,0.0013671898,0.0004891472,0.00023868136,0.00027193277,0.00004518019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003161337,0.0007341313,0.0011816038,0.000643279,0.00030762193,0.00085512624,0.0010908595,0.00083613134,0.0010501806],"category_scores_gemma":[0.0069224196,0.0007768665,0.00084172946,0.0005418122,0.001438956,0.0011723567,0.0012599258,0.0008198883,0.00021690692],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031647043,0.0000064988017,0.00014641191,0.000030337063,0.000019160736,0.00002423105,0.000020416757,0.9774116,0.0018975491,0.0131869605,0.000061687024,0.007163516],"study_design_scores_gemma":[0.0000102051,0.000027859905,0.0000814003,0.00000514396,0.000007752954,0.000018733212,0.0000030238139,0.99009913,0.00083968404,0.008514967,0.00038449786,0.0000075767375],"about_ca_topic_score_codex":0.00076710375,"about_ca_topic_score_gemma":0.0006656525,"teacher_disagreement_score":0.003161337,"about_ca_system_score_codex":0.0008612018,"about_ca_system_score_gemma":0.00070811965,"threshold_uncertainty_score":0.016718984},"labels":[],"label_agreement":null},{"id":"W2083061379","doi":"10.1007/s001580100104","title":"On buckling optimization under uncertain loading combinations","year":2001,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Christian Studies; Pontifical Institute of Mediaeval Studies","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Buckling; Mathematical optimization; Regular polygon; Boundary (topology); Mathematics; Stability (learning theory); Set (abstract data type); Critical load; Boundary value problem; Optimization problem; Engineering design process; Structural engineering; Computer science; Engineering; Geometry; Mathematical analysis; Mechanical engineering","score_opus":0.059339430602743166,"score_gpt":0.3382383270295422,"score_spread":0.27889889642679905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083061379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038594432,0.0023681342,0.92987406,0.0007644045,0.00016122435,0.00007956137,0.00016706776,0.00010782005,0.02788331],"genre_scores_gemma":[0.8634615,0.0056217457,0.10474955,0.00048205815,0.0004508897,0.0003466758,0.00031041607,0.0003376853,0.024239495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993474,0.0003061358,0.00002394011,0.00008409154,0.00018250868,0.000055964283],"domain_scores_gemma":[0.9983352,0.0013247919,0.00013338192,0.00005796812,0.000113507864,0.0000350978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016884748,0.001609843,0.001979957,0.0013763476,0.0005055202,0.0011992239,0.00092472957,0.0018367016,0.00400901],"category_scores_gemma":[0.0053081303,0.00095118576,0.0008128828,0.0015545979,0.0015163386,0.0015988027,0.0017289494,0.0009120269,0.0003463218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018063252,0.000009908488,0.000050454968,0.000057837697,0.000016256668,0.000025086714,0.000014795488,0.984518,0.0004668868,0.008479316,0.00028889105,0.0060545765],"study_design_scores_gemma":[0.0000044494914,0.00002008526,0.00008735854,0.000014125359,0.000009313472,0.000008581725,0.000008877068,0.9831518,0.00021975805,0.016026996,0.0004410918,0.000007561856],"about_ca_topic_score_codex":0.002445582,"about_ca_topic_score_gemma":0.0015274254,"teacher_disagreement_score":0.00400901,"about_ca_system_score_codex":0.0008114382,"about_ca_system_score_gemma":0.0005258755,"threshold_uncertainty_score":0.013411462},"labels":[],"label_agreement":null},{"id":"W2087299534","doi":"10.1142/s0218539311004263","title":"METAMODEL-BASED PROBABILISTIC DESIGN OPTIMIZATION OF STATIC SYSTEMS WITH AN EXTENSION TO DYNAMIC SYSTEMS","year":2011,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Metamodeling; Probabilistic logic; Probabilistic design; Kriging; Computer science; Monte Carlo method; Mathematical optimization; Measure (data warehouse); Probability distribution; First-order reliability method; Reliability (semiconductor); Surrogate model; Engineering design process; Reliability engineering; Data mining; Engineering; Machine learning; Mathematics; Artificial intelligence","score_opus":0.1225072616762708,"score_gpt":0.33842990385215443,"score_spread":0.21592264217588364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087299534","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003466459,0.00011525671,0.99499655,0.00004870907,0.000007842546,0.000017337736,0.000016836615,0.000101634374,0.0012293727],"genre_scores_gemma":[0.53765106,0.00074847566,0.45688733,0.00010180603,0.000040346036,0.00041038904,0.00015983636,0.00015528068,0.0038454316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994747,0.0002137999,0.000018689718,0.000088423134,0.00016718122,0.00003724818],"domain_scores_gemma":[0.99950266,0.000306784,0.000058671372,0.000045647015,0.00007224593,0.000014110407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012322553,0.00088537915,0.0009081284,0.00056178466,0.00029170775,0.00070553774,0.0007428235,0.0008843118,0.0016746751],"category_scores_gemma":[0.0019409836,0.0005467083,0.0010950252,0.0006751061,0.00060802145,0.0007158056,0.00086092734,0.0007614506,0.000297689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007426995,0.000007389627,0.00007447702,0.000021630913,0.000008107881,0.000008191039,0.000011106059,0.98644316,0.00059979444,0.005621912,0.00006815468,0.0071286815],"study_design_scores_gemma":[0.0000023905725,0.000018744728,0.00003936011,0.000004249939,0.0000040935824,0.0000072442976,0.0000025832826,0.99558264,0.00019359005,0.0034658343,0.0006767404,0.0000025614931],"about_ca_topic_score_codex":0.0019774465,"about_ca_topic_score_gemma":0.001672263,"teacher_disagreement_score":0.0019774465,"about_ca_system_score_codex":0.00054892927,"about_ca_system_score_gemma":0.0010099814,"threshold_uncertainty_score":0.006516874},"labels":[],"label_agreement":null},{"id":"W2088864649","doi":"10.1198/00401700152672519","title":"A Profile-Based Approach to Parametric Sensitivity Analysis of Nonlinear Regression Models","year":2001,"lang":"en","type":"article","venue":"Technometrics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sensitivity (control systems); Parametric statistics; Nonlinear regression; Nonlinear system; Mathematics; Regression analysis; Parametric model; Measure (data warehouse); Statistics; Regression; Applied mathematics; Computer science; Data mining; Engineering","score_opus":0.14602504067533423,"score_gpt":0.3430785200124298,"score_spread":0.19705347933709555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088864649","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037778057,0.00005582026,0.9955064,0.000031879095,0.000004875669,0.000014993375,0.000019049861,0.00006741067,0.00052174873],"genre_scores_gemma":[0.6913233,0.0004759837,0.30596045,0.00007728421,0.00007028222,0.0002661157,0.0001709433,0.00013737462,0.0015182516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971016,0.0016569892,0.000107628475,0.00022876308,0.0007948433,0.00011017841],"domain_scores_gemma":[0.99387807,0.0045486344,0.00042876048,0.00053350156,0.0005266682,0.00008441885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004864395,0.001022342,0.0007910369,0.0024355447,0.00040614657,0.0013554766,0.0011389403,0.0008475387,0.0017549418],"category_scores_gemma":[0.020078646,0.0006074648,0.0011592192,0.0013036267,0.00092458294,0.0017793061,0.0020882038,0.0014887927,0.0002496968],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030062793,0.000025737256,0.00057181553,0.000049672777,0.00005767872,0.00007893624,0.00007364343,0.92677873,0.002239942,0.033360984,0.0002801892,0.036452617],"study_design_scores_gemma":[0.0000016261955,0.000018629518,0.00020218306,0.000007600242,0.000007582592,0.000031005333,0.0000073641454,0.97944343,0.00079573406,0.019110585,0.00036284915,0.00001147669],"about_ca_topic_score_codex":0.0018104401,"about_ca_topic_score_gemma":0.0009566087,"teacher_disagreement_score":0.004864395,"about_ca_system_score_codex":0.0009833351,"about_ca_system_score_gemma":0.00081158476,"threshold_uncertainty_score":0.025725663},"labels":[],"label_agreement":null},{"id":"W2089598455","doi":"10.1016/j.jsv.2013.11.019","title":"Stochastic stability of a fractional viscoelastic column under bounded noise excitation","year":2013,"lang":"en","type":"article","venue":"Journal of Sound and Vibration","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Viscoelasticity; Parametric statistics; Bounded function; Mathematical analysis; Lyapunov exponent; Noise (video); Eigenvalues and eigenvectors; Stability (learning theory); Statistical physics; Applied mathematics; Physics; Nonlinear system; Statistics","score_opus":0.0683108415300452,"score_gpt":0.31184327348376406,"score_spread":0.24353243195371888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089598455","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68528295,0.0002283237,0.3048875,0.0008310301,0.00008324217,0.000028203722,0.00015150226,0.00018037122,0.008326866],"genre_scores_gemma":[0.9961945,0.000040736577,0.0013769642,0.000021574806,0.000009680427,0.000008426856,0.000024475756,0.0000092287455,0.002314403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977,0.00004035654,0.000007034148,0.00007189626,0.00005842906,0.00005239056],"domain_scores_gemma":[0.9990452,0.00041494318,0.00024697496,0.000032666434,0.00017506517,0.00008512457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043651537,0.0004939215,0.0006970888,0.00051431323,0.0006475288,0.0012140318,0.0005358699,0.0011044783,0.0013460468],"category_scores_gemma":[0.0018086401,0.00025687512,0.0003597452,0.0002613039,0.0016675069,0.00052488944,0.0008647479,0.00047929338,0.000096016855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020421596,0.00004633718,0.0013138674,0.000057364763,0.000050145452,0.0003409312,0.00010989237,0.94944173,0.019390337,0.02535002,0.000295611,0.0033995993],"study_design_scores_gemma":[0.0000037926873,0.000020562718,0.0002837148,0.000002145193,0.0000046351065,0.000015214999,0.000011675415,0.99809974,0.0004501274,0.0010599534,0.000042303727,0.000006197001],"about_ca_topic_score_codex":0.010334286,"about_ca_topic_score_gemma":0.003331304,"teacher_disagreement_score":0.010334286,"about_ca_system_score_codex":0.0008136178,"about_ca_system_score_gemma":0.0008554518,"threshold_uncertainty_score":0.020548284},"labels":[],"label_agreement":null},{"id":"W2090914054","doi":"10.1007/s00348-014-1756-y","title":"General perspectives on model construction and evaluation for stochastic estimation, with application to a blunt trailing edge wake","year":2014,"lang":"en","type":"article","venue":"Experiments in Fluids","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Estimator; Computer science; Context (archaeology); Wake; Noise (video); Linear regression; Econometrics; Statistics; Mathematics; Geology; Machine learning; Physics; Mechanics; Artificial intelligence","score_opus":0.061857055723769004,"score_gpt":0.3743492948878257,"score_spread":0.3124922391640567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090914054","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00050651864,0.00015080032,0.99862003,0.00025948632,0.000021879916,0.00001968919,0.000021922526,0.00005123173,0.00034854293],"genre_scores_gemma":[0.12360799,0.002035913,0.8673521,0.0007652818,0.0007596182,0.00076984725,0.00048391215,0.00038113585,0.003844194],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99123657,0.0056687784,0.0005836424,0.0007758767,0.001460045,0.0002751689],"domain_scores_gemma":[0.95787233,0.034021307,0.0016470912,0.0029000572,0.0030970734,0.0004621697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025294462,0.0031251947,0.0034744164,0.002717985,0.0012540867,0.004834365,0.0052691833,0.006290957,0.0059059155],"category_scores_gemma":[0.06926506,0.0019265108,0.0034958208,0.0018377169,0.0050617824,0.006835593,0.0059515573,0.0059551946,0.00095198443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095924996,0.00010228609,0.0006458148,0.00030991674,0.00010596898,0.00019103588,0.00012323663,0.48184657,0.0029394045,0.48641184,0.0015360211,0.025691863],"study_design_scores_gemma":[0.000013821087,0.000050240145,0.00010486104,0.000049749393,0.000019707562,0.000043176016,0.000021385666,0.82002485,0.00088282407,0.17758338,0.0011775916,0.000028447417],"about_ca_topic_score_codex":0.004670473,"about_ca_topic_score_gemma":0.0033095602,"teacher_disagreement_score":0.025294462,"about_ca_system_score_codex":0.0021885494,"about_ca_system_score_gemma":0.003023176,"threshold_uncertainty_score":0.13377154},"labels":[],"label_agreement":null},{"id":"W2093193141","doi":"10.1007/s00477-005-0020-7","title":"Grain yield reliability analysis with crop water demand uncertainty","year":2006,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":76,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Evapotranspiration; Probability density function; Reliability (semiconductor); Discretization; Mathematics; Irrigation scheduling; Statistics; Crop yield; Irrigation; Mathematical optimization; Soil science; Computer science; Applied mathematics; Environmental science; Soil water; Agronomy","score_opus":0.041289588248241076,"score_gpt":0.34507837847085604,"score_spread":0.30378879022261496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093193141","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5050601,0.0007076444,0.49021426,0.0003698729,0.000025856305,0.00002760599,0.00019115485,0.00023853318,0.0031649766],"genre_scores_gemma":[0.9931672,0.00010630868,0.005995999,0.000010357489,0.000012772543,0.000010929958,0.000050019793,0.00003152011,0.0006148824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996232,0.00014327394,0.000016868122,0.00005635909,0.00011894898,0.00004133323],"domain_scores_gemma":[0.99463886,0.004354917,0.0003365185,0.00022426245,0.000403729,0.00004176065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015846948,0.00046976912,0.0005987256,0.00083928916,0.00018704365,0.00047984376,0.00055411115,0.0005600738,0.0007563745],"category_scores_gemma":[0.0074109198,0.0005185582,0.00059485517,0.00058520935,0.0004763163,0.0008472089,0.00043000237,0.00045741012,0.000113636765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004801129,0.000008766567,0.0009458719,0.000018890134,0.000027422253,0.00007044178,0.000020787156,0.99060726,0.0015908985,0.0024118088,0.000107149805,0.0041426807],"study_design_scores_gemma":[0.0000016530589,0.000013011382,0.00047947667,0.0000013143688,0.000010631133,0.000015023229,0.000004817638,0.99712247,0.00045916816,0.0018503013,0.000038832775,0.0000033502276],"about_ca_topic_score_codex":0.0033037346,"about_ca_topic_score_gemma":0.001925983,"teacher_disagreement_score":0.0033037346,"about_ca_system_score_codex":0.0007000954,"about_ca_system_score_gemma":0.00035052348,"threshold_uncertainty_score":0.008380771},"labels":[],"label_agreement":null},{"id":"W2095410057","doi":"10.1139/l99-060","title":"Assessment of time-dependent reliability of reinforced concrete columns with uncertain load eccentricity","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Structural engineering; Bending moment; Eccentricity (behavior); Moment (physics); Bending; Sensitivity (control systems); First-order reliability method; Structural load; Reinforcement; Computer science; Engineering; Random variable; Mathematics; Statistics; Power (physics); Physics","score_opus":0.017930021495087572,"score_gpt":0.24938568813062834,"score_spread":0.23145566663554076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095410057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26334432,0.00021604076,0.73565966,0.000035600573,0.0000071509553,0.000023399853,0.00004877553,0.000114832634,0.0005501513],"genre_scores_gemma":[0.97636694,0.00011965103,0.02304413,0.000004055686,0.0000074256777,0.000022228192,0.000060078368,0.000012270834,0.0003632633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993291,0.00021022509,0.000022058866,0.000074567564,0.00031547362,0.000048649086],"domain_scores_gemma":[0.99631006,0.0024410675,0.00051665306,0.00019384404,0.00046920934,0.000069061345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011732477,0.0005950743,0.00036285946,0.00085945893,0.00015536338,0.0003672057,0.0006014173,0.0005227213,0.00043142625],"category_scores_gemma":[0.0050189397,0.00028899606,0.00047362648,0.00038085305,0.0005113727,0.00056325994,0.00034298692,0.00051900075,0.00008426375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008858903,0.00002141084,0.0032014663,0.00004451589,0.00004983742,0.0001570333,0.000055106,0.9626785,0.012392946,0.0029720527,0.00008053956,0.018258],"study_design_scores_gemma":[0.0000026949692,0.000050994524,0.0017599086,0.000003960019,0.000014508899,0.00006830229,0.000010773979,0.9927411,0.0041427943,0.0011110225,0.00008299725,0.000010917208],"about_ca_topic_score_codex":0.0016907839,"about_ca_topic_score_gemma":0.0015927576,"teacher_disagreement_score":0.0016907839,"about_ca_system_score_codex":0.0005326287,"about_ca_system_score_gemma":0.00039549658,"threshold_uncertainty_score":0.006204784},"labels":[],"label_agreement":null},{"id":"W2095587821","doi":"10.1088/1755-1315/15/2/022005","title":"The role of high cycle fatigue (HCF) onset in Francis runner reliability","year":2012,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; École de Technologie Supérieure","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Context (archaeology); Range (aeronautics); Stress (linguistics); Diagram; Limit (mathematics); Computer science; Structural engineering; Engineering; Mathematics; Statistics; Physics; Power (physics); Thermodynamics; Mathematical analysis; Geography","score_opus":0.024473805773888448,"score_gpt":0.2440770565520182,"score_spread":0.21960325077812975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095587821","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.821094,0.0019921616,0.16985916,0.00040750898,0.000028079692,0.000030456797,0.0001248613,0.00016072218,0.0063031297],"genre_scores_gemma":[0.99804974,0.00012725429,0.0014192641,0.0000117314175,0.000005530057,0.0000070502274,0.000018689841,0.000008750961,0.00035205614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992506,0.00021071215,0.00003087602,0.00015257018,0.00023370379,0.00012154794],"domain_scores_gemma":[0.9935906,0.0046248497,0.0009167632,0.00023644097,0.0005094312,0.00012189337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017405048,0.00043315496,0.0006066346,0.0009415689,0.0003578999,0.00079450395,0.00077778724,0.0014756303,0.0009457398],"category_scores_gemma":[0.0068435604,0.00028354742,0.00045858952,0.00033869906,0.0011635392,0.0013946148,0.0005381605,0.0006189311,0.00011849519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026165284,0.00008337389,0.034699935,0.00024672967,0.00006324877,0.0009014949,0.00043805988,0.8749034,0.027340205,0.026838617,0.00052206573,0.033701234],"study_design_scores_gemma":[0.000009033955,0.00039414281,0.021167008,0.00004218916,0.000042278654,0.0005876924,0.00013301038,0.9535116,0.008252505,0.015208311,0.0005704544,0.00008182818],"about_ca_topic_score_codex":0.004140524,"about_ca_topic_score_gemma":0.0039672377,"teacher_disagreement_score":0.004140524,"about_ca_system_score_codex":0.000798437,"about_ca_system_score_gemma":0.00044451404,"threshold_uncertainty_score":0.009204745},"labels":[],"label_agreement":null},{"id":"W2096186970","doi":"10.1007/s00158-014-1144-5","title":"Solving multiobjective optimization problems using quasi-separable MDO formulations and analytical target cascading","year":2014,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematical optimization; Separable space; Pareto principle; Multi-objective optimization; Multidisciplinary design optimization; Engineering design process; Decomposition; Computer science; Aggregate (composite); Optimization problem; Function (biology); Decomposition method (queueing theory); Mathematics; Multidisciplinary approach; Engineering","score_opus":0.05171122976834131,"score_gpt":0.3296961774502628,"score_spread":0.2779849476819215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096186970","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0141152665,0.00027489857,0.9802971,0.00029715683,0.000046116267,0.000059078855,0.000046321315,0.00008762145,0.0047764056],"genre_scores_gemma":[0.7018223,0.00043946624,0.2910654,0.00019322625,0.00007225359,0.0005011831,0.00014335259,0.00009972678,0.0056631207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943143,0.00025892488,0.00002584385,0.000075069474,0.00014269778,0.000066011264],"domain_scores_gemma":[0.9976211,0.0017087429,0.00026164888,0.00010105768,0.0002201129,0.00008729048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018739242,0.0013284638,0.001522795,0.00080216577,0.00053109817,0.0015846918,0.001433653,0.0019442036,0.0029883434],"category_scores_gemma":[0.004402664,0.0011101558,0.0011209784,0.000928495,0.00095147034,0.0014142825,0.0019977686,0.001716367,0.00026850717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011553085,0.000013051941,0.00005103097,0.000026113607,0.000014660006,0.000014246155,0.000008230845,0.99410075,0.00012121363,0.0035599023,0.00008812763,0.0019910615],"study_design_scores_gemma":[0.0000026789735,0.0000059013314,0.000013191274,0.000002330911,0.0000020715306,0.0000018616475,0.0000027068402,0.998448,0.00003526958,0.0014311854,0.00005369538,0.0000010214607],"about_ca_topic_score_codex":0.00526387,"about_ca_topic_score_gemma":0.0048554656,"teacher_disagreement_score":0.00526387,"about_ca_system_score_codex":0.00090334046,"about_ca_system_score_gemma":0.0013506808,"threshold_uncertainty_score":0.010466456},"labels":[],"label_agreement":null},{"id":"W2096385857","doi":"10.24908/pceea.v0i0.3648","title":"Development of a Derivative Design Process Considering Uncertainties from Low Fidelity Analysis Tools","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Engineering design process; Sensitivity (control systems); Conceptual design; Fidelity; Probabilistic design; Computer science; Derivative (finance); Reliability engineering; Process (computing); Product design; Reliability (semiconductor); Material derivative; Concurrent engineering; Design process; Systems engineering; Industrial engineering; Product (mathematics); Engineering; Work in process; Process engineering; Mathematics; Mechanical engineering; Operations management","score_opus":0.09332293645265861,"score_gpt":0.28365247726228826,"score_spread":0.19032954080962966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096385857","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011054173,0.00001119474,0.99829334,0.000009168783,0.0000027679687,0.00003459672,0.0000058018036,0.00013643214,0.0004012919],"genre_scores_gemma":[0.07457737,0.0000692,0.9241248,0.000020326514,0.000009766367,0.00020232225,0.00007334247,0.00010764351,0.000815207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727875,0.0007081415,0.00017423842,0.00029811196,0.0014494142,0.00009130629],"domain_scores_gemma":[0.9943698,0.0034948494,0.0003516812,0.00057023397,0.0011466291,0.00006677951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056496635,0.0011320891,0.001251433,0.0014584159,0.0006611261,0.0014067079,0.001018368,0.00091645645,0.003208038],"category_scores_gemma":[0.009559788,0.00089948456,0.0013555759,0.0004925076,0.001055917,0.0009561928,0.0016752151,0.0020457015,0.00075618457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013487757,0.00010427332,0.0005502692,0.00039951736,0.000055602202,0.00026159504,0.00041954778,0.6783533,0.03954936,0.06508971,0.0008315638,0.21425049],"study_design_scores_gemma":[0.000018947438,0.00009646918,0.00016836707,0.00003547113,0.000017445927,0.00006290356,0.00001813771,0.9680162,0.010121461,0.017828694,0.0035953075,0.000020644538],"about_ca_topic_score_codex":0.0011308866,"about_ca_topic_score_gemma":0.0007775464,"teacher_disagreement_score":0.0056496635,"about_ca_system_score_codex":0.00059407664,"about_ca_system_score_gemma":0.0016641238,"threshold_uncertainty_score":0.029878676},"labels":[],"label_agreement":null},{"id":"W2096451443","doi":"10.1139/cjce-2013-0227","title":"Discussion on “Plotting positions for fitting distributions and extreme value analysis”","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Academy of Finland","keywords":"Weibull distribution; Monte Carlo method; Extreme value theory; Position (finance); Statistical physics; Distribution fitting; Generalized extreme value distribution; Cumulative distribution function; Distribution (mathematics); Probability distribution; Mathematics; Computer science; Statistics; Applied mathematics; Probability density function; Mathematical analysis; Physics","score_opus":0.04739453966466127,"score_gpt":0.26554033177135233,"score_spread":0.21814579210669105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096451443","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017917986,0.0018797893,0.96310365,0.017594235,0.004141767,0.00008804352,0.00012476732,0.00047544247,0.010800525],"genre_scores_gemma":[0.09443164,0.0026887308,0.8667428,0.019406298,0.0050548986,0.00084997824,0.00025510916,0.0010804613,0.009490137],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9584119,0.030386223,0.0025600297,0.002393322,0.00563231,0.0006163197],"domain_scores_gemma":[0.9206085,0.059934825,0.0030147436,0.0073900055,0.008391405,0.00066052936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039013848,0.0016838121,0.0010894119,0.002571054,0.002771921,0.0065638362,0.006761961,0.009263754,0.008321632],"category_scores_gemma":[0.14794552,0.0009671486,0.0027253376,0.00424081,0.009085527,0.009321486,0.003075877,0.011809864,0.002743935],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054415417,0.000027448848,0.0006858941,0.00017988498,0.000040018705,0.00032486778,0.00061327784,0.014151783,0.00067861273,0.9205967,0.025056247,0.037590947],"study_design_scores_gemma":[0.00004564254,0.000121331126,0.00076461333,0.000497728,0.000044481098,0.0007764388,0.00036451424,0.05471809,0.0042447317,0.756626,0.18163794,0.00015851975],"about_ca_topic_score_codex":0.001759152,"about_ca_topic_score_gemma":0.001109781,"teacher_disagreement_score":0.039013848,"about_ca_system_score_codex":0.002486254,"about_ca_system_score_gemma":0.0017814306,"threshold_uncertainty_score":0.20632744},"labels":[],"label_agreement":null},{"id":"W2096921648","doi":"10.1002/cjs.10045","title":"An efficient computational approach for prior sensitivity analysis and cross‐validation","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Sensitivity (control systems); Bayesian probability; Monte Carlo method; Cross-validation; Regularization (linguistics); Model selection; Approximate Bayesian computation; Computation; Context (archaeology); Machine learning; Path (computing); Data mining; Algorithm; Artificial intelligence; Statistics; Mathematics; Engineering","score_opus":0.04569505760470724,"score_gpt":0.3198627982856546,"score_spread":0.2741677406809474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096921648","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00070635753,0.000026010359,0.9986608,0.000040262355,0.000010536109,0.000035669724,0.000009910472,0.00025939426,0.00025110203],"genre_scores_gemma":[0.053867027,0.00006169181,0.94429684,0.000094735806,0.000044305085,0.0004778891,0.00010966199,0.00026562068,0.000782205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9892302,0.006706436,0.0005481472,0.0008671352,0.002310673,0.0003374619],"domain_scores_gemma":[0.96029437,0.031615872,0.0010860441,0.0028296807,0.0038504063,0.00032364772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017358592,0.0023487748,0.002530513,0.0046036798,0.0014534607,0.0020718884,0.003323977,0.0023727415,0.008904719],"category_scores_gemma":[0.058893308,0.0017643733,0.0023655712,0.0027621882,0.0020339873,0.002315635,0.004531765,0.004719386,0.0016794021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017937153,0.00021616516,0.0009430593,0.00016989504,0.00019757381,0.00020408764,0.00013164943,0.7360671,0.0035204885,0.07570549,0.0034298871,0.17923522],"study_design_scores_gemma":[0.000019887759,0.000022123244,0.00010335029,0.000014161157,0.000011651028,0.00003661538,0.0000058663545,0.9771881,0.00086184737,0.020850439,0.00087296334,0.000013008288],"about_ca_topic_score_codex":0.0055218106,"about_ca_topic_score_gemma":0.0052123535,"teacher_disagreement_score":0.017358592,"about_ca_system_score_codex":0.00179703,"about_ca_system_score_gemma":0.0037615753,"threshold_uncertainty_score":0.09180206},"labels":[],"label_agreement":null},{"id":"W2097364769","doi":"10.5555/2431518.2431693","title":"Panel discussion: integrating data from multiple simulation models of different fidelity","year":2011,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Extrapolation; Computational model; Fidelity; Markov chain Monte Carlo; Context (archaeology); Calibration; Presentation (obstetrics); Computation; Bayesian probability; Markov model; Field (mathematics); Markov chain; Machine learning; Artificial intelligence; Algorithm; Statistics; Mathematics","score_opus":0.5161449601405311,"score_gpt":0.38369697257892266,"score_spread":0.13244798756160847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097364769","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011809479,0.0061503835,0.1944527,0.57006663,0.06635334,0.0027858135,0.0037539308,0.0020750726,0.14255264],"genre_scores_gemma":[0.1588948,0.007978711,0.114291914,0.28700802,0.037920643,0.0058079264,0.0051465337,0.002342878,0.3806085],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99507266,0.0015853539,0.00021466955,0.0008807114,0.001788484,0.00045810058],"domain_scores_gemma":[0.98585653,0.0066692987,0.0005321804,0.0011157029,0.004924203,0.000902122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016070768,0.0015744751,0.0010866377,0.0008634161,0.0041146767,0.00585855,0.003713768,0.015358208,0.062590584],"category_scores_gemma":[0.025220381,0.00066305907,0.003071155,0.0009650247,0.0014930086,0.0051671327,0.0058420934,0.013053352,0.019927252],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003603882,0.00011569033,0.0008635289,0.00026447707,0.00007183485,0.00043826818,0.00043495043,0.0041908496,0.003740919,0.044954125,0.9013979,0.043167133],"study_design_scores_gemma":[0.00015814093,0.0002195714,0.0014467741,0.00077374827,0.00009528778,0.00025494787,0.0008602136,0.0075903856,0.00612454,0.06500782,0.91731286,0.00015582719],"about_ca_topic_score_codex":0.0022523906,"about_ca_topic_score_gemma":0.0026129882,"teacher_disagreement_score":0.062590584,"about_ca_system_score_codex":0.0027932501,"about_ca_system_score_gemma":0.0031365769,"threshold_uncertainty_score":0.20938635},"labels":[],"label_agreement":null},{"id":"W2097548601","doi":"10.1111/j.1467-8667.2007.00485.x","title":"Reliability‐Based Optimal Design of Electrical Transmission Towers Using Multi‐Objective Genetic Algorithms","year":2007,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":82,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Truss; Reliability (semiconductor); Tower; Finite element method; Optimal design; Reliability engineering; Genetic algorithm; Mathematical optimization; Transmission tower; Multi-objective optimization; Computer science; Pareto principle; Engineering; Structural engineering; Algorithm; Mathematics; Power (physics); Machine learning","score_opus":0.02931576540461784,"score_gpt":0.27495962918931494,"score_spread":0.2456438637846971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097548601","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07581974,0.0003586692,0.9157792,0.0002120239,0.000048039867,0.00011688799,0.00007880908,0.00030531586,0.0072812885],"genre_scores_gemma":[0.8023606,0.00024012495,0.19385663,0.000051655643,0.00003186369,0.0003080156,0.00011804275,0.00009573023,0.0029372957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995283,0.00020479412,0.000016353722,0.00007036516,0.00012116687,0.00005911231],"domain_scores_gemma":[0.99868554,0.0008094633,0.00018097865,0.000041850417,0.00023580845,0.00004631456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013922375,0.0013256939,0.0014325904,0.0020061247,0.00054911897,0.0010976708,0.0013347378,0.0017104686,0.0023142705],"category_scores_gemma":[0.003992214,0.0013044726,0.0010880469,0.0009778435,0.0008792229,0.0008226814,0.00069933286,0.00076769537,0.00030638237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006276446,0.0000042767288,0.000039341197,0.00000619546,0.0000050320814,0.0000074997392,0.0000058911173,0.9975823,0.00014085171,0.00037969026,0.000033445485,0.0017891361],"study_design_scores_gemma":[0.0000074229665,0.0000108108125,0.000029898041,0.0000032001165,0.0000050709086,0.0000024727876,0.000003155215,0.99929976,0.000106894644,0.00047753728,0.000051837585,0.000001929423],"about_ca_topic_score_codex":0.01022544,"about_ca_topic_score_gemma":0.008491452,"teacher_disagreement_score":0.01022544,"about_ca_system_score_codex":0.0016504162,"about_ca_system_score_gemma":0.0016831077,"threshold_uncertainty_score":0.02033186},"labels":[],"label_agreement":null},{"id":"W2097677959","doi":"10.3901/cjme.2009.01.027","title":"Reliable Space Pursuing for Reliability-based Design Optimization with Black-box Performance Functions","year":2009,"lang":"en","type":"article","venue":"Chinese Journal of Mechanical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Black box; Reliability (semiconductor); Space (punctuation); Reliability engineering; Computer science; Engineering; Physics; Artificial intelligence; Operating system","score_opus":0.029528969042311698,"score_gpt":0.2689866752676212,"score_spread":0.2394577062253095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097677959","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009773988,0.00019435032,0.9865319,0.00008640223,0.000018411889,0.0000396143,0.000027223625,0.0001241601,0.0032040416],"genre_scores_gemma":[0.6756098,0.0005297806,0.31656277,0.00015195884,0.000071122784,0.0004964363,0.00017614981,0.00026944507,0.0061325445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990972,0.0005151951,0.000024360716,0.000078283876,0.00021566525,0.000069149544],"domain_scores_gemma":[0.9980287,0.0014032548,0.00014095625,0.00013110157,0.00023740988,0.000058568876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003840821,0.0020858878,0.0018698018,0.0014994136,0.000549923,0.0013119407,0.001213571,0.0013297492,0.0041305185],"category_scores_gemma":[0.006436833,0.0008749165,0.0012462712,0.0008426596,0.0015415025,0.0015190026,0.0021962163,0.001415686,0.0004682124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008060427,0.00003100503,0.0001436595,0.000080385194,0.000040939674,0.00003056442,0.000050136998,0.95034146,0.0009750677,0.035599336,0.00041194048,0.012214842],"study_design_scores_gemma":[0.000006062256,0.000026022799,0.000021114764,0.000006882341,0.000006360962,0.0000039811084,0.0000033050596,0.98980343,0.00021044635,0.009715607,0.00019372255,0.0000030533854],"about_ca_topic_score_codex":0.0014914165,"about_ca_topic_score_gemma":0.000973791,"teacher_disagreement_score":0.0041305185,"about_ca_system_score_codex":0.0008090494,"about_ca_system_score_gemma":0.0010913927,"threshold_uncertainty_score":0.020312428},"labels":[],"label_agreement":null},{"id":"W2099127348","doi":"10.1007/978-3-642-12659-8_19","title":"Domain Decomposition of Stochastic PDEs: A Novel Preconditioner and Its Parallel Performance","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Preconditioner; Computer science; Domain decomposition methods; Domain (mathematical analysis); Decomposition; Parallel computing; Computational science; Algorithm; Mathematics; Iterative method; Finite element method","score_opus":0.03650840049300456,"score_gpt":0.2893831063925972,"score_spread":0.2528747058995926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099127348","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043641212,0.00016571542,0.99335206,0.00011359851,0.00007936582,0.000014989134,0.000042580836,0.00034133415,0.0015261341],"genre_scores_gemma":[0.08495956,0.00049301365,0.9066938,0.00012574923,0.00018690286,0.00011275895,0.00019684255,0.00032430072,0.0069070826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996836,0.00009191107,0.000017336113,0.000048810994,0.00012988377,0.00002849369],"domain_scores_gemma":[0.99957496,0.00017638622,0.00003556773,0.000090701506,0.000082371895,0.000040095158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005678967,0.0006206695,0.0006941391,0.0003137247,0.00027262702,0.0005409946,0.0005110221,0.0008455652,0.0029191375],"category_scores_gemma":[0.0010597657,0.00027335915,0.0005767544,0.0004991776,0.00048976863,0.000745631,0.0010909464,0.0015242172,0.0011298949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004503303,0.00025862647,0.00054492225,0.00043982835,0.000087369925,0.00025897374,0.000189781,0.26054162,0.1475146,0.13109793,0.017909572,0.44070637],"study_design_scores_gemma":[0.000035345,0.00004639694,0.0001373688,0.000010413851,0.0000121961875,0.00009316147,0.000008610109,0.9657467,0.014967029,0.01208619,0.0068449043,0.000011707613],"about_ca_topic_score_codex":0.00056317943,"about_ca_topic_score_gemma":0.0009134132,"teacher_disagreement_score":0.0029191375,"about_ca_system_score_codex":0.00016574157,"about_ca_system_score_gemma":0.0005577543,"threshold_uncertainty_score":0.009765506},"labels":[],"label_agreement":null},{"id":"W2099758012","doi":"10.1002/nme.4341","title":"Galerkin reduced‐order modeling scheme for time‐dependent randomly parametrized linear partial differential equations","year":2012,"lang":"en","type":"article","venue":"International Journal for Numerical Methods in Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Southampton","keywords":"Galerkin method; Basis function; Mathematics; Projection (relational algebra); Applied mathematics; Partial differential equation; Basis (linear algebra); Mathematical optimization; Mathematical analysis; Algorithm; Finite element method; Geometry","score_opus":0.16767182430382202,"score_gpt":0.46911232336910513,"score_spread":0.30144049906528314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099758012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012279845,0.00006959103,0.9859002,0.00008895074,0.000014199646,0.00002754953,0.00003125369,0.00011632006,0.0014720861],"genre_scores_gemma":[0.6203284,0.0003437378,0.37001213,0.000079954785,0.000028361777,0.00036291307,0.00022849905,0.00011909445,0.008496874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997054,0.000110377754,0.000010709954,0.000030496498,0.00012404876,0.000018911775],"domain_scores_gemma":[0.9997166,0.00012180473,0.000050953495,0.000037302132,0.000059982838,0.000013332691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006837506,0.00045107087,0.0007391228,0.0002946828,0.00026202615,0.0005960708,0.0009088966,0.0008025871,0.0014879162],"category_scores_gemma":[0.0009510938,0.00032257027,0.00072767,0.0002921489,0.0005347436,0.00056885503,0.00059956015,0.00088879437,0.00035767854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018576358,0.000014670283,0.00016538354,0.000033561468,0.000009474087,0.0000263753,0.000045411954,0.9709038,0.0035588904,0.016905753,0.00018100072,0.008137169],"study_design_scores_gemma":[0.000001188761,0.0000028619877,0.00000958193,8.933807e-7,5.9676177e-7,0.0000023324849,0.0000013129654,0.99906605,0.0001947612,0.0005265709,0.00019276295,0.0000010994745],"about_ca_topic_score_codex":0.0037468334,"about_ca_topic_score_gemma":0.0024186277,"teacher_disagreement_score":0.0037468334,"about_ca_system_score_codex":0.0006535981,"about_ca_system_score_gemma":0.00096990075,"threshold_uncertainty_score":0.007450044},"labels":[],"label_agreement":null},{"id":"W2100028952","doi":"10.1198/tech.2007.s464","title":"Uncertainty Analysis With High Dimensional Dependence Modelling","year":2007,"lang":"en","type":"article","venue":"Technometrics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":118,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SaskTel (Canada)","funders":"","keywords":"Statistical physics; Uncertainty quantification; Econometrics; Mathematics; Statistics; Physics","score_opus":0.07811162964312866,"score_gpt":0.3071532787814636,"score_spread":0.2290416491383349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100028952","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036843957,0.0022173424,0.99012846,0.0008624303,0.00010253531,0.000020131893,0.00022416114,0.00015251717,0.002608051],"genre_scores_gemma":[0.66253424,0.007142074,0.31769416,0.0011016408,0.000976001,0.0004502427,0.0017078979,0.00037825032,0.008015501],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99557364,0.0017711079,0.0002512406,0.00035943676,0.00183739,0.00020712028],"domain_scores_gemma":[0.98791933,0.008027974,0.0012482621,0.0012275374,0.0013061014,0.00027080436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006390522,0.0012427564,0.0014788292,0.0035143376,0.00079459976,0.0024733841,0.0015855981,0.0018888903,0.0029346282],"category_scores_gemma":[0.019410588,0.0011365516,0.0022042633,0.0030854046,0.0031673247,0.005215725,0.0039955466,0.0036676305,0.0008899545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013640658,0.00007746934,0.001606886,0.0002677837,0.00027322414,0.00020892249,0.00021134652,0.3776968,0.0014732768,0.5086081,0.02022525,0.089214526],"study_design_scores_gemma":[0.000012316538,0.000030605544,0.0007381172,0.00004723033,0.000051499235,0.00008590416,0.000016354135,0.5564351,0.0010550821,0.43746686,0.004007587,0.00005341368],"about_ca_topic_score_codex":0.0030390238,"about_ca_topic_score_gemma":0.0021619485,"teacher_disagreement_score":0.006390522,"about_ca_system_score_codex":0.001989131,"about_ca_system_score_gemma":0.0008825542,"threshold_uncertainty_score":0.033796728},"labels":[],"label_agreement":null},{"id":"W2101095696","doi":"10.1109/tsp.2004.828941","title":"Spectrum Estimation Using Multirate Observations","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Simon Fraser University; University of Delaware","keywords":"Spectral density; Autocorrelation; Spectral density estimation; Algorithm; Mathematics; Computation; SIGNAL (programming language); Discrete-time signal; Sampling (signal processing); Maximum entropy spectral estimation; Entropy (arrow of time); Signal processing; Stationary process; Computer science; Principle of maximum entropy; Applied mathematics; Statistics; Signal transfer function; Fourier transform; Mathematical analysis; Digital signal processing; Analog signal; Telecommunications","score_opus":0.16491045934704734,"score_gpt":0.34432544567450646,"score_spread":0.17941498632745911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101095696","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024554476,0.00020622155,0.97446096,0.00006566134,0.000016657199,0.000012286524,0.000030178833,0.000108079,0.0005454274],"genre_scores_gemma":[0.7335686,0.0006943349,0.26394892,0.00007210091,0.000100864425,0.000055889497,0.00018909933,0.000053225158,0.0013169592],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993017,0.0002743579,0.00003996255,0.00015061053,0.00018352082,0.000049877257],"domain_scores_gemma":[0.99754435,0.0015904426,0.0003582274,0.00029807593,0.00016493986,0.00004401965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001265848,0.00068872684,0.0009668469,0.0006530315,0.0002469742,0.0009985922,0.000747568,0.0009136704,0.0007870691],"category_scores_gemma":[0.007838011,0.0005308477,0.00063357153,0.00055957516,0.00072046445,0.0026408564,0.0011677453,0.0009408537,0.00028521675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004778161,0.00006757541,0.004376459,0.0002606769,0.00011347521,0.0003108709,0.00021142374,0.8152513,0.021070497,0.028360626,0.00044788126,0.12905136],"study_design_scores_gemma":[0.000008770392,0.000037157984,0.00064694113,0.000019047162,0.000012012046,0.00008249892,0.000016843162,0.98590565,0.004375192,0.008439686,0.00044192086,0.000014372685],"about_ca_topic_score_codex":0.0008893353,"about_ca_topic_score_gemma":0.0008379003,"teacher_disagreement_score":0.001265848,"about_ca_system_score_codex":0.00032175114,"about_ca_system_score_gemma":0.00032030104,"threshold_uncertainty_score":0.0066945553},"labels":[],"label_agreement":null},{"id":"W2101527983","doi":"10.3141/1701-06","title":"Reliability Approach to Intersection Sight Distance Design","year":2000,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intersection (aeronautics); Percentile; Reliability (semiconductor); Sight; Random variable; Statistics; Variance (accounting); Variable (mathematics); Mathematics; Engineering; Transport engineering; Mathematical analysis","score_opus":0.205848274280149,"score_gpt":0.4111759805251299,"score_spread":0.20532770624498087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101527983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005137133,0.000105855404,0.99300295,0.00004245633,0.00001537502,0.00003353758,0.000025119305,0.00013488645,0.0015027027],"genre_scores_gemma":[0.59561366,0.00039545164,0.40033817,0.00007262001,0.000110929635,0.00035578347,0.00025321628,0.00014366426,0.002716443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99294704,0.0028129714,0.00038269756,0.00086702185,0.002664999,0.00032534412],"domain_scores_gemma":[0.9899997,0.0048623597,0.0008427411,0.0007368065,0.0033954536,0.00016291477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004306847,0.0013600211,0.0012197125,0.0027288795,0.00058008614,0.0013406632,0.0014853163,0.000853636,0.0028179528],"category_scores_gemma":[0.015308424,0.0007623397,0.0011605179,0.0012792288,0.0012758754,0.0013902527,0.0016990313,0.001435582,0.00063970516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017862687,0.000049532315,0.0027521905,0.00020020052,0.00007807702,0.00008898887,0.00017396211,0.8419708,0.0029560223,0.052657295,0.0011011931,0.097793125],"study_design_scores_gemma":[0.000021701906,0.00027343945,0.00078421575,0.00003440055,0.000033649776,0.00015678856,0.00004001539,0.9619614,0.0028481537,0.030531557,0.0032856255,0.000029011544],"about_ca_topic_score_codex":0.002234489,"about_ca_topic_score_gemma":0.001292082,"teacher_disagreement_score":0.004306847,"about_ca_system_score_codex":0.0013007964,"about_ca_system_score_gemma":0.0016892939,"threshold_uncertainty_score":0.02277708},"labels":[],"label_agreement":null},{"id":"W2101811205","doi":"10.1177/0021998310366062","title":"Application of Response Sensitivity in Composite Processing","year":2010,"lang":"en","type":"article","venue":"Journal of Composite Materials","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"AUTO21 Network of Centres of Excellence; University of British Columbia","keywords":"Sensitivity (control systems); Calibration; Computation; Computer science; Reliability (semiconductor); Composite number; Software; Process (computing); Suite; Algorithm; Reliability engineering; Mathematics; Electronic engineering; Engineering","score_opus":0.030790373032466935,"score_gpt":0.3278419921789999,"score_spread":0.29705161914653294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101811205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019135073,0.0003263505,0.9726452,0.00013787393,0.00003761112,0.000044209384,0.00003209615,0.00038493756,0.007256606],"genre_scores_gemma":[0.8271426,0.0008427837,0.16936477,0.00011794942,0.000034524714,0.00010592861,0.00004365682,0.00010979636,0.0022379868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989655,0.0004105585,0.00003732533,0.00010924607,0.0004325718,0.00004491581],"domain_scores_gemma":[0.9983406,0.0012209079,0.00010945359,0.00014868066,0.00016215115,0.000018223833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013283613,0.00077205425,0.00050379947,0.00072505994,0.00033843485,0.00068954093,0.0005577283,0.0008305372,0.0014730884],"category_scores_gemma":[0.003913329,0.0003513396,0.0006712868,0.0004503817,0.0009464465,0.0007469452,0.0011409221,0.00077773904,0.00030061806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004059231,0.000023309045,0.00037862273,0.000094323615,0.000022480703,0.0001154419,0.00006659801,0.9357925,0.015086713,0.028242648,0.00019024043,0.01994648],"study_design_scores_gemma":[0.0000041738103,0.000039893817,0.00011963083,0.000013470027,0.0000061370592,0.000069258225,0.000007123541,0.97955954,0.009755674,0.009012737,0.0013978808,0.000014401728],"about_ca_topic_score_codex":0.0011164108,"about_ca_topic_score_gemma":0.0004589166,"teacher_disagreement_score":0.0014730884,"about_ca_system_score_codex":0.0005149836,"about_ca_system_score_gemma":0.0004737367,"threshold_uncertainty_score":0.0070251226},"labels":[],"label_agreement":null},{"id":"W2102599847","doi":"10.1115/wind2003-866","title":"Probabilistic Analysis of List Data for the Estimation of Extreme Design Loads for Wind Turbine Components","year":2003,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gumbel distribution; Extreme value theory; Weibull distribution; Wind power; Extrapolation; Turbine; Computer science; Probabilistic logic; Wind speed; Statistics; Mathematics; Engineering; Meteorology; Aerospace engineering","score_opus":0.37526170451858726,"score_gpt":0.37956244677896783,"score_spread":0.0043007422603805745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102599847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07607298,0.00008366862,0.9223559,0.000109298184,0.000010162544,0.00007085367,0.00038327175,0.00038400787,0.00052987627],"genre_scores_gemma":[0.8453952,0.00017619244,0.1511594,0.000057240162,0.0000401995,0.00033939476,0.0023343642,0.00006853756,0.00042937245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99602926,0.002326935,0.00023456103,0.00028648635,0.0010150169,0.00010777419],"domain_scores_gemma":[0.9548522,0.035802323,0.003991605,0.003043575,0.0021033639,0.00020684567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008312433,0.0006620425,0.00068485376,0.0018575734,0.0004743544,0.0008690796,0.0012122182,0.0009937969,0.0014115222],"category_scores_gemma":[0.04589628,0.0005342183,0.0007352876,0.0014998856,0.00076891773,0.002041106,0.0013567293,0.0012283758,0.0004111526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000636841,0.00016475086,0.027307386,0.0003496398,0.00014447808,0.00017650038,0.00023377573,0.81128544,0.0052411146,0.026678288,0.0013232052,0.12645854],"study_design_scores_gemma":[0.000019161722,0.00013776904,0.005611004,0.000029939309,0.00001495877,0.00006975485,0.000041673502,0.979301,0.0026426024,0.011403647,0.0006946028,0.000033915556],"about_ca_topic_score_codex":0.0007113157,"about_ca_topic_score_gemma":0.0012528767,"teacher_disagreement_score":0.008312433,"about_ca_system_score_codex":0.00060231506,"about_ca_system_score_gemma":0.0005562381,"threshold_uncertainty_score":0.04396087},"labels":[],"label_agreement":null},{"id":"W2102634391","doi":"10.1080/02331888.2011.637629","title":"Reliability estimation in stress–strength models: an MCMC approach","year":2011,"lang":"en","type":"article","venue":"Statistics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Memorial University of Newfoundland","keywords":"Mathematics; Reliability (semiconductor); Markov chain Monte Carlo; Estimation; Statistics; Econometrics; Stress (linguistics); Applied mathematics; Monte Carlo method","score_opus":0.1834230484285477,"score_gpt":0.33609137025678865,"score_spread":0.15266832182824094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102634391","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014089222,0.000256602,0.98407054,0.00024972303,0.000032851007,0.00009670534,0.00012014636,0.0002830399,0.0008012393],"genre_scores_gemma":[0.41828325,0.0004237663,0.57727003,0.00024306022,0.00014821396,0.000738275,0.0007976435,0.0002801444,0.0018156455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643683,0.0023272345,0.00011739482,0.00032037887,0.00066283526,0.00013533107],"domain_scores_gemma":[0.97501737,0.020248024,0.0011366269,0.0014495861,0.0019044077,0.00024394535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008866343,0.0010183147,0.0019499451,0.003489257,0.0012867068,0.0015049637,0.004053745,0.0021037292,0.004126637],"category_scores_gemma":[0.0420215,0.001413239,0.0016458554,0.0023496118,0.0016844672,0.001994742,0.001631308,0.003351879,0.0005571412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006949,0.000051149364,0.0019366512,0.00009915941,0.0001517898,0.000088545894,0.00012128219,0.91386986,0.0006052788,0.051160954,0.0010576937,0.030788148],"study_design_scores_gemma":[0.000008548846,0.000003844289,0.00012643797,0.000013709726,0.000009414311,0.000007863251,0.0000049128853,0.9860016,0.0001284466,0.013467779,0.00021964198,0.000007803183],"about_ca_topic_score_codex":0.025450483,"about_ca_topic_score_gemma":0.02076389,"teacher_disagreement_score":0.025450483,"about_ca_system_score_codex":0.0022269674,"about_ca_system_score_gemma":0.002801192,"threshold_uncertainty_score":0.05060464},"labels":[],"label_agreement":null},{"id":"W2103625133","doi":"10.5539/ass.v9n7p231","title":"Electricity Consumption Analysis Using Spline Regression Models: The Case of a Turkish Province","year":2013,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Polynomial regression; Spline (mechanical); Regression analysis; Electricity; Econometrics; Turkish; Statistics; Energy consumption; Consumption (sociology); Quadratic function; Linear regression; Mathematics; Quadratic equation; Electric potential energy; Regression; Economics; Energy (signal processing); Engineering; Electrical engineering; Sociology; Social science","score_opus":0.0924996668288118,"score_gpt":0.3584446122317315,"score_spread":0.2659449454029197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103625133","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9804717,0.00018739761,0.017034635,0.00022036617,0.000012681161,0.000023796209,0.0003129628,0.000051411575,0.0016850907],"genre_scores_gemma":[0.99601704,0.00012122697,0.0025819733,0.000004937777,0.0000061773326,0.000012160936,0.00021090402,0.000008709864,0.0010369499],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992292,0.0004059947,0.000031246163,0.00009270912,0.00007324682,0.00016764716],"domain_scores_gemma":[0.99762434,0.001530455,0.00025609834,0.0001418567,0.00036884806,0.00007856127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019926794,0.00038792848,0.00062093016,0.00105869,0.000504161,0.00096624,0.0009307106,0.0006938279,0.0016786866],"category_scores_gemma":[0.0048537813,0.00023106718,0.0013577021,0.0018711222,0.0005051365,0.0004337518,0.00062318635,0.0007758582,0.00021756289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006021495,0.00028974324,0.099225804,0.00011841386,0.0002380386,0.001729844,0.00042591363,0.8616413,0.00068905164,0.014065664,0.0011342203,0.01983986],"study_design_scores_gemma":[0.000016160888,0.00007051412,0.0137892505,0.000009828143,0.00005271004,0.000080368154,0.00041380417,0.9829275,0.00016579362,0.0018380092,0.00061622896,0.000019797544],"about_ca_topic_score_codex":0.16113181,"about_ca_topic_score_gemma":0.09159829,"teacher_disagreement_score":0.16113181,"about_ca_system_score_codex":0.0013266065,"about_ca_system_score_gemma":0.0013619945,"threshold_uncertainty_score":0.32038784},"labels":[],"label_agreement":null},{"id":"W2104993276","doi":"10.1109/tbme.2006.889203","title":"Reduced-Order Preconditioning for Bidomain Simulations","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Preconditioner; Conjugate gradient method; Computer science; Speedup; Iterative method; Linear system; Algorithm; Mathematical optimization; Reduction (mathematics); Sparse matrix; Arnoldi iteration; Applied mathematics; Mathematics; Parallel computing; Physics; Mathematical analysis","score_opus":0.05209055773577082,"score_gpt":0.33196056055820744,"score_spread":0.2798700028224366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104993276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031304747,0.00023286897,0.9610539,0.00015782405,0.00008497291,0.000082467974,0.00012091848,0.0011636822,0.005798738],"genre_scores_gemma":[0.4853793,0.00039196957,0.5096138,0.00012705586,0.000032251377,0.0003626838,0.00025920768,0.0004502256,0.003383481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997693,0.00008418868,0.000011722436,0.000024263361,0.00008356881,0.000027074964],"domain_scores_gemma":[0.9995383,0.00020344017,0.000044455235,0.000091040405,0.00007863217,0.000044142103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039507338,0.00039173488,0.00058563787,0.0002285908,0.00045868222,0.0005276904,0.0006366565,0.0006753854,0.0033477591],"category_scores_gemma":[0.0013480117,0.0002636784,0.00031274045,0.00027503647,0.00042764714,0.0004951202,0.00088613626,0.00082395435,0.0007741189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001842096,0.00008735685,0.0013187755,0.00023612096,0.000033091088,0.0002826313,0.0002551893,0.87253124,0.04123233,0.034735065,0.0030095635,0.04609434],"study_design_scores_gemma":[0.000008797586,0.0000145388085,0.00006529103,0.000003608247,0.0000016937967,0.000011905337,0.0000075148196,0.99420905,0.00218914,0.0021073935,0.0013772553,0.000003848736],"about_ca_topic_score_codex":0.0027664697,"about_ca_topic_score_gemma":0.0022549683,"teacher_disagreement_score":0.0033477591,"about_ca_system_score_codex":0.00034781318,"about_ca_system_score_gemma":0.00082608644,"threshold_uncertainty_score":0.011199355},"labels":[],"label_agreement":null},{"id":"W2106315576","doi":"10.1016/j.ress.2006.11.003","title":"Gamma processes and peaks-over-threshold distributions for time-dependent reliability","year":2006,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":217,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gamma process; Reliability (semiconductor); Stochastic process; Gamma distribution; Poisson distribution; Generalized Pareto distribution; Pareto principle; Stochastic modelling; Process (computing); Event (particle physics); Mathematics; Poisson process; Statistical physics; Reliability engineering; Computer science; Mathematical optimization; Statistics; Engineering; Physics; Extreme value theory","score_opus":0.01558084392964842,"score_gpt":0.25271440404775064,"score_spread":0.23713356011810222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106315576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033890508,0.0026527308,0.9530621,0.0010902702,0.00017802705,0.00007192957,0.0002025131,0.00042360427,0.008428375],"genre_scores_gemma":[0.89736325,0.005727696,0.075940855,0.00089414633,0.0011993933,0.00038909627,0.0005046659,0.00046454373,0.017516382],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974698,0.00085928437,0.000135597,0.0004944324,0.0005749138,0.00046607893],"domain_scores_gemma":[0.9725739,0.020768343,0.0021440682,0.001977749,0.0017790484,0.00075692165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011030382,0.0023852307,0.0025600682,0.0037383477,0.0010689013,0.0041998182,0.0049100094,0.0036949746,0.0062989746],"category_scores_gemma":[0.045380067,0.0014692436,0.0026707635,0.0029620572,0.0069208266,0.008976885,0.0029011848,0.0054905163,0.0010394373],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055368353,0.000024226363,0.0005245613,0.00009185347,0.000047105015,0.00015869131,0.00026419148,0.064603195,0.000661463,0.9262151,0.0013694087,0.005984891],"study_design_scores_gemma":[0.000018407705,0.000015190043,0.00033031678,0.000037893766,0.00003534965,0.00014700969,0.00006574091,0.21490519,0.00027923236,0.7834173,0.00070340413,0.000045011893],"about_ca_topic_score_codex":0.0022850046,"about_ca_topic_score_gemma":0.0013891523,"teacher_disagreement_score":0.011030382,"about_ca_system_score_codex":0.002711325,"about_ca_system_score_gemma":0.0014723008,"threshold_uncertainty_score":0.058334887},"labels":[],"label_agreement":null},{"id":"W2106605796","doi":"10.1007/s40072-015-0049-7","title":"Optimization of mesh hierarchies in multilevel Monte Carlo samplers","year":2015,"lang":"en","type":"article","venue":"Stochastic Partial Differential Equations Analysis and Computations","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Discretization; Mathematics; Monte Carlo method; Applied mathematics; Solver; Mathematical optimization; Piecewise; Mathematical analysis","score_opus":0.1198611132401476,"score_gpt":0.34360968545169013,"score_spread":0.22374857221154254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106605796","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023959318,0.00034660232,0.97260225,0.00037029685,0.000045457917,0.000072866555,0.00009963278,0.0002738223,0.0022297814],"genre_scores_gemma":[0.517562,0.0004049019,0.4765563,0.00034608148,0.0001345419,0.0006371936,0.0004381222,0.00036406398,0.0035567896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983334,0.0008716033,0.00008637358,0.00017694115,0.00034370387,0.00018795254],"domain_scores_gemma":[0.9839258,0.01333335,0.0006098992,0.0007468836,0.0008474369,0.0005366292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049080155,0.0007466919,0.0023028334,0.0014187408,0.00091194967,0.0019306118,0.002626405,0.0027400765,0.0053175744],"category_scores_gemma":[0.024618926,0.0016585088,0.0012981038,0.0012526929,0.001699189,0.0021284248,0.0037446965,0.0023392944,0.0006431126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086743166,0.000034648907,0.0006366938,0.00006531608,0.000030898744,0.000028356473,0.000042936026,0.95730597,0.000528425,0.029108603,0.0005502015,0.011581148],"study_design_scores_gemma":[0.000009685612,0.0000071836225,0.00003158519,0.000005191395,0.00000306976,0.0000024589694,0.0000028538202,0.99487996,0.0000666539,0.004901299,0.000088103974,0.0000019564047],"about_ca_topic_score_codex":0.006357498,"about_ca_topic_score_gemma":0.008349652,"teacher_disagreement_score":0.006357498,"about_ca_system_score_codex":0.0023952492,"about_ca_system_score_gemma":0.0018557213,"threshold_uncertainty_score":0.025956333},"labels":[],"label_agreement":null},{"id":"W2109255107","doi":"10.5402/2012/465320","title":"Inverse Dispersion for an Unknown Number of Sources: Model Selection and Uncertainty Analysis","year":2012,"lang":"en","type":"article","venue":"ISRN Applied Mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Dispersion (optics); Inverse; Algorithm; Residual; Bayesian probability; Computer science; Atmospheric dispersion modeling; Noise (video); Inverse problem; Statistics; Mathematics; Artificial intelligence; Physics; Chemistry; Mathematical analysis; Optics","score_opus":0.09275253035563048,"score_gpt":0.34586995797692155,"score_spread":0.2531174276212911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109255107","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011611979,0.000059058537,0.9984372,0.00004569408,0.0000042342795,0.000011659421,0.000018273546,0.00008566129,0.00017695315],"genre_scores_gemma":[0.15690291,0.0005970621,0.83786464,0.000099591336,0.00009805853,0.00043466076,0.00043993792,0.0002465384,0.003316498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808204,0.00072104495,0.00008437828,0.00035725455,0.00066226587,0.00009306651],"domain_scores_gemma":[0.99478865,0.0039712326,0.00041035708,0.00034950676,0.00041114504,0.000069148184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004104772,0.0013692825,0.0015717219,0.0015154104,0.00073605217,0.0016523508,0.0022220649,0.0015617015,0.0014252146],"category_scores_gemma":[0.010698628,0.0012323098,0.0021250795,0.0012990958,0.0015431157,0.002038203,0.002347047,0.0025661392,0.0006071543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058506634,0.000033535754,0.0007875411,0.00009104138,0.00009408725,0.00011822296,0.00009719137,0.90197504,0.0029837247,0.03660865,0.0007832231,0.056369174],"study_design_scores_gemma":[0.0000049552,0.000010618308,0.00011685927,0.0000087469025,0.000008466711,0.000031276577,0.000005943031,0.9846726,0.000734438,0.013752603,0.0006378417,0.000015672562],"about_ca_topic_score_codex":0.0074025304,"about_ca_topic_score_gemma":0.005180028,"teacher_disagreement_score":0.0074025304,"about_ca_system_score_codex":0.0014899537,"about_ca_system_score_gemma":0.0018369806,"threshold_uncertainty_score":0.021708429},"labels":[],"label_agreement":null},{"id":"W2109865897","doi":"10.1007/s00226-011-0401-7","title":"Duration-of-load and creep effects in strand-based wood composite: a creep-rupture model","year":2011,"lang":"en","type":"article","venue":"Wood Science and Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Canadian Wood Council","funders":"","keywords":"Creep; Deflection (physics); Materials science; Composite material; Composite number; Structural engineering; Flexural strength; Engineering","score_opus":0.04402019403427464,"score_gpt":0.2839311454180328,"score_spread":0.23991095138375818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109865897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51428145,0.0011613446,0.4736514,0.00078370655,0.00010652357,0.000097764845,0.00041890424,0.00032699597,0.009171861],"genre_scores_gemma":[0.9871414,0.00039818892,0.0046438114,0.000050265855,0.00003862384,0.000064710395,0.000104936014,0.000046995723,0.0075112213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999665,0.00008953377,0.000021151674,0.00008153815,0.00006285182,0.000080068494],"domain_scores_gemma":[0.99801874,0.0011488553,0.0003768678,0.00011569624,0.00021719169,0.00012257845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017126903,0.0009124546,0.0011338681,0.00076740724,0.00045654908,0.0008735021,0.002094516,0.002760766,0.0014956931],"category_scores_gemma":[0.0037178653,0.00082005747,0.0011495595,0.00065193395,0.0012671364,0.0015564814,0.00084456714,0.0011194055,0.00047125792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007471076,0.000059547976,0.00070222165,0.000030266416,0.00002169427,0.000099609555,0.00005513136,0.98997796,0.002188162,0.004517427,0.00015229691,0.002121047],"study_design_scores_gemma":[0.000004315276,0.000022660313,0.00023379507,0.0000025346696,0.000019350182,0.000013121777,0.000006104911,0.99853563,0.00018038893,0.0009227403,0.000052896652,0.000006492375],"about_ca_topic_score_codex":0.006911178,"about_ca_topic_score_gemma":0.006600131,"teacher_disagreement_score":0.006911178,"about_ca_system_score_codex":0.0008884379,"about_ca_system_score_gemma":0.0007957049,"threshold_uncertainty_score":0.01374191},"labels":[],"label_agreement":null},{"id":"W2110410763","doi":"10.1239/jap/996986650","title":"Boundary crossing probability for Brownian motion","year":2001,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":104,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mathematics; Boundary (topology); Piecewise; Brownian motion; Piecewise linear function; Mathematical analysis; Monte Carlo method; Applied mathematics; Statistics","score_opus":0.10990585948246177,"score_gpt":0.3361828883833318,"score_spread":0.22627702890087004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110410763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07489239,0.0015433396,0.9112746,0.00038660766,0.000111492605,0.000050476378,0.00006990636,0.0002762544,0.01139488],"genre_scores_gemma":[0.88982546,0.0017693648,0.0999609,0.00018550716,0.00015682034,0.0001794487,0.00020487883,0.00015001639,0.007567689],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985845,0.00030606784,0.00005730473,0.00034811196,0.0004911849,0.0002129391],"domain_scores_gemma":[0.99465203,0.0033883064,0.00057437463,0.00043064935,0.0005734486,0.00038120555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036173416,0.00046331252,0.0011174275,0.0018832809,0.0009434323,0.0020952625,0.0017922294,0.0021947892,0.0060596787],"category_scores_gemma":[0.021936951,0.00043445383,0.0009658267,0.0009015932,0.0026929753,0.004449482,0.0022855417,0.0023108567,0.0008702916],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007783878,0.000020784153,0.0011630564,0.000106811334,0.000020985955,0.00017342929,0.00021791465,0.18453154,0.002476767,0.7942271,0.00093085377,0.016052792],"study_design_scores_gemma":[0.000014912282,0.000030916148,0.0006826295,0.00004056796,0.000013109511,0.00020130929,0.000030422532,0.68838197,0.0013322282,0.30763775,0.0015975712,0.00003662627],"about_ca_topic_score_codex":0.0014366173,"about_ca_topic_score_gemma":0.0003713663,"teacher_disagreement_score":0.0060596787,"about_ca_system_score_codex":0.0014670423,"about_ca_system_score_gemma":0.00051865017,"threshold_uncertainty_score":0.020271659},"labels":[],"label_agreement":null},{"id":"W2111666498","doi":"10.1093/biomet/ass065","title":"Strong orthogonal arrays and associated Latin hypercubes for computer experiments","year":2012,"lang":"en","type":"article","venue":"Biometrika","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":96,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Latin hypercube sampling; Hypercube; Beijing; China; Mathematics; Library science; Combinatorics; Statistics; Computer science; Geography; Monte Carlo method; Archaeology","score_opus":0.20759496997999877,"score_gpt":0.3704516957843537,"score_spread":0.16285672580435495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111666498","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015499528,0.00024279735,0.9965146,0.00007985712,0.000066144945,0.000091417925,0.00006023329,0.00011774339,0.0012772322],"genre_scores_gemma":[0.060043037,0.0007441915,0.93387645,0.00029008364,0.00021555767,0.0030553604,0.0002062336,0.00011929762,0.0014497336],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9815749,0.014382913,0.00069567474,0.0012495519,0.0018183751,0.00027863221],"domain_scores_gemma":[0.97167814,0.022274366,0.0017821963,0.0024338027,0.001530656,0.0003008782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010365588,0.0014578709,0.0014169195,0.0010047815,0.0004884751,0.0018665307,0.0012431465,0.0010671488,0.0053602266],"category_scores_gemma":[0.031556454,0.0006345529,0.0011725253,0.0016731874,0.002276255,0.0021774098,0.0018274062,0.002209099,0.0015989298],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007263598,0.00012152067,0.00081197143,0.000739025,0.00016056985,0.00011084556,0.00027987355,0.124324255,0.010093653,0.6823647,0.0030384168,0.17722882],"study_design_scores_gemma":[0.00025396663,0.0010247484,0.00045786682,0.00016421857,0.00006343041,0.00013798106,0.00010427233,0.2735908,0.008016651,0.6883167,0.027777046,0.00009216062],"about_ca_topic_score_codex":0.00020637996,"about_ca_topic_score_gemma":0.00020100224,"teacher_disagreement_score":0.010365588,"about_ca_system_score_codex":0.0006945666,"about_ca_system_score_gemma":0.0011617551,"threshold_uncertainty_score":0.054819167},"labels":[],"label_agreement":null},{"id":"W2112020065","doi":"10.1061/(asce)0887-3801(2008)22:5(281)","title":"Software Framework for Parameter Updating and Finite-Element Response Sensitivity Analysis","year":2008,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"OpenSees; Finite element method; Sensitivity (control systems); Computer science; Software; Domain (mathematical analysis); Engineering; Structural engineering; Programming language; Mathematics; Electronic engineering","score_opus":0.05873932865167243,"score_gpt":0.3182777727816025,"score_spread":0.2595384441299301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112020065","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004094648,0.000031935073,0.97610414,0.000027367536,0.000018935094,0.00006412272,0.0001650458,0.022066541,0.0011124501],"genre_scores_gemma":[0.043645862,0.0002412889,0.93478197,0.00014691085,0.000063963926,0.0010013798,0.0024690153,0.011757408,0.005892211],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971138,0.0003833518,0.00032387616,0.00029195187,0.0016941178,0.00019283319],"domain_scores_gemma":[0.99719256,0.0011076519,0.00018012893,0.00059319753,0.0008146291,0.00011186636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00408325,0.0018353105,0.0016370424,0.0019625393,0.0008516293,0.0022781065,0.005679573,0.0017268915,0.023543444],"category_scores_gemma":[0.0068182694,0.0017272045,0.0023455278,0.0011692239,0.0012580832,0.0021728314,0.0027543525,0.0030542936,0.0077211815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040813224,0.00032901103,0.002338511,0.0011373811,0.00036075123,0.00077175826,0.00083199673,0.26838067,0.03009603,0.19418094,0.05628724,0.44487762],"study_design_scores_gemma":[0.00020590043,0.00008283395,0.0005061092,0.00013644407,0.00006221609,0.00043389137,0.00007535273,0.7438655,0.020277077,0.07994003,0.15426911,0.00014555371],"about_ca_topic_score_codex":0.0042123226,"about_ca_topic_score_gemma":0.004475553,"teacher_disagreement_score":0.023543444,"about_ca_system_score_codex":0.0011007625,"about_ca_system_score_gemma":0.0020658763,"threshold_uncertainty_score":0.07876068},"labels":[],"label_agreement":null},{"id":"W2112534584","doi":"","title":"Scaling and Probabilistic Smoothing (SAPS)","year":2007,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Software; Computer science; Smoothing; Solver; Set (abstract data type); Algorithm; Code (set theory); Scaling; Probabilistic logic; Theoretical computer science; Programming language; Mathematics; Artificial intelligence","score_opus":0.08846140776524263,"score_gpt":0.3587119264338947,"score_spread":0.2702505186686521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112534584","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031908643,0.00030092816,0.9870684,0.00018173966,0.00016489219,0.000051480183,0.00015840508,0.0021667054,0.0067166584],"genre_scores_gemma":[0.20559825,0.0005436391,0.77298445,0.00037455495,0.00045536173,0.00029256023,0.0010247863,0.0021026584,0.016623756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976361,0.00065252924,0.00015646356,0.00046650623,0.0009342594,0.00015407188],"domain_scores_gemma":[0.9960741,0.0012306249,0.00029475594,0.0015722378,0.00067225145,0.00015618908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027540266,0.0013144385,0.001383744,0.0012449737,0.0008427026,0.0018103393,0.0020042115,0.0013975531,0.021619428],"category_scores_gemma":[0.009888684,0.00071304955,0.0016100515,0.0019292587,0.0012411146,0.0016445678,0.0032891475,0.0019154578,0.007539837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002572738,0.00009645265,0.0012678861,0.00042366458,0.00019255817,0.00022017107,0.0001751606,0.17627181,0.008489158,0.2759322,0.03690434,0.4997694],"study_design_scores_gemma":[0.000052576426,0.00011114201,0.00070946,0.000042724936,0.00005929903,0.0003089375,0.00003399416,0.77954555,0.006139197,0.17160827,0.041328594,0.000060206206],"about_ca_topic_score_codex":0.0019338266,"about_ca_topic_score_gemma":0.0020475267,"teacher_disagreement_score":0.021619428,"about_ca_system_score_codex":0.0006768658,"about_ca_system_score_gemma":0.0012363872,"threshold_uncertainty_score":0.07232422},"labels":[],"label_agreement":null},{"id":"W2113187029","doi":"10.1016/j.cam.2009.12.026","title":"Analytical existence of solutions to a system of nonlinear equations with application","year":2010,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport","keywords":"Mathematics; Nonlinear system; Schur complement; Measure (data warehouse); Applied mathematics; Iterative method; Complement (music); Simple (philosophy); Newton's method; Numerical analysis; System of linear equations; Mathematical analysis; Mathematical optimization; Computer science; Eigenvalues and eigenvectors","score_opus":0.06160751295487764,"score_gpt":0.31478577319774304,"score_spread":0.2531782602428654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113187029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21981153,0.0021918358,0.6876333,0.00443057,0.0005296161,0.00018042682,0.00012801305,0.00022440289,0.08487036],"genre_scores_gemma":[0.9416569,0.0008643036,0.03385622,0.00024810818,0.00017417179,0.00009645833,0.00006414349,0.000054292184,0.022985432],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99971324,0.00008809682,0.000018557528,0.000053252574,0.00008252798,0.0000443004],"domain_scores_gemma":[0.9988539,0.0006050025,0.000120080294,0.000055996854,0.00030109574,0.00006395218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006884647,0.0007006871,0.00068790134,0.0009560507,0.0009028049,0.0015559633,0.0007812722,0.002131295,0.0035128784],"category_scores_gemma":[0.006056678,0.00048022743,0.0006487868,0.00063241314,0.0019047743,0.0010756319,0.0016684533,0.0011662812,0.00047979024],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015608934,0.00012505322,0.0014214531,0.00043324177,0.00007885616,0.0010339196,0.0010509965,0.18622106,0.029022887,0.74787515,0.004437754,0.02814351],"study_design_scores_gemma":[0.000022586197,0.000057143166,0.00039166873,0.00004902545,0.000020013314,0.00040287277,0.00024342825,0.83672905,0.0028763025,0.15548214,0.003692289,0.000033415843],"about_ca_topic_score_codex":0.0013587407,"about_ca_topic_score_gemma":0.0009993298,"teacher_disagreement_score":0.0035128784,"about_ca_system_score_codex":0.0006123757,"about_ca_system_score_gemma":0.0011252349,"threshold_uncertainty_score":0.011751771},"labels":[],"label_agreement":null},{"id":"W2115382766","doi":"10.1109/tcst.2005.847328","title":"Convex integrated design (CID) method and its application to the design of a linear positioning system","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Control Systems Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Control theory (sociology); Convex optimization; Control system; Transfer function; Controller (irrigation); Loop (graph theory); Closed-loop transfer function; Set (abstract data type); Linear system; Closed loop; Mathematical optimization; Computer science; Control engineering; Regular polygon; Engineering; Mathematics; Control (management)","score_opus":0.0407495678291811,"score_gpt":0.3051473580653966,"score_spread":0.2643977902362155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115382766","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038266837,0.000031134183,0.9989417,0.000013303313,0.0000062836866,0.000010844716,0.0000030493447,0.000046986108,0.00056395784],"genre_scores_gemma":[0.11791695,0.00021326802,0.8798995,0.00007687951,0.00003423155,0.00022328908,0.00006510619,0.000085921565,0.0014848516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878114,0.00042846176,0.00005618657,0.0001676094,0.0005006226,0.00006593988],"domain_scores_gemma":[0.9987325,0.00068298273,0.00010556887,0.00011501106,0.00032352016,0.00004045161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020989797,0.0015106471,0.0010566526,0.0007571457,0.00048599893,0.0009859805,0.000959341,0.0007612986,0.0025201293],"category_scores_gemma":[0.0032465374,0.00067003944,0.0008148658,0.0006903536,0.0009503536,0.00052736304,0.0008954531,0.0014703121,0.00056068244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000741364,0.000051046827,0.00032152137,0.0002053896,0.00006536577,0.0001003031,0.00010446885,0.8234281,0.0052389232,0.044702366,0.0013704391,0.12433792],"study_design_scores_gemma":[0.000014282949,0.00008046178,0.00006648821,0.000013743941,0.0000145436,0.000039998886,0.000009237187,0.988931,0.0025742222,0.005277382,0.002965979,0.000012713517],"about_ca_topic_score_codex":0.0028894932,"about_ca_topic_score_gemma":0.0020628201,"teacher_disagreement_score":0.0028894932,"about_ca_system_score_codex":0.000970637,"about_ca_system_score_gemma":0.0012903325,"threshold_uncertainty_score":0.01110059},"labels":[],"label_agreement":null},{"id":"W2117146521","doi":"10.1243/09596518jsce573","title":"Stochastic feedforward—feedback control design in a discrete-time case","year":2008,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Alberta","funders":"","keywords":"Feed forward; Control theory (sociology); Probabilistic logic; Controller (irrigation); Stochastic control; Discrete time and continuous time; Quadratic equation; Random variable; Term (time); Stochastic process; Computer science; Control (management); Mathematics; Probability density function; Linear-quadratic regulator; Mathematical optimization; Optimal control; Control engineering; Engineering; Statistics","score_opus":0.02976213377652003,"score_gpt":0.2376280130681494,"score_spread":0.20786587929162936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117146521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052535716,0.00013537121,0.9929904,0.00015497372,0.00003133308,0.000012108685,0.00000827553,0.00006009032,0.0013538315],"genre_scores_gemma":[0.9417501,0.00041221044,0.055337816,0.0001263433,0.00007912901,0.000104517356,0.000028464174,0.000020943777,0.002140503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890184,0.0002849011,0.000058330854,0.00021018372,0.00046594976,0.000078800935],"domain_scores_gemma":[0.9985892,0.0007961435,0.00024038344,0.00007027888,0.00024496872,0.000059048434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017605033,0.00061743625,0.00068032084,0.00030305315,0.00034565103,0.0008350974,0.0009049864,0.0011027687,0.0009905683],"category_scores_gemma":[0.003392781,0.00038736663,0.0005255966,0.0003084992,0.0011621027,0.00088833674,0.00078274176,0.00081151206,0.0001291988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056732664,0.000030044293,0.00017042336,0.0000980837,0.000031718817,0.00010571961,0.000046159763,0.8860661,0.003622075,0.09493591,0.00025788535,0.014579237],"study_design_scores_gemma":[0.000011940773,0.000052613363,0.00007865278,0.0000075665066,0.000008381025,0.000019663328,0.000003412851,0.9815833,0.00069889816,0.017139697,0.00038860168,0.000007264013],"about_ca_topic_score_codex":0.0017228993,"about_ca_topic_score_gemma":0.0013065614,"teacher_disagreement_score":0.0017605033,"about_ca_system_score_codex":0.000804496,"about_ca_system_score_gemma":0.0010283701,"threshold_uncertainty_score":0.009310484},"labels":[],"label_agreement":null},{"id":"W2117624816","doi":"10.1061/(asce)co.1943-7862.0000326","title":"Discussion of “Multiobjective Optimization of Time-Cost Trade-Off Using Harmony Search” by Zong Woo Geem","year":2011,"lang":"en","type":"article","venue":"Journal of Construction Engineering and Management","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Harmony search; Harmony (color); Multi-objective optimization; Mathematical optimization; Computer science; Mathematical economics; Economics; Mathematics; Art","score_opus":0.04287800904839086,"score_gpt":0.2659047987145206,"score_spread":0.22302678966612974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117624816","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013884033,0.018788224,0.8079737,0.06455209,0.004650793,0.00019162586,0.000115482835,0.00010380098,0.089740284],"genre_scores_gemma":[0.60179186,0.018744666,0.26271868,0.021643605,0.004159434,0.0009474184,0.00013377244,0.0002741387,0.089586504],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99929774,0.00038228926,0.000026782978,0.00008728422,0.00015484421,0.00005095013],"domain_scores_gemma":[0.9994186,0.0004076179,0.000039738465,0.000033981585,0.00007735674,0.000022639639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002393921,0.000514906,0.000752623,0.00041848063,0.00079651014,0.0013067139,0.0014483401,0.0023303118,0.0068567246],"category_scores_gemma":[0.0050180587,0.00035885532,0.0011902387,0.0010233549,0.0015240315,0.0018589062,0.0014092932,0.0019111172,0.0005539474],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006802013,0.000036302798,0.00028702588,0.00028292084,0.00010284749,0.0002713459,0.00022047537,0.15675335,0.00065572304,0.76192164,0.039687436,0.03971291],"study_design_scores_gemma":[0.00007769536,0.0001489713,0.0005689448,0.0003114403,0.000064737185,0.00019725763,0.00021954907,0.33345208,0.0009810156,0.5183383,0.14557546,0.000064516964],"about_ca_topic_score_codex":0.003215535,"about_ca_topic_score_gemma":0.0030911318,"teacher_disagreement_score":0.0068567246,"about_ca_system_score_codex":0.0010206695,"about_ca_system_score_gemma":0.0008116478,"threshold_uncertainty_score":0.022937953},"labels":[],"label_agreement":null},{"id":"W2117819612","doi":"10.1139/cjce-2013-0498","title":"Assessment of the structural reliability of loadbearing concrete masonry designed to the Canadian Standard S304.1","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Masonry; Reliability (semiconductor); Structural engineering; Structural load; Geotechnical engineering; Engineering; Index (typography); Forensic engineering; Computer science","score_opus":0.02215720602867456,"score_gpt":0.26378514074803106,"score_spread":0.2416279347193565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117819612","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.991617,0.000084260704,0.0060863993,0.000009997521,0.000006076144,0.000047498685,0.00026119396,0.000067678644,0.0018198716],"genre_scores_gemma":[0.9941825,0.000056726647,0.0042038597,0.0000039230736,0.0000012517344,0.00001804156,0.0004211928,0.0000118717535,0.0011005624],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99746704,0.000098294324,0.000062075924,0.00014791879,0.0020405096,0.00018409686],"domain_scores_gemma":[0.99714273,0.00024484794,0.00024410234,0.00020560672,0.0020522815,0.000110340035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014773724,0.0005840381,0.000355701,0.0014372551,0.0006016937,0.00029847078,0.001107069,0.00041887353,0.0010144119],"category_scores_gemma":[0.0030609246,0.0003697521,0.00037038856,0.0009847076,0.0006878898,0.0002009735,0.00034202856,0.00032085512,0.0002368987],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015141868,0.00027390494,0.14350668,0.00036886983,0.00012684292,0.0007235979,0.0015758654,0.23425826,0.5008755,0.0025571722,0.002035816,0.11218336],"study_design_scores_gemma":[0.000067179346,0.0016661885,0.6712615,0.000040318508,0.00014351125,0.0003823585,0.0005238669,0.16041456,0.15913498,0.0004465426,0.005739879,0.0001791017],"about_ca_topic_score_codex":0.4106155,"about_ca_topic_score_gemma":0.5931223,"teacher_disagreement_score":0.5893845,"about_ca_system_score_codex":0.0040142606,"about_ca_system_score_gemma":0.0047806026,"threshold_uncertainty_score":0.8164509},"labels":[],"label_agreement":null},{"id":"W2119126832","doi":"10.1109/mwsym.2013.6697507","title":"Efficient analysis of parameter uncertainty in FDTD models of microwave circuits using polynomial chaos","year":2013,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Polynomial chaos; Monte Carlo method; Finite-difference time-domain method; Stub (electronics); Microstrip; Polynomial; Uncertainty quantification; Algorithm; Electronic circuit; Applied mathematics; Electronic engineering; Microwave; Computer science; Mathematics; Mathematical optimization; Mathematical analysis; Engineering; Telecommunications; Statistics; Physics; Electrical engineering","score_opus":0.11194118677096426,"score_gpt":0.3160817392323362,"score_spread":0.20414055246137192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119126832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018984986,0.00008459776,0.9796514,0.000057456975,0.0000072947405,0.000013941887,0.000034276163,0.00012979817,0.0010362164],"genre_scores_gemma":[0.87520355,0.00031547324,0.12186439,0.000029615416,0.000016896416,0.000097330034,0.0001007718,0.00009148901,0.0022805075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998317,0.000042737774,0.000007101516,0.000018041137,0.00008184977,0.000018476281],"domain_scores_gemma":[0.9994764,0.00035659876,0.00005530286,0.000042589163,0.000058771748,0.000010398097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036710923,0.00042537172,0.00051934546,0.00039142297,0.00027429537,0.0005382492,0.00050758675,0.0005617643,0.00051985285],"category_scores_gemma":[0.0014085459,0.0003418944,0.00047280916,0.0003475911,0.00048734376,0.000620349,0.00035544066,0.00055981096,0.00011649134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008832593,0.0000034482944,0.00014798534,0.00001200889,0.0000061768437,0.000022803564,0.000018007631,0.9890948,0.0019168111,0.005507034,0.000058929938,0.0032032],"study_design_scores_gemma":[7.1579103e-7,0.0000021535943,0.0000198606,7.602238e-7,8.7659083e-7,0.0000055088503,9.512654e-7,0.99880683,0.00032967364,0.0007288165,0.00010258085,0.0000012132226],"about_ca_topic_score_codex":0.0034893982,"about_ca_topic_score_gemma":0.0020489595,"teacher_disagreement_score":0.0034893982,"about_ca_system_score_codex":0.0007247529,"about_ca_system_score_gemma":0.00057496456,"threshold_uncertainty_score":0.0069381595},"labels":[],"label_agreement":null},{"id":"W2120640238","doi":"10.1109/tpwrs.2003.821620","title":"Order Reduction of the Dynamic Model of a Linear Weakly Periodic System–Part I: General Methodology","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"LTI system theory; Linearization; Reduction (mathematics); Control theory (sociology); Mathematics; Linear system; Model order reduction; Convergence (economics); Singular value decomposition; Applied mathematics; Gauss–Seidel method; Gauss; Electric power system; Computer science; Mathematical optimization; Nonlinear system; Algorithm; Mathematical analysis; Iterative method; Power (physics); Physics; Control (management)","score_opus":0.10129837277519098,"score_gpt":0.3259367602345679,"score_spread":0.22463838745937692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120640238","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011698516,0.00009909105,0.9972863,0.000031193442,0.000014473132,0.000034448658,0.000032544245,0.00008704675,0.0012449515],"genre_scores_gemma":[0.18613195,0.0016204813,0.79344934,0.000120652636,0.0001885122,0.00073711504,0.00039956893,0.00030222922,0.01705023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997558,0.00007819255,0.000016852833,0.000037782644,0.00009301867,0.000018405291],"domain_scores_gemma":[0.9998165,0.00006468036,0.000024049381,0.00004369853,0.000044321074,0.000006761061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005071684,0.000834149,0.0008676308,0.0005128713,0.00035303886,0.00070306234,0.0007865536,0.0004979358,0.0047244458],"category_scores_gemma":[0.00072400045,0.00041329343,0.0011278404,0.0003566769,0.0005347767,0.00051140046,0.00076610694,0.0012096075,0.0018318413],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006274416,0.000097503114,0.00048544756,0.0007011332,0.000076263466,0.0002778551,0.00028406896,0.64237994,0.051016815,0.15230747,0.0029864467,0.14932428],"study_design_scores_gemma":[0.000009008995,0.0000993816,0.00014894523,0.00002658716,0.000021575728,0.000082137776,0.000029889097,0.9516382,0.0055861813,0.03293179,0.00940522,0.000021081974],"about_ca_topic_score_codex":0.0018991125,"about_ca_topic_score_gemma":0.0014427904,"teacher_disagreement_score":0.0047244458,"about_ca_system_score_codex":0.00042241404,"about_ca_system_score_gemma":0.0008576567,"threshold_uncertainty_score":0.015804827},"labels":[],"label_agreement":null},{"id":"W2121009990","doi":"10.24908/pceea.v0i0.4654","title":"ENGINEERING DESIGN FROM A SAFETY PERSPECTIVE","year":2012,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Probabilistic design; Engineering design process; Reliability (semiconductor); Reliability engineering; Computer science; Function (biology); Process (computing); Mathematical optimization; Product (mathematics); Focus (optics); Product design; New product development; Perspective (graphical); Industrial engineering; Engineering; Mathematics; Mechanical engineering","score_opus":0.03208127903715614,"score_gpt":0.2647264883734953,"score_spread":0.23264520933633914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121009990","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023626722,0.015670197,0.747557,0.025855592,0.0014438416,0.000088719135,0.00017702323,0.00020897362,0.20663613],"genre_scores_gemma":[0.337673,0.06447814,0.42562664,0.009447167,0.0043376167,0.00053390366,0.0004071005,0.0003505335,0.15714589],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99745315,0.0008246388,0.00011752252,0.00024945353,0.0012171263,0.00013813625],"domain_scores_gemma":[0.9978981,0.0010201099,0.00013762862,0.000277113,0.00053664506,0.00013036645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024195334,0.0016976174,0.0008214422,0.001946091,0.0011169582,0.00419334,0.0013820042,0.0026099798,0.0092430245],"category_scores_gemma":[0.0033045576,0.0005836547,0.0007889934,0.00097489107,0.0065976493,0.002882292,0.0016991672,0.0036208355,0.0030722618],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064960345,0.000016819602,0.00014734881,0.00018974513,0.000016444807,0.00006961772,0.00012374051,0.0225708,0.00061900826,0.9413163,0.0057031675,0.029220516],"study_design_scores_gemma":[0.00000980509,0.00007228706,0.0001795191,0.00027017086,0.000014407792,0.00014882244,0.0002081074,0.010549902,0.00062621315,0.7595036,0.22839506,0.00002215033],"about_ca_topic_score_codex":0.003590162,"about_ca_topic_score_gemma":0.003230061,"teacher_disagreement_score":0.0092430245,"about_ca_system_score_codex":0.0027660565,"about_ca_system_score_gemma":0.0035130808,"threshold_uncertainty_score":0.030920982},"labels":[],"label_agreement":null},{"id":"W2121209755","doi":"10.1109/tcad.2010.2061553","title":"Advanced Variance Reduction and Sampling Techniques for Efficient Statistical Timing Analysis","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IGNIS Innovation (Canada)","funders":"","keywords":"Control variates; Variance reduction; Quantile; Monte Carlo method; Random variate; Computer science; Reduction (mathematics); Statistics; Sampling (signal processing); Variance (accounting); Algorithm; Importance sampling; Mathematics; Markov chain Monte Carlo; Random variable; Hybrid Monte Carlo","score_opus":0.08450524157312866,"score_gpt":0.32439264676199175,"score_spread":0.2398874051888631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121209755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013792615,0.00010629162,0.99808156,0.000017571007,0.0000085898355,0.000010499493,0.000009611219,0.000115738396,0.0002708997],"genre_scores_gemma":[0.1015136,0.00041900433,0.8961238,0.000074414995,0.00007480905,0.00013680122,0.00009647251,0.00012560879,0.0014354505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987142,0.00049606006,0.000044618762,0.00012329801,0.00057788176,0.00004394302],"domain_scores_gemma":[0.9984688,0.00092369196,0.000102475264,0.00021768456,0.00026711027,0.00002022107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014313359,0.00058828626,0.00053886086,0.000995486,0.00023909366,0.00044031738,0.00062769605,0.00046547342,0.0019421902],"category_scores_gemma":[0.0048595294,0.0003383326,0.00064532046,0.0010303712,0.0005296857,0.0005940803,0.0005465447,0.0012239924,0.0005224962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022595619,0.00009933017,0.0010625828,0.00026949059,0.00010222527,0.00015333277,0.00019973834,0.20503618,0.09475277,0.2035358,0.0023751827,0.4921874],"study_design_scores_gemma":[0.000025032798,0.00009232165,0.0004937615,0.000015988386,0.000022810342,0.00014274995,0.000007886698,0.94618154,0.025965603,0.021206442,0.0058189677,0.000026833179],"about_ca_topic_score_codex":0.0008916814,"about_ca_topic_score_gemma":0.0010074808,"teacher_disagreement_score":0.0019421902,"about_ca_system_score_codex":0.0003882948,"about_ca_system_score_gemma":0.00060350716,"threshold_uncertainty_score":0.0075696707},"labels":[],"label_agreement":null},{"id":"W2121755197","doi":"10.3141/2060-02","title":"Three-Dimensional, Probabilistic Highway Design","year":2008,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Sight; Reliability (semiconductor); Monte Carlo method; Probabilistic logic; Probabilistic analysis of algorithms; Hazard; Computer science; Geometric design; Algorithm; Reliability engineering; Simulation; Statistics; Mathematics; Engineering; Geometry; Power (physics)","score_opus":0.31027203958424976,"score_gpt":0.41161689730271056,"score_spread":0.1013448577184608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121755197","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011537227,0.00009546443,0.98518395,0.00007041744,0.000011140998,0.000037684556,0.00015813748,0.0003094573,0.0025965148],"genre_scores_gemma":[0.6363301,0.00035838908,0.35998788,0.00005191103,0.000018318176,0.00033222346,0.0004295418,0.0000903353,0.002401373],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910754,0.0002849932,0.000050523,0.00012929742,0.0003692165,0.000058393183],"domain_scores_gemma":[0.9991702,0.00033038508,0.00014032262,0.00010008809,0.00023984436,0.000019184336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013196699,0.0005346832,0.00048550812,0.000880579,0.00038191615,0.00093859434,0.0009162535,0.0006352663,0.0017666833],"category_scores_gemma":[0.0028040214,0.0005357073,0.0008256355,0.000781854,0.0007142302,0.00078904856,0.0007936966,0.0004859489,0.00038465386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022276434,0.0000081805565,0.00057421223,0.00002863462,0.000012902698,0.000022943334,0.00002410509,0.97275734,0.0007673165,0.011598959,0.00032685348,0.013856219],"study_design_scores_gemma":[0.00000580252,0.00002981197,0.00041779302,0.0000053025874,0.000009648284,0.000042372038,0.000008679138,0.9895176,0.0006267962,0.00775375,0.0015709444,0.00001141981],"about_ca_topic_score_codex":0.002853408,"about_ca_topic_score_gemma":0.0027548464,"teacher_disagreement_score":0.002853408,"about_ca_system_score_codex":0.00065476337,"about_ca_system_score_gemma":0.0010587439,"threshold_uncertainty_score":0.006979227},"labels":[],"label_agreement":null},{"id":"W2122383369","doi":"10.1115/1.4024448","title":"Plastic Response Estimation in Repeated Elastic Analyses for Strain Hardening Material Model","year":2013,"lang":"en","type":"article","venue":"Journal of Pressure Vessel Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Finite element method; Limit load; Strain hardening exponent; Hardening (computing); Structural engineering; Elastic modulus; Materials science; Finite strain theory; Strain energy; Linear elasticity; Context (archaeology); Composite material; Engineering","score_opus":0.07989668608942145,"score_gpt":0.36510099323105966,"score_spread":0.2852043071416382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122383369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05293796,0.00003773173,0.94583505,0.000021707963,0.00000367485,0.00001675944,0.000009885411,0.00022296577,0.0009143152],"genre_scores_gemma":[0.7790207,0.00006373145,0.21911462,0.000014529841,0.0000056312283,0.000054879933,0.000041334544,0.00005782871,0.0016267073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973935,0.00008119648,0.0000132525975,0.00005320239,0.000094796225,0.000018296278],"domain_scores_gemma":[0.99946314,0.00025796692,0.000088502595,0.00009427128,0.00008341513,0.000012652017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057345047,0.00039351126,0.00037276596,0.00047676216,0.00021210467,0.00036322023,0.00067721005,0.0005092242,0.0012914952],"category_scores_gemma":[0.001630385,0.00034639478,0.00043462368,0.0002166556,0.00038204071,0.0006119611,0.000494885,0.00040091004,0.00033172106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000090780064,0.00006894476,0.0012053065,0.00006743872,0.00002661172,0.0000930476,0.000106373605,0.8358779,0.051368546,0.0066766706,0.0001848483,0.104233526],"study_design_scores_gemma":[0.0000010859502,0.000012446402,0.00014990057,0.0000010991639,0.0000022123152,0.000010939514,0.000002915108,0.99651754,0.002489644,0.00068277324,0.0001266679,0.0000028610486],"about_ca_topic_score_codex":0.0017285693,"about_ca_topic_score_gemma":0.0020195164,"teacher_disagreement_score":0.0017285693,"about_ca_system_score_codex":0.00027059135,"about_ca_system_score_gemma":0.00033003849,"threshold_uncertainty_score":0.0043204427},"labels":[],"label_agreement":null},{"id":"W2122839776","doi":"10.1007/s00184-014-0485-9","title":"On extremes of bivariate residual lifetimes from generalized Marshall–Olkin and time transformed exponential models","year":2014,"lang":"en","type":"article","venue":"Metrika","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Residual; Mathematics; Bivariate analysis; Exponential function; Majorization; Applied mathematics; Statistics; Statistical physics; Mathematical analysis; Combinatorics; Algorithm","score_opus":0.06741545556025746,"score_gpt":0.2865873617956848,"score_spread":0.21917190623542732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122839776","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13559571,0.0023727855,0.847665,0.0019941356,0.00016885728,0.000088091765,0.00035814787,0.00031663614,0.011440688],"genre_scores_gemma":[0.943613,0.0029443419,0.037977505,0.00046050886,0.0004645516,0.00020348864,0.0006712426,0.0003086481,0.01335666],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99754363,0.0012929772,0.0001082975,0.0003789786,0.0003540348,0.00032206535],"domain_scores_gemma":[0.9768878,0.016074527,0.0034582585,0.00089761405,0.0016009043,0.0010809347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011346572,0.001659842,0.002541436,0.003486864,0.0011643481,0.0038476451,0.0036086866,0.0030449878,0.00491299],"category_scores_gemma":[0.04588005,0.0012184955,0.002706721,0.0024833311,0.0062361653,0.0070390124,0.004907033,0.0052611916,0.0005712901],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006978786,0.000026990783,0.0009834332,0.00009038311,0.000050497947,0.0001585048,0.00036274758,0.12205876,0.00059646624,0.8709627,0.00097466883,0.0036650782],"study_design_scores_gemma":[0.000014017913,0.00003082158,0.0005333738,0.000056155826,0.00002926358,0.00010953898,0.00013797908,0.47842637,0.0001477528,0.51951516,0.00093788997,0.000061667466],"about_ca_topic_score_codex":0.004833874,"about_ca_topic_score_gemma":0.002841284,"teacher_disagreement_score":0.011346572,"about_ca_system_score_codex":0.0028444335,"about_ca_system_score_gemma":0.0015109234,"threshold_uncertainty_score":0.060007155},"labels":[],"label_agreement":null},{"id":"W2122941807","doi":"10.1139/s08-031","title":"Latin hypercube sampling for uncertainty analysis in multiphase modelling","year":2008,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Government of Newfoundland and Labrador","funders":"Memorial University of Newfoundland","keywords":"Latin hypercube sampling; Replicate; Monte Carlo method; BTEX; Sampling (signal processing); Statistics; Mathematics; Environmental science; Ethylbenzene; Chemistry; Computer science; Toluene; Filter (signal processing)","score_opus":0.09900382908387827,"score_gpt":0.2962500049544743,"score_spread":0.197246175870596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122941807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017712675,0.0001083708,0.9969602,0.000047115023,0.0000207321,0.00013705155,0.00008443872,0.00021698906,0.0006538657],"genre_scores_gemma":[0.085773855,0.00022094649,0.9106391,0.00006913493,0.000047448317,0.0020024925,0.00039397815,0.00020682516,0.000646267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98490405,0.012303007,0.00040040334,0.00052451785,0.0017131965,0.00015486499],"domain_scores_gemma":[0.9697621,0.025254345,0.0010569745,0.0018726151,0.0019052549,0.00014869319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017401459,0.0015720235,0.0019737114,0.002038335,0.0009364621,0.0015714436,0.0017396292,0.0011270914,0.005453163],"category_scores_gemma":[0.043714974,0.0011238604,0.0014845564,0.0025893447,0.0011278436,0.0014865594,0.002090015,0.0023416346,0.0008025332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020967744,0.00008034459,0.00079838524,0.0002564031,0.0001776625,0.000106271924,0.00014943515,0.8797576,0.0011743609,0.05637758,0.0016300072,0.05928238],"study_design_scores_gemma":[0.000035842248,0.00005050731,0.00010741948,0.000025782174,0.000008692415,0.000019069506,0.000013811671,0.96776396,0.001102023,0.028831962,0.002022925,0.000018017026],"about_ca_topic_score_codex":0.0031218228,"about_ca_topic_score_gemma":0.0025628363,"teacher_disagreement_score":0.017401459,"about_ca_system_score_codex":0.0011663557,"about_ca_system_score_gemma":0.0017387351,"threshold_uncertainty_score":0.0920288},"labels":[],"label_agreement":null},{"id":"W2123573061","doi":"10.1299/jsmeicone.2007.15._icone1510_234","title":"ICONE15-10441 Initiating Statistical Maintenance Optimization","year":2007,"lang":"en","type":"article","venue":"The Proceedings of the International Conference on Nuclear Engineering (ICONE)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bruce Power (Canada)","funders":"","keywords":"Preventive maintenance; Reliability (semiconductor); Computer science; Operations research; Point (geometry); Reliability engineering; Stochastic optimization; Engineering; Mathematical optimization; Mathematics","score_opus":0.0760156316231426,"score_gpt":0.2983725742245265,"score_spread":0.22235694260138392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123573061","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009454604,0.0038295898,0.63811016,0.0028321526,0.0009302345,0.00046463247,0.0029624258,0.004439702,0.33697644],"genre_scores_gemma":[0.24255191,0.0049453876,0.46896312,0.0014168076,0.00073555077,0.0009727333,0.008183522,0.0018354192,0.2703955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979055,0.00051429006,0.000072089366,0.00020330881,0.0012080114,0.000096745294],"domain_scores_gemma":[0.99650455,0.0011028915,0.00020730958,0.00054170814,0.0014940632,0.00014951857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034515564,0.0010667208,0.0009683878,0.0014427773,0.00042926354,0.0020684917,0.0011709053,0.0012059021,0.034518767],"category_scores_gemma":[0.0057891896,0.00043351576,0.000573042,0.0017968998,0.00074344606,0.00085561647,0.0011912538,0.0014587096,0.011235483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042664024,0.00016752032,0.001048783,0.00039251687,0.000079180754,0.00013160039,0.00006868061,0.21529362,0.0071928534,0.13647826,0.16109288,0.4776275],"study_design_scores_gemma":[0.00012453472,0.00045572405,0.0019578831,0.00021200806,0.00003835393,0.0001601964,0.000027885893,0.44157645,0.0097866515,0.037347727,0.5082443,0.000068343346],"about_ca_topic_score_codex":0.0069167903,"about_ca_topic_score_gemma":0.009778994,"teacher_disagreement_score":0.034518767,"about_ca_system_score_codex":0.0018722694,"about_ca_system_score_gemma":0.003031643,"threshold_uncertainty_score":0.11547685},"labels":[],"label_agreement":null},{"id":"W2123744743","doi":"10.2139/ssrn.357821","title":"A Misspecification-Robust Impulse Response Estimator","year":2003,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Estimator; Impulse response; Econometrics; Mathematics; Computer science; Statistics; Mathematical analysis","score_opus":0.06077344304225287,"score_gpt":0.3177277696435287,"score_spread":0.2569543266012759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123744743","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028132081,0.000092593946,0.99618405,0.00010425033,0.000030274818,0.0000134640995,0.0000440891,0.00022118342,0.00049701525],"genre_scores_gemma":[0.31810078,0.0005145374,0.6673438,0.0005278003,0.0003313114,0.00017834074,0.00071713026,0.00030181702,0.011984523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99687827,0.0014211129,0.000113224894,0.0006743674,0.0006587664,0.00025423904],"domain_scores_gemma":[0.9919497,0.0050832946,0.00063065405,0.0011507721,0.001050294,0.00013537334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061133,0.0009765765,0.002127947,0.0012410228,0.0003782041,0.0014125166,0.0017271274,0.0028535924,0.0048167855],"category_scores_gemma":[0.019930879,0.0008188717,0.0013103854,0.0012300061,0.0009258713,0.0017907361,0.0018532138,0.0019902608,0.0027321565],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085901236,0.00026476503,0.004267123,0.0004940958,0.00084650866,0.00039262828,0.00015637159,0.43587238,0.024592679,0.1727941,0.007128658,0.3523316],"study_design_scores_gemma":[0.00007703176,0.00014600449,0.0010866306,0.00003462956,0.00013013316,0.00022341593,0.000015657892,0.9590124,0.004371663,0.03208539,0.0027582317,0.0000588437],"about_ca_topic_score_codex":0.0006947,"about_ca_topic_score_gemma":0.00070001965,"teacher_disagreement_score":0.0061133,"about_ca_system_score_codex":0.00047462215,"about_ca_system_score_gemma":0.0013126677,"threshold_uncertainty_score":0.032330632},"labels":[],"label_agreement":null},{"id":"W2124330816","doi":"10.5539/mer.v3n1p99","title":"Probabilistic Design with Gerber Fatigue Model","year":2013,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Probabilistic design; Reliability engineering; Probabilistic logic; Reliability (semiconductor); Sizing; Product design; Product (mathematics); Component (thermodynamics); Optimal design; Quality (philosophy); Computer science; Reduction (mathematics); Engineering; Engineering design process; Mathematics; Power (physics); Machine learning","score_opus":0.3513993420575405,"score_gpt":0.4022913991173712,"score_spread":0.05089205705983074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124330816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002513636,0.00016883567,0.99394715,0.0000550695,0.000014377236,0.000052193627,0.00003539062,0.00016336888,0.0030499038],"genre_scores_gemma":[0.54200137,0.0011206267,0.44390348,0.00023762093,0.00009883316,0.0010195185,0.00029581727,0.0002605164,0.011062186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99710697,0.001059292,0.0001232837,0.00039914978,0.0011352089,0.00017618494],"domain_scores_gemma":[0.99804807,0.0011036425,0.00028629572,0.00016233536,0.00036535252,0.00003435635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029714082,0.0014744386,0.0015010858,0.0017232759,0.0005092905,0.0011002236,0.0019496973,0.0016591927,0.004503922],"category_scores_gemma":[0.00472261,0.0011497911,0.0025124778,0.0007455837,0.00097496255,0.0011502152,0.0014376828,0.0014340995,0.0010153513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001961253,0.000010364145,0.00014342151,0.00004477902,0.000022399941,0.00003756823,0.000030957235,0.97485864,0.0009119091,0.0148233175,0.00016580212,0.008931274],"study_design_scores_gemma":[0.000006766788,0.000038759743,0.00007586503,0.000011747533,0.000015287955,0.00003405546,0.0000035915834,0.9911109,0.00033115695,0.0071885544,0.0011736767,0.000009600418],"about_ca_topic_score_codex":0.002678205,"about_ca_topic_score_gemma":0.0020317615,"teacher_disagreement_score":0.004503922,"about_ca_system_score_codex":0.001016953,"about_ca_system_score_gemma":0.000978648,"threshold_uncertainty_score":0.015714526},"labels":[],"label_agreement":null},{"id":"W2125201692","doi":"10.2514/6.2002-1464","title":"Probabilistic Design of a Plate-Like Wing to Meet Flutter and Strength Requirements","year":2002,"lang":"en","type":"article","venue":"43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Flutter; Reliability (semiconductor); Probabilistic logic; Structural engineering; Monte Carlo method; Stress (linguistics); Wing; Minimum weight; Optimal design; Engineering; Reliability engineering; Control theory (sociology); Computer science; Mathematics; Aerodynamics; Power (physics); Statistics; Aerospace engineering","score_opus":0.08476043935357504,"score_gpt":0.29382496138447595,"score_spread":0.20906452203090092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125201692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030614147,0.000055302226,0.96656823,0.00006047774,0.000009957444,0.000056569967,0.00003163772,0.00012843717,0.0024752673],"genre_scores_gemma":[0.77647334,0.00014282344,0.22066464,0.000045861532,0.000017020486,0.00023646667,0.00008961407,0.000050985524,0.002279191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993937,0.00015816299,0.000021122756,0.0000690016,0.00029734802,0.00006065537],"domain_scores_gemma":[0.999421,0.00022388174,0.00013564294,0.00004816999,0.00014454684,0.000026716507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009821652,0.0005619373,0.00049691927,0.0004812789,0.00029940417,0.0004133228,0.00078946,0.0005701138,0.0010289027],"category_scores_gemma":[0.0015715754,0.00054902927,0.0007041664,0.00021725797,0.000475087,0.0003750024,0.0004826148,0.00040851734,0.00026582522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021707177,0.000009355107,0.00029319845,0.00002039121,0.000012735912,0.000029618208,0.000013278815,0.9856312,0.0046527064,0.003160119,0.00009055024,0.0060650944],"study_design_scores_gemma":[0.000007967531,0.00009765641,0.0002879661,0.000003683079,0.000010360675,0.00003816978,0.0000062785807,0.9953752,0.0018597065,0.0018220093,0.00048437595,0.0000066928264],"about_ca_topic_score_codex":0.0014047407,"about_ca_topic_score_gemma":0.0018299059,"teacher_disagreement_score":0.0014047407,"about_ca_system_score_codex":0.00046583856,"about_ca_system_score_gemma":0.00095345813,"threshold_uncertainty_score":0.005194247},"labels":[],"label_agreement":null},{"id":"W2127313930","doi":"10.5539/mer.v3n1p185","title":"Failure Dependence Analysis of Shear Walls with Different Openings under Fortification Earthquakes","year":2013,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Office of Science","keywords":"Shear wall; Structural engineering; Shear (geology); Geotechnical engineering; Monte Carlo method; Geology; Materials science; Engineering; Composite material; Mathematics","score_opus":0.12341264878439907,"score_gpt":0.36096612751125845,"score_spread":0.2375534787268594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127313930","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99218655,0.000049328024,0.0073723607,0.000015387572,0.0000022111587,0.0000031780585,0.000035916448,0.000028675919,0.00030632774],"genre_scores_gemma":[0.9995011,0.000017556287,0.00033275862,0.0000013860862,0.0000012833477,0.0000016901703,0.000042064046,0.0000043084588,0.00009777591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997696,0.00004799266,0.000012929066,0.000053880973,0.000064380656,0.00005126973],"domain_scores_gemma":[0.99825865,0.00075157615,0.00044913343,0.00019443242,0.0002516375,0.00009449374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007521832,0.00031311155,0.0003487251,0.00070294406,0.0001840836,0.00021659402,0.00035905122,0.00039478045,0.00064383686],"category_scores_gemma":[0.0021405118,0.00023774747,0.00057276216,0.00020885521,0.0004780821,0.0004177343,0.0003085845,0.00032889424,0.00008469854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005764827,0.00010098052,0.09055728,0.00008632322,0.00012484637,0.0008332848,0.00019481404,0.79997754,0.0906814,0.0026731389,0.00027585958,0.013918072],"study_design_scores_gemma":[0.000006134299,0.00014296657,0.08675876,0.000006439336,0.00005549512,0.000170685,0.000053020263,0.89869326,0.013129784,0.00085603754,0.00010175624,0.000025751671],"about_ca_topic_score_codex":0.0022329863,"about_ca_topic_score_gemma":0.0018986822,"teacher_disagreement_score":0.0022329863,"about_ca_system_score_codex":0.00029923065,"about_ca_system_score_gemma":0.00014381926,"threshold_uncertainty_score":0.00443995},"labels":[],"label_agreement":null},{"id":"W2127485070","doi":"10.1080/10556780802079958","title":"The smoothed Monte Carlo method in robustness optimization","year":2008,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Vancouver Island University","keywords":"Robustness (evolution); Monte Carlo method; Mathematical optimization; Classification of discontinuities; Computer science; Nonlinear system; Monte Carlo integration; Robust optimization; Hybrid Monte Carlo; Algorithm; Mathematics; Markov chain Monte Carlo; Statistics; Physics","score_opus":0.0963803541285717,"score_gpt":0.40177578685875887,"score_spread":0.30539543273018716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127485070","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006475439,0.00062399963,0.99721956,0.000087825465,0.000036664944,0.000013112032,0.000015110867,0.000111801775,0.0012443786],"genre_scores_gemma":[0.20151938,0.0033484888,0.78652185,0.000407492,0.00046133433,0.0004187949,0.00021472848,0.0004546667,0.0066532665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977163,0.0012306023,0.00006194074,0.00021804347,0.0007012483,0.00007184854],"domain_scores_gemma":[0.99568653,0.003306753,0.00024147311,0.0003418371,0.00034363286,0.000079885336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003851635,0.0010855187,0.0015715199,0.001825326,0.0005249955,0.0012447033,0.0013666286,0.001689623,0.0031886294],"category_scores_gemma":[0.010121703,0.00075588626,0.0014969835,0.0018851202,0.0024594786,0.001540275,0.0014989633,0.0023824559,0.00082547026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055074983,0.000020371352,0.00022384705,0.00015924305,0.00009333825,0.00005850726,0.00004409071,0.7337896,0.001968321,0.22341052,0.0014361729,0.0387409],"study_design_scores_gemma":[0.0000112488515,0.000030403722,0.000099231824,0.00002608417,0.000018658235,0.00003670328,0.000003690508,0.9299486,0.0007691001,0.06435739,0.004675914,0.000022929018],"about_ca_topic_score_codex":0.0033413693,"about_ca_topic_score_gemma":0.0017973379,"teacher_disagreement_score":0.003851635,"about_ca_system_score_codex":0.0013235417,"about_ca_system_score_gemma":0.0016706429,"threshold_uncertainty_score":0.020369649},"labels":[],"label_agreement":null},{"id":"W2133474653","doi":"10.1137/120889733","title":"Parameter Estimation for ODEs Using a Cross-Entropy Approach","year":2013,"lang":"en","type":"article","venue":"SIAM Journal on Scientific Computing","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Mathematical optimization; Cross entropy; Algorithm; Ode; Entropy (arrow of time); Estimation theory; Applied mathematics; Principle of maximum entropy; Statistics","score_opus":0.17099823582488005,"score_gpt":0.39687291482209053,"score_spread":0.22587467899721048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133474653","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032897852,0.00020631537,0.9958177,0.000060171657,0.000012780887,0.000013688895,0.00001861464,0.00008460264,0.0004963047],"genre_scores_gemma":[0.4393253,0.0015324886,0.55331254,0.00025539636,0.00013800152,0.00038396817,0.00036684005,0.00028476396,0.004400618],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992799,0.0002774295,0.000055073848,0.00013927979,0.00020872192,0.000039665065],"domain_scores_gemma":[0.9964833,0.0026317858,0.00034123153,0.00018544529,0.00029604047,0.00006212077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027350725,0.001172192,0.0012261133,0.0014195518,0.00044356316,0.0010434755,0.0010787207,0.0016603258,0.0014915655],"category_scores_gemma":[0.0071699126,0.0005774719,0.0012276365,0.0007525618,0.0012414821,0.0019570263,0.0019765857,0.001729789,0.000348049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031995347,0.000026132651,0.0006623906,0.000119534234,0.00006898523,0.00006142348,0.000053055643,0.9463425,0.0034158262,0.020414822,0.00031122548,0.028492128],"study_design_scores_gemma":[0.0000016601007,0.0000086491045,0.0000757902,0.0000070020005,0.0000044438716,0.000010910245,0.000002854809,0.9958332,0.00058776315,0.0032098782,0.00025033997,0.0000074777986],"about_ca_topic_score_codex":0.0027708246,"about_ca_topic_score_gemma":0.0015237893,"teacher_disagreement_score":0.0027708246,"about_ca_system_score_codex":0.0006575888,"about_ca_system_score_gemma":0.0010401257,"threshold_uncertainty_score":0.014464676},"labels":[],"label_agreement":null},{"id":"W2134543762","doi":"10.1109/icma.2009.5246062","title":"On-line fouling detection of aircraft environmental control system cross flow heat exchanger","year":2009,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Environmental control system; Heat exchanger; Fin; Control theory (sociology); Fouling; Mass flow rate; Heat capacity rate; Nonlinear system; Extended Kalman filter; Mechanics; Kalman filter; Engineering; Plate heat exchanger; Computer science; Mechanical engineering; Chemistry; Automotive engineering; Control (management); Physics","score_opus":0.040118417913858916,"score_gpt":0.2917707056909035,"score_spread":0.2516522877770446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134543762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39896116,0.00020437958,0.59704274,0.00017467281,0.000035892095,0.00008552412,0.00008271853,0.0011304348,0.0022825112],"genre_scores_gemma":[0.9907996,0.000025967758,0.008587797,0.000014806047,0.0000048688034,0.000016462911,0.00003109066,0.00000564397,0.00051376683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997179,0.00006886808,0.000011957313,0.0000620624,0.00010157941,0.000037744478],"domain_scores_gemma":[0.9995944,0.00017221898,0.00008657122,0.000029126715,0.0000996477,0.000017976976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037827442,0.000503374,0.00051876216,0.00036621754,0.00024189378,0.00042129267,0.0004449041,0.00051976513,0.00054433726],"category_scores_gemma":[0.00096701126,0.00022723523,0.00024772203,0.00009167032,0.000219567,0.00032711183,0.00031188646,0.0002786259,0.00009853573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005500241,0.00031864116,0.011706168,0.00012415196,0.00007258612,0.00026700558,0.000163983,0.76161367,0.048446853,0.0013379379,0.00092599523,0.17447293],"study_design_scores_gemma":[0.000007140423,0.000075445554,0.0014727742,0.000002173834,0.0000051867733,0.000026917109,0.0000072826647,0.99280155,0.005370451,0.00013019088,0.00009588392,0.000005000464],"about_ca_topic_score_codex":0.003927339,"about_ca_topic_score_gemma":0.0036820248,"teacher_disagreement_score":0.003927339,"about_ca_system_score_codex":0.0004349509,"about_ca_system_score_gemma":0.00048965827,"threshold_uncertainty_score":0.0078089833},"labels":[],"label_agreement":null},{"id":"W2135799075","doi":"10.1002/mats.201500017","title":"Reactivity Ratio Estimation in Non‐Linear Polymerization Models using Markov Chain Monte Carlo Techniques and an Error‐In‐Variables Framework","year":2015,"lang":"en","type":"article","venue":"Macromolecular Theory and Simulations","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Monte Carlo method; Markov chain; Applied mathematics; Linear regression; Computer science; Nonlinear regression; Mathematics; Regression analysis; Mathematical optimization; Statistics","score_opus":0.06389378851723654,"score_gpt":0.35470267471926625,"score_spread":0.2908088862020297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135799075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016503097,0.00009642011,0.9826691,0.00005882862,0.000007163499,0.00003170889,0.000023000166,0.00016388572,0.00044680762],"genre_scores_gemma":[0.60304916,0.00033710295,0.39438343,0.000056725712,0.000020034831,0.00021201176,0.0001469167,0.00016499004,0.0016295474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970686,0.0017021266,0.00012417855,0.0004305132,0.00053223706,0.00014235172],"domain_scores_gemma":[0.976715,0.020011649,0.0016435963,0.00083683146,0.0006663161,0.0001265806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009000435,0.0010420459,0.0011697682,0.0012879218,0.0005168338,0.0015032156,0.0017474964,0.0012974241,0.001219626],"category_scores_gemma":[0.023934403,0.0008649465,0.0012788001,0.0011545676,0.0013246944,0.0017873538,0.0013600502,0.0022647833,0.00021835488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039438248,0.000037242375,0.00076031295,0.00005449698,0.000026610593,0.000030282803,0.000045939796,0.9714389,0.0013634528,0.015561752,0.00007929075,0.010562275],"study_design_scores_gemma":[0.000002619631,0.000012310918,0.00007301052,0.0000025641166,0.000004063366,0.0000051834822,0.0000028076224,0.99600935,0.001009087,0.0027993252,0.000073527735,0.000006085785],"about_ca_topic_score_codex":0.0060599456,"about_ca_topic_score_gemma":0.0035551756,"teacher_disagreement_score":0.009000435,"about_ca_system_score_codex":0.0014898209,"about_ca_system_score_gemma":0.001520452,"threshold_uncertainty_score":0.047599435},"labels":[],"label_agreement":null},{"id":"W2137691917","doi":"10.1109/jlt.2007.899171","title":"Efficient Adjoint Sensitivity Analysis Exploiting the FD-BPM","year":2007,"lang":"en","type":"article","venue":"Journal of Lightwave Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Sensitivity (control systems); Beam propagation method; Simple (philosophy); Mathematics; Finite difference method; Finite difference; Variable (mathematics); Adjoint equation; Algorithm; Mathematical optimization; Computer science; Electronic engineering; Physics; Mathematical analysis; Partial differential equation; Engineering","score_opus":0.05267290627197443,"score_gpt":0.3310756681460348,"score_spread":0.2784027618740604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137691917","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031687587,0.000036561636,0.995462,0.000034382916,0.0000084663725,0.000012615749,0.000014534048,0.00013556043,0.0011272602],"genre_scores_gemma":[0.38380957,0.00022442653,0.6130539,0.000085122694,0.000029807872,0.00019250595,0.0000895176,0.00015667109,0.0023584152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994056,0.00020401017,0.000018310484,0.000043783668,0.00029044456,0.000037825786],"domain_scores_gemma":[0.9992705,0.0005238364,0.000039657505,0.00006817447,0.0000804437,0.000017361534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013741821,0.00076734176,0.0008278216,0.0008479026,0.0003018671,0.00075930223,0.0006589304,0.00070894614,0.0019314573],"category_scores_gemma":[0.002132218,0.00047618238,0.00069134386,0.00035535832,0.00066172396,0.00064188574,0.0010959593,0.0008423714,0.00037791362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008475863,0.00006569908,0.00038330653,0.00018849553,0.00006106251,0.00011951925,0.0000708724,0.81455797,0.041212104,0.07107827,0.0007990333,0.07137883],"study_design_scores_gemma":[0.000004572948,0.000009992952,0.000042270774,0.000004826781,0.0000039956694,0.00001839239,0.0000027844462,0.9869599,0.003960432,0.008356567,0.00062935153,0.000007042631],"about_ca_topic_score_codex":0.0007008564,"about_ca_topic_score_gemma":0.00047199766,"teacher_disagreement_score":0.0019314573,"about_ca_system_score_codex":0.00042651818,"about_ca_system_score_gemma":0.00057677744,"threshold_uncertainty_score":0.007267475},"labels":[],"label_agreement":null},{"id":"W2138590658","doi":"10.4208/cicp.271109.150710s","title":"Reduction of Linear Systems of ODEs with Optimal Replacement Variables","year":2010,"lang":"en","type":"article","venue":"Communications in Computational Physics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Hong Kong Baptist University","keywords":"Ode; Applied mathematics; Linear system; Mathematics; System of linear equations; Variable (mathematics); Set (abstract data type); Simple (philosophy); Linear equation; Reduction (mathematics); Ordinary differential equation; Mathematical optimization; Computer science; Differential equation; Mathematical analysis","score_opus":0.10443096011706879,"score_gpt":0.3634499870475961,"score_spread":0.25901902693052736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138590658","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035819758,0.00017573373,0.961059,0.00012413468,0.000045644912,0.00005223313,0.000039570095,0.00016659188,0.0025173991],"genre_scores_gemma":[0.5869872,0.00031488793,0.40481377,0.00010674329,0.00008260676,0.0002733736,0.00019826011,0.00014294733,0.00708021],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994617,0.00022614234,0.000027663405,0.00007154883,0.0001746045,0.000038409034],"domain_scores_gemma":[0.9991062,0.00057141215,0.00008490291,0.00010191633,0.00010568132,0.00002986541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010875325,0.00051432085,0.0011822607,0.00041198294,0.00027309373,0.00066394656,0.0007593413,0.00049363653,0.0015359268],"category_scores_gemma":[0.0026900095,0.00035487927,0.00084778096,0.0003084248,0.0010061129,0.0005152755,0.0010844712,0.0009054047,0.00027047528],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029496941,0.000028148677,0.0002461904,0.00006390393,0.000023607865,0.000049349397,0.000051028303,0.9579222,0.002595751,0.026467428,0.00031286123,0.012210065],"study_design_scores_gemma":[0.0000054698403,0.000017432987,0.000031528612,0.0000026284183,0.000004187046,0.00000806096,0.0000050469985,0.99403596,0.0005032496,0.0047860392,0.0005973881,0.0000030972317],"about_ca_topic_score_codex":0.0030955605,"about_ca_topic_score_gemma":0.0022028857,"teacher_disagreement_score":0.0030955605,"about_ca_system_score_codex":0.0006561245,"about_ca_system_score_gemma":0.00077745266,"threshold_uncertainty_score":0.0061550736},"labels":[],"label_agreement":null},{"id":"W2139604219","doi":"10.1007/s11081-007-9003-5","title":"Application of a sensitivity equation method to the k–ε model of turbulence","year":2007,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Air Force Office of Scientific Research","keywords":"Sensitivity (control systems); Turbulence; Closure (psychology); K-epsilon turbulence model; Turbulence modeling; K-omega turbulence model; Flow (mathematics); Applied mathematics; Mathematics; Mathematical optimization; Mechanics; Physics; Engineering","score_opus":0.057841064846744845,"score_gpt":0.3137366843848266,"score_spread":0.25589561953808176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139604219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009074331,0.00014720752,0.98822,0.00014912341,0.000051101662,0.000030571377,0.00001871332,0.000102840655,0.0022060587],"genre_scores_gemma":[0.76915115,0.00064556877,0.22096492,0.00016866392,0.00015308127,0.00020565731,0.00010040502,0.000218862,0.0083916355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996101,0.00019605111,0.00001859191,0.000052348623,0.00009902931,0.000023865085],"domain_scores_gemma":[0.99864405,0.0010215379,0.00008688839,0.00007163388,0.00013617545,0.00003967152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010813794,0.00067015406,0.001004948,0.00068022945,0.00052732206,0.0008729031,0.0008423011,0.0012956088,0.00158566],"category_scores_gemma":[0.003451109,0.0006054239,0.0013781531,0.0004607464,0.0011195873,0.0008930269,0.001506957,0.0013781998,0.00022331542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023684725,0.000045935147,0.00030364623,0.000051392548,0.000060546503,0.000071703544,0.00004978202,0.9452223,0.004382203,0.035890706,0.00033632986,0.013561742],"study_design_scores_gemma":[0.0000018257717,0.0000044858566,0.000032473225,0.0000015688425,0.0000026639689,0.0000067995893,0.0000013171025,0.9973437,0.00022648888,0.0022121707,0.0001625795,0.000004056025],"about_ca_topic_score_codex":0.005933412,"about_ca_topic_score_gemma":0.0025473733,"teacher_disagreement_score":0.005933412,"about_ca_system_score_codex":0.00060895603,"about_ca_system_score_gemma":0.0010676307,"threshold_uncertainty_score":0.011797726},"labels":[],"label_agreement":null},{"id":"W2141686428","doi":"10.1002/9781118445112.stat00520","title":"Monte Carlo Studies, Empirical Response Surfaces in","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Monte Carlo method; Statistical physics; Monte Carlo method in statistical physics; Monte Carlo molecular modeling; Simple (philosophy); Computer science; Bridge (graph theory); Dynamic Monte Carlo method; Hybrid Monte Carlo; Econometrics; Markov chain Monte Carlo; Mathematics; Statistics; Physics; Medicine; Epistemology","score_opus":0.2686926434435685,"score_gpt":0.4582546654181565,"score_spread":0.189562021974588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141686428","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014029182,0.0046879207,0.9598702,0.0027313048,0.00022668623,0.00023769386,0.00018796998,0.0003414437,0.017687617],"genre_scores_gemma":[0.67157876,0.0064978814,0.3106045,0.0017955535,0.000505196,0.001586139,0.0003706018,0.00041141568,0.0066499356],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96030927,0.034914237,0.00064266333,0.0012797114,0.00251592,0.00033819998],"domain_scores_gemma":[0.7834517,0.19848059,0.0049855025,0.008586326,0.004095723,0.0004001639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040297024,0.0012716826,0.001622498,0.0026581392,0.00052484515,0.003081481,0.0017606398,0.0023261805,0.009247641],"category_scores_gemma":[0.19936778,0.0006232805,0.00095541606,0.0018369011,0.004739832,0.0042412425,0.0019556093,0.0031536464,0.00085149135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007406884,0.00007412623,0.001124609,0.0003848287,0.00009658261,0.00005975793,0.00019125,0.18115996,0.00025798898,0.7877584,0.002006775,0.026811643],"study_design_scores_gemma":[0.00003754892,0.00010110865,0.0006643555,0.0003043642,0.000029277508,0.000043497737,0.000114200215,0.2458396,0.00062507903,0.7440427,0.008161674,0.00003671221],"about_ca_topic_score_codex":0.00141117,"about_ca_topic_score_gemma":0.0008608839,"teacher_disagreement_score":0.040297024,"about_ca_system_score_codex":0.0017793076,"about_ca_system_score_gemma":0.0014531526,"threshold_uncertainty_score":0.21311355},"labels":[],"label_agreement":null},{"id":"W2141721194","doi":"10.1109/icsmc.2003.1245692","title":"Design flow robustness evaluation","year":2004,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Computer science; Reliability engineering; Design flow; Mathematical optimization; Risk analysis (engineering); Control theory (sociology); Engineering; Mathematics; Artificial intelligence; Control (management)","score_opus":0.21862832751154734,"score_gpt":0.3702857565994957,"score_spread":0.15165742908794835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141721194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052668408,0.00031269164,0.93634385,0.00015338244,0.000037383175,0.00023138909,0.00015238617,0.0007180295,0.009382579],"genre_scores_gemma":[0.8333549,0.00031553282,0.16139136,0.00010873619,0.000036054695,0.00042359848,0.00038797842,0.00028252017,0.0036992782],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964689,0.0013161375,0.00016746139,0.00033718045,0.0014490102,0.00026128738],"domain_scores_gemma":[0.9926495,0.004316824,0.00074938574,0.00086446304,0.0013291498,0.0000907117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005323886,0.0013974413,0.00064839487,0.001994221,0.0003083369,0.0012239831,0.00053496764,0.0007724561,0.0044182083],"category_scores_gemma":[0.0175721,0.00027647614,0.00085070066,0.00039390608,0.00066527136,0.0009810078,0.00082302565,0.0006916912,0.000591141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039663244,0.00013590498,0.003908631,0.00046086693,0.00014326173,0.00017678842,0.00016685955,0.7379535,0.05056619,0.037898466,0.00120273,0.16699022],"study_design_scores_gemma":[0.000035130215,0.0007856049,0.0022130958,0.00009900663,0.00009231127,0.00016865092,0.00008027502,0.9031507,0.06016395,0.026067529,0.0070912903,0.000052506453],"about_ca_topic_score_codex":0.00058644754,"about_ca_topic_score_gemma":0.00025896978,"teacher_disagreement_score":0.005323886,"about_ca_system_score_codex":0.00090339687,"about_ca_system_score_gemma":0.00067157665,"threshold_uncertainty_score":0.028155744},"labels":[],"label_agreement":null},{"id":"W2142194074","doi":"10.1139/l10-011","title":"Application of reliability techniques for the estimation of uncertainties in fluvial hydraulics simulationsThis article is one of a selection of papers published in this Special Issue on Hydrotechnical Engineering.","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Hydro-Québec","funders":"Core Research for Evolutional Science and Technology","keywords":"Computation; Reliability (semiconductor); Hydraulics; Monte Carlo method; Computer science; Mathematical optimization; Applied mathematics; Algorithm; Mathematics; Engineering; Statistics","score_opus":0.01591151095210464,"score_gpt":0.2591219937343592,"score_spread":0.24321048278225454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142194074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009574646,0.0001851383,0.989769,0.000050349594,0.000009922501,0.0000120136365,0.000012586186,0.00011326531,0.00027305313],"genre_scores_gemma":[0.69599223,0.00055777264,0.30244792,0.000033243545,0.000058864513,0.00010885066,0.00007696484,0.00012281178,0.0006012517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998403,0.00096197624,0.000071780705,0.00012531379,0.00038315784,0.000054854136],"domain_scores_gemma":[0.99167365,0.0066363486,0.00059754535,0.0003512118,0.0006726653,0.000068583075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029883687,0.0010010693,0.0008411522,0.0014979793,0.00034632097,0.0006308345,0.000728923,0.0009754117,0.00056971534],"category_scores_gemma":[0.015880601,0.00062178023,0.00071447284,0.0006009804,0.0008846733,0.00076862576,0.0009953831,0.00090334605,0.00015263802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030235833,0.000010604245,0.00052317255,0.000054637927,0.00003492285,0.000033020486,0.000054045428,0.95992213,0.001433367,0.007495519,0.00012837729,0.030280063],"study_design_scores_gemma":[0.0000022293484,0.000015674861,0.00012715899,0.0000067167994,0.0000047151757,0.000015247666,0.000004642224,0.9956232,0.0007402685,0.0032973883,0.00015729482,0.000005514513],"about_ca_topic_score_codex":0.0025575382,"about_ca_topic_score_gemma":0.0014568466,"teacher_disagreement_score":0.0029883687,"about_ca_system_score_codex":0.0005207977,"about_ca_system_score_gemma":0.00060090306,"threshold_uncertainty_score":0.015804172},"labels":[],"label_agreement":null},{"id":"W2142373740","doi":"10.1515/2156-6674.1011","title":"What Do Kernel Density Estimators Optimize?","year":2012,"lang":"en","type":"article","venue":"Journal of Econometric Methods","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Variable kernel density estimation; Kernel density estimation; Mathematics; Multivariate kernel density estimation; Kernel (algebra); Estimator; Applied mathematics; Kernel embedding of distributions; Laplace transform; Gaussian; Kernel smoother; Gaussian function; Mathematical optimization; Density estimation; Kernel method; Statistics; Mathematical analysis; Radial basis function kernel; Computer science; Artificial intelligence; Combinatorics; Physics","score_opus":0.21733665254641757,"score_gpt":0.45420399871694933,"score_spread":0.23686734617053176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142373740","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050149657,0.005680504,0.9342072,0.005170779,0.00015227438,0.0000785633,0.0002688555,0.00033976152,0.0039524767],"genre_scores_gemma":[0.847888,0.0059751454,0.1392592,0.00086296495,0.00064194883,0.00019237648,0.00066244905,0.000493313,0.0040246043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99428,0.0039035678,0.00025531818,0.0007693402,0.0005345474,0.00025726622],"domain_scores_gemma":[0.9566843,0.035781436,0.0025673478,0.0020062907,0.0025476976,0.00041287133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013502182,0.000809933,0.002335175,0.0021112554,0.00044978788,0.0032053129,0.0016647982,0.002784907,0.003263608],"category_scores_gemma":[0.120882064,0.00075509795,0.0008277171,0.0022654599,0.0018667841,0.00645865,0.0016089603,0.0015193947,0.00073419744],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027786015,0.00014598189,0.014943606,0.0009750609,0.0006974716,0.00008724844,0.0003714157,0.3570985,0.00068561,0.34416792,0.009884733,0.27066454],"study_design_scores_gemma":[0.00004764058,0.000052877644,0.0033322207,0.00018750275,0.00006706492,0.00007195489,0.00014553776,0.5640458,0.00060594716,0.4275799,0.0038168135,0.000046787438],"about_ca_topic_score_codex":0.0032726154,"about_ca_topic_score_gemma":0.0016198786,"teacher_disagreement_score":0.013502182,"about_ca_system_score_codex":0.0015328134,"about_ca_system_score_gemma":0.0011659247,"threshold_uncertainty_score":0.0714072},"labels":[],"label_agreement":null},{"id":"W2143811188","doi":"10.2514/6.2013-1684","title":"Structural Reliability Analysis of the Advanced Composite Cargo Aircraft","year":2013,"lang":"en","type":"article","venue":"54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Reliability (semiconductor); Composite number; Reliability engineering; Computer science; Aerospace engineering; Engineering; Physics","score_opus":0.02307055820163893,"score_gpt":0.27343284840695986,"score_spread":0.2503622902053209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143811188","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9433949,0.000131349,0.05148439,0.00008338826,0.000009184855,0.000024974262,0.0002114877,0.00011624177,0.004544031],"genre_scores_gemma":[0.9947566,0.00004605336,0.0040424094,0.000004973094,0.000003501138,0.000007960543,0.00014028167,0.00000825008,0.0009899191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999721,0.000036297824,0.000008257842,0.000037404785,0.00016540005,0.000031658346],"domain_scores_gemma":[0.9992537,0.00019176878,0.00007892949,0.000054285483,0.00039064643,0.000030652584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056789536,0.0003010724,0.0002287216,0.0011864161,0.0002611099,0.00024959518,0.0003102607,0.00022815616,0.001453633],"category_scores_gemma":[0.0012583416,0.00014269503,0.00039649315,0.00034167056,0.00024139085,0.0001886551,0.00024445343,0.00024736894,0.0002883941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002697829,0.000071829156,0.026791766,0.000070534305,0.00005472537,0.0005942712,0.00016741587,0.8012818,0.10274443,0.0039109173,0.0007826457,0.06325984],"study_design_scores_gemma":[0.000007901448,0.00030722134,0.025319172,0.000007152619,0.000025705549,0.00022779968,0.000051251485,0.9628985,0.009585851,0.00080171425,0.0007518623,0.000015908508],"about_ca_topic_score_codex":0.006488048,"about_ca_topic_score_gemma":0.004551961,"teacher_disagreement_score":0.006488048,"about_ca_system_score_codex":0.0005612861,"about_ca_system_score_gemma":0.00053972675,"threshold_uncertainty_score":0.012900591},"labels":[],"label_agreement":null},{"id":"W2144620433","doi":"10.1142/s0218202513500784","title":"Optimal monodomain approximations of the bidomain equations used in cardiac electrophysiology","year":2013,"lang":"en","type":"article","venue":"Mathematical Models and Methods in Applied Sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Agence Nationale de la Recherche","keywords":"Bidomain model; Pointwise; Cardiac electrophysiology; Bounded function; Mathematics; Mathematical analysis; Physics; Electrophysiology","score_opus":0.14191965518182856,"score_gpt":0.3996121521627092,"score_spread":0.2576924969808806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144620433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043412883,0.0003231338,0.95071876,0.00026685387,0.00005545651,0.00003879708,0.00009126001,0.00012472422,0.0049680774],"genre_scores_gemma":[0.66144407,0.0007646995,0.33142912,0.00018635891,0.000045680063,0.00019745524,0.00022686626,0.0001761487,0.005529729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996202,0.00014896781,0.000022850814,0.000043921766,0.00012723872,0.00003672637],"domain_scores_gemma":[0.99901867,0.00054392905,0.000110242734,0.00011969577,0.00016546,0.000041994976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008415146,0.0006245378,0.00067042495,0.00035906237,0.0003753749,0.0010454875,0.00084353687,0.0009919693,0.00089520094],"category_scores_gemma":[0.0031555104,0.0003608237,0.00051209703,0.00030574275,0.0008899133,0.0010774666,0.00091004436,0.0012226723,0.00030548725],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011341634,0.00005783434,0.0009879703,0.00011149009,0.000020533944,0.00014327794,0.0001863301,0.8696383,0.010075668,0.10607965,0.00093546236,0.01164997],"study_design_scores_gemma":[0.0000040369937,0.00001017381,0.000048208767,0.0000059689087,0.000001917774,0.000012696408,0.000012348455,0.99313194,0.00096135476,0.00534705,0.00046005234,0.0000042197003],"about_ca_topic_score_codex":0.004241658,"about_ca_topic_score_gemma":0.002296713,"teacher_disagreement_score":0.004241658,"about_ca_system_score_codex":0.00075706997,"about_ca_system_score_gemma":0.0009692564,"threshold_uncertainty_score":0.008433938},"labels":[],"label_agreement":null},{"id":"W2148381357","doi":"10.1080/1061856032000101448","title":"A General Continuous Sensitivity Equation Formulation for the <i>k</i> - <i>ε</i> Model of Turbulence","year":2004,"lang":"en","type":"article","venue":"International journal of computational fluid dynamics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Air Force Office of Scientific Research","keywords":"Turbulence; Sensitivity (control systems); Turbulence modeling; K-epsilon turbulence model; Domain (mathematical analysis); Range (aeronautics); Applied mathematics; Finite element method; Computer science; Computational fluid dynamics; Software; Flow (mathematics); K-omega turbulence model; Mathematical optimization; Mathematics; Mathematical analysis; Mechanics; Physics; Geometry; Engineering; Aerospace engineering","score_opus":0.0548996746798474,"score_gpt":0.32108624926877377,"score_spread":0.26618657458892636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148381357","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041036755,0.00015167102,0.9888775,0.00031671606,0.00006554544,0.000048782127,0.00011672698,0.000062034276,0.00625739],"genre_scores_gemma":[0.5243048,0.001392045,0.4366725,0.0010833422,0.00035425686,0.0006672898,0.0005783705,0.0002676967,0.034679655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995067,0.00009016984,0.00002611903,0.00012926708,0.0002045471,0.000043133772],"domain_scores_gemma":[0.9994685,0.00027103384,0.00005428959,0.000057505527,0.00011740416,0.000031324664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011461747,0.00081551325,0.0005468138,0.0005247214,0.0002936563,0.0010380385,0.0012368261,0.001768484,0.0034101445],"category_scores_gemma":[0.002143291,0.00045326326,0.0011685449,0.0003316003,0.0012451255,0.0015569445,0.0012345855,0.0018562372,0.0005366246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021379035,0.000050286493,0.00057739916,0.0001697794,0.000031725034,0.00022555044,0.00010745201,0.6166357,0.015016804,0.35235062,0.002224312,0.012589029],"study_design_scores_gemma":[0.000007350487,0.00001984965,0.00017013696,0.000014091599,0.00000820272,0.000081143306,0.000010497865,0.9642618,0.0015711165,0.030398745,0.0034383922,0.000018712719],"about_ca_topic_score_codex":0.0030206046,"about_ca_topic_score_gemma":0.0016985962,"teacher_disagreement_score":0.0034101445,"about_ca_system_score_codex":0.00079790904,"about_ca_system_score_gemma":0.0012768892,"threshold_uncertainty_score":0.011408031},"labels":[],"label_agreement":null},{"id":"W2148526875","doi":"10.2514/6.2010-2866","title":"A Probabilistic Study of Composite Impact Damage Design Strain Allowables","year":2010,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Composite number; Probabilistic logic; Strain (injury); Computer science; Reliability engineering; Structural engineering; Engineering; Artificial intelligence; Algorithm","score_opus":0.10339351597946853,"score_gpt":0.3688487730751625,"score_spread":0.26545525709569395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148526875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32283428,0.00042891127,0.66430646,0.00013405234,0.000018100174,0.000072359835,0.00014395,0.00016291463,0.011898992],"genre_scores_gemma":[0.96959466,0.00010490526,0.028893584,0.000013908873,0.000011996844,0.00005988762,0.00008855815,0.000025682117,0.0012067736],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974395,0.00064201944,0.0000720731,0.000297205,0.0014035805,0.00014571083],"domain_scores_gemma":[0.9910909,0.0063498346,0.00084734574,0.00061890826,0.0010283932,0.00006469972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036824096,0.00044716577,0.00039102076,0.0014035925,0.00030931467,0.0005827909,0.0007603615,0.00044502385,0.0014169944],"category_scores_gemma":[0.011426432,0.00047538412,0.0005992259,0.00074518094,0.00063044345,0.00075427594,0.0005087565,0.0005916528,0.00017168348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003704627,0.000019917816,0.0030047756,0.00002658079,0.00001648944,0.000043345357,0.000032493444,0.97263384,0.0020004653,0.008174665,0.00011215826,0.013898189],"study_design_scores_gemma":[0.0000026167509,0.00008083074,0.002814954,0.0000050559024,0.000008037992,0.00008027554,0.0000111068775,0.9927496,0.0013601056,0.0023123769,0.00056471693,0.000010329751],"about_ca_topic_score_codex":0.002024363,"about_ca_topic_score_gemma":0.0024877812,"teacher_disagreement_score":0.0036824096,"about_ca_system_score_codex":0.001003358,"about_ca_system_score_gemma":0.00062101625,"threshold_uncertainty_score":0.019474626},"labels":[],"label_agreement":null},{"id":"W2150625653","doi":"10.23919/acc.2004.1384394","title":"Optimization of stochastic uncertain systems: large deviations and robustness for partially observable diffusions","year":2004,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Minimax; Monotonic function; Mathematical optimization; Observable; Mathematics; Robustness (evolution); Kullback–Leibler divergence; Entropy (arrow of time); Optimal control; Stochastic control; Optimization problem; Stochastic optimization; Probability measure; Measure (data warehouse); Mathematical economics; Computer science","score_opus":0.09897846634734672,"score_gpt":0.3264781135348516,"score_spread":0.2274996471875049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150625653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03343249,0.0020398628,0.95686054,0.001475131,0.00006934141,0.00003327875,0.00007373623,0.000074669326,0.0059409244],"genre_scores_gemma":[0.9544432,0.0022819068,0.037824675,0.00017937772,0.00016598495,0.0001508278,0.00010404274,0.00006125574,0.0047887256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987495,0.0005913324,0.000065616994,0.00022588056,0.00028261502,0.00008499945],"domain_scores_gemma":[0.9959637,0.0030399847,0.0005408841,0.00011104917,0.00022012662,0.00012421982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024399874,0.0012959131,0.0013977848,0.00072896545,0.0004615636,0.0018731653,0.00083915517,0.0015278246,0.0010809808],"category_scores_gemma":[0.008445236,0.000506104,0.0009180033,0.00077696645,0.0028015224,0.0020339573,0.0017657427,0.0015135248,0.00011858968],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029545316,0.000013545188,0.0001804665,0.000087626155,0.00005091505,0.00005920415,0.00005041641,0.7964831,0.0011535588,0.19695875,0.00030377015,0.0046290294],"study_design_scores_gemma":[0.000007946928,0.000024398762,0.0001401494,0.000012647329,0.000007807965,0.000015471216,0.000012786177,0.9098492,0.00024474677,0.08930053,0.00037322828,0.000010979455],"about_ca_topic_score_codex":0.0022391586,"about_ca_topic_score_gemma":0.001178905,"teacher_disagreement_score":0.0024399874,"about_ca_system_score_codex":0.0016534375,"about_ca_system_score_gemma":0.00089436036,"threshold_uncertainty_score":0.012904048},"labels":[],"label_agreement":null},{"id":"W2154309891","doi":"10.1109/iscas.1990.112514","title":"A decomposition-aggregation approach for 2-D systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Decomposition; Extension (predicate logic); Curse of dimensionality; Dimensionality reduction; Computer science; Order (exchange); Perturbation (astronomy); Singular value decomposition; Algorithm; Theoretical computer science; Artificial intelligence; Chemistry; Physics; Programming language","score_opus":0.15067540765333812,"score_gpt":0.3379910731466813,"score_spread":0.1873156654933432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154309891","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008912279,0.0001416764,0.9977246,0.0000554072,0.000041438238,0.000014816828,0.000021394979,0.00010422262,0.0010052308],"genre_scores_gemma":[0.13522308,0.00081908616,0.85809183,0.00015786603,0.00014042719,0.0002252416,0.00020405045,0.00012359383,0.0050148084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964976,0.00011150503,0.000024562194,0.000052601627,0.0001329529,0.000028593444],"domain_scores_gemma":[0.9997323,0.00009360023,0.000029347002,0.000063269705,0.00006201797,0.000019508252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000491667,0.00072935043,0.00076264417,0.0006025063,0.00058257894,0.00079419895,0.0006345656,0.0006728698,0.002637746],"category_scores_gemma":[0.0007747812,0.00035521877,0.0008928903,0.0006016183,0.0005322326,0.0008543574,0.0015315769,0.0009973077,0.0006632487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046259694,0.000039839008,0.0004115135,0.00026062096,0.00006609361,0.00016551121,0.00021527441,0.6974277,0.021106374,0.15835828,0.0055231918,0.116379365],"study_design_scores_gemma":[0.0000030847855,0.00001616821,0.000074129384,0.0000080254895,0.0000061504934,0.000037247264,0.000009451919,0.9746663,0.0013103896,0.019111553,0.004746322,0.000011133674],"about_ca_topic_score_codex":0.002079655,"about_ca_topic_score_gemma":0.0017263785,"teacher_disagreement_score":0.002637746,"about_ca_system_score_codex":0.0005501652,"about_ca_system_score_gemma":0.0006292849,"threshold_uncertainty_score":0.00882411},"labels":[],"label_agreement":null},{"id":"W2158481978","doi":"10.1115/1.1688780","title":"Lower Bound Limit Load Determination: The mβ-Multiplier Method","year":2004,"lang":"en","type":"article","venue":"Journal of Pressure Vessel Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Multiplier (economics); Upper and lower bounds; Mathematics; Limit load; Limit (mathematics); Mathematical analysis; Linear elasticity; Applied mathematics; Finite element method; Physics; Thermodynamics","score_opus":0.04566944307350159,"score_gpt":0.3421959340326007,"score_spread":0.2965264909590991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158481978","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018909564,0.00010282261,0.99597734,0.00003734686,0.000015443886,0.000018967992,0.000012587011,0.00009982926,0.0018447277],"genre_scores_gemma":[0.16672078,0.00043974485,0.8235022,0.0001697045,0.00013870269,0.00028824763,0.00012144426,0.00019413493,0.008425132],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99885094,0.0003843943,0.000039930273,0.00018575105,0.00046811465,0.00007090428],"domain_scores_gemma":[0.9986389,0.000653065,0.00017328493,0.00016031726,0.00033031148,0.000044037366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017652428,0.001230087,0.00089449587,0.0018211833,0.0005052805,0.0011345498,0.001974617,0.0014677116,0.004901441],"category_scores_gemma":[0.0049124816,0.00058905326,0.0006452546,0.0008638684,0.0008863068,0.0014193875,0.0016994486,0.0014248426,0.002606214],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037425078,0.00011802981,0.0014458344,0.0003561382,0.00007959638,0.00022697616,0.00025264313,0.28712246,0.036127098,0.11984859,0.0066390573,0.5474093],"study_design_scores_gemma":[0.000015246176,0.0000633364,0.00022443185,0.000034698598,0.000009823996,0.00010828168,0.000012146065,0.9708476,0.007890841,0.015853412,0.004914341,0.000025822255],"about_ca_topic_score_codex":0.00084473577,"about_ca_topic_score_gemma":0.00066796923,"teacher_disagreement_score":0.004901441,"about_ca_system_score_codex":0.0005957965,"about_ca_system_score_gemma":0.0009696803,"threshold_uncertainty_score":0.01639694},"labels":[],"label_agreement":null},{"id":"W2158966261","doi":"10.1002/num.20166","title":"Numerical analysis of the stochastic Stokes equations of Wick type","year":2006,"lang":"en","type":"article","venue":"Numerical Methods for Partial Differential Equations","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Mathematics; Uniqueness; Type (biology); Partial differential equation; Finite element method; Applied mathematics; Set (abstract data type); Numerical analysis; Stochastic differential equation; Stokes problem; Stochastic partial differential equation; Mathematical analysis; Computer science","score_opus":0.13782384173476883,"score_gpt":0.4318276288498558,"score_spread":0.294003787115087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158966261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09053465,0.00025575512,0.9042926,0.000308948,0.00015071576,0.000049214686,0.000037376092,0.000117298325,0.004253459],"genre_scores_gemma":[0.8275479,0.00027577401,0.16727298,0.000110706664,0.00006846573,0.0001367629,0.000054850618,0.000049599974,0.0044828868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994236,0.00022733337,0.000029944555,0.000044181972,0.00022755678,0.000047418107],"domain_scores_gemma":[0.99893254,0.00045433777,0.00015621066,0.00011442138,0.00021715208,0.00012520925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015301032,0.0003134921,0.0008125752,0.0005842533,0.00037325453,0.00089093874,0.00055297965,0.0008686884,0.00096992566],"category_scores_gemma":[0.0032057227,0.00031323457,0.00056088634,0.00034320034,0.0018062312,0.0007359466,0.0009832287,0.0006413027,0.0001468995],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063598745,0.000046871708,0.00081425975,0.000060352184,0.000036395304,0.00009586124,0.00006681346,0.82342017,0.008743804,0.15988238,0.00038252436,0.0063869874],"study_design_scores_gemma":[0.0000048018437,0.000006532014,0.000037801354,0.000003157209,0.0000019784839,0.0000112525295,0.0000034838836,0.99450004,0.00075648475,0.004406796,0.00026281463,0.000004887715],"about_ca_topic_score_codex":0.0013809653,"about_ca_topic_score_gemma":0.0008114541,"teacher_disagreement_score":0.0015301032,"about_ca_system_score_codex":0.0005428331,"about_ca_system_score_gemma":0.0007607536,"threshold_uncertainty_score":0.008092046},"labels":[],"label_agreement":null},{"id":"W2160789306","doi":"10.1061/9780784413609.207","title":"Estimation of Failure Probability by Limit State Sampling","year":2014,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Probability density function; Probabilistic logic; Limit (mathematics); Sampling (signal processing); Importance sampling; Computer science; Limit state design; Statistical model; Algorithm; Mathematical optimization; Mathematics; Statistics; Monte Carlo method; Artificial intelligence; Engineering","score_opus":0.0799499452723707,"score_gpt":0.3209604405351082,"score_spread":0.2410104952627375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160789306","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008516041,0.000050634273,0.99088717,0.00002209492,0.000006999631,0.000019649719,0.000019234132,0.00011573831,0.000362427],"genre_scores_gemma":[0.64891344,0.00039757168,0.3480358,0.00007773766,0.0000672684,0.00036231452,0.00043063247,0.000105645915,0.0016095758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982146,0.00082919875,0.000067627516,0.00021879701,0.000599972,0.0000698542],"domain_scores_gemma":[0.9924803,0.0057752063,0.0004187888,0.0006640207,0.0005629699,0.00009876684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032660982,0.0008046083,0.0012683517,0.0017141168,0.0004614587,0.0009825886,0.0015483585,0.0010941093,0.0015796894],"category_scores_gemma":[0.014709743,0.0006330349,0.00087637495,0.00079920347,0.0013559032,0.0023756723,0.0011649278,0.0010927286,0.00037808137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015936929,0.00009609883,0.002972314,0.000094710245,0.000060843668,0.000102776605,0.000086572836,0.90389544,0.003851325,0.041034695,0.00048266558,0.047163267],"study_design_scores_gemma":[0.0000043817504,0.000022018805,0.00016252494,0.000004951876,0.0000035449725,0.00002262183,0.0000037681734,0.9905956,0.00094663055,0.007997902,0.0002279268,0.000008174266],"about_ca_topic_score_codex":0.001981999,"about_ca_topic_score_gemma":0.0010992164,"teacher_disagreement_score":0.0032660982,"about_ca_system_score_codex":0.0006857063,"about_ca_system_score_gemma":0.00088223984,"threshold_uncertainty_score":0.01727295},"labels":[],"label_agreement":null},{"id":"W2161933013","doi":"10.1016/j.envsoft.2015.10.001","title":"A modified Sobol′ sensitivity analysis method for decision-making in environmental problems","year":2015,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Sobol sequence; Multiple-criteria decision analysis; Ranking (information retrieval); Robustness (evolution); Sensitivity (control systems); Stakeholder; Computer science; Decision analysis; Variance (accounting); Operations research; Mathematics; Machine learning; Engineering; Statistics; Economics","score_opus":0.08992107902443713,"score_gpt":0.3244997859705622,"score_spread":0.2345787069461251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161933013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00151121,0.000110134286,0.9968906,0.000047423266,0.000024846957,0.000033911663,0.000041063067,0.00012845524,0.0012123643],"genre_scores_gemma":[0.18702032,0.0005222759,0.8029326,0.0002074248,0.00012739172,0.0006234675,0.0002792352,0.00055150676,0.0077358186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99746835,0.0014660762,0.0000926275,0.00020580397,0.0006436691,0.00012354203],"domain_scores_gemma":[0.9956155,0.0036015562,0.00009114279,0.00017284042,0.000450908,0.00006808732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040601394,0.0013303149,0.0017073376,0.0021098906,0.00062794855,0.0011939979,0.0018139302,0.0013233268,0.0075869826],"category_scores_gemma":[0.009505239,0.00088787137,0.002398607,0.001217223,0.00088852725,0.0012492929,0.0020202566,0.0021993804,0.0008655088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017528108,0.00014040884,0.00044648317,0.00047922996,0.0002559042,0.00016682043,0.0001242821,0.7553622,0.0073513957,0.06635071,0.002106562,0.16704066],"study_design_scores_gemma":[0.000010799216,0.000043785993,0.00010946493,0.00001723031,0.000027187782,0.000031467418,0.000007317604,0.9752714,0.0012186572,0.02070058,0.002542054,0.00002004816],"about_ca_topic_score_codex":0.0038213746,"about_ca_topic_score_gemma":0.0030025141,"teacher_disagreement_score":0.0075869826,"about_ca_system_score_codex":0.0008438605,"about_ca_system_score_gemma":0.001303127,"threshold_uncertainty_score":0.025380969},"labels":[],"label_agreement":null},{"id":"W2162776646","doi":"10.1061/(asce)0733-9445(2006)132:2(260)","title":"Efficient Computation of Response Sensitivities for Inelastic Structures","year":2006,"lang":"en","type":"article","venue":"Journal of Structural Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Truss; Computation; Finite element method; Sensitivity (control systems); Structural engineering; Computer science; Frame (networking); Reinforced concrete; Algorithm; Engineering","score_opus":0.03041629825279113,"score_gpt":0.2985893040961466,"score_spread":0.26817300584335546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162776646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034871902,0.00004994748,0.9625804,0.0000477396,0.000010024931,0.000022951848,0.000043615055,0.00043200413,0.0019415007],"genre_scores_gemma":[0.71441686,0.0001599701,0.28240988,0.000049119175,0.000014882532,0.00008045052,0.00018288473,0.00014474218,0.0025411865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996557,0.00008804614,0.000018013208,0.000042331267,0.00016443695,0.000031398373],"domain_scores_gemma":[0.9993206,0.00042222888,0.000059760645,0.000073144765,0.00009859736,0.000025608193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066996313,0.0005743866,0.0006357939,0.00062216405,0.00026272758,0.00054140366,0.0004834696,0.0006454693,0.001701721],"category_scores_gemma":[0.002151766,0.00046724366,0.00045958534,0.000377888,0.00040229168,0.0006134026,0.00091050926,0.000620663,0.00033746622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003820586,0.000021261194,0.0004947364,0.000058270653,0.000018135404,0.000117649804,0.00006276816,0.9385821,0.01677506,0.010509575,0.00031601766,0.0330062],"study_design_scores_gemma":[0.0000021805367,0.0000077520845,0.00012866383,0.0000027075746,0.000002290358,0.000025136354,0.0000062225386,0.99296653,0.003437768,0.003086357,0.00032940137,0.0000050750177],"about_ca_topic_score_codex":0.0010790136,"about_ca_topic_score_gemma":0.0009511277,"teacher_disagreement_score":0.001701721,"about_ca_system_score_codex":0.00041984118,"about_ca_system_score_gemma":0.00061929866,"threshold_uncertainty_score":0.00569278},"labels":[],"label_agreement":null},{"id":"W2163216628","doi":"10.1139/l11-081","title":"Effect of relative failure consequences in reliability based dual performance design","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Bhabha Atomic Research Centre; Indian Institute of Technology Kharagpur","keywords":"Reliability engineering; Reliability (semiconductor); Dual (grammatical number); Computer science; Environmental science; Engineering","score_opus":0.04720599252535208,"score_gpt":0.246960029593805,"score_spread":0.1997540370684529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163216628","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7742753,0.00066529313,0.21227683,0.00032637783,0.000027223145,0.000052495107,0.000023883093,0.00012659942,0.012225965],"genre_scores_gemma":[0.9916904,0.00006421741,0.007900492,0.00001813233,0.0000045379443,0.00001100136,0.000008608607,0.000010776461,0.000291894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795985,0.0011543928,0.000060271715,0.000153837,0.00048957835,0.00018200533],"domain_scores_gemma":[0.9950466,0.003619486,0.00053942885,0.00027582824,0.0003472103,0.00017137431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040595015,0.00085260684,0.00048355723,0.0007763374,0.00042740523,0.00092191447,0.0005844343,0.0006490215,0.0013593237],"category_scores_gemma":[0.010263752,0.0003686919,0.00048353735,0.00027096414,0.0013881631,0.0009954552,0.0012434734,0.00067462306,0.000110046116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006425368,0.000109923196,0.0046857307,0.00010317016,0.000053370553,0.00030717085,0.00012357016,0.9211235,0.013869487,0.032173973,0.00014664247,0.026660899],"study_design_scores_gemma":[0.00008466669,0.0021231044,0.006135189,0.00005438315,0.00014023103,0.00039793274,0.00016753585,0.9196771,0.020748546,0.04953794,0.00087824045,0.000055174114],"about_ca_topic_score_codex":0.00031164987,"about_ca_topic_score_gemma":0.00046489076,"teacher_disagreement_score":0.0040595015,"about_ca_system_score_codex":0.00062973035,"about_ca_system_score_gemma":0.00036776043,"threshold_uncertainty_score":0.021468997},"labels":[],"label_agreement":null},{"id":"W2163634216","doi":"10.1155/2015/989542","title":"Optimal Design of Stochastic Distributed Order Linear SISO Systems Using Hybrid Spectral Method","year":2015,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Research Foundation of Korea; Ministry of Education; National Research Foundation","keywords":"Linear system; Polynomial chaos; Computer science; Mathematical optimization; Stochastic process; Monte Carlo method; Mathematics","score_opus":0.15332937425393292,"score_gpt":0.3454075811458249,"score_spread":0.192078206891892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163634216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02216107,0.0001868783,0.97480506,0.00012476127,0.000024782665,0.000042353786,0.000023207363,0.000101504804,0.0025302677],"genre_scores_gemma":[0.93971515,0.00019483328,0.058055054,0.00006340227,0.000019759998,0.00021610588,0.00003704307,0.00002613469,0.001672479],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996358,0.0001283315,0.00001986594,0.000066408706,0.000105828636,0.000043732027],"domain_scores_gemma":[0.9993543,0.00039816438,0.00008985686,0.000023989622,0.00010903417,0.000024715659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093141367,0.0007000123,0.0010343273,0.00041444943,0.00031581122,0.00078918133,0.00047896494,0.000750986,0.0011495919],"category_scores_gemma":[0.0013101646,0.00047474995,0.0005691761,0.00037366664,0.00089826965,0.0004700858,0.0008217644,0.0005178159,0.00014176304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003547475,0.000013662305,0.00012095552,0.00005346652,0.00001943817,0.000023199373,0.000033102926,0.984251,0.0017635425,0.007069922,0.00012757868,0.006488638],"study_design_scores_gemma":[0.0000045947727,0.000015373567,0.000020031835,0.0000026364255,0.0000026971686,0.0000017824202,0.000003208124,0.99885345,0.00015963,0.00085485226,0.000079805905,0.0000019590739],"about_ca_topic_score_codex":0.0033281615,"about_ca_topic_score_gemma":0.0027626823,"teacher_disagreement_score":0.0033281615,"about_ca_system_score_codex":0.00055014103,"about_ca_system_score_gemma":0.0009825306,"threshold_uncertainty_score":0.0066176057},"labels":[],"label_agreement":null},{"id":"W2164452475","doi":"10.1177/0954410011408941","title":"Estimation models for the preliminary design of electromechanical actuators","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Actuator; Sizing; Airframe; Aerospace; Component (thermodynamics); Reliability (semiconductor); Engineering; Control engineering; Scaling; Computer science; Scaling law; Power (physics); Control theory (sociology); Aerospace engineering; Control (management); Mathematics","score_opus":0.09141489043655751,"score_gpt":0.27129531732079165,"score_spread":0.17988042688423414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164452475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004342802,0.00016034498,0.9942291,0.00005219708,0.000008575404,0.00003228152,0.00004553205,0.00014829868,0.0009808915],"genre_scores_gemma":[0.85182023,0.0009306057,0.14055559,0.000080249905,0.00004517308,0.0007266775,0.00053806556,0.000106435175,0.0051969984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911207,0.0003129124,0.000054066797,0.00015680358,0.00029120478,0.00007293545],"domain_scores_gemma":[0.9981201,0.0011853244,0.0002989203,0.00008436235,0.0002835902,0.000027713273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001802377,0.0011661501,0.000975028,0.0005788518,0.0002550925,0.0010547423,0.0010768459,0.00089903176,0.001993663],"category_scores_gemma":[0.0047902022,0.00081670424,0.0009208552,0.0003272347,0.00059545727,0.00096738304,0.0007966343,0.0014718322,0.0006541746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002810709,0.000011886964,0.0001585394,0.000050020746,0.000011584868,0.000017758595,0.000028127362,0.98687255,0.0010037727,0.004627405,0.0001537788,0.0070364424],"study_design_scores_gemma":[0.0000051523775,0.000029141644,0.000104839855,0.000010586177,0.0000073598458,0.0000060990656,0.0000039538045,0.9967284,0.0006785393,0.0018001737,0.000619677,0.000006059012],"about_ca_topic_score_codex":0.002395378,"about_ca_topic_score_gemma":0.002119254,"teacher_disagreement_score":0.002395378,"about_ca_system_score_codex":0.00077735935,"about_ca_system_score_gemma":0.0008630961,"threshold_uncertainty_score":0.009531975},"labels":[],"label_agreement":null},{"id":"W2164686924","doi":"10.1080/03610920802401138","title":"Assessing Sensitivity to Priors Using Higher Order Approximations","year":2010,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Prior probability; Quantile; Sensitivity (control systems); Marginal likelihood; Marginal distribution; Posterior probability; Applied mathematics; Simple (philosophy); Econometrics; Mathematics; Joint probability distribution; Statistics; Computer science; Mathematical optimization; Bayesian probability; Random variable; Engineering","score_opus":0.1869870177137185,"score_gpt":0.5193842610771522,"score_spread":0.3323972433634337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164686924","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041464917,0.00042692007,0.9551976,0.0003779034,0.00003537741,0.00005394616,0.00011512884,0.00036300995,0.0019652222],"genre_scores_gemma":[0.7326116,0.0007766297,0.26359254,0.00035921423,0.00009379077,0.00018997601,0.00040652146,0.0003201994,0.0016495554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9828554,0.010810201,0.00074400776,0.0014084236,0.0036618095,0.0005201677],"domain_scores_gemma":[0.7493235,0.22445124,0.0062567308,0.015723126,0.0034328287,0.0008126059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04106767,0.001338154,0.001654933,0.002664149,0.0009017178,0.0040915036,0.0019991696,0.003035244,0.0028342502],"category_scores_gemma":[0.21961688,0.0011893989,0.0012235269,0.0017907617,0.0029696387,0.004349106,0.003651595,0.004656034,0.00055070635],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022918501,0.00004740324,0.004982711,0.00017125873,0.00019299828,0.00021265761,0.00035934892,0.89166445,0.0017242163,0.072571866,0.00061063305,0.027233247],"study_design_scores_gemma":[0.000029708042,0.00007320381,0.0032056551,0.00006910417,0.000047526002,0.00018004642,0.00007499513,0.8702062,0.0025060368,0.12229082,0.0012423475,0.00007447264],"about_ca_topic_score_codex":0.005417195,"about_ca_topic_score_gemma":0.0026032708,"teacher_disagreement_score":0.04106767,"about_ca_system_score_codex":0.0019729855,"about_ca_system_score_gemma":0.0013286424,"threshold_uncertainty_score":0.2171892},"labels":[],"label_agreement":null},{"id":"W2164729989","doi":"10.3138/infor.45.2.51","title":"On Approximating the Distribution of Random Distances Within and Between Certain Regions of Space","year":2007,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Mathematics; Euclidean space; Norm (philosophy); Euclidean distance; Distribution (mathematics); Point (geometry); Moment (physics); Space (punctuation); Line segment; Combinatorics; Line (geometry); Real line; Euclidean geometry; Mathematical analysis; Geometry; Computer science; Physics","score_opus":0.13326376370370185,"score_gpt":0.3927791841115961,"score_spread":0.25951542040789427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164729989","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004907933,0.000151851,0.9945574,0.000050788192,0.000011144477,0.0000146575385,0.000028110431,0.000060089944,0.00021798853],"genre_scores_gemma":[0.33212826,0.002188429,0.66156226,0.00015507301,0.00024808024,0.00036341356,0.00051214447,0.00014733523,0.002694959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988682,0.00050736626,0.00004036161,0.00017300717,0.00030720915,0.00010382306],"domain_scores_gemma":[0.9935703,0.004981265,0.00049406075,0.00034233232,0.0004955666,0.00011645549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040301112,0.0011054634,0.0015503229,0.002051462,0.0005406077,0.0016475776,0.0024851405,0.0015413896,0.0010821223],"category_scores_gemma":[0.014651905,0.0007287199,0.0010904061,0.0021528322,0.0019930664,0.0027006574,0.0013116372,0.0014897444,0.000368197],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010402722,0.000030415415,0.0010205545,0.00010922023,0.000047000216,0.00008318345,0.00008862528,0.89023095,0.001656679,0.07101692,0.00068495906,0.03492742],"study_design_scores_gemma":[0.0000050277854,0.000020107864,0.00015033108,0.000012018479,0.000006023328,0.000045530098,0.000009755907,0.98851573,0.000425484,0.010450617,0.00034669103,0.000012572167],"about_ca_topic_score_codex":0.003771554,"about_ca_topic_score_gemma":0.0021228257,"teacher_disagreement_score":0.0040301112,"about_ca_system_score_codex":0.0015432186,"about_ca_system_score_gemma":0.0011534953,"threshold_uncertainty_score":0.021313488},"labels":[],"label_agreement":null},{"id":"W2166017965","doi":"10.2514/1.j052161","title":"Robust and Reliability-Based Design Optimization Framework for Wing Design","year":2014,"lang":"en","type":"article","venue":"AIAA Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Robustness (evolution); Mathematical optimization; Computer science; Surrogate model; Optimization problem; Probabilistic logic; Kriging; Multi-objective optimization; Robust optimization; Probabilistic design; First-order reliability method; Reliability (semiconductor); Reliability engineering; Engineering design process; Mathematics; Engineering; Machine learning; Artificial intelligence","score_opus":0.13558900094620352,"score_gpt":0.3205841751478428,"score_spread":0.1849951742016393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166017965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025088261,0.00014146278,0.99772817,0.000048849033,0.000013092235,0.000014841264,0.000022663766,0.0000711532,0.0017088171],"genre_scores_gemma":[0.118680194,0.0012460261,0.87354773,0.00014095593,0.00017240562,0.0005558479,0.00025905078,0.00024230962,0.005155445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998588,0.00047592688,0.000055062545,0.0001370259,0.00067504233,0.00006884479],"domain_scores_gemma":[0.99916124,0.00040417426,0.00010437867,0.00008774498,0.00021864515,0.000023776829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028576574,0.0017693898,0.0013296067,0.001364043,0.00039999821,0.0012995333,0.0017565733,0.0010622395,0.003482142],"category_scores_gemma":[0.0028964449,0.0007151766,0.0015847711,0.0010334251,0.001091004,0.0009315241,0.0013901249,0.0019071439,0.0013953388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009099833,0.000012141645,0.000057227007,0.000069384165,0.000025445044,0.000023680279,0.000016612352,0.90648806,0.0008484756,0.0778724,0.0006167881,0.013960757],"study_design_scores_gemma":[0.0000052924265,0.000022430486,0.000028396982,0.000017284967,0.000008041789,0.000013984596,0.00000436478,0.9691566,0.00031359427,0.027244294,0.003178448,0.0000072353096],"about_ca_topic_score_codex":0.0025204013,"about_ca_topic_score_gemma":0.0023491683,"teacher_disagreement_score":0.003482142,"about_ca_system_score_codex":0.001097987,"about_ca_system_score_gemma":0.0019407369,"threshold_uncertainty_score":0.015112877},"labels":[],"label_agreement":null},{"id":"W2170476331","doi":"","title":"Using simulation and symbolic computing in linear programming","year":2006,"lang":"en","type":"article","venue":"SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Linear programming; Linear inequality; Computer science; Mathematics; Mathematical optimization; Inequality","score_opus":0.15000775650238302,"score_gpt":0.3643049273701701,"score_spread":0.2142971708677871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170476331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003912955,0.0013192132,0.98098516,0.0009020617,0.00009551364,0.000024267469,0.000040958952,0.00030356375,0.012416326],"genre_scores_gemma":[0.35361022,0.004046135,0.6334084,0.0003844231,0.00045536752,0.00036490944,0.00017497824,0.00023822072,0.007317355],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828005,0.0010603113,0.00007811549,0.00013509314,0.0003845023,0.000061990395],"domain_scores_gemma":[0.9975701,0.0019698734,0.000104815364,0.00020472202,0.000111285044,0.000039204908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015961156,0.0010631942,0.0009867243,0.0009832395,0.00051874283,0.0022644806,0.00089949137,0.0009333249,0.004712504],"category_scores_gemma":[0.0049184244,0.00041935575,0.0012647996,0.0014437566,0.004501996,0.0027202892,0.0021439195,0.0024965543,0.00089467876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025200465,0.000018708954,0.00018540598,0.00014386828,0.000030692383,0.000056973906,0.00012564461,0.15871386,0.00061028765,0.8121904,0.000719806,0.02717914],"study_design_scores_gemma":[0.000011906135,0.000019219075,0.00003482356,0.000047005506,0.000007242962,0.000023822298,0.000015957321,0.40689138,0.00063884427,0.5865349,0.005763087,0.000011878463],"about_ca_topic_score_codex":0.0018129563,"about_ca_topic_score_gemma":0.0016609534,"teacher_disagreement_score":0.004712504,"about_ca_system_score_codex":0.001598445,"about_ca_system_score_gemma":0.0011803017,"threshold_uncertainty_score":0.015764832},"labels":[],"label_agreement":null},{"id":"W2171902472","doi":"10.2139/ssrn.1639543","title":"Further Results on the Limiting Distribution of GMM Sample Moment Conditions","year":2012,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sample (material); Moment (physics); Mathematics; Distribution (mathematics); Limiting; Statistics; Econometrics; Statistical physics; Mathematical analysis; Physics; Thermodynamics; Engineering; Classical mechanics","score_opus":0.06177230345320511,"score_gpt":0.3141639663516956,"score_spread":0.25239166289849047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171902472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008390711,0.0014743771,0.96665245,0.0030648115,0.00025153233,0.000103050435,0.0005668918,0.00065811566,0.018838141],"genre_scores_gemma":[0.5536552,0.0161578,0.33829653,0.0077917962,0.006983462,0.0020768999,0.0048170495,0.0035452112,0.06667612],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9943468,0.0027675559,0.0002867663,0.000970881,0.001098432,0.00052954716],"domain_scores_gemma":[0.8973537,0.0832859,0.0047919736,0.006647399,0.0061564613,0.0017644841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018401397,0.002757705,0.0032285661,0.004206774,0.0017734297,0.0046237865,0.0036306644,0.003339047,0.035740674],"category_scores_gemma":[0.14797619,0.0014525828,0.0035266965,0.0042790356,0.004808922,0.0129073365,0.0053790645,0.009283701,0.0060611176],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015254004,0.000114280556,0.0022961167,0.00064852956,0.00014310828,0.0005452686,0.00072432857,0.046404637,0.0033208323,0.89173603,0.0097274985,0.04418693],"study_design_scores_gemma":[0.00003355347,0.00005974176,0.0021184525,0.00031704322,0.00011541055,0.00049706927,0.00020758135,0.21381453,0.002926184,0.7686887,0.011081748,0.00014006557],"about_ca_topic_score_codex":0.0032549547,"about_ca_topic_score_gemma":0.002543244,"teacher_disagreement_score":0.035740674,"about_ca_system_score_codex":0.0029147258,"about_ca_system_score_gemma":0.0023701582,"threshold_uncertainty_score":0.11956447},"labels":[],"label_agreement":null},{"id":"W2172237364","doi":"10.1115/icone16-48078","title":"A Point Process Model for Piping Failure Frequency Analysis Using OPDE Data","year":2008,"lang":"en","type":"article","venue":"Volume 1: Plant Operations, Maintenance, Installations and Life Cycle; Component Reliability and Materials Issues; Advanced Applications of Nuclear Technology; Codes, Standards, Licensing and Regulatory Issues","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nuclear Safety Commission; University of Waterloo","funders":"Canadian Nuclear Safety Commission; University Network of Excellence in Nuclear Engineering","keywords":"Piping; Probabilistic logic; Computer science; Failure rate; Process (computing); Point estimation; Statistical model; Reliability engineering; Nuclear power plant; Task (project management); Point (geometry); Engineering; Data mining; Artificial intelligence; Statistics; Mathematics","score_opus":0.03835283942844863,"score_gpt":0.3157963644360744,"score_spread":0.27744352500762576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172237364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025914352,0.00013398533,0.9712215,0.0002246408,0.000026722013,0.00012701964,0.00080004433,0.00026781266,0.001283884],"genre_scores_gemma":[0.7844636,0.0010878993,0.19735079,0.00014311197,0.00012405856,0.0016843915,0.0027547556,0.00015065353,0.012240684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978005,0.00080067315,0.00014636827,0.00047603776,0.00059612724,0.00018015267],"domain_scores_gemma":[0.9931004,0.005076777,0.0006586516,0.00043626456,0.0006535424,0.00007431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048850463,0.0011313569,0.0016328645,0.001807862,0.0005154477,0.0017121686,0.0032790443,0.0024123748,0.0035033037],"category_scores_gemma":[0.011860747,0.0009439182,0.0016910633,0.0021345427,0.00091511454,0.0018689816,0.0011266174,0.0024608662,0.0012099013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046360074,0.000040093917,0.0015647473,0.000051235384,0.000038552425,0.00008057652,0.00006877085,0.9589465,0.0006976709,0.029052235,0.00036247697,0.009050801],"study_design_scores_gemma":[0.000006260194,0.00002984422,0.0003881627,0.0000059779863,0.00001259259,0.000026212241,0.000008038747,0.99353796,0.000163777,0.005366394,0.00043963187,0.0000151104305],"about_ca_topic_score_codex":0.011528247,"about_ca_topic_score_gemma":0.007165353,"teacher_disagreement_score":0.011528247,"about_ca_system_score_codex":0.0011679528,"about_ca_system_score_gemma":0.0010806199,"threshold_uncertainty_score":0.025834918},"labels":[],"label_agreement":null},{"id":"W2178278354","doi":"10.1115/icone17-75366","title":"Bayesian Analysis of Piping Failure Frequency Using OECD/NEA Data","year":2009,"lang":"en","type":"article","venue":"Volume 1: Plant Operations, Maintenance, Engineering, Modifications and Life Cycle; Component Reliability and Materials Issues; Next Generation Systems","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nuclear Safety Commission; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Nuclear Safety Commission; University Network of Excellence in Nuclear Engineering","keywords":"Piping; Bayesian probability; Nuclear power plant; Probabilistic logic; Failure rate; Poisson distribution; Reliability engineering; Computer science; Stage (stratigraphy); Bayes estimator; Engineering; Statistics; Mathematics; Artificial intelligence; Environmental engineering","score_opus":0.11062060672309773,"score_gpt":0.30236905805668873,"score_spread":0.191748451333591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2178278354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4381543,0.00021822931,0.5551594,0.00019667065,0.000011957369,0.00012390237,0.0024600683,0.00047350148,0.0032020474],"genre_scores_gemma":[0.92953396,0.00025768921,0.065578,0.00002752202,0.00001883958,0.00013486821,0.0038027975,0.000048510945,0.00059782574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980323,0.0009855767,0.000098757744,0.00022731561,0.0005587827,0.00009729418],"domain_scores_gemma":[0.9912934,0.006131609,0.0009672228,0.0006661784,0.0008796759,0.0000619099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066356324,0.0005445669,0.0006400403,0.002478473,0.0003029916,0.00068762305,0.0007112737,0.00071680255,0.00057264563],"category_scores_gemma":[0.018448345,0.00034601023,0.00086392387,0.0014676111,0.0005164637,0.0008085276,0.0006500565,0.0006200762,0.00016181807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007453106,0.000032365355,0.02698159,0.00004651637,0.00007925109,0.00014804612,0.00008375486,0.9397777,0.0009300391,0.0066534057,0.00056868413,0.024624145],"study_design_scores_gemma":[0.000013169543,0.00003181298,0.028992957,0.000015887501,0.000028555642,0.00008260966,0.000039662904,0.9619259,0.0011876787,0.006738307,0.0008951686,0.00004821107],"about_ca_topic_score_codex":0.025334513,"about_ca_topic_score_gemma":0.018538004,"teacher_disagreement_score":0.025334513,"about_ca_system_score_codex":0.0013024886,"about_ca_system_score_gemma":0.00084986375,"threshold_uncertainty_score":0.05037409},"labels":[],"label_agreement":null},{"id":"W2178402216","doi":"10.1139/cjce-2013-0491","title":"A comprehensive collapse fragility assessment of moment resisting steel frames considering various sources of uncertainties","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"UC Berkeley College of Chemistry","keywords":"Fragility; Moment (physics); Standard deviation; Quality (philosophy); Monte Carlo method; Uncertainty analysis; Mathematics; Structural engineering; Applied mathematics; Engineering; Statistics; Physics","score_opus":0.08041342477875885,"score_gpt":0.2996985307951482,"score_spread":0.21928510601638934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2178402216","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7784459,0.00028577753,0.21585676,0.00004518672,0.000006627657,0.00005752772,0.0002592284,0.00020413678,0.004838844],"genre_scores_gemma":[0.9931258,0.00006335131,0.0063079963,0.0000037529132,0.0000032088492,0.000014210104,0.00012986417,0.0000077017185,0.00034412649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993735,0.0000916627,0.00002411429,0.00007641269,0.00036464157,0.00006966651],"domain_scores_gemma":[0.99930525,0.00021831554,0.00013430764,0.00006370181,0.00024659128,0.00003191532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011313872,0.00088232174,0.00050758355,0.001972499,0.0003455341,0.0005763318,0.0004238019,0.0006307581,0.00073787005],"category_scores_gemma":[0.001594649,0.00023438217,0.0007119495,0.0007384496,0.0004153906,0.000581121,0.00056556106,0.00027833358,0.000105949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012699851,0.00003379437,0.010852639,0.0000993395,0.00006329167,0.0003556775,0.00017319465,0.9066349,0.036264163,0.0028820282,0.00021472664,0.04229932],"study_design_scores_gemma":[0.000004365924,0.00020707416,0.022804767,0.000025272284,0.000054415934,0.00014188484,0.00009773994,0.96245384,0.011800301,0.0017539564,0.0006117194,0.00004459107],"about_ca_topic_score_codex":0.0057106246,"about_ca_topic_score_gemma":0.0046342094,"teacher_disagreement_score":0.0057106246,"about_ca_system_score_codex":0.0005168806,"about_ca_system_score_gemma":0.00052084186,"threshold_uncertainty_score":0.011354804},"labels":[],"label_agreement":null},{"id":"W2183782348","doi":"","title":"Delta diagram based test for the Halphen (A and B) and the Gamma distributions","year":2014,"lang":"en","type":"article","venue":"EGUGA","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Gamma distribution; Mathematics; Skewness; Statistics; Diagram; Class (philosophy); Combinatorics; Computer science; Artificial intelligence","score_opus":0.04666729649808188,"score_gpt":0.2967891954680778,"score_spread":0.2501218989699959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2183782348","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16220608,0.0005549257,0.8277104,0.0003085772,0.00010555203,0.00039972106,0.0009506479,0.00121886,0.0065451795],"genre_scores_gemma":[0.8024636,0.00020826231,0.19249482,0.00020851292,0.000080658356,0.00094124593,0.0013795005,0.00012985354,0.002093403],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98936206,0.0060586142,0.00055873767,0.0016891076,0.0020546785,0.00027683377],"domain_scores_gemma":[0.9255396,0.06545165,0.0030976243,0.0022260025,0.0030316447,0.00065350794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00982741,0.0006185695,0.0011368841,0.0037063179,0.0007789328,0.0018023478,0.0016167976,0.0017240314,0.007507248],"category_scores_gemma":[0.06456997,0.00025442237,0.001058982,0.0018983326,0.0018665644,0.0022727577,0.0015806548,0.0014664899,0.0010081106],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036306882,0.00067202625,0.21914828,0.0011554606,0.001161186,0.002354082,0.0013558606,0.09330457,0.017802486,0.15669592,0.009321409,0.493398],"study_design_scores_gemma":[0.00040006958,0.0031307887,0.087051444,0.0003808571,0.000319639,0.0043690344,0.0013332575,0.7324482,0.019486599,0.13658328,0.014221967,0.00027481333],"about_ca_topic_score_codex":0.00072768936,"about_ca_topic_score_gemma":0.00050333294,"teacher_disagreement_score":0.00982741,"about_ca_system_score_codex":0.00079905003,"about_ca_system_score_gemma":0.0013063612,"threshold_uncertainty_score":0.051972926},"labels":[],"label_agreement":null},{"id":"W2184965969","doi":"","title":"A New Framework for Effective and Efficient Global Sensitivity Analysis of Earth and Environmental Systems Models","year":2015,"lang":"en","type":"article","venue":"EGU General Assembly Conference Abstracts","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Sobol sequence; Sensitivity (control systems); Metric (unit); Computer science; Set (abstract data type); Range (aeronautics); Performance metric; Mathematical optimization; Mathematics; Engineering","score_opus":0.08046749974514922,"score_gpt":0.3171221023714227,"score_spread":0.23665460262627347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184965969","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002818601,0.00005040987,0.9987508,0.00006852271,0.000010371363,0.000018153523,0.00002754851,0.000057979087,0.0007344441],"genre_scores_gemma":[0.10454088,0.00047078615,0.89187443,0.00023767282,0.00016960685,0.0006015649,0.00028169973,0.00033921565,0.0014841454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948088,0.0028489286,0.00027827127,0.0005311956,0.0012954244,0.00023738286],"domain_scores_gemma":[0.9937237,0.0042139348,0.00040369274,0.0007723097,0.0007417144,0.00014471007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010178181,0.0029226646,0.0021791623,0.0035319235,0.0008729193,0.0027718723,0.0025756706,0.0016941879,0.0039280364],"category_scores_gemma":[0.0144139705,0.0013937027,0.0040820693,0.0018060019,0.0026687293,0.0032462752,0.0051084845,0.0052164947,0.0006475605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010666655,0.000025279858,0.00021241022,0.00010487477,0.00008798992,0.000053529555,0.00008177695,0.7537427,0.0015481258,0.22794956,0.0008184724,0.015364622],"study_design_scores_gemma":[0.0000055133282,0.000028075381,0.000075629505,0.00003129324,0.000019094798,0.000029424713,0.000021670614,0.85165334,0.0005036827,0.14383201,0.0037810556,0.000019151043],"about_ca_topic_score_codex":0.0036786976,"about_ca_topic_score_gemma":0.0030474176,"teacher_disagreement_score":0.010178181,"about_ca_system_score_codex":0.0019415729,"about_ca_system_score_gemma":0.0023401815,"threshold_uncertainty_score":0.053828},"labels":[],"label_agreement":null},{"id":"W2185909196","doi":"10.18057/ijasc.2013.9.1.6","title":"SYSTEM RELIABILITY ASSESSMENT OF 3D STEEL FRAMES DESIGNED PER AISC LRFD SPECIFICATIONS","year":2013,"lang":"en","type":"book-chapter","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Structural engineering; Structural load; Frame (networking); Limit state design; Engineering; Steel frame; First-order reliability method; Mathematics; Statistics; Monte Carlo method; Mechanical engineering; Physics","score_opus":0.12002466399534817,"score_gpt":0.32117204137318855,"score_spread":0.2011473773778404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2185909196","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83720946,0.00046740967,0.1563057,0.000054393837,0.0000205621,0.00006651278,0.0004481868,0.00050300534,0.0049247304],"genre_scores_gemma":[0.9817015,0.00012898215,0.016772658,0.0000066028624,0.000003665788,0.000027968132,0.00028164018,0.00002849776,0.001048558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995665,0.0000992802,0.000015802227,0.00005969161,0.00022713376,0.000031599346],"domain_scores_gemma":[0.99942315,0.00021356124,0.00007431877,0.000057316218,0.00022045602,0.000011192552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067557755,0.00050950167,0.00032945533,0.000898606,0.00018549387,0.0002866939,0.00038600655,0.00037950612,0.0008802644],"category_scores_gemma":[0.0010903437,0.00018004945,0.00046510977,0.00033011776,0.00023863703,0.00023363942,0.00020478516,0.00018958053,0.0002404862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002827922,0.000037159272,0.0057678246,0.000121578,0.00004292771,0.00021944063,0.00014152582,0.8354079,0.07912436,0.0011268038,0.00075293833,0.07697479],"study_design_scores_gemma":[0.000014895857,0.00064813445,0.020969668,0.000021487524,0.00004167026,0.00017462777,0.000079347694,0.9414123,0.034260325,0.0007982985,0.001534391,0.00004487991],"about_ca_topic_score_codex":0.0031249197,"about_ca_topic_score_gemma":0.0036724166,"teacher_disagreement_score":0.0031249197,"about_ca_system_score_codex":0.00066466484,"about_ca_system_score_gemma":0.0002958846,"threshold_uncertainty_score":0.006213486},"labels":[],"label_agreement":null},{"id":"W2186233747","doi":"10.56748/ejse.655","title":"Decision Tools for the Engineering of Steel Structures","year":2006,"lang":"en","type":"article","venue":"Electronic Journal of Structural Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Realm; Decision engineering; Computer science; Management science; Process (computing); Field (mathematics); Business decision mapping; Engineering; Systems engineering; Engineering management; Construction engineering; Software engineering; Decision support system; Artificial intelligence","score_opus":0.024521006079621675,"score_gpt":0.2798573404147037,"score_spread":0.25533633433508207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186233747","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006269074,0.0016664484,0.98727465,0.00068437716,0.00015047622,0.00007036507,0.00020780435,0.00063526485,0.0086837],"genre_scores_gemma":[0.045009233,0.0033880079,0.9445185,0.00035336995,0.0002838005,0.0005135265,0.00048789338,0.00024885288,0.0051968605],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963336,0.0015534215,0.00030884947,0.00030125194,0.0013787197,0.00012415367],"domain_scores_gemma":[0.9930949,0.0052449377,0.00043295653,0.00043683586,0.000656754,0.00013363881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048144157,0.0023851672,0.0015831905,0.0029671113,0.001141178,0.0032828974,0.002149569,0.0021414624,0.018136716],"category_scores_gemma":[0.014941967,0.0008014128,0.0018048382,0.0024687648,0.0020230908,0.0031148884,0.0026460297,0.003911194,0.0048066857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043064316,0.000057438778,0.0002857909,0.00054666295,0.000061143845,0.00013532535,0.00020252452,0.12534611,0.0006518739,0.7226679,0.0107213,0.13928086],"study_design_scores_gemma":[0.000037193004,0.000034225297,0.00010364568,0.00028410798,0.000025216232,0.00009418157,0.000062826526,0.2055645,0.00063017744,0.72136986,0.07175768,0.000036336696],"about_ca_topic_score_codex":0.0028246765,"about_ca_topic_score_gemma":0.0028257645,"teacher_disagreement_score":0.018136716,"about_ca_system_score_codex":0.00169252,"about_ca_system_score_gemma":0.0022112401,"threshold_uncertainty_score":0.060673356},"labels":[],"label_agreement":null},{"id":"W2186242840","doi":"","title":"METHODOLOGY FOR SELECTING SSC FOR TIME-DEPENDENT RELIABILITY MODELLING IN PSA","year":2008,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Risk analysis (engineering); Computer science; Reliability engineering; Probabilistic logic; Probabilistic risk assessment; Set (abstract data type); Resource (disambiguation); Selection (genetic algorithm); Risk assessment; Operations research; Engineering; Machine learning; Artificial intelligence; Business","score_opus":0.39312872261141546,"score_gpt":0.40316357664326474,"score_spread":0.010034854031849283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186242840","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006370182,0.000022949356,0.9986228,0.000016797365,0.0000033555852,0.00007945753,0.000022411814,0.000085206644,0.0005100555],"genre_scores_gemma":[0.03630083,0.00015851903,0.9617015,0.0000382573,0.000020776046,0.00079497823,0.00019259659,0.000064767235,0.0007277436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997182,0.0013864138,0.00022152728,0.00021633947,0.00085249246,0.00014119495],"domain_scores_gemma":[0.995317,0.0028178988,0.00040624026,0.00027352045,0.0010956721,0.00008976002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005558042,0.0016398209,0.001092817,0.0027647205,0.00072373933,0.0017135459,0.0020536957,0.0013259888,0.004053329],"category_scores_gemma":[0.010508311,0.0006358636,0.001870789,0.0022376815,0.00067449897,0.0012288077,0.0017728328,0.0020660588,0.0016677212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003723856,0.00009929506,0.0012367083,0.0003606888,0.0000857172,0.00025061096,0.00030447272,0.78776926,0.005123707,0.07296159,0.0018976105,0.12987313],"study_design_scores_gemma":[0.000014127138,0.00007304831,0.00015552074,0.0000699281,0.000029701363,0.00008731399,0.00004888318,0.9521485,0.0015948662,0.04183543,0.003923704,0.000018993305],"about_ca_topic_score_codex":0.002154073,"about_ca_topic_score_gemma":0.0029468266,"teacher_disagreement_score":0.005558042,"about_ca_system_score_codex":0.0009321003,"about_ca_system_score_gemma":0.002701599,"threshold_uncertainty_score":0.02939409},"labels":[],"label_agreement":null},{"id":"W2189423432","doi":"10.1109/epeps.2015.7347120","title":"Statistical analysis via generalized decoupled polynomial chaos","year":2015,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Polynomial chaos; Polynomial; Decoupling (probability); Polynomial matrix; Computer science; CHAOS (operating system); Galerkin method; Projection (relational algebra); Applied mathematics; Matrix polynomial; Mathematics; Algorithm; Nonlinear system; Monte Carlo method; Mathematical analysis; Control engineering; Engineering","score_opus":0.1557158010163663,"score_gpt":0.3778695748281133,"score_spread":0.22215377381174697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189423432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013298475,0.0001956367,0.9845521,0.00011945046,0.000020406509,0.000012708058,0.00003980583,0.00009669648,0.0016647392],"genre_scores_gemma":[0.86227095,0.0008409771,0.13131475,0.00015814637,0.00021284372,0.00010847464,0.0001685609,0.000113283706,0.0048120897],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992367,0.00022009353,0.000024452991,0.00011391306,0.00034602595,0.00005883246],"domain_scores_gemma":[0.9988722,0.00055064535,0.00019066253,0.00015493453,0.00018316349,0.000048440317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005989793,0.0004958683,0.00055302185,0.0010490958,0.0003251505,0.0007310117,0.0006555901,0.00047198462,0.001035725],"category_scores_gemma":[0.002773556,0.00024121141,0.00063219684,0.00065661495,0.0011836456,0.0012973382,0.0010228017,0.0009694951,0.00022478214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033610755,0.000016881155,0.0005046419,0.00006235134,0.00004717841,0.00011007927,0.00006545453,0.41098705,0.010379352,0.555478,0.0005955692,0.02171991],"study_design_scores_gemma":[0.0000042323177,0.000019310044,0.00023813327,0.0000035449723,0.0000045982315,0.000033449636,0.000005738779,0.90444195,0.0010486977,0.093140624,0.00104946,0.000010295488],"about_ca_topic_score_codex":0.00097457576,"about_ca_topic_score_gemma":0.00058825547,"teacher_disagreement_score":0.0010490958,"about_ca_system_score_codex":0.00065409695,"about_ca_system_score_gemma":0.0006646522,"threshold_uncertainty_score":0.004745841},"labels":[],"label_agreement":null},{"id":"W2193187935","doi":"10.1007/s11009-015-9475-2","title":"First Passage Time for Brownian Motion and Piecewise Linear Boundaries","year":2015,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Piecewise linear function; First-hitting-time model; Brownian motion; Piecewise; Boundary (topology); Differentiable function; Computation; Numerical integration; Mathematical analysis; Monte Carlo method; Nonlinear system; Applied mathematics; Statistical physics; Algorithm","score_opus":0.26448190864393767,"score_gpt":0.37346164624840567,"score_spread":0.108979737604468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2193187935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02336119,0.0026868954,0.9642013,0.0011108486,0.00022251013,0.00003696088,0.0000927203,0.00015989509,0.008127689],"genre_scores_gemma":[0.66197,0.004754237,0.27232194,0.0007340183,0.0006744434,0.0003728335,0.00062979053,0.0006667973,0.05787577],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986827,0.00051958003,0.00007038922,0.00025081792,0.0003083281,0.00016818063],"domain_scores_gemma":[0.9906647,0.006784962,0.0007869031,0.00034881735,0.0007923056,0.0006223438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004277045,0.0011454009,0.0018218523,0.0030233574,0.0012717842,0.0037739666,0.0032598935,0.0027080574,0.0051258104],"category_scores_gemma":[0.021051,0.0009046446,0.0016434095,0.0014731846,0.0042594643,0.0051727593,0.0028749658,0.0037754872,0.0006581233],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002252393,0.000017487235,0.0002205623,0.00012038296,0.00002793357,0.00007359944,0.00013528105,0.07407472,0.0007043322,0.92062956,0.0006160484,0.0033575941],"study_design_scores_gemma":[0.000008471804,0.0000134091815,0.00017764035,0.000029592129,0.00001695039,0.000042919284,0.000028500326,0.54944265,0.0002862652,0.44861224,0.0013226855,0.000018658806],"about_ca_topic_score_codex":0.005449226,"about_ca_topic_score_gemma":0.0020999329,"teacher_disagreement_score":0.005449226,"about_ca_system_score_codex":0.0028133804,"about_ca_system_score_gemma":0.001618384,"threshold_uncertainty_score":0.022619486},"labels":[],"label_agreement":null},{"id":"W2208159239","doi":"10.1109/tcpmt.2015.2490240","title":"Fast Variability Analysis of General Nonlinear Circuits Using Decoupled Polynomial Chaos","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Components Packaging and Manufacturing Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Jacobian matrix and determinant; Polynomial chaos; Nonlinear system; Diagonal; Hermite polynomials; Applied mathematics; Electronic circuit; Polynomial; Mathematics; CHAOS (operating system); Control theory (sociology); Algorithm; Computer science; Mathematical analysis; Monte Carlo method; Engineering; Geometry; Statistics","score_opus":0.08322118390759257,"score_gpt":0.315203127111291,"score_spread":0.23198194320369842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2208159239","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014473495,0.00005365422,0.984677,0.000028844861,0.000005890305,0.000008856852,0.000015807005,0.000053399242,0.0006830079],"genre_scores_gemma":[0.85058796,0.0003117894,0.14625762,0.00003512123,0.000053372278,0.00007054621,0.000101214995,0.000071964634,0.0025104154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975973,0.000058605816,0.000008304806,0.000039710536,0.00011368696,0.000019921301],"domain_scores_gemma":[0.99943656,0.00037744982,0.00005828571,0.000057432662,0.000054154192,0.000016093467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045875763,0.0004579552,0.00044029244,0.0003830896,0.00020634652,0.00048008785,0.00055890915,0.0003361954,0.0006566325],"category_scores_gemma":[0.0016933744,0.00022989171,0.0005651628,0.00027154657,0.00054856914,0.0007774138,0.00060575746,0.0007700315,0.00012005825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080125326,0.000019641999,0.00054319925,0.00006582531,0.00004360986,0.000094989606,0.000071713825,0.87439644,0.020675909,0.07114794,0.00023448265,0.032626193],"study_design_scores_gemma":[0.0000018307514,0.000010646876,0.00007009524,0.0000010930291,0.000002038401,0.000011514873,0.0000015830105,0.99405634,0.00093205785,0.0047439984,0.0001657627,0.0000029402897],"about_ca_topic_score_codex":0.0009872401,"about_ca_topic_score_gemma":0.0007954502,"teacher_disagreement_score":0.0009872401,"about_ca_system_score_codex":0.00036098302,"about_ca_system_score_gemma":0.00041471046,"threshold_uncertainty_score":0.0026190877},"labels":[],"label_agreement":null},{"id":"W2209897355","doi":"10.1002/2015wr017558","title":"A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory","year":2015,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":208,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"University of Saskatchewan","keywords":"Sobol sequence; Sensitivity (control systems); Robustness (evolution); Computer science; Dimension (graph theory); Variance (accounting); Mathematical optimization; Econometrics; Data mining; Mathematics; Engineering","score_opus":0.2750037768540003,"score_gpt":0.4282374762467771,"score_spread":0.1532336993927768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2209897355","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003341927,0.00010323073,0.9984535,0.00008929402,0.00001383404,0.000014058074,0.000020446127,0.00003619757,0.00093523494],"genre_scores_gemma":[0.16402441,0.0012964166,0.8297008,0.00038171472,0.00039985584,0.0006213611,0.00018011351,0.00024494476,0.0031503572],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9957682,0.0024095,0.00019537695,0.00050407316,0.00093558425,0.00018717644],"domain_scores_gemma":[0.9944581,0.0037301902,0.0004053526,0.0005113007,0.00077697437,0.00011811326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008584386,0.002069294,0.0016749719,0.0034461925,0.0007884649,0.002699332,0.0019309517,0.001487841,0.004152754],"category_scores_gemma":[0.011424994,0.0011066351,0.0027922755,0.0017998385,0.003931131,0.0027653214,0.0038381128,0.0032413108,0.0006287082],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000983167,0.000023997702,0.00019213207,0.00013122315,0.00006358482,0.000046741643,0.000056315785,0.32539243,0.0012625963,0.6503568,0.0014701468,0.020994183],"study_design_scores_gemma":[0.0000062798144,0.00003094324,0.00008519709,0.000042521406,0.000018710802,0.000026210148,0.000014132951,0.7366736,0.0004419925,0.25845486,0.0041857264,0.000019884938],"about_ca_topic_score_codex":0.0026357824,"about_ca_topic_score_gemma":0.0017393593,"teacher_disagreement_score":0.008584386,"about_ca_system_score_codex":0.0020402253,"about_ca_system_score_gemma":0.0023335423,"threshold_uncertainty_score":0.04539907},"labels":[],"label_agreement":null},{"id":"W2216124413","doi":"10.1017/cbo9781139017657.012","title":"Approximate solutions: reduction to the engineering theories","year":2011,"lang":"en","type":"book-chapter","venue":"Continuum Mechanics and Thermodynamics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Reduction (mathematics); Mathematics; Applied mathematics; Mathematical economics; Calculus (dental); Computer science; Theoretical physics; Physics; Geometry; Medicine","score_opus":0.04762994246529761,"score_gpt":0.2405776390190519,"score_spread":0.1929476965537543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2216124413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062214895,0.012535374,0.6770585,0.00432777,0.001226912,0.000060972947,0.0004488938,0.0003873081,0.29773277],"genre_scores_gemma":[0.32442757,0.03796558,0.3838182,0.0022424068,0.0028612958,0.00061041414,0.0015311296,0.0010217074,0.24552165],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99955565,0.000114685965,0.000018292758,0.000046009398,0.00024144589,0.000023877534],"domain_scores_gemma":[0.99973446,0.00012237113,0.000018920786,0.00005737378,0.000051178773,0.00001566969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004880718,0.00071165676,0.00093956955,0.0009412155,0.0005641728,0.002209218,0.0012063782,0.0010074193,0.010765468],"category_scores_gemma":[0.002034152,0.0004224924,0.0009849815,0.00097553356,0.0018295601,0.0030632217,0.0018210298,0.0031830615,0.0030921588],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029179662,0.000004774557,0.000015095197,0.00006014504,0.0000049097825,0.000012023492,0.000036093992,0.0033004007,0.00016540808,0.97784674,0.0053523504,0.013199148],"study_design_scores_gemma":[0.0000020804027,0.0000031560494,0.00002495949,0.000021990993,0.0000023444577,0.000025479483,0.0000176537,0.011738259,0.00011350266,0.97023183,0.017815819,0.000003056609],"about_ca_topic_score_codex":0.00093379326,"about_ca_topic_score_gemma":0.00088911667,"teacher_disagreement_score":0.010765468,"about_ca_system_score_codex":0.0010356965,"about_ca_system_score_gemma":0.0007174767,"threshold_uncertainty_score":0.03601408},"labels":[],"label_agreement":null},{"id":"W2221870291","doi":"10.1002/2015wr017559","title":"A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application","year":2015,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":153,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Australian Research Council","keywords":"Sobol sequence; Robustness (evolution); Sensitivity (control systems); Computer science; Parameter space; Variance (accounting); Range (aeronautics); Econometrics; Data mining; Statistics; Mathematics; Monte Carlo method; Engineering","score_opus":0.25871299681721627,"score_gpt":0.4311630373920318,"score_spread":0.17245004057481556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2221870291","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005210442,0.000033720913,0.99886346,0.000030521067,0.000005494995,0.000013430058,0.000016911112,0.00003950048,0.0004759946],"genre_scores_gemma":[0.19112249,0.0005506544,0.8048977,0.00019672762,0.00012583603,0.00045762505,0.00020588266,0.00023919732,0.002203854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99630094,0.002268378,0.00015709476,0.0004428717,0.000702961,0.00012768913],"domain_scores_gemma":[0.9959698,0.0027376486,0.00022791926,0.0004104296,0.0005891458,0.00006511818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007607936,0.0016157473,0.0013644757,0.00227976,0.0004481904,0.0013206103,0.001165037,0.00086982024,0.0031006194],"category_scores_gemma":[0.011291566,0.00077087147,0.0023061673,0.0014382353,0.0018579528,0.0015927395,0.0026725726,0.0019673242,0.00044347794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022157305,0.000035641096,0.0004446481,0.00013503713,0.000107077736,0.00008274895,0.00007302388,0.68760073,0.0027949584,0.2565162,0.0010480853,0.051139675],"study_design_scores_gemma":[0.0000061403307,0.000028068884,0.000120298166,0.00001522555,0.000013369484,0.000025637983,0.000010786404,0.9238786,0.0005132881,0.07320325,0.0021745367,0.000010792537],"about_ca_topic_score_codex":0.0020624362,"about_ca_topic_score_gemma":0.0015439454,"teacher_disagreement_score":0.007607936,"about_ca_system_score_codex":0.00092347176,"about_ca_system_score_gemma":0.0014607628,"threshold_uncertainty_score":0.040235102},"labels":[],"label_agreement":null},{"id":"W2228271964","doi":"","title":"목구조 설계를 위한 확정론적 구조 설계법과 확률 기반 구조 설계법의 비교 연구","year":2009,"lang":"ko","type":"article","venue":"한국가구학회지","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Probabilistic logic; Reliability (semiconductor); Reliability engineering; Structural reliability; Process (computing); Probabilistic design; Engineering; Probabilistic method; Computer science; Engineering design process; Mechanical engineering; Power (physics)","score_opus":0.060520236905952846,"score_gpt":0.3188137451081615,"score_spread":0.25829350820220864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2228271964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014423574,0.00048015782,0.991646,0.00011804667,0.000070057504,0.00007928409,0.00010513958,0.00022260936,0.0058363937],"genre_scores_gemma":[0.14635861,0.0026401787,0.83964795,0.00019561943,0.00016506681,0.000844646,0.0004250555,0.00021446125,0.009508346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766505,0.00075576524,0.0001773151,0.00042432672,0.00091021875,0.00006739114],"domain_scores_gemma":[0.9977512,0.0011749605,0.00023211529,0.00019526987,0.0006188655,0.000027583159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002231186,0.0010276899,0.00060732936,0.001629076,0.0005622724,0.001410092,0.000986368,0.0007538742,0.005735427],"category_scores_gemma":[0.0037230703,0.0005445835,0.0009881821,0.0013334353,0.001438958,0.0010575358,0.0006356554,0.0012714821,0.0013669769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013908469,0.00007484018,0.0045893444,0.0013560398,0.0001470248,0.00017359841,0.00034762075,0.24729991,0.012695261,0.25118816,0.009902236,0.47208688],"study_design_scores_gemma":[0.0001250671,0.0005562497,0.00430889,0.000529836,0.00024809004,0.0011977594,0.00027658977,0.6138535,0.02245132,0.19304776,0.16313052,0.00027438134],"about_ca_topic_score_codex":0.002680356,"about_ca_topic_score_gemma":0.0023751808,"teacher_disagreement_score":0.005735427,"about_ca_system_score_codex":0.0011446903,"about_ca_system_score_gemma":0.0016461923,"threshold_uncertainty_score":0.019186914},"labels":[],"label_agreement":null},{"id":"W2232343343","doi":"10.4271/2005-01-0814","title":"Robust Design for Occupant Restraint System","year":2005,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Illinois at Chicago; Ryerson University","keywords":"Computer science","score_opus":0.09267062852720465,"score_gpt":0.30913575938423854,"score_spread":0.2164651308570339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2232343343","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029881988,0.00021085958,0.99355894,0.000048276805,0.000015393138,0.000029469224,0.000034321936,0.0002231956,0.0028913396],"genre_scores_gemma":[0.77650976,0.00085352914,0.21222644,0.00015603761,0.00007218667,0.00051237765,0.00030656334,0.00015007777,0.009212977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990195,0.0002849273,0.000042088788,0.00023138175,0.00034419834,0.00007800512],"domain_scores_gemma":[0.99943596,0.00020777255,0.00013104426,0.000047516154,0.00015798605,0.000019702853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011713593,0.0010754223,0.0008538484,0.00047713882,0.00028274674,0.0007976362,0.0009087685,0.0008488719,0.0040096724],"category_scores_gemma":[0.0017635428,0.00037892043,0.0008527677,0.00034547958,0.00053322886,0.00051267404,0.0008959993,0.00066381594,0.0007910787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006860321,0.000026071464,0.00019188681,0.00013856971,0.000047919435,0.000048814632,0.000031849704,0.9443015,0.0072094244,0.0122467615,0.0007798648,0.034908686],"study_design_scores_gemma":[0.000011746293,0.00009916338,0.00016623853,0.000009676584,0.000014668995,0.000015197092,0.0000053601284,0.99420303,0.0012984272,0.0025128373,0.0016559976,0.000007657058],"about_ca_topic_score_codex":0.0032985383,"about_ca_topic_score_gemma":0.0018197942,"teacher_disagreement_score":0.0040096724,"about_ca_system_score_codex":0.00062701263,"about_ca_system_score_gemma":0.0009000564,"threshold_uncertainty_score":0.013413668},"labels":[],"label_agreement":null},{"id":"W2237934651","doi":"10.1145/2775106","title":"Static Network Reliability Estimation under the Marshall-Olkin Copula","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Modeling and Computer Simulation","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Australian Research Council; Canada Research Chairs","keywords":"Monte Carlo method; Estimator; Copula (linguistics); Computer science; Mathematical optimization; Bounded function; Mathematics; Algorithm; Econometrics; Statistics","score_opus":0.08701479158394684,"score_gpt":0.32700985599127497,"score_spread":0.23999506440732812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2237934651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015755994,0.000095971074,0.98328155,0.00007976209,0.000009000506,0.000018827111,0.000041883053,0.00016523102,0.0005518171],"genre_scores_gemma":[0.77957875,0.00051699916,0.21700573,0.00011821166,0.000070459486,0.00014145697,0.0003994679,0.00019147524,0.0019774095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988927,0.00046551955,0.00003977987,0.00028486943,0.000214794,0.000102364604],"domain_scores_gemma":[0.99494135,0.002929939,0.0007247206,0.00076533674,0.0005181096,0.00012067376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002980422,0.0010208256,0.0012394536,0.0010175292,0.00044903968,0.00080931577,0.0018125336,0.0008865758,0.0016411586],"category_scores_gemma":[0.0148508,0.000724956,0.000975049,0.0011318799,0.0012163804,0.002697546,0.0014145573,0.0019702213,0.00031546847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024213658,0.000012209122,0.0012132737,0.00002694616,0.000039436873,0.000053022934,0.000040154602,0.9446225,0.0006017463,0.040478263,0.0005572275,0.01233096],"study_design_scores_gemma":[0.0000021336725,0.000007970204,0.00021437382,0.0000032568428,0.000004636563,0.000013900481,0.0000054054576,0.98387605,0.00018731946,0.015515252,0.00016381698,0.0000058576784],"about_ca_topic_score_codex":0.00767829,"about_ca_topic_score_gemma":0.0041865595,"teacher_disagreement_score":0.00767829,"about_ca_system_score_codex":0.0012375183,"about_ca_system_score_gemma":0.0012046357,"threshold_uncertainty_score":0.01576221},"labels":[],"label_agreement":null},{"id":"W2244834432","doi":"10.4271/2006-01-1139","title":"Incorporating Input Data Uncertainties in Computer Models of Vehicle Systems using the Polynomial Chaos Quadrature Method","year":2006,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Dynamics (Canada)","funders":"Federal Highway Administration","keywords":"Polynomial chaos; Quadrature (astronomy); CHAOS (operating system); Computer science; Polynomial; Applied mathematics; Control theory (sociology); Mathematics; Mathematical analysis; Statistics; Engineering; Artificial intelligence; Electronic engineering; Monte Carlo method","score_opus":0.07893970688620852,"score_gpt":0.3244039136391969,"score_spread":0.24546420675298838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2244834432","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055529666,0.000040769144,0.99361366,0.000042333933,0.000008451829,0.000011188828,0.000017452308,0.00010264318,0.0006105311],"genre_scores_gemma":[0.7567676,0.00036218052,0.2391004,0.000064555774,0.00004751146,0.00016564921,0.000108834916,0.00013974414,0.0032435446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995515,0.00016199613,0.00001776454,0.000035858702,0.00020225487,0.00003065672],"domain_scores_gemma":[0.99900705,0.0006501471,0.00008080688,0.0000876214,0.0001554077,0.000018975934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010311552,0.000425212,0.00069722364,0.0004191178,0.0002643603,0.0006871383,0.0007258979,0.00058158895,0.0008945164],"category_scores_gemma":[0.003560441,0.00033538995,0.0004909844,0.0004693551,0.000484461,0.0008949323,0.00066580006,0.0007508279,0.00023486538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007843296,0.0000053115814,0.00011596821,0.0000098600985,0.00000592821,0.000015737545,0.000013672535,0.985158,0.00042474418,0.008332871,0.000083407926,0.005826531],"study_design_scores_gemma":[8.782325e-7,0.0000021974488,0.000011417128,6.925772e-7,6.871797e-7,0.0000015386511,6.940358e-7,0.9987382,0.0000892278,0.0010792544,0.000074268544,9.764203e-7],"about_ca_topic_score_codex":0.0074859606,"about_ca_topic_score_gemma":0.004031764,"teacher_disagreement_score":0.0074859606,"about_ca_system_score_codex":0.0006518712,"about_ca_system_score_gemma":0.0008663396,"threshold_uncertainty_score":0.01488477},"labels":[],"label_agreement":null},{"id":"W2252812103","doi":"10.1007/s11431-011-4603-x","title":"A numerical method for structural uncertainty response computation","year":2011,"lang":"en","type":"article","venue":"Science in China. Series E, Technological sciences/Science in China. Series E, Technological Sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Finite element method; Cumulative distribution function; Probability density function; Applied mathematics; Mathematics; Random variable; Probability distribution; Numerical integration; Monte Carlo method; Hessian matrix; Mathematical optimization; Algorithm; Mathematical analysis; Structural engineering; Statistics; Engineering","score_opus":0.09859402596473928,"score_gpt":0.38401798848443053,"score_spread":0.28542396251969127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2252812103","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00080215465,0.000056023422,0.99751115,0.000047645597,0.00006212072,0.00003423468,0.000033029904,0.000320528,0.0011331298],"genre_scores_gemma":[0.051176075,0.00012656041,0.944637,0.00009833653,0.00007309186,0.00039831235,0.00012105756,0.00030841777,0.0030612128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931014,0.00022750316,0.00005077221,0.00007707192,0.0002951872,0.000039298397],"domain_scores_gemma":[0.99787724,0.0011661748,0.00009287055,0.00024382828,0.0005269082,0.00009306365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015592225,0.0007395172,0.0011208102,0.000987823,0.0009905456,0.00096191093,0.0019475586,0.0016769471,0.010662546],"category_scores_gemma":[0.0058122724,0.00068254065,0.0010038391,0.0009841509,0.001019955,0.0010172568,0.0019300568,0.002368419,0.0029208018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018606143,0.00017571528,0.00049697736,0.00037112218,0.000110837456,0.00013233362,0.00016693573,0.6541726,0.01274025,0.06694819,0.006833765,0.25766528],"study_design_scores_gemma":[0.000025918567,0.000019987789,0.00003376578,0.00001527799,0.000006333352,0.000019646519,0.0000067979286,0.9885343,0.00064381113,0.00758524,0.0031010944,0.00000783471],"about_ca_topic_score_codex":0.003936817,"about_ca_topic_score_gemma":0.0040852497,"teacher_disagreement_score":0.010662546,"about_ca_system_score_codex":0.00056367775,"about_ca_system_score_gemma":0.0015750597,"threshold_uncertainty_score":0.035669804},"labels":[],"label_agreement":null},{"id":"W2253681433","doi":"10.1115/pvp2015-45427","title":"A Probabilistic Approach to Fired Heater Tube Remaining Life Assessments","year":2015,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"J. D. Irving (Canada)","funders":"","keywords":"Creep; Probabilistic logic; Tube (container); Monte Carlo method; Margin (machine learning); Measurement uncertainty; Computer science; Nuclear engineering; Reliability engineering; Materials science; Structural engineering; Mechanical engineering; Engineering; Mathematics; Statistics; Machine learning; Composite material; Artificial intelligence","score_opus":0.31320787914390624,"score_gpt":0.39453033475587695,"score_spread":0.08132245561197071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2253681433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015674373,0.0002452234,0.9785024,0.00025996074,0.000015579226,0.00013820124,0.00024245107,0.00012251614,0.0047992063],"genre_scores_gemma":[0.6107667,0.001031628,0.38213503,0.00009837396,0.00009759464,0.00057885837,0.00046971196,0.00009414486,0.004727982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962877,0.0016423333,0.00018661963,0.00041765228,0.0013008489,0.00016482463],"domain_scores_gemma":[0.9898673,0.0077080787,0.0009881529,0.00038737332,0.00093153486,0.00011753819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006316564,0.0011431598,0.0008037783,0.003717051,0.00095217174,0.0021317776,0.001977423,0.0012493101,0.004174835],"category_scores_gemma":[0.0155246155,0.0009477942,0.001373176,0.0017810923,0.0012303864,0.0020229667,0.0015591016,0.0014238104,0.00037232804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022501119,0.000019531271,0.0010106479,0.00005888918,0.000035408582,0.000111068424,0.000073644136,0.9442236,0.00057255983,0.03843985,0.00024786557,0.015184418],"study_design_scores_gemma":[0.0000048905117,0.000061889026,0.0006999172,0.000023025887,0.000019735244,0.00012828193,0.000047720343,0.95320326,0.0006439815,0.043395635,0.0017428887,0.000028773109],"about_ca_topic_score_codex":0.005534162,"about_ca_topic_score_gemma":0.0073718843,"teacher_disagreement_score":0.006316564,"about_ca_system_score_codex":0.002076564,"about_ca_system_score_gemma":0.0018119772,"threshold_uncertainty_score":0.033405542},"labels":[],"label_agreement":null},{"id":"W2256121065","doi":"10.1007/978-0-8176-4807-7_26","title":"Optimal Experimental Designs","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Censoring (clinical trials); Fisher information; Statistics; Computer science; Mathematics; Variance (accounting); Econometrics; Mathematical optimization; Economics","score_opus":0.1699855187500097,"score_gpt":0.33863112930562145,"score_spread":0.16864561055561175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2256121065","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015679585,0.0029451433,0.91923857,0.0008122581,0.00047189026,0.00018106963,0.00044650814,0.0004843891,0.07385225],"genre_scores_gemma":[0.10838176,0.006788347,0.7786398,0.0020168342,0.000837749,0.0019846705,0.0014247057,0.00069890195,0.09922726],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9943204,0.0027456603,0.0002060124,0.0009839439,0.0015576599,0.00018636073],"domain_scores_gemma":[0.9964749,0.0020967664,0.00020492011,0.00078519195,0.00035771198,0.00008052862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052377684,0.001419733,0.0013443468,0.0011631049,0.00050803594,0.0017099801,0.0019871064,0.0016452083,0.04127372],"category_scores_gemma":[0.009793896,0.0009783654,0.00077301206,0.00088000065,0.0021972572,0.0019352795,0.0016408508,0.0023015966,0.008160492],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032682318,0.00015489267,0.00021633484,0.0009192705,0.00008668265,0.00003535155,0.000063924985,0.022866035,0.0070554037,0.5494113,0.027237134,0.39162686],"study_design_scores_gemma":[0.0001506403,0.00027752426,0.00052544044,0.0004155823,0.000088567,0.000116104464,0.0000490892,0.05229251,0.011280604,0.7822604,0.1524929,0.00005062148],"about_ca_topic_score_codex":0.00040480882,"about_ca_topic_score_gemma":0.0007558625,"teacher_disagreement_score":0.04127372,"about_ca_system_score_codex":0.0013267271,"about_ca_system_score_gemma":0.0016992715,"threshold_uncertainty_score":0.13807434},"labels":[],"label_agreement":null},{"id":"W2258481197","doi":"","title":"Modelling Multi-level Power Usage with Latent States and Smooth Functions","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Frequentist inference; Function (biology); Computer science; Process (computing); Power (physics); Chiller; State (computer science); Mathematical optimization; Mathematics; Algorithm; Artificial intelligence","score_opus":0.3313646863874762,"score_gpt":0.24194945978737928,"score_spread":0.0894152266000969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2258481197","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042058453,0.00011864855,0.95650095,0.00028660824,0.000016735887,0.000029843795,0.0001998571,0.00016806173,0.0006208179],"genre_scores_gemma":[0.8813495,0.00032835285,0.11249191,0.00010870805,0.00008460324,0.00021225808,0.000577787,0.00011687579,0.0047298754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998004,0.0008791706,0.0000848141,0.00054045045,0.0002753488,0.00021628111],"domain_scores_gemma":[0.98975533,0.0077578602,0.0010988598,0.0008638048,0.00034592216,0.00017817716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004039568,0.0009678759,0.0014023582,0.0012491192,0.0004232565,0.0021738396,0.0022522458,0.0019697666,0.0027547965],"category_scores_gemma":[0.013103186,0.0009960058,0.001862454,0.0016714726,0.0019878603,0.0028668996,0.0019769112,0.0031414605,0.00058067613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010880234,0.00009430418,0.0055711446,0.00006396629,0.000112279864,0.00010846057,0.0002630729,0.89853984,0.0011204412,0.074546784,0.00044689496,0.019023873],"study_design_scores_gemma":[0.000004386952,0.00001162345,0.0005311726,0.000004380033,0.0000069493217,0.000009709227,0.000010585835,0.9788969,0.00013483985,0.020164194,0.00021476013,0.000010385255],"about_ca_topic_score_codex":0.0057260427,"about_ca_topic_score_gemma":0.005906047,"teacher_disagreement_score":0.0057260427,"about_ca_system_score_codex":0.0012610104,"about_ca_system_score_gemma":0.0008568734,"threshold_uncertainty_score":0.021363556},"labels":[],"label_agreement":null},{"id":"W2266960822","doi":"10.1080/14488353.2007.11463926","title":"Consequence modelling based on stated preferences","year":2007,"lang":"en","type":"article","venue":"Australian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Preference; Probabilistic logic; Decision analysis; Operations research; Engineering; Risk analysis (engineering); Decision model; Management science; Computer science; Economics; Business; Artificial intelligence","score_opus":0.12928401599185446,"score_gpt":0.3247356164506295,"score_spread":0.19545160045877502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2266960822","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03650715,0.0002370434,0.9507639,0.0010636189,0.000038851875,0.00016941439,0.00023716867,0.00011372762,0.010869106],"genre_scores_gemma":[0.85556006,0.00029949145,0.13904801,0.00018280809,0.00008135922,0.0004733187,0.00034947429,0.000032322117,0.0039731264],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98463887,0.010015362,0.0006434854,0.0011260621,0.0026935244,0.00088278775],"domain_scores_gemma":[0.96100676,0.030931925,0.0022971446,0.0019328204,0.0031029114,0.00072838843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017085237,0.0012380106,0.0011219382,0.0020442808,0.00088647957,0.004091515,0.002088821,0.0016678189,0.0074408543],"category_scores_gemma":[0.040912814,0.000598446,0.0022957495,0.0015852397,0.0022485317,0.004560337,0.0024004404,0.00281072,0.0005292078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002003287,0.000101091806,0.0021978745,0.00017632608,0.0001393442,0.00044625904,0.0006187341,0.3592143,0.0005196917,0.60547745,0.0011949488,0.0297136],"study_design_scores_gemma":[0.000039913262,0.00008816711,0.00044333944,0.00005189863,0.000039856208,0.000089028596,0.000094019844,0.61812043,0.00031574274,0.3796319,0.0010565709,0.000029128683],"about_ca_topic_score_codex":0.002423528,"about_ca_topic_score_gemma":0.00224892,"teacher_disagreement_score":0.017085237,"about_ca_system_score_codex":0.002539393,"about_ca_system_score_gemma":0.00149755,"threshold_uncertainty_score":0.09035647},"labels":[],"label_agreement":null},{"id":"W2270849607","doi":"10.14288/1.0076076","title":"Finite element reliability analysis of structures using the dimensional reduction method","year":2015,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Finite element method; Reliability (semiconductor); Reduction (mathematics); Computer science; Reliability engineering; Mathematics; Structural engineering; Algorithm; Engineering; Geometry; Physics","score_opus":0.06259024837273355,"score_gpt":0.2820839576454978,"score_spread":0.21949370927276426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2270849607","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061629573,0.00017607327,0.9912477,0.00004766747,0.000010410871,0.000028553019,0.000037362493,0.00018197784,0.0021073623],"genre_scores_gemma":[0.44618064,0.0006222687,0.54876995,0.00007763482,0.000046318622,0.00036328105,0.00022391047,0.00014331819,0.0035727054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994199,0.00022227612,0.000022070153,0.00005107361,0.00026240107,0.000022339917],"domain_scores_gemma":[0.9996182,0.00019507541,0.000040147013,0.000052042316,0.00008797175,0.0000065278145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007265509,0.0004731475,0.0007311016,0.00076300284,0.0002436281,0.0005175805,0.0005589374,0.0004922283,0.0013783271],"category_scores_gemma":[0.0013842105,0.00031880336,0.0006835714,0.00043445217,0.0003751486,0.00046387443,0.00047133586,0.0006118089,0.00052230945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001571735,0.000023613493,0.0005259304,0.000078609926,0.00003625149,0.00004466705,0.000062583225,0.9196605,0.012906315,0.020095512,0.0005216286,0.046028737],"study_design_scores_gemma":[0.0000013590866,0.000010500509,0.00014806462,0.0000046331943,0.0000030198034,0.000017829156,0.0000042278266,0.9962931,0.000776246,0.002014616,0.00072181405,0.000004507667],"about_ca_topic_score_codex":0.0013374997,"about_ca_topic_score_gemma":0.0012666585,"teacher_disagreement_score":0.0013783271,"about_ca_system_score_codex":0.00038134825,"about_ca_system_score_gemma":0.00052188896,"threshold_uncertainty_score":0.0046109557},"labels":[],"label_agreement":null},{"id":"W2271473454","doi":"10.1016/j.apm.2017.09.016","title":"Probabilistic modeling and global sensitivity analysis for CO2 storage in geological formations: a spectral approach","year":2017,"lang":"en","type":"preprint","venue":"Applied Mathematical Modelling","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Saudi Basic Industries Corporation; King Abdullah University of Science and Technology; University of Texas at Austin","keywords":"Sobol sequence; Monte Carlo method; Polynomial chaos; Probabilistic logic; Sensitivity (control systems); Applied mathematics; Uncertainty quantification; Leakage (economics); Observable; Computer science; Mathematical optimization; Mathematics; Statistics; Engineering; Physics","score_opus":0.16404433800988327,"score_gpt":0.33441429060678884,"score_spread":0.17036995259690557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2271473454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035015147,0.00034539122,0.96022433,0.000594741,0.000033946337,0.000029125255,0.00008441717,0.000085086394,0.0035879042],"genre_scores_gemma":[0.95926285,0.0005550476,0.036048833,0.00017155247,0.00011134393,0.000115393115,0.00011840501,0.00013306997,0.003483376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988248,0.00061384076,0.00004526128,0.0001969429,0.00022233005,0.00009689096],"domain_scores_gemma":[0.99568355,0.003326832,0.0003475908,0.00020739438,0.000344346,0.00009037831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032577438,0.0013890305,0.0011562703,0.001448378,0.0005727223,0.0017916332,0.001520004,0.0016732735,0.0018733506],"category_scores_gemma":[0.008947611,0.0009761196,0.0019860542,0.00086603593,0.002534469,0.0025970999,0.002824338,0.0014424366,0.00015450681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011025787,0.00001685425,0.00018335789,0.000030287061,0.00003122661,0.000026552147,0.000025645408,0.97034866,0.0005973997,0.026508292,0.00013275517,0.0020879149],"study_design_scores_gemma":[0.000001089856,0.000002768311,0.0000484066,0.0000020275083,0.0000038916032,0.000004334617,0.000004089114,0.98766625,0.00009032313,0.0121186,0.000055152374,0.000003079246],"about_ca_topic_score_codex":0.0063521983,"about_ca_topic_score_gemma":0.0031784715,"teacher_disagreement_score":0.0063521983,"about_ca_system_score_codex":0.0013962646,"about_ca_system_score_gemma":0.0009639518,"threshold_uncertainty_score":0.017228782},"labels":[],"label_agreement":null},{"id":"W2284235417","doi":"","title":"Inverse-free second moment method for electrical systems with uncertain parameters: Research Articles","year":2005,"lang":"en","type":"article","venue":"International Journal of Circuit Theory and Applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Acadia University","funders":"","keywords":"Inverse; Moment (physics); Inverse problem; Applied mathematics; Computer science; Matrix (chemical analysis); Reliability (semiconductor); Mathematical optimization; Monte Carlo method; Probabilistic logic; Diagonal; Moore–Penrose pseudoinverse; Algorithm; Mathematics; Statistics; Artificial intelligence; Mathematical analysis","score_opus":0.15372663663559316,"score_gpt":0.4177350933289739,"score_spread":0.26400845669338074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2284235417","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00096849207,0.00057950756,0.99686265,0.00010247584,0.000043629218,0.000010038769,0.000017907905,0.00015973356,0.0012554775],"genre_scores_gemma":[0.17956086,0.0038350583,0.7992957,0.0002531119,0.00048236016,0.00015185101,0.00025789277,0.00040342196,0.015759615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964976,0.000115312374,0.0000133082485,0.000041958876,0.00016074943,0.000018819004],"domain_scores_gemma":[0.9992293,0.00047878703,0.000071266564,0.00007964528,0.00011808296,0.000022931323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007469388,0.00069273345,0.00096082786,0.0008791563,0.00028918486,0.0006560307,0.0007019252,0.0009952685,0.0037005225],"category_scores_gemma":[0.0021113306,0.0003401922,0.00093982386,0.0007164843,0.0007711473,0.0010136343,0.00057420187,0.0012052004,0.0013362494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014820514,0.00006534277,0.0004260502,0.0005536936,0.00013337693,0.00023303516,0.00016374835,0.5329228,0.0173726,0.16202758,0.008828962,0.27712455],"study_design_scores_gemma":[0.000013804058,0.000027641796,0.00013736746,0.000020589023,0.000018947194,0.00014169434,0.000007819438,0.96021885,0.0027404139,0.027107773,0.009543422,0.000021686325],"about_ca_topic_score_codex":0.0011464063,"about_ca_topic_score_gemma":0.0011394699,"teacher_disagreement_score":0.0037005225,"about_ca_system_score_codex":0.00045778154,"about_ca_system_score_gemma":0.0007453549,"threshold_uncertainty_score":0.0123794675},"labels":[],"label_agreement":null},{"id":"W2288584778","doi":"10.1109/nemo.2015.7415047","title":"Fast multidimensional statistical analysis of microwave networks via stroud cubature approach","year":2015,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Collocation (remote sensing); Computer science; Key (lock); Collocation method; Tensor product; Applied mathematics; Microwave; Algorithm; Product (mathematics); Mathematical optimization; Mathematics; Telecommunications; Mathematical analysis; Machine learning","score_opus":0.08147512152884696,"score_gpt":0.32165832998901467,"score_spread":0.24018320846016772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2288584778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034459473,0.00008177609,0.9957273,0.00006425275,0.000012410444,0.000010998714,0.000015263287,0.00005777761,0.00058424764],"genre_scores_gemma":[0.56889814,0.0010290263,0.42402524,0.00017690785,0.00012227165,0.00042855841,0.00021657412,0.00019699807,0.0049062073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952996,0.00023698669,0.000015345222,0.000044922082,0.00013702807,0.00003577554],"domain_scores_gemma":[0.99886477,0.00073206663,0.000115983414,0.00009200357,0.00016283832,0.00003229198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011159026,0.00058482576,0.0007692995,0.00081731885,0.0003688179,0.0010167161,0.0007268192,0.00062481617,0.0014704927],"category_scores_gemma":[0.0026843639,0.00047408522,0.00059082726,0.0006760033,0.00089500356,0.0012864028,0.00076505786,0.00085931976,0.00034980426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031290994,0.000013822789,0.0003271108,0.000058396196,0.000030958403,0.00004089094,0.000042347336,0.87160754,0.0030024063,0.109983005,0.0005440384,0.014318229],"study_design_scores_gemma":[9.096745e-7,0.0000031491477,0.00002322562,0.0000013142295,8.110335e-7,0.0000028271777,0.0000017461228,0.9955052,0.00014685288,0.0040906263,0.00022138415,0.0000019091897],"about_ca_topic_score_codex":0.0021681034,"about_ca_topic_score_gemma":0.0017642112,"teacher_disagreement_score":0.0021681034,"about_ca_system_score_codex":0.0007696241,"about_ca_system_score_gemma":0.0007239377,"threshold_uncertainty_score":0.0059015155},"labels":[],"label_agreement":null},{"id":"W2289836461","doi":"","title":"BUCKLING LOAD PREDICTIONS IN PRESSURE VESSELS UTILIZING MONTE CARLO METHOD","year":2009,"lang":"en","type":"other","venue":"NC Digital Online Collection of Knowledge and Scholarship (The University of North Carolina at Greensboro)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Monte Carlo method; Buckling; Structural engineering; Computer science; Mathematics; Engineering; Statistics","score_opus":0.04187518759621444,"score_gpt":0.2882577552551745,"score_spread":0.24638256765896005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2289836461","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3494735,0.00057184044,0.62278247,0.00033188253,0.0000728414,0.00016209918,0.00039616652,0.0012485879,0.02496063],"genre_scores_gemma":[0.92862475,0.00024704915,0.06493984,0.00005767963,0.000020911146,0.00020059304,0.00029595054,0.00014397118,0.0054692533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980766,0.000045037785,0.000008668283,0.000024288045,0.000081255326,0.000033071457],"domain_scores_gemma":[0.998806,0.0008222224,0.00010286968,0.000040180596,0.00018776431,0.00004098199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073994306,0.00043909534,0.00055717834,0.0010554715,0.0007153913,0.00077997934,0.00096894207,0.0013363111,0.0023089577],"category_scores_gemma":[0.0019592561,0.00053508877,0.0006066206,0.0007140699,0.0004767435,0.00059363205,0.00036796578,0.0006112885,0.00041783167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009102263,0.000008929547,0.0003806397,0.000007736543,0.0000027863537,0.0000225077,0.000008953346,0.9970266,0.00033252063,0.0006977639,0.000063745574,0.0014387849],"study_design_scores_gemma":[4.4950798e-7,0.0000017624681,0.000049417973,9.787605e-7,3.74625e-7,0.000001914358,0.0000018192387,0.9997255,0.00006666266,0.00011020181,0.000039679835,0.0000011114082],"about_ca_topic_score_codex":0.022003409,"about_ca_topic_score_gemma":0.013842451,"teacher_disagreement_score":0.022003409,"about_ca_system_score_codex":0.0009163958,"about_ca_system_score_gemma":0.0011267377,"threshold_uncertainty_score":0.043750703},"labels":[],"label_agreement":null},{"id":"W2290364503","doi":"10.5555/2888619.2888661","title":"Efficient probability estimation and simulation of the truncated multivariate student-t distribution","year":2015,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Multivariate statistics; Markov chain Monte Carlo; Multivariate normal distribution; Markov chain; Exponential distribution; Applied mathematics; Monte Carlo method; Probability distribution; Mathematics; Distribution (mathematics); Normal-Wishart distribution; Multivariate t-distribution; Univariate distribution; Computer science; Multivariate random variable; Statistics; Mathematical optimization; Random variable; Mathematical analysis","score_opus":0.1563098546172481,"score_gpt":0.37978680247289676,"score_spread":0.22347694785564864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290364503","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034033896,0.000018603198,0.9961599,0.000020518351,0.000007920622,0.000015527226,0.000016057573,0.00013790006,0.00022022627],"genre_scores_gemma":[0.3670431,0.0002199024,0.6301624,0.00010332832,0.000046601268,0.0003014326,0.00039592123,0.00022467619,0.0015026599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759656,0.0009332295,0.00013570338,0.00029438105,0.0008541264,0.00018607115],"domain_scores_gemma":[0.99110574,0.0053148502,0.000738142,0.0011724237,0.001376875,0.00029190836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035890397,0.0007950008,0.001326633,0.0010552397,0.0005047096,0.0012680684,0.002321671,0.0010043976,0.0036779046],"category_scores_gemma":[0.020266883,0.000715376,0.0011483107,0.0012197281,0.0012653026,0.002204538,0.002275392,0.0022322352,0.0009223392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012292762,0.000029798004,0.0014821626,0.00004680295,0.000031918542,0.00008063506,0.000049440037,0.92689043,0.0023743578,0.038925264,0.00060350203,0.029362665],"study_design_scores_gemma":[0.0000043697323,0.0000075815633,0.0000686327,0.0000029234213,0.0000020458658,0.000014364124,0.0000026477794,0.99552405,0.0006639206,0.0035735788,0.00012953764,0.000006367899],"about_ca_topic_score_codex":0.004240522,"about_ca_topic_score_gemma":0.0024579265,"teacher_disagreement_score":0.004240522,"about_ca_system_score_codex":0.0010065511,"about_ca_system_score_gemma":0.002038689,"threshold_uncertainty_score":0.01898086},"labels":[],"label_agreement":null},{"id":"W2292128163","doi":"10.2139/ssrn.2568435","title":"Mathematical Appendices to: 'The Probability of Backtest Overfitting'","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Society of Canada","funders":"","keywords":"Overfitting; Monte Carlo method; Extreme value theory; Computer science; Carry (investment); Value (mathematics); Econometrics; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning; Statistics; Economics; Artificial neural network; Finance","score_opus":0.09443576931675676,"score_gpt":0.33474453493440065,"score_spread":0.2403087656176439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2292128163","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031543644,0.01297557,0.49072286,0.039605998,0.069342345,0.0005422591,0.03500453,0.0043026945,0.3443494],"genre_scores_gemma":[0.11431387,0.020973377,0.17135192,0.026248833,0.051993128,0.0028578723,0.036954466,0.008976923,0.5663296],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983341,0.0004493756,0.00017247212,0.00023192375,0.00072273565,0.000089393856],"domain_scores_gemma":[0.97412246,0.015488878,0.0012279031,0.0024005515,0.006396296,0.0003639944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025446147,0.0015914465,0.0019720213,0.003327657,0.0011319115,0.0016427477,0.003418949,0.0024587684,0.2650985],"category_scores_gemma":[0.050989393,0.0008238782,0.0013905447,0.004578028,0.0013886794,0.004631059,0.0025482676,0.0040408364,0.11215381],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027180824,0.00006891523,0.0002639152,0.00032317438,0.000017934288,0.00013199831,0.0000527469,0.0060976115,0.0001960305,0.09898425,0.8517042,0.042132065],"study_design_scores_gemma":[0.000041458075,0.000058305282,0.0014024116,0.0004247469,0.00003435376,0.0006427515,0.00006257136,0.020294378,0.0007589859,0.5009181,0.4752594,0.00010263634],"about_ca_topic_score_codex":0.0036660766,"about_ca_topic_score_gemma":0.004811273,"teacher_disagreement_score":0.2650985,"about_ca_system_score_codex":0.0022948834,"about_ca_system_score_gemma":0.0017437297,"threshold_uncertainty_score":0.88684285},"labels":[],"label_agreement":null},{"id":"W2300941020","doi":"10.1016/j.jcp.2016.05.044","title":"Data-driven probability concentration and sampling on manifold","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":109,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Innovation, Science and Economic Development Canada; Advanced Scientific Computing Research; U.S. Department of Energy","keywords":"Mathematics; Multivariate random variable; Probability distribution; Algorithm; Statistical manifold; Matrix (chemical analysis); Applied mathematics; Random variable; Statistics; Information geometry; Geometry","score_opus":0.22426289777609157,"score_gpt":0.3716983697016835,"score_spread":0.14743547192559195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2300941020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02620308,0.00035500719,0.97112375,0.00082993694,0.000044164608,0.000069439615,0.00012978348,0.00017169218,0.0010730651],"genre_scores_gemma":[0.7717763,0.0010063992,0.21794385,0.0004932135,0.00031412416,0.00038772964,0.0007583207,0.00041072757,0.0069094044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99678385,0.0015040165,0.00016842113,0.0007138814,0.0006219978,0.00020775944],"domain_scores_gemma":[0.95698816,0.032563686,0.002997492,0.0026450225,0.0033969674,0.00140866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007841923,0.0015478699,0.0030083405,0.0030327963,0.0011320297,0.003646586,0.004981012,0.004124268,0.0031340371],"category_scores_gemma":[0.06892387,0.0021959194,0.0017818139,0.002059357,0.005274287,0.0071573346,0.0064419163,0.0028970398,0.0004134448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018986165,0.00007894341,0.0019952161,0.00021942124,0.00009790061,0.00017044801,0.00025644834,0.5767192,0.0016553081,0.40635717,0.001344052,0.010915929],"study_design_scores_gemma":[0.000008328083,0.000014169868,0.000115788454,0.000008518027,0.0000054463094,0.000027275524,0.000008747579,0.93340015,0.0003635549,0.06583509,0.00020101175,0.000011925639],"about_ca_topic_score_codex":0.0056519383,"about_ca_topic_score_gemma":0.0028739187,"teacher_disagreement_score":0.007841923,"about_ca_system_score_codex":0.0036510986,"about_ca_system_score_gemma":0.002116529,"threshold_uncertainty_score":0.041472614},"labels":[],"label_agreement":null},{"id":"W2303382453","doi":"10.1109/tmag.2015.2488360","title":"A Rational Approach to Curve Representation","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extrapolation; Interpolation (computer graphics); Rational function; Computer science; Finite element method; Curve fitting; Applied mathematics; Polynomial and rational function modeling; Representation (politics); Data point; Algorithm; Mathematical optimization; Mathematical analysis; Mathematics; Polynomial; Artificial intelligence","score_opus":0.21111794805710835,"score_gpt":0.35896124732924695,"score_spread":0.1478432992721386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2303382453","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016058035,0.00010643767,0.9945411,0.00006945174,0.000027979162,0.000017379916,0.000042767897,0.0002663774,0.0033226188],"genre_scores_gemma":[0.1851694,0.0011402196,0.80157757,0.00018209669,0.00017191937,0.00017688576,0.0005053822,0.00067862915,0.01039789],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99901485,0.00018940131,0.0000476688,0.00019475326,0.00045927594,0.00009417366],"domain_scores_gemma":[0.9990318,0.00032390704,0.000077865596,0.00023757147,0.0002959361,0.000032962602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012677273,0.0008814172,0.0007844391,0.0019938091,0.00046760746,0.0023561441,0.0016154636,0.0011245512,0.00536013],"category_scores_gemma":[0.0039608646,0.0004919546,0.0010947885,0.0014383956,0.0016459344,0.0021149707,0.001311165,0.0018322633,0.0023551534],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054379627,0.000042158845,0.00040944314,0.00018533057,0.000024604828,0.000094640716,0.00022476236,0.21593328,0.007935473,0.6320614,0.0035653766,0.13946906],"study_design_scores_gemma":[0.0000108325485,0.000053023177,0.000149465,0.000037091482,0.000009966656,0.00013248256,0.0000620867,0.799263,0.0035267652,0.16526796,0.03145291,0.000034454373],"about_ca_topic_score_codex":0.0019637025,"about_ca_topic_score_gemma":0.0010488809,"teacher_disagreement_score":0.00536013,"about_ca_system_score_codex":0.00091250724,"about_ca_system_score_gemma":0.00082986953,"threshold_uncertainty_score":0.017931402},"labels":[],"label_agreement":null},{"id":"W2305950301","doi":"10.1016/j.renene.2016.03.031","title":"Power curve monitoring using weighted moving average control charts","year":2016,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Desjardins; École de Technologie Supérieure","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"EWMA chart; Control chart; Moving average; Turbine; Wind power; Bin; Robustness (evolution); Statistics; Control theory (sociology); Computer science; Engineering; Mathematics; Process (computing); Algorithm; Control (management); Artificial intelligence","score_opus":0.043311968652652966,"score_gpt":0.2821480385190283,"score_spread":0.23883606986637534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2305950301","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029878687,0.00018154382,0.96797633,0.000040594026,0.000038484304,0.000022254511,0.00005094697,0.0006929233,0.0011181067],"genre_scores_gemma":[0.9081098,0.00015840991,0.09050566,0.00002923146,0.00003854147,0.00004936713,0.00009806186,0.000103885766,0.0009071138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855405,0.0004105597,0.00010358156,0.00032947486,0.00053427514,0.00006812196],"domain_scores_gemma":[0.99666554,0.0015997322,0.0005955074,0.00037263535,0.00070134166,0.000065238164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017259686,0.00087615364,0.0008539573,0.0012362335,0.0003052226,0.0015696208,0.0007381975,0.00046292163,0.0008704856],"category_scores_gemma":[0.008776715,0.00027208042,0.0003989386,0.0012298656,0.00038869237,0.0019236159,0.0006549072,0.0006871766,0.00018293453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053200463,0.00011126034,0.0028454862,0.00016250699,0.00014389785,0.00011642195,0.00013946978,0.6361882,0.0125215305,0.017019065,0.0014407589,0.3287794],"study_design_scores_gemma":[0.0000054273346,0.00004262555,0.00038963073,0.000005751804,0.0000126016985,0.000014313912,0.000003979712,0.99376464,0.0029009944,0.0025000705,0.00035053075,0.000009463247],"about_ca_topic_score_codex":0.0022624484,"about_ca_topic_score_gemma":0.0015602212,"teacher_disagreement_score":0.0022624484,"about_ca_system_score_codex":0.0004547622,"about_ca_system_score_gemma":0.00038572846,"threshold_uncertainty_score":0.009127915},"labels":[],"label_agreement":null},{"id":"W2313700874","doi":"10.2514/6.2013-1467","title":"Two-Level Domain Decomposition Method for Uncertainty Quantification","year":2013,"lang":"en","type":"article","venue":"54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Innovation Trust","keywords":"Computer science; Decomposition; Domain (mathematical analysis); Domain decomposition methods; Mathematics; Physics; Chemistry; Finite element method","score_opus":0.06990014270604977,"score_gpt":0.3629099515374286,"score_spread":0.29300980883137884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2313700874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000359761,0.000046261353,0.9992244,0.00001410161,0.000007268017,0.000005271608,0.000020002293,0.00006413247,0.00025884135],"genre_scores_gemma":[0.07154589,0.00027974998,0.9247613,0.00007844178,0.000037498372,0.00013195803,0.00034112963,0.00017740949,0.0026465494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945647,0.00017101249,0.000029941482,0.00007704294,0.00022349482,0.000042002317],"domain_scores_gemma":[0.99921286,0.0003904027,0.000044252432,0.000089620626,0.0002285219,0.00003427098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010820121,0.0007260854,0.0009036047,0.0008242209,0.00036304284,0.0011498234,0.00080614316,0.0008772998,0.0038827502],"category_scores_gemma":[0.0021557114,0.00043589287,0.0010351755,0.0007198509,0.00046077935,0.0011424355,0.0012564295,0.0016357142,0.0011105033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013960381,0.00008551659,0.00037157699,0.00038224732,0.00011443003,0.000088405446,0.00008137918,0.4827235,0.020168701,0.08862743,0.005889983,0.4013273],"study_design_scores_gemma":[0.0000037944426,0.0000100258785,0.000052856078,0.000008000374,0.000006284532,0.00001757448,0.0000038510366,0.99051124,0.0012307123,0.0067161713,0.0014330734,0.000006352842],"about_ca_topic_score_codex":0.0017253681,"about_ca_topic_score_gemma":0.0014168087,"teacher_disagreement_score":0.0038827502,"about_ca_system_score_codex":0.00040553475,"about_ca_system_score_gemma":0.00100212,"threshold_uncertainty_score":0.012989044},"labels":[],"label_agreement":null},{"id":"W2314274275","doi":"10.1061/9780784479117.178","title":"Ductility Estimation for a Novel Timber-Steel-Hybrid System with Consideration of Uncertainty","year":2015,"lang":"en","type":"article","venue":"Structures Congress 2015","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Ductility (Earth science); Estimation; Computer science; Structural engineering; Engineering; Materials science; Composite material; Systems engineering","score_opus":0.1028301785576087,"score_gpt":0.34702774424011096,"score_spread":0.24419756568250225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2314274275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26381394,0.000107453605,0.7324641,0.00008244916,0.0000065418603,0.000035771052,0.00011858625,0.00018162193,0.0031895423],"genre_scores_gemma":[0.98586977,0.000034871053,0.013140316,0.000009362531,0.0000041800968,0.000023157158,0.0000690877,0.000007070486,0.0008420941],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999728,0.000054536406,0.000015922984,0.00008107477,0.000083430554,0.00003695703],"domain_scores_gemma":[0.99915266,0.0005261002,0.00013174757,0.000032043983,0.0001374321,0.000019995134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007701849,0.00045874607,0.00048401605,0.00049419864,0.00027428727,0.00051650533,0.0004475929,0.0005473063,0.0010663957],"category_scores_gemma":[0.0013374838,0.00026102204,0.00050354504,0.00026771676,0.000353656,0.00049496145,0.00059232005,0.00037307257,0.00015341546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000738478,0.000013321048,0.0016680254,0.000033343567,0.000017634853,0.00006863431,0.00004334045,0.98072904,0.0046142354,0.0019914059,0.00010355785,0.01064362],"study_design_scores_gemma":[0.0000016105041,0.000022608161,0.00047208584,0.000001748329,0.000004512689,0.000011447211,0.000005478057,0.9984023,0.00062953663,0.00037441793,0.00007028677,0.0000040081068],"about_ca_topic_score_codex":0.0049935323,"about_ca_topic_score_gemma":0.0049277013,"teacher_disagreement_score":0.0049935323,"about_ca_system_score_codex":0.000584059,"about_ca_system_score_gemma":0.00046473305,"threshold_uncertainty_score":0.009928942},"labels":[],"label_agreement":null},{"id":"W2314370321","doi":"10.2514/6.2002-1640","title":"Fuzzy Probabilistic Assessment of the Impact of Corrosion on Fatigue of Aircraft Structures","year":2002,"lang":"en","type":"article","venue":"43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Martec (Canada)","funders":"","keywords":"Probabilistic logic; Fuzzy logic; Corrosion; Corrosion fatigue; Reliability engineering; Computer science; Artificial intelligence; Engineering; Structural engineering; Materials science; Metallurgy","score_opus":0.06817505017770843,"score_gpt":0.33691830756398744,"score_spread":0.268743257386279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2314370321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020647869,0.00013920528,0.9780773,0.000062848034,0.00000515773,0.000021503274,0.000018093195,0.00004751268,0.0009805404],"genre_scores_gemma":[0.80450475,0.00033761404,0.19385673,0.000032563254,0.000027589122,0.00007633462,0.000053656502,0.0000130553535,0.0010976717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995709,0.00012383515,0.000017650875,0.00005234537,0.00020355782,0.000031721444],"domain_scores_gemma":[0.99907637,0.00061654137,0.000099335295,0.00003422188,0.00014538261,0.00002809619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096668204,0.0005141258,0.00050877157,0.00070908223,0.000287974,0.000708067,0.0006840494,0.0006642906,0.0007863558],"category_scores_gemma":[0.0031022306,0.0002840604,0.00050850294,0.000312846,0.0005861025,0.00072997273,0.00056476437,0.00048495375,0.00011275027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054257976,0.000026559357,0.0008928279,0.00006456273,0.000031088694,0.00009767451,0.000067176305,0.9187108,0.0069403695,0.015728503,0.00024302988,0.057143185],"study_design_scores_gemma":[0.0000027948465,0.000035083038,0.00032783631,0.000004751651,0.0000072013154,0.0000306021,0.000008501148,0.9937604,0.0008998581,0.0047353236,0.00018020434,0.00000729602],"about_ca_topic_score_codex":0.0027692835,"about_ca_topic_score_gemma":0.0026459768,"teacher_disagreement_score":0.0027692835,"about_ca_system_score_codex":0.0006767452,"about_ca_system_score_gemma":0.0005817408,"threshold_uncertainty_score":0.0055063367},"labels":[],"label_agreement":null},{"id":"W2314878479","doi":"10.2514/6.2011-1766","title":"Application of the stochastic normal form for a nonlinear aeroelastic model","year":2011,"lang":"en","type":"article","venue":"52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aeroelasticity; Nonlinear system; Computer science; Aerodynamics; Engineering; Aerospace engineering; Physics","score_opus":0.049945755247141566,"score_gpt":0.27936490674897474,"score_spread":0.22941915150183317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2314878479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011032759,0.00016281035,0.9835277,0.00032785124,0.000048074213,0.000033781685,0.00005480141,0.00006172244,0.0047506024],"genre_scores_gemma":[0.8436845,0.001000996,0.13990219,0.00021159348,0.00022800232,0.00032234768,0.00026703218,0.00014553944,0.014237826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991678,0.00034101796,0.000031654505,0.00012907617,0.00028053258,0.000049959068],"domain_scores_gemma":[0.99867934,0.0006909057,0.00019086801,0.00009334485,0.00027903364,0.000066452056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016737147,0.00095664157,0.0008645716,0.0008482622,0.00046195555,0.0009459547,0.0010236045,0.0011993255,0.0016974201],"category_scores_gemma":[0.0052840603,0.00039196198,0.0011006612,0.00067587796,0.0013671109,0.0011287385,0.001609954,0.0012154302,0.00028809014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008813092,0.000013710872,0.00018882401,0.000022445945,0.000011730942,0.000049806724,0.000032080632,0.91039103,0.0007658218,0.08526633,0.00023502395,0.0030143245],"study_design_scores_gemma":[0.000001054003,0.0000042788893,0.000019422894,0.0000011784206,9.739505e-7,0.000006268828,0.000001818316,0.99046636,0.00005525354,0.009245703,0.00019526355,0.0000025360957],"about_ca_topic_score_codex":0.007914829,"about_ca_topic_score_gemma":0.0029579687,"teacher_disagreement_score":0.007914829,"about_ca_system_score_codex":0.0010689812,"about_ca_system_score_gemma":0.0014124897,"threshold_uncertainty_score":0.015737534},"labels":[],"label_agreement":null},{"id":"W2314941735","doi":"10.1115/pvp2005-71596","title":"Elastic Modulus Adjustment Improved Convergement Schemes Using Variable Local Constraints","year":2005,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Finite element method; Convergence (economics); Limit (mathematics); Basis (linear algebra); Elastic modulus; Linear elasticity; Limit load; Node (physics); Modulus; Mathematics; Applied mathematics; Mathematical analysis; Structural engineering; Geometry; Physics; Engineering","score_opus":0.06386729791446051,"score_gpt":0.31339164089585786,"score_spread":0.24952434298139736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2314941735","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066694994,0.000049516853,0.9919853,0.000017036831,0.000010301835,0.000025712363,0.0000060101643,0.00012069459,0.0011158194],"genre_scores_gemma":[0.23744692,0.00014308095,0.7561427,0.00004396405,0.000027812788,0.00025262145,0.000045571902,0.00017648369,0.005720802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993311,0.0002334947,0.0000424797,0.00010148174,0.00024221443,0.000049245373],"domain_scores_gemma":[0.9985373,0.0007162339,0.00019311161,0.00026060385,0.00024661387,0.00004609197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024199812,0.0010131096,0.0009516806,0.0008689794,0.0004960265,0.00075321534,0.0017710759,0.00088537124,0.0029758755],"category_scores_gemma":[0.0039935904,0.00040814994,0.00068789115,0.00067801954,0.0010234285,0.0010908975,0.0021342908,0.0015001484,0.000689466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017385224,0.00007714404,0.0004885684,0.0001211301,0.00004519788,0.00010520293,0.0002821505,0.7131516,0.016767042,0.075358726,0.00097557803,0.19245385],"study_design_scores_gemma":[0.000012898407,0.00004541794,0.00005620693,0.0000065112163,0.000004700478,0.000017877632,0.000006470991,0.9929704,0.0028309585,0.003101192,0.0009379058,0.000009292198],"about_ca_topic_score_codex":0.0012150481,"about_ca_topic_score_gemma":0.0012174322,"teacher_disagreement_score":0.0029758755,"about_ca_system_score_codex":0.00046323967,"about_ca_system_score_gemma":0.00057526876,"threshold_uncertainty_score":0.01279825},"labels":[],"label_agreement":null},{"id":"W2315264522","doi":"10.1016/j.mechmachtheory.2016.03.012","title":"Continuous approximate synthesis of planar function-generators minimising the design error","year":2016,"lang":"en","type":"article","venue":"Mechanism and Machine Theory","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Norm (philosophy); Cardinality (data modeling); Mathematics; Euclidean geometry; Nonlinear system; Euclidean distance; Mathematical optimization; Applied mathematics; Computer science; Geometry","score_opus":0.05543045156492336,"score_gpt":0.26306912932030313,"score_spread":0.20763867775537978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315264522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012932456,0.00010030139,0.9841499,0.000032228905,0.000010285368,0.00001730266,0.000016809076,0.000092084316,0.0026487513],"genre_scores_gemma":[0.7738824,0.00019420379,0.22223331,0.000033812343,0.000019313466,0.00010826329,0.00007596294,0.0000635012,0.0033893446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997404,0.00006314405,0.000010673907,0.000050148952,0.00011030997,0.000025148318],"domain_scores_gemma":[0.99957436,0.00024528356,0.000060840965,0.00004953029,0.000057675952,0.000012241755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066179584,0.0005745131,0.0007362272,0.0003676959,0.00017978955,0.0007817839,0.00061272335,0.0008313638,0.0019865485],"category_scores_gemma":[0.00167784,0.00032532384,0.00046307454,0.0004485289,0.0007132595,0.0005403228,0.0006268385,0.00049715687,0.00035120867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006582608,0.000019768268,0.00014065481,0.0001298061,0.000024283405,0.000029697767,0.000049168295,0.90305597,0.008657084,0.027108617,0.00029324682,0.060425863],"study_design_scores_gemma":[0.000012958297,0.00007868578,0.000071907554,0.000011582335,0.000009350545,0.000023082823,0.000011550997,0.9878676,0.0027794652,0.008203952,0.0009236472,0.0000062019426],"about_ca_topic_score_codex":0.0005860237,"about_ca_topic_score_gemma":0.000832405,"teacher_disagreement_score":0.0019865485,"about_ca_system_score_codex":0.00043573772,"about_ca_system_score_gemma":0.0005784952,"threshold_uncertainty_score":0.006645739},"labels":[],"label_agreement":null},{"id":"W2315270774","doi":"10.1139/cjce-2013-0507","title":"Inversion analysis to determine design parameters for reliability assessment in pavement structures","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea","keywords":"Limit state design; Monte Carlo method; Inversion (geology); Reliability (semiconductor); Probabilistic logic; Structural engineering; Reliability engineering; Computer science; Engineering; Mathematics; Statistics; Geology","score_opus":0.05764098626355071,"score_gpt":0.29053934156795036,"score_spread":0.23289835530439965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315270774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032483857,0.00010205279,0.9653149,0.000035322693,0.0000044384365,0.000049090817,0.000024224179,0.00018061543,0.0018053589],"genre_scores_gemma":[0.7046299,0.00016305913,0.29414436,0.000020042284,0.000006833196,0.00019336346,0.00009230984,0.000075495154,0.0006745731],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932075,0.0002275455,0.000028207352,0.00004810869,0.0003417769,0.000033569046],"domain_scores_gemma":[0.99830997,0.0011102031,0.00014523868,0.00009852179,0.00032063847,0.000015463267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017734466,0.0006243807,0.0005147549,0.00086944486,0.00033567284,0.0005055551,0.0004803439,0.0005764411,0.00113767],"category_scores_gemma":[0.00626551,0.00040147567,0.00047747363,0.00042391205,0.00047047893,0.00076891365,0.00047389246,0.0007108214,0.0002602202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034773766,0.000047292273,0.0009211285,0.000095391086,0.000021050764,0.000031328625,0.000085042106,0.9239885,0.015822126,0.010419424,0.00021376037,0.04832018],"study_design_scores_gemma":[0.000003822029,0.000029879233,0.00024477975,0.0000076091715,0.000006178984,0.000020516904,0.000009511665,0.9927845,0.004375243,0.0021168524,0.00039367913,0.0000073969195],"about_ca_topic_score_codex":0.0018426487,"about_ca_topic_score_gemma":0.0019925314,"teacher_disagreement_score":0.0018426487,"about_ca_system_score_codex":0.0006482605,"about_ca_system_score_gemma":0.0012476286,"threshold_uncertainty_score":0.00937897},"labels":[],"label_agreement":null},{"id":"W2315713888","doi":"10.1061/40642(253)32","title":"Reliability-Based Design of Transmission Line Structures - Direct Approach Using the Inverse Reliability Method","year":2002,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Powertech Labs (Canada)","funders":"","keywords":"Serviceability (structure); Structural engineering; Transmission line; Reliability (semiconductor); Inverse; Reliability engineering; Design methods; Deflection (physics); Computer science; Engineering; Mathematics; Mechanical engineering; Electrical engineering","score_opus":0.1954412076838479,"score_gpt":0.35369209060423923,"score_spread":0.15825088292039133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315713888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027154635,0.000034847533,0.9960045,0.000013288507,0.000004203998,0.000019482126,0.000008056267,0.000083274215,0.0011168456],"genre_scores_gemma":[0.25250444,0.0002613435,0.7444229,0.000028979019,0.000024663934,0.00027023928,0.00008341029,0.00009928074,0.0023046765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951184,0.00012589186,0.000016851709,0.00005401616,0.00026342692,0.000027953924],"domain_scores_gemma":[0.9993523,0.00028202336,0.00007061156,0.000055897934,0.00022358343,0.000015489542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008184119,0.0007074849,0.00066019135,0.000673677,0.00022418241,0.00050933246,0.0008971983,0.0004480524,0.0019477867],"category_scores_gemma":[0.0018750535,0.00049087213,0.0007352357,0.00021698303,0.0005460136,0.00046645795,0.00047364592,0.00070625346,0.00050506403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002616536,0.000025135543,0.000262509,0.00012381379,0.00002488344,0.000060902465,0.00007199919,0.9088126,0.012383759,0.018002177,0.00043894016,0.05976707],"study_design_scores_gemma":[0.0000137432535,0.00007824806,0.0001085969,0.000010652738,0.000014659449,0.000060937036,0.000007231833,0.989994,0.0030306785,0.0048219743,0.0018498915,0.000009406128],"about_ca_topic_score_codex":0.0010900233,"about_ca_topic_score_gemma":0.0012246666,"teacher_disagreement_score":0.0019477867,"about_ca_system_score_codex":0.00043832295,"about_ca_system_score_gemma":0.00082237966,"threshold_uncertainty_score":0.0065159798},"labels":[],"label_agreement":null},{"id":"W2316665742","doi":"10.2514/6.2013-1699","title":"A Robust ASE Correlation and Analysis Method","year":2013,"lang":"en","type":"article","venue":"54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Correlation; Mathematics","score_opus":0.048360729124806774,"score_gpt":0.29882363635840925,"score_spread":0.2504629072336025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316665742","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00057548727,0.000024178058,0.99819916,0.000014869568,0.000012143643,0.000024837613,0.000043066753,0.00034070085,0.00076561794],"genre_scores_gemma":[0.082597606,0.00017520419,0.9095875,0.000089846064,0.00009228352,0.00036259193,0.00063818943,0.00050244044,0.0059542814],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996415,0.00084527093,0.0001623369,0.00060625875,0.0017886701,0.0001824818],"domain_scores_gemma":[0.9969138,0.00095186237,0.00032864176,0.00042447873,0.0013096817,0.00007160695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022720366,0.0014621504,0.001288947,0.001972178,0.00067433895,0.0013818194,0.0016291218,0.0011698228,0.0076171253],"category_scores_gemma":[0.0078118294,0.0006523811,0.0015595552,0.0013705506,0.0007397407,0.0012697914,0.0018403307,0.0018575673,0.0039784852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022349911,0.00013563354,0.0014136696,0.00021542396,0.00015910549,0.00022834667,0.00010213006,0.3496563,0.030438237,0.0419229,0.0069259414,0.5685789],"study_design_scores_gemma":[0.000012135696,0.00006152169,0.0004456863,0.000016082751,0.000019700301,0.0001242713,0.000014949942,0.9809693,0.0063117975,0.005603895,0.006385675,0.000034954952],"about_ca_topic_score_codex":0.0028356183,"about_ca_topic_score_gemma":0.0018860401,"teacher_disagreement_score":0.0076171253,"about_ca_system_score_codex":0.0005901218,"about_ca_system_score_gemma":0.0018634896,"threshold_uncertainty_score":0.02548182},"labels":[],"label_agreement":null},{"id":"W2317152369","doi":"10.2514/6.2010-2926","title":"Hopf Bifurcation Scenario of a Stochastic Aeroelastic Model with Cubic Nonlinearities","year":2010,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MacEwan University","funders":"","keywords":"Aeroelasticity; Hopf bifurcation; Bifurcation; Control theory (sociology); Biological applications of bifurcation theory; Applied mathematics; Computer science; Mathematics; Nonlinear system; Physics; Aerodynamics; Mechanics; Artificial intelligence","score_opus":0.053750780034213415,"score_gpt":0.309241095570054,"score_spread":0.2554903155358406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317152369","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.959,0.00015475517,0.03507317,0.00034428955,0.000026931119,0.000030161647,0.00008024941,0.00006162778,0.0052286936],"genre_scores_gemma":[0.998245,0.000026302769,0.0012530542,0.000012639321,0.000004511677,0.000009041775,0.000016077223,0.000003099816,0.0004303262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998678,0.000042968102,0.0000073132032,0.00002203051,0.00003235645,0.000027637667],"domain_scores_gemma":[0.99944144,0.00027893067,0.00010596367,0.00003157356,0.000061203435,0.00008086993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047330157,0.00030317798,0.00058761606,0.00048222786,0.0005732821,0.00062187476,0.00050453166,0.0010629264,0.0010530038],"category_scores_gemma":[0.0018097234,0.00021048631,0.0005150803,0.00024477363,0.0010621105,0.0007555298,0.0006368194,0.0005158163,0.000055656343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005446466,0.000027630476,0.0015717021,0.000026168842,0.000021965623,0.00027047997,0.000055961722,0.9824091,0.0029819165,0.011878491,0.00010449573,0.0005975903],"study_design_scores_gemma":[0.00000921535,0.000015204369,0.0002692229,0.0000016014258,0.000003359187,0.000015934012,0.0000126600535,0.9982218,0.00019492855,0.0012074527,0.00004456097,0.0000040219006],"about_ca_topic_score_codex":0.008966591,"about_ca_topic_score_gemma":0.0032715346,"teacher_disagreement_score":0.008966591,"about_ca_system_score_codex":0.00065098866,"about_ca_system_score_gemma":0.0005549862,"threshold_uncertainty_score":0.017828822},"labels":[],"label_agreement":null},{"id":"W2317445759","doi":"10.2514/6.2008-5847","title":"Aerostructural Optimization of Aircraft Structures Using Asymmetric Subspace Optimization","year":2008,"lang":"en","type":"article","venue":"12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Subspace topology; Computer science; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.08238258530710522,"score_gpt":0.3247730691608307,"score_spread":0.24239048385372547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317445759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014187234,0.00012260863,0.9819802,0.00008045949,0.00001257884,0.000023103417,0.000031564716,0.00006919194,0.003493103],"genre_scores_gemma":[0.6253896,0.00044910124,0.36868516,0.00008226816,0.000057274963,0.00028553052,0.00021326786,0.00012353035,0.0047143395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996667,0.0001224044,0.000009756784,0.000030513527,0.0001433377,0.000027227643],"domain_scores_gemma":[0.9996486,0.00017185783,0.000046871213,0.000037219143,0.00007187463,0.000023654726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056486175,0.0006461359,0.00074490835,0.0005601702,0.00034591148,0.00061197346,0.00049157144,0.000496126,0.0012869426],"category_scores_gemma":[0.0008104572,0.0002691166,0.00054702366,0.00046401905,0.00060338754,0.0007660913,0.0010035378,0.00062222785,0.000250552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016752576,0.000021197506,0.00014046732,0.000023261176,0.000013448382,0.000018740482,0.0000140696775,0.9623294,0.0025352172,0.013430785,0.00028144027,0.021175237],"study_design_scores_gemma":[0.000002467832,0.000012691716,0.00003201487,0.0000014766216,0.0000010568226,0.000005871162,0.00000343317,0.9952638,0.00029586686,0.004064693,0.00031470816,0.000001879122],"about_ca_topic_score_codex":0.0010650946,"about_ca_topic_score_gemma":0.0012004268,"teacher_disagreement_score":0.0012869426,"about_ca_system_score_codex":0.00037056903,"about_ca_system_score_gemma":0.0006851034,"threshold_uncertainty_score":0.004305303},"labels":[],"label_agreement":null},{"id":"W2318437144","doi":"10.2514/6.2012-1503","title":"Adjoint Based Sensitivity Analysis for Geometrically Exact Beam Theory and Variational Asymptotic Beam Section Analysis with Applications to Wind Turbine Design Optimization","year":2012,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Sensitivity (control systems); Beam (structure); Section (typography); Turbine; Physics; Computer science; Engineering; Mechanical engineering; Electronic engineering; Optics","score_opus":0.046798781208123315,"score_gpt":0.2921026968194661,"score_spread":0.2453039156113428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2318437144","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034328771,0.0003288948,0.9916021,0.00030447546,0.0000433431,0.000020927819,0.000030400437,0.00006644748,0.0041705538],"genre_scores_gemma":[0.74831504,0.0017134581,0.23484834,0.0007567752,0.00033242823,0.00036081747,0.00020160041,0.00030349864,0.01316795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853873,0.0008159536,0.00004244521,0.00012223666,0.00041215788,0.00006854071],"domain_scores_gemma":[0.99649197,0.0028104607,0.00021316858,0.00013201761,0.0002977183,0.00005474517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040018894,0.0011448675,0.0009587585,0.0013876859,0.00047703306,0.0013909572,0.0007619195,0.0012969329,0.0036177626],"category_scores_gemma":[0.0068248184,0.0007395807,0.0013596177,0.0006425716,0.002194618,0.0014469167,0.0019911076,0.0017634353,0.00033026576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022723249,0.000035343626,0.00023839879,0.000114655624,0.000049022477,0.000074323,0.000060330025,0.83132,0.0035623822,0.14848895,0.0010040669,0.015029875],"study_design_scores_gemma":[0.0000023095552,0.000010503516,0.00008359647,0.00001518988,0.000005341239,0.000016100437,0.000006232487,0.9478181,0.00050580787,0.050732072,0.0007943956,0.0000102922095],"about_ca_topic_score_codex":0.0012693711,"about_ca_topic_score_gemma":0.0010124447,"teacher_disagreement_score":0.0040018894,"about_ca_system_score_codex":0.0010005264,"about_ca_system_score_gemma":0.0009317713,"threshold_uncertainty_score":0.021164238},"labels":[],"label_agreement":null},{"id":"W2319704964","doi":"10.1071/aseg2012ab248","title":"Finite element based inversion of AEM data using stochastic optimization","year":2012,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Inverse problem; Inversion (geology); Electromagnetics; Computer science; Inverse; Finite element method; Stochastic optimization; Software; Cover (algebra); Mathematical optimization; Algorithm; Time domain; Computational electromagnetics; Optimization algorithm; Optimization problem; Mathematics; Electronic engineering; Engineering; Geology; Mechanical engineering; Geometry; Mathematical analysis; Electromagnetic field","score_opus":0.19099298155066632,"score_gpt":0.3590496596848088,"score_spread":0.16805667813414246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2319704964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00905764,0.000047181187,0.989643,0.000108688924,0.0000107991345,0.000014317995,0.000036898484,0.00012690261,0.00095461315],"genre_scores_gemma":[0.5366589,0.00021690142,0.45875478,0.00012415787,0.000050751954,0.00020868397,0.00031258477,0.0001259645,0.0035473993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956757,0.00014806046,0.000022035827,0.00005417539,0.00018006821,0.000028141709],"domain_scores_gemma":[0.99891186,0.00071341184,0.00011871083,0.000069095,0.00015549666,0.000031371834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011083569,0.00038548568,0.00072525896,0.00043223554,0.00023393231,0.0006812217,0.00056713057,0.00073233305,0.0017615795],"category_scores_gemma":[0.0026024128,0.00038941743,0.0005188517,0.00043283254,0.00062817155,0.00040721195,0.0007448867,0.00084604963,0.00037367304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023167395,0.000012817064,0.00016814975,0.000016196385,0.000008837606,0.0000103000475,0.000011054722,0.987874,0.001892209,0.0031363633,0.00018190515,0.0066649984],"study_design_scores_gemma":[0.0000015135282,0.0000030127528,0.000026348349,0.0000018172229,6.15251e-7,0.0000026010864,0.0000012987442,0.9989398,0.0003347275,0.00054670003,0.00014006942,0.0000015224269],"about_ca_topic_score_codex":0.0036219505,"about_ca_topic_score_gemma":0.003078554,"teacher_disagreement_score":0.0036219505,"about_ca_system_score_codex":0.0004807273,"about_ca_system_score_gemma":0.0009543583,"threshold_uncertainty_score":0.007201731},"labels":[],"label_agreement":null},{"id":"W2320277382","doi":"10.2514/6.2005-2127","title":"The Application of the MISTC Framework to Structural Design Optimization","year":2005,"lang":"en","type":"article","venue":"46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"Air Force Research Laboratory; National Aeronautics and Space Administration; U.S. Department of Defense","keywords":"Computer science; Systems engineering; Engineering","score_opus":0.02979344724189607,"score_gpt":0.3005237815076488,"score_spread":0.2707303342657527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2320277382","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012395448,0.00014179564,0.9878656,0.00020000794,0.000046616933,0.000059009744,0.00016728159,0.0003692635,0.009910899],"genre_scores_gemma":[0.124071024,0.00052602816,0.8674828,0.0002233712,0.000087290005,0.000609044,0.00061617506,0.00043643982,0.005947855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984059,0.00040749967,0.000062117586,0.000125757,0.000927786,0.00007101287],"domain_scores_gemma":[0.99852955,0.0005581387,0.00009926389,0.0002471372,0.00052437984,0.00004153849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015868911,0.0010376426,0.0010463613,0.0012682738,0.0007321108,0.0011394287,0.0016166653,0.0011385509,0.006472747],"category_scores_gemma":[0.003796547,0.00055202097,0.0012987284,0.0012976768,0.00085376255,0.00076080655,0.0011549795,0.0014964652,0.0014718439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001393057,0.000023043956,0.00023589247,0.0001153811,0.00002485146,0.00004916339,0.000026026053,0.7989435,0.0009528509,0.12830366,0.0047006463,0.06661096],"study_design_scores_gemma":[0.0000074523236,0.00001615506,0.000064330256,0.000024636789,0.000005913867,0.00003118882,0.0000063499924,0.9666185,0.0003290753,0.022620907,0.0102681555,0.000007355534],"about_ca_topic_score_codex":0.01381193,"about_ca_topic_score_gemma":0.01584319,"teacher_disagreement_score":0.01381193,"about_ca_system_score_codex":0.0014619137,"about_ca_system_score_gemma":0.0022846968,"threshold_uncertainty_score":0.027463019},"labels":[],"label_agreement":null},{"id":"W2320594082","doi":"10.2514/6.2013-1938","title":"Model Selection Methods for Nonlinear Aeroelastic Systems Using Wind Tunnel Data","year":2013,"lang":"en","type":"article","venue":"54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Carleton University","funders":"","keywords":"Aeroelasticity; Wind tunnel; Nonlinear system; Selection (genetic algorithm); Computer science; Aerodynamics; Structural engineering; Aerospace engineering; Marine engineering; Engineering; Physics; Artificial intelligence","score_opus":0.17896040813205374,"score_gpt":0.39520401450306974,"score_spread":0.216243606371016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2320594082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053803597,0.00039406557,0.9427633,0.00024360172,0.000047007317,0.00009725875,0.00028524964,0.0009769242,0.0013889541],"genre_scores_gemma":[0.82377285,0.0003971948,0.16900015,0.00011672483,0.00009818976,0.00045771847,0.0012179605,0.00038558984,0.0045536244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994005,0.0003756341,0.00003824462,0.00007131032,0.000072711315,0.000041590818],"domain_scores_gemma":[0.99549025,0.0037191226,0.0001730154,0.0001096932,0.00042793225,0.000080041755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024365424,0.0010589609,0.0013799432,0.0011472064,0.00087570335,0.0010202607,0.0011454669,0.0009673283,0.0031206494],"category_scores_gemma":[0.006142926,0.001099399,0.00102941,0.0007435225,0.00037450614,0.001036939,0.0008587861,0.0012900543,0.0006297055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099642195,0.000052527583,0.0006466376,0.000050686835,0.00007232875,0.000033778175,0.000021636035,0.9714409,0.00047240328,0.0010553013,0.0004273465,0.025626846],"study_design_scores_gemma":[0.000007513516,0.000008062878,0.00007196385,0.0000018566952,0.000004491521,0.0000020525379,0.000002550024,0.99939287,0.00009349932,0.0003522243,0.000060564566,0.0000024429341],"about_ca_topic_score_codex":0.014714794,"about_ca_topic_score_gemma":0.014506285,"teacher_disagreement_score":0.014714794,"about_ca_system_score_codex":0.00048219107,"about_ca_system_score_gemma":0.001330448,"threshold_uncertainty_score":0.02925831},"labels":[],"label_agreement":null},{"id":"W2321798531","doi":"10.2514/6.2007-1866","title":"Flexible transonic wing design optimization with discipline-oriented decompositions","year":2007,"lang":"en","type":"article","venue":"48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Transonic; Wing; Computer science; Aerospace engineering; Aerodynamics; Engineering","score_opus":0.04105390010617572,"score_gpt":0.3059006065684188,"score_spread":0.26484670646224306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2321798531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04778823,0.00021000793,0.9465151,0.00006894427,0.000028056522,0.000053574313,0.00008433762,0.00014018592,0.005111661],"genre_scores_gemma":[0.53796446,0.00040147125,0.4572245,0.00010806749,0.00003166667,0.00029199437,0.00034341044,0.00015386114,0.0034805248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964285,0.00015170596,0.000014101409,0.000041732354,0.00009937864,0.000050207604],"domain_scores_gemma":[0.99959105,0.00017014287,0.000048090096,0.000052870208,0.000098099954,0.000039763097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010508022,0.00095976103,0.0007475277,0.00058607286,0.0003062641,0.0007768084,0.00047094806,0.00074702036,0.0016313547],"category_scores_gemma":[0.0014242159,0.0003967336,0.0010447527,0.00055325596,0.00041383575,0.00056257425,0.00096838735,0.00093911943,0.0003539054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003484526,0.000033312077,0.00024173154,0.000028251268,0.000018803976,0.000021372494,0.000021926535,0.9750265,0.001868886,0.004040491,0.00018218876,0.018481785],"study_design_scores_gemma":[0.000007454704,0.000032805016,0.00008482658,0.000004541901,0.0000037600394,0.00000635159,0.000008386458,0.99729353,0.00044296237,0.0016792372,0.00043348878,0.0000026102223],"about_ca_topic_score_codex":0.0026977,"about_ca_topic_score_gemma":0.0022112348,"teacher_disagreement_score":0.0026977,"about_ca_system_score_codex":0.00048814694,"about_ca_system_score_gemma":0.0008597043,"threshold_uncertainty_score":0.005557239},"labels":[],"label_agreement":null},{"id":"W2323970846","doi":"10.2514/6.2007-1867","title":"A New Subspace Optimization Method for Aero-Structural Design","year":2007,"lang":"en","type":"article","venue":"48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Subspace topology; Computer science; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.0602296327409602,"score_gpt":0.34653484873230184,"score_spread":0.2863052159913416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2323970846","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036266187,0.000085879976,0.9981311,0.000024880677,0.000024301558,0.000013897809,0.000026907763,0.00015756115,0.0011728749],"genre_scores_gemma":[0.031336706,0.00035905148,0.96283627,0.0000643369,0.00006997671,0.00025160846,0.00018141836,0.00019064937,0.0047099604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999286,0.00021156858,0.000025027572,0.00007167843,0.0003766456,0.000029117698],"domain_scores_gemma":[0.99963796,0.00011765925,0.000030318452,0.00005597255,0.00013120906,0.00002691397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006262419,0.00085696357,0.0007951963,0.00072407705,0.00047667194,0.00068572105,0.0009151358,0.00066190434,0.0069566504],"category_scores_gemma":[0.0010256623,0.00038424923,0.00071396166,0.000754667,0.0005546007,0.0010516664,0.001156539,0.0011778381,0.002574523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006132826,0.00008307626,0.00040629995,0.00033153757,0.00007586937,0.00008312825,0.00009152518,0.47587594,0.021920623,0.11604608,0.010363335,0.3746613],"study_design_scores_gemma":[0.000009268572,0.000035838355,0.00007099461,0.000012861195,0.0000056857866,0.00006322568,0.000007718876,0.96936953,0.0020524561,0.01269067,0.015666278,0.000015402613],"about_ca_topic_score_codex":0.0013717663,"about_ca_topic_score_gemma":0.0020765194,"teacher_disagreement_score":0.0069566504,"about_ca_system_score_codex":0.00034619882,"about_ca_system_score_gemma":0.0010059202,"threshold_uncertainty_score":0.023272276},"labels":[],"label_agreement":null},{"id":"W2325409323","doi":"10.1061/40642(253)9","title":"4.0 Strength of Single Pole Utility Structures","year":2002,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro One (Canada); BC Hydro (Canada)","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Range (aeronautics); Basis (linear algebra); Probability density function; Computer science; Probability distribution; Structural engineering; Engineering; Mathematics; Statistics","score_opus":0.14325050883496604,"score_gpt":0.3096543427140607,"score_spread":0.1664038338790947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2325409323","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58668816,0.0013321287,0.095525965,0.0005622588,0.00020661781,0.00018923012,0.0015497024,0.0011245691,0.31282145],"genre_scores_gemma":[0.9803241,0.00018436692,0.0045275344,0.000049437233,0.00001997233,0.000031353542,0.00027411917,0.00005011151,0.014538935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992854,0.00005707118,0.000015706553,0.00007901198,0.00049466104,0.00006815663],"domain_scores_gemma":[0.9993772,0.00009120281,0.00008978859,0.00006029012,0.00032242935,0.00005912581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006817189,0.00050329504,0.000390034,0.0016441756,0.00038941766,0.001032682,0.0010380612,0.0006788869,0.012232722],"category_scores_gemma":[0.0012308283,0.00034445972,0.00032372988,0.00060324953,0.00073313713,0.0005507699,0.0007173448,0.0004085939,0.0024918397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058062334,0.00019766859,0.05084803,0.0009095141,0.00012275377,0.0013036383,0.00063937646,0.27955407,0.20542352,0.062484335,0.012891099,0.38504535],"study_design_scores_gemma":[0.00010619446,0.0016877226,0.23250945,0.0004430639,0.0001889065,0.0039316397,0.0013216512,0.3387153,0.1862524,0.049607605,0.18499278,0.00024328564],"about_ca_topic_score_codex":0.0017174656,"about_ca_topic_score_gemma":0.0022301534,"teacher_disagreement_score":0.012232722,"about_ca_system_score_codex":0.00085241115,"about_ca_system_score_gemma":0.00056911266,"threshold_uncertainty_score":0.040922523},"labels":[],"label_agreement":null},{"id":"W2325938468","doi":"10.2514/6.2008-5956","title":"Reconfigurable Semi-Analytic Sensitivity Methods and MDO Architectures within the piMDO Framework","year":2008,"lang":"en","type":"article","venue":"12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sensitivity (control systems); Computer science; Computer architecture; Electronic engineering; Engineering","score_opus":0.08066532835160008,"score_gpt":0.3662120968079168,"score_spread":0.2855467684563167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2325938468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029383656,0.00010957426,0.99259174,0.00010537704,0.000020169105,0.00003137658,0.000026296442,0.00020701968,0.0039701196],"genre_scores_gemma":[0.2589678,0.00035978152,0.7369303,0.00012936784,0.00005053648,0.00037653645,0.00010031706,0.00016051748,0.0029248383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957174,0.00018409373,0.00001899222,0.00004704526,0.0001402721,0.000037897837],"domain_scores_gemma":[0.99904126,0.0006274361,0.00008887112,0.000103520375,0.000095837444,0.000043036827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013252351,0.00084746967,0.0007242171,0.00068579713,0.0004134773,0.0010744203,0.001234922,0.0007450911,0.002718528],"category_scores_gemma":[0.0028259929,0.0004618924,0.000691328,0.00041294948,0.0009513018,0.00091166375,0.0019537404,0.0012171424,0.0004064026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046403693,0.000034788187,0.00046012225,0.00013307485,0.000037307327,0.000057320372,0.000058591893,0.8560903,0.0028810664,0.09689826,0.0006209847,0.0426817],"study_design_scores_gemma":[0.0000050717085,0.000012115439,0.000040335995,0.000008548521,0.0000037617308,0.000007971685,0.000007311142,0.98568183,0.00049150496,0.012342666,0.0013939647,0.0000047955136],"about_ca_topic_score_codex":0.0018574232,"about_ca_topic_score_gemma":0.0016211247,"teacher_disagreement_score":0.002718528,"about_ca_system_score_codex":0.00071865134,"about_ca_system_score_gemma":0.0010582614,"threshold_uncertainty_score":0.009094417},"labels":[],"label_agreement":null},{"id":"W2326193222","doi":"10.1115/pvp2006-icpvt-11-93273","title":"Simplified Limit Load Determination Using the Reference Two-Bar Structure","year":2006,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Bar (unit); Limit (mathematics); Equivalence (formal languages); Scaling; Component (thermodynamics); Simple (philosophy); Computer science; Mathematics; Applied mathematics; Mathematical analysis; Physics; Geometry; Discrete mathematics; Thermodynamics","score_opus":0.12476256317579552,"score_gpt":0.35390844866166526,"score_spread":0.22914588548586973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2326193222","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012739104,0.000078154175,0.9804817,0.00003313156,0.000024512645,0.000023927734,0.000019272387,0.00019975286,0.006400476],"genre_scores_gemma":[0.41808137,0.00023188577,0.5760239,0.00006986485,0.0000376852,0.00012625867,0.00016126601,0.00011692297,0.005150799],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993973,0.00011444711,0.000022244232,0.0000976166,0.0003386955,0.000029684059],"domain_scores_gemma":[0.9995622,0.00012617139,0.000041063588,0.00012027952,0.00013549569,0.000014714744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000616931,0.00058444013,0.0005659396,0.0009553349,0.00041935756,0.00092477625,0.0014417995,0.00086027343,0.004785935],"category_scores_gemma":[0.0018208761,0.00040159628,0.00037692487,0.0005134908,0.0007808588,0.0017508267,0.0008854576,0.0006681714,0.0012725282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001927103,0.00008909899,0.0008416024,0.0002697046,0.000029692595,0.00044076357,0.0002507487,0.24268359,0.10832273,0.4068418,0.0023739843,0.23766361],"study_design_scores_gemma":[0.000032880653,0.00015552987,0.000531116,0.000027946677,0.000014142102,0.00025518998,0.000038762875,0.8969211,0.029376594,0.06383688,0.008755407,0.00005435236],"about_ca_topic_score_codex":0.00054431154,"about_ca_topic_score_gemma":0.00078521087,"teacher_disagreement_score":0.004785935,"about_ca_system_score_codex":0.00038352882,"about_ca_system_score_gemma":0.00043680216,"threshold_uncertainty_score":0.016010523},"labels":[],"label_agreement":null},{"id":"W2327973210","doi":"10.2514/6.2009-1625","title":"A Method of Code Comparison for CFD Verification","year":2009,"lang":"en","type":"article","venue":"47th AIAA Aerospace Sciences Meeting including The New Horizons Forum and Aerospace Exposition","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Polytechnique Montréal","keywords":"Computer science; Computational fluid dynamics; Code (set theory); Programming language; Aerospace engineering; Engineering","score_opus":0.11356432659979412,"score_gpt":0.4054401699709187,"score_spread":0.2918758433711246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327973210","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034471063,0.00015260262,0.9905944,0.000089309666,0.00023342935,0.00020608459,0.00010551822,0.0013147944,0.003856677],"genre_scores_gemma":[0.05864648,0.00011528007,0.93670404,0.0000830598,0.000062816405,0.00068099657,0.0002563825,0.0008764661,0.00257445],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9838875,0.006568366,0.00078229164,0.0010633343,0.007364072,0.0003344499],"domain_scores_gemma":[0.97648126,0.009509653,0.0010295411,0.0052029695,0.007571038,0.00020560861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009634421,0.000941433,0.0010449308,0.0028008712,0.0014564735,0.0016080015,0.0025536534,0.001532628,0.013246424],"category_scores_gemma":[0.043191876,0.0004925686,0.0011327874,0.0016853935,0.0012265221,0.0018827862,0.0029778313,0.0021888358,0.0017781248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007951006,0.0002682811,0.0025195307,0.000698591,0.00020193274,0.0003020744,0.00045814278,0.0995047,0.033152003,0.1639593,0.016383411,0.68175685],"study_design_scores_gemma":[0.00032949846,0.0008914047,0.0024580255,0.00029641954,0.000121310426,0.0009840253,0.00018546825,0.77253366,0.051072113,0.052398436,0.11850644,0.00022325215],"about_ca_topic_score_codex":0.0013789835,"about_ca_topic_score_gemma":0.0011233087,"teacher_disagreement_score":0.013246424,"about_ca_system_score_codex":0.0010562243,"about_ca_system_score_gemma":0.002888305,"threshold_uncertainty_score":0.050952315},"labels":[],"label_agreement":null},{"id":"W2328294936","doi":"10.1016/j.compchemeng.2016.03.020","title":"Uncertainty quantification and global sensitivity analysis of complex chemical process using a generalized polynomial chaos approach","year":2016,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Research Foundation of Korea; Ministry of Education; National Research Foundation","keywords":"Sobol sequence; Polynomial chaos; Uncertainty quantification; Sensitivity (control systems); Monte Carlo method; Propagation of uncertainty; Process (computing); Surrogate model; Computer science; Polynomial; Uncertainty analysis; Sensitivity analysis; Mathematical optimization; Chemical process; Algorithm; Mathematics; Machine learning; Engineering; Statistics; Simulation","score_opus":0.0843854686183037,"score_gpt":0.3189386808915958,"score_spread":0.23455321227329212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2328294936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038005266,0.00026230488,0.95951676,0.00015741285,0.000018229648,0.000023590377,0.000028202123,0.000054372522,0.0019337522],"genre_scores_gemma":[0.9587137,0.000343301,0.03875199,0.000047507943,0.00004525572,0.00006317907,0.00003933164,0.000059853774,0.0019358292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928826,0.0003119842,0.000026514606,0.000117344935,0.00019753684,0.000058404137],"domain_scores_gemma":[0.9983997,0.0012053901,0.00012866025,0.00008388137,0.00015056401,0.000031877425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017878294,0.0008417584,0.0011759099,0.0011239542,0.000499214,0.0012455306,0.00077106216,0.0007447568,0.00068579195],"category_scores_gemma":[0.0034914268,0.000495561,0.0014702891,0.0006229559,0.0016897887,0.0016173336,0.0018045444,0.0010525666,0.00005852068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027788685,0.000013367635,0.00019219669,0.00005222786,0.000049992308,0.000048452912,0.00004225703,0.9547978,0.0031321151,0.036049895,0.00008847208,0.005505535],"study_design_scores_gemma":[0.0000010580646,0.000007251891,0.00006562421,0.0000015250594,0.000005068317,0.00000484606,0.000002886989,0.99202526,0.00028504364,0.0075456193,0.00005141025,0.0000044669637],"about_ca_topic_score_codex":0.0028510767,"about_ca_topic_score_gemma":0.0014723361,"teacher_disagreement_score":0.0028510767,"about_ca_system_score_codex":0.0010578764,"about_ca_system_score_gemma":0.000727463,"threshold_uncertainty_score":0.009455025},"labels":[],"label_agreement":null},{"id":"W2329490400","doi":"10.2514/6.2009-6237","title":"Aircraft Conceptual Design Optimization with Uncertain Contributing Analyses","year":2009,"lang":"en","type":"article","venue":"AIAA Modeling and Simulation Technologies Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Conceptual design; Computer science; Risk analysis (engineering); Systems engineering; Engineering; Human–computer interaction; Business","score_opus":0.26651629596258847,"score_gpt":0.38516475540615347,"score_spread":0.118648459443565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329490400","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011081577,0.00020850501,0.9855884,0.00012173836,0.00001353002,0.000028210898,0.000039292398,0.00006260414,0.0028561412],"genre_scores_gemma":[0.6612416,0.00055269897,0.33432904,0.00010081448,0.000055041797,0.00031970075,0.00014473396,0.00010158853,0.0031548352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985682,0.0007060452,0.00004741951,0.00015895707,0.00042483822,0.0000946162],"domain_scores_gemma":[0.99768686,0.0016445143,0.0002432968,0.00014037712,0.00023076766,0.000054181397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032804378,0.0011479257,0.001243526,0.0008468522,0.00044365684,0.0013863392,0.0008550049,0.0008996298,0.0011961163],"category_scores_gemma":[0.0062755975,0.0009088265,0.0009330715,0.00060215004,0.001019564,0.0010483194,0.0017940148,0.0010637225,0.00021357251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001037236,0.0000032818673,0.00009082961,0.000017772038,0.000007661677,0.000010549757,0.0000082278575,0.9902688,0.00018622298,0.0055833072,0.000048301525,0.0037644722],"study_design_scores_gemma":[0.0000031666416,0.000011227212,0.000044051994,0.0000045821785,0.0000045417487,0.0000047932335,0.000003868146,0.9949479,0.00013344086,0.0045581837,0.00028183582,0.0000024496599],"about_ca_topic_score_codex":0.0019842526,"about_ca_topic_score_gemma":0.0014691987,"teacher_disagreement_score":0.0032804378,"about_ca_system_score_codex":0.000995752,"about_ca_system_score_gemma":0.0013853804,"threshold_uncertainty_score":0.017348766},"labels":[],"label_agreement":null},{"id":"W2330124367","doi":"10.2514/6.2013-1810","title":"Impact of Wing Box Geometrical Parameters on Stick Model Prediction Accuracy","year":2013,"lang":"en","type":"article","venue":"54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Wing; Computer science; Engineering; Structural engineering","score_opus":0.05763931188743964,"score_gpt":0.3186414711913,"score_spread":0.26100215930386034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330124367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92500085,0.0006198061,0.07035223,0.00021063289,0.000053760145,0.000030032894,0.0006390678,0.0007357295,0.0023577486],"genre_scores_gemma":[0.9948152,0.000086106214,0.004615855,0.000013433806,0.0000034611533,0.0000064958294,0.00023558151,0.00005394142,0.00016994146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986029,0.00035400334,0.00012831218,0.0002451174,0.0004660774,0.0002035984],"domain_scores_gemma":[0.9866982,0.009230058,0.00085950724,0.0022031332,0.0008668195,0.00014241117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023213914,0.00068284804,0.00069896306,0.00056452054,0.00029469543,0.00076888915,0.0007547695,0.0010371262,0.0012684176],"category_scores_gemma":[0.013687399,0.0003526228,0.0005499825,0.00042691742,0.0004550046,0.00091269286,0.00069971214,0.00063709106,0.0004077503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000922348,0.000087723834,0.030435188,0.00022148639,0.00007756263,0.00026961512,0.00022444477,0.8934203,0.020477643,0.00086850417,0.00058383396,0.052411422],"study_design_scores_gemma":[0.00001857848,0.00046779512,0.013026488,0.00006913086,0.00004640566,0.0001564191,0.00016423158,0.94979644,0.034752734,0.0005973791,0.0008561049,0.00004826461],"about_ca_topic_score_codex":0.00442519,"about_ca_topic_score_gemma":0.002813901,"teacher_disagreement_score":0.00442519,"about_ca_system_score_codex":0.00026632217,"about_ca_system_score_gemma":0.00039882568,"threshold_uncertainty_score":0.012276828},"labels":[],"label_agreement":null},{"id":"W2331310660","doi":"10.2514/6.2016-1446","title":"Stochastic Models for Fast Analysis of Unsteady Wing Aerodynamics","year":2016,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Aerodynamics; Wing; Computer science; Aerospace engineering; Aeronautics; Engineering","score_opus":0.10305905463516385,"score_gpt":0.33204528321946614,"score_spread":0.2289862285843023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331310660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004904718,0.0005579531,0.99287933,0.00023267367,0.0000621945,0.000014165125,0.000059150116,0.000099792844,0.0011899185],"genre_scores_gemma":[0.8260577,0.003918831,0.1487094,0.0004383806,0.00083993754,0.00045151392,0.0006824946,0.0004315216,0.01847027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863356,0.00059111713,0.000055706703,0.00014568909,0.00042785544,0.00014598742],"domain_scores_gemma":[0.99403495,0.0042186286,0.0006311173,0.00029468065,0.00061753613,0.00020314193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029609855,0.0012980322,0.0019123117,0.0015266853,0.0006426454,0.0016393055,0.0019175584,0.0017194246,0.0026195042],"category_scores_gemma":[0.010461358,0.0011613988,0.0015059449,0.0010701077,0.001758772,0.0019814596,0.0021070254,0.0024833751,0.0004908272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018134875,0.00001334065,0.00015009964,0.000042326446,0.000039574265,0.000017842554,0.000020969142,0.85355467,0.0005254278,0.14186525,0.00048537672,0.0032670074],"study_design_scores_gemma":[0.0000018149256,0.0000029685868,0.000025825386,0.0000030375272,0.0000029739451,0.000002325992,0.0000014266383,0.98107564,0.00004511137,0.018644754,0.00019092661,0.000003135943],"about_ca_topic_score_codex":0.007406603,"about_ca_topic_score_gemma":0.005088882,"teacher_disagreement_score":0.007406603,"about_ca_system_score_codex":0.0016620252,"about_ca_system_score_gemma":0.0014313156,"threshold_uncertainty_score":0.015659332},"labels":[],"label_agreement":null},{"id":"W2332325192","doi":"10.2514/6.2008-8875","title":"An Approach for Verification and Validation of the Environmental Design Space","year":2008,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Suite; Computer science; Process (computing); Credibility; Probabilistic logic; Systems engineering; Airframe; Identification (biology); Operations research; Headway; Noise (video); Domain (mathematical analysis); Task (project management); Relation (database); Sensitivity (control systems); Simulation; Engineering; Data mining; Aerospace engineering; Artificial intelligence","score_opus":0.13509848626602933,"score_gpt":0.2937983446888832,"score_spread":0.1586998584228539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332325192","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005256868,0.00009374026,0.9880497,0.00036458013,0.00006654549,0.0002665928,0.00013433064,0.00042188406,0.005345898],"genre_scores_gemma":[0.10635543,0.0001490456,0.89066124,0.00022612418,0.00003731072,0.00072205905,0.00038829475,0.00020574727,0.0012547398],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9215853,0.042289004,0.0047841314,0.0058398764,0.023600152,0.0019015119],"domain_scores_gemma":[0.85148895,0.065496214,0.0053594,0.049417254,0.02751267,0.00072556915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07933293,0.002169704,0.0014208381,0.0044262656,0.0025762036,0.008631907,0.0058341636,0.0038168305,0.0045955777],"category_scores_gemma":[0.13459347,0.0013476234,0.0041784626,0.0020264247,0.0065110703,0.0063540046,0.007996944,0.005918482,0.0008853412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001360793,0.00038870738,0.0036158282,0.00048232125,0.00029684926,0.0002905505,0.0012671743,0.3941966,0.0078653935,0.44988775,0.002274279,0.13929841],"study_design_scores_gemma":[0.00017981131,0.0007316493,0.001473596,0.001156674,0.000144281,0.00023817948,0.0007942179,0.6517828,0.020124331,0.2624591,0.0607043,0.00021104471],"about_ca_topic_score_codex":0.007784086,"about_ca_topic_score_gemma":0.0041160486,"teacher_disagreement_score":0.07933293,"about_ca_system_score_codex":0.0056275376,"about_ca_system_score_gemma":0.016347058,"threshold_uncertainty_score":0.4195577},"labels":[],"label_agreement":null},{"id":"W2333098343","doi":"10.2514/6.2014-1526","title":"Calculation of Equivalent Initial Flaw Size Distributions for Multiple Site Damage","year":2014,"lang":"en","type":"article","venue":"55th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Materials science","score_opus":0.04752229187363993,"score_gpt":0.32516675263538203,"score_spread":0.2776444607617421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333098343","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06131218,0.000042342977,0.9349763,0.000019224159,0.0000070104547,0.00008032672,0.00013589297,0.00032379638,0.003103013],"genre_scores_gemma":[0.7707594,0.00008844419,0.22664982,0.000020836822,0.0000064840683,0.00022011125,0.00026944428,0.00019614906,0.0017892027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996848,0.00004901574,0.000016868826,0.000043901153,0.00017545612,0.00002994601],"domain_scores_gemma":[0.99915135,0.00042126657,0.00014226895,0.000072703515,0.00018823845,0.000024215458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007820343,0.00044963896,0.00058459135,0.0010834375,0.00018393745,0.0004780411,0.00085267384,0.0004883161,0.0016325884],"category_scores_gemma":[0.0021178548,0.0004107534,0.000497478,0.0004580803,0.00028833747,0.00056515634,0.00042165225,0.00039044267,0.00023017378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012450667,0.000013726751,0.00066122593,0.000021687836,0.000006230563,0.000038467013,0.000015737316,0.9801002,0.0030764826,0.0025167393,0.00011985555,0.013417246],"study_design_scores_gemma":[0.000003040477,0.000009961329,0.0004359698,0.0000027162232,0.000002139063,0.000017405071,0.000002987373,0.9969709,0.001493287,0.0008386429,0.00021844835,0.0000044380836],"about_ca_topic_score_codex":0.0030075184,"about_ca_topic_score_gemma":0.003740211,"teacher_disagreement_score":0.0030075184,"about_ca_system_score_codex":0.0009730078,"about_ca_system_score_gemma":0.0009976579,"threshold_uncertainty_score":0.0070596337},"labels":[],"label_agreement":null},{"id":"W2334920724","doi":"10.1115/pvp2003-1887","title":"Lower Bound Limit Load Determination: The mβ-Multiplier Method","year":2003,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Multiplier (economics); Upper and lower bounds; Mathematics; Limit load; Limit (mathematics); Mathematical analysis; Sensitivity (control systems); Stress (linguistics); Linear elasticity; Applied mathematics; Finite element method; Physics; Thermodynamics","score_opus":0.10007498523088543,"score_gpt":0.36427143927166217,"score_spread":0.26419645404077674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2334920724","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018901444,0.00011324142,0.99609715,0.000038814604,0.000015870768,0.000019193158,0.000012822325,0.000101277365,0.0017115152],"genre_scores_gemma":[0.16936228,0.00047535446,0.8212924,0.00017877821,0.00014870576,0.0002918115,0.00012496654,0.00017954108,0.007946188],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988017,0.00040339702,0.000042673153,0.00019653009,0.00048501982,0.000070704766],"domain_scores_gemma":[0.998514,0.00071775983,0.00019084843,0.00017377593,0.0003571,0.000046611163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018324683,0.0012634383,0.0009054836,0.0019032294,0.000504335,0.0011251373,0.0020056886,0.0014332728,0.0045366785],"category_scores_gemma":[0.005155577,0.0005933795,0.0006467889,0.0008953801,0.0008917188,0.0014018781,0.0016859078,0.0014141863,0.0024942008],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038052953,0.00011716,0.00151413,0.0003494124,0.000083553154,0.00022707749,0.00023499034,0.2732977,0.03526847,0.11593966,0.0065779374,0.56600934],"study_design_scores_gemma":[0.000016573757,0.00007028001,0.00025407254,0.000036286478,0.000010931458,0.0001209441,0.00001262275,0.96794975,0.008665935,0.017730456,0.0051029255,0.00002908541],"about_ca_topic_score_codex":0.00083961594,"about_ca_topic_score_gemma":0.0006725874,"teacher_disagreement_score":0.0045366785,"about_ca_system_score_codex":0.00059443625,"about_ca_system_score_gemma":0.00096131733,"threshold_uncertainty_score":0.0151767135},"labels":[],"label_agreement":null},{"id":"W2335506059","doi":"10.2514/6.2004-742","title":"Second Order Sensitivity and Uncertainty Analysis of Laminar Airfoil Flows","year":2004,"lang":"en","type":"article","venue":"42nd AIAA Aerospace Sciences Meeting and Exhibit","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Airfoil; Laminar flow; Sensitivity (control systems); Computer science; Order (exchange); Mechanics; Physics; Engineering; Electronic engineering; Economics","score_opus":0.03967307090387425,"score_gpt":0.3002776642155421,"score_spread":0.26060459331166785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2335506059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41465864,0.0012318476,0.5745046,0.0007109141,0.00009417089,0.00005137341,0.00015764087,0.00032442593,0.008266394],"genre_scores_gemma":[0.99450994,0.00012952805,0.004118309,0.000026207426,0.000025637592,0.000013762905,0.000035402827,0.00002639283,0.0011149192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986864,0.0005376296,0.000038934282,0.00011484573,0.00045390477,0.00016828314],"domain_scores_gemma":[0.9934807,0.0054809875,0.00031110743,0.00019208038,0.00041218774,0.00012301587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002727844,0.0009542784,0.0011577989,0.0020670993,0.00058303645,0.0020967827,0.0006599608,0.0010908087,0.0007146949],"category_scores_gemma":[0.00898739,0.00075449946,0.0012046908,0.0007276984,0.001336793,0.0013951296,0.0016099457,0.0012532114,0.000046711444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101349906,0.000025036865,0.0004698905,0.00003723228,0.00003919816,0.000063184925,0.00004201083,0.9806277,0.0043457667,0.010456203,0.00010183405,0.0036907236],"study_design_scores_gemma":[0.0000025234383,0.000019066096,0.00043970533,0.0000033716815,0.000007779048,0.000012211916,0.000006354374,0.99332255,0.0016089567,0.0044836085,0.000081629645,0.000012201497],"about_ca_topic_score_codex":0.004839936,"about_ca_topic_score_gemma":0.0018045848,"teacher_disagreement_score":0.004839936,"about_ca_system_score_codex":0.0019778889,"about_ca_system_score_gemma":0.0008484461,"threshold_uncertainty_score":0.01442641},"labels":[],"label_agreement":null},{"id":"W2335707893","doi":"10.2514/6.2012-1543","title":"Industry Perspectives on Composite Structural Certification and Design","year":2012,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Certification; Composite number; Computer science; Manufacturing engineering; Engineering; Materials science; Composite material; Management","score_opus":0.16814047684743447,"score_gpt":0.35533315338069926,"score_spread":0.1871926765332648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2335707893","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019792141,0.08833147,0.32181,0.17563961,0.0025613287,0.00006758991,0.00014618736,0.00016966807,0.391482],"genre_scores_gemma":[0.63276446,0.12172271,0.15167135,0.030821567,0.00641284,0.000243163,0.00022113633,0.00015526717,0.055987462],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9956487,0.0016697214,0.00015900178,0.0004156313,0.0018905281,0.00021647268],"domain_scores_gemma":[0.9924379,0.0045618643,0.0004153286,0.00042990467,0.00190471,0.00025022175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008549766,0.0006186123,0.0005454076,0.0013243644,0.0009995288,0.003218772,0.0010738055,0.0043168846,0.007852173],"category_scores_gemma":[0.0069960933,0.00029031304,0.0004332644,0.00096534926,0.0047597676,0.0030450788,0.0018109056,0.0039055229,0.001551067],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003640723,0.00004951609,0.00029844657,0.00019478066,0.000008243887,0.00012418427,0.00025349515,0.008069178,0.0007399487,0.9174967,0.008856695,0.06387241],"study_design_scores_gemma":[0.000019440447,0.00018225232,0.00037216806,0.0003922948,0.000009658604,0.00027079482,0.0004418983,0.011396741,0.0013254352,0.74167234,0.2438848,0.000032212884],"about_ca_topic_score_codex":0.001073428,"about_ca_topic_score_gemma":0.0013105645,"teacher_disagreement_score":0.008549766,"about_ca_system_score_codex":0.0020662742,"about_ca_system_score_gemma":0.0018210937,"threshold_uncertainty_score":0.045216024},"labels":[],"label_agreement":null},{"id":"W2340096680","doi":"10.5539/mas.v10n6p147","title":"Fuzzy Numbers Applied in Reliability Assessment of Unreinforced Masonry Shear Wall","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fuzzy logic; Reliability (semiconductor); Probabilistic logic; Consistency (knowledge bases); Computer science; Masonry; Reliability engineering; Range (aeronautics); Uncertainty analysis; Fuzzy number; Unreinforced masonry building; Uncertainty quantification; Mathematical optimization; Mathematics; Fuzzy set; Structural engineering; Engineering; Machine learning; Simulation; Artificial intelligence","score_opus":0.044961446081039455,"score_gpt":0.3257150061949548,"score_spread":0.28075356011391533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2340096680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16439068,0.0018049298,0.8275272,0.00014261411,0.00009756419,0.00008009202,0.000105510546,0.00013544114,0.0057158754],"genre_scores_gemma":[0.9324896,0.00058180466,0.06601092,0.000015638427,0.000020891912,0.000048805647,0.000060601673,0.000009464439,0.0007622288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859935,0.0005549776,0.00008526805,0.00015961922,0.0005368224,0.00006395205],"domain_scores_gemma":[0.9984302,0.000894547,0.00018290948,0.00007843092,0.000380243,0.000033622942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018901817,0.00067905843,0.0005329479,0.0018098122,0.00047903848,0.0009590078,0.000584069,0.0006235559,0.0007064662],"category_scores_gemma":[0.004596629,0.0002815886,0.00067254313,0.000992512,0.000549733,0.000592895,0.00032990982,0.00048442153,0.00010096516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020522189,0.000026922284,0.0023340506,0.00021912067,0.000067366105,0.0002793867,0.00021335144,0.9199808,0.012349541,0.016134927,0.00035208053,0.047837142],"study_design_scores_gemma":[0.0000055816786,0.00009223039,0.0011171096,0.00003119252,0.000024926523,0.00006823731,0.00005590333,0.98507196,0.0047820625,0.00803684,0.0006889871,0.000024958872],"about_ca_topic_score_codex":0.0038066942,"about_ca_topic_score_gemma":0.002190015,"teacher_disagreement_score":0.0038066942,"about_ca_system_score_codex":0.0008159617,"about_ca_system_score_gemma":0.0006469332,"threshold_uncertainty_score":0.009996355},"labels":[],"label_agreement":null},{"id":"W2356605520","doi":"","title":"Estimation of Functional Failure Probability of Passive Systems Based on Adaptive Importance Sampling Method","year":2012,"lang":"en","type":"article","venue":"Hedongli gongcheng","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Sampling (signal processing); Residual; Adaptive sampling; Importance sampling; Probability distribution; Computer science; Probability density function; Statistics; Mathematics; Algorithm; Monte Carlo method","score_opus":0.1569446332088771,"score_gpt":0.34924349546533073,"score_spread":0.19229886225645362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2356605520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017793007,0.000080958904,0.98175967,0.000018493956,0.000006037365,0.000017527318,0.000008574281,0.00007321606,0.00024258514],"genre_scores_gemma":[0.8859416,0.00025884176,0.11292071,0.000016750528,0.000034159802,0.000115818286,0.000073718984,0.000024601894,0.0006138555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993179,0.00022889208,0.00003115752,0.00010654901,0.00026698047,0.000048562782],"domain_scores_gemma":[0.9981165,0.0012599083,0.00015978064,0.00009367068,0.0003341118,0.000036004254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012379427,0.00063155184,0.00072276505,0.0009662465,0.00021677646,0.0004071669,0.0006792879,0.00042988718,0.00054995547],"category_scores_gemma":[0.003672673,0.00030948373,0.0005929416,0.00043637725,0.0004836683,0.00074889493,0.0005333005,0.00049358903,0.00006088047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010787862,0.000043533277,0.0033392687,0.00013658052,0.000060784714,0.00009923376,0.00006703327,0.91309613,0.010317508,0.007038714,0.00026717255,0.065426156],"study_design_scores_gemma":[0.000002791628,0.000013486158,0.00034553502,0.0000016101436,0.0000035079267,0.00001125801,0.0000021737385,0.99842346,0.00061723293,0.00052571355,0.00004970736,0.0000035342093],"about_ca_topic_score_codex":0.0030913344,"about_ca_topic_score_gemma":0.0012282581,"teacher_disagreement_score":0.0030913344,"about_ca_system_score_codex":0.0003591986,"about_ca_system_score_gemma":0.0005204458,"threshold_uncertainty_score":0.006546974},"labels":[],"label_agreement":null},{"id":"W2373042927","doi":"10.1016/j.aap.2016.04.023","title":"Multi-mode reliability-based design of horizontal curves","year":2016,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Reliability (semiconductor); Mode (computer interface); Sight; Reliability engineering; Failure mode and effects analysis; Process (computing); Engineering; Computer science","score_opus":0.09373016017910966,"score_gpt":0.3758091424269489,"score_spread":0.2820789822478392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2373042927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015276864,0.000070236885,0.9819936,0.000027205748,0.000009857009,0.000029359393,0.00002064927,0.00018071943,0.0023915512],"genre_scores_gemma":[0.82929987,0.00011167262,0.16764227,0.00003090461,0.000014942199,0.00012101503,0.00005927857,0.00011121008,0.0026087796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944764,0.00019280477,0.00002210723,0.00009559304,0.0001754617,0.000066276894],"domain_scores_gemma":[0.9989059,0.0003758392,0.00015775702,0.00012477192,0.0003872092,0.000048408285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001221289,0.00072889007,0.00057030836,0.00090804795,0.00031941212,0.0007184124,0.001352644,0.0006197592,0.003232446],"category_scores_gemma":[0.0023795564,0.00047899576,0.0006060324,0.0003869538,0.00039625022,0.00075099786,0.00085387885,0.00059291755,0.0005322445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007890161,0.000025548316,0.00039714517,0.000070394926,0.000021840922,0.000025037614,0.00006565078,0.93226665,0.007973641,0.009185548,0.0002932065,0.04959642],"study_design_scores_gemma":[0.0000061326714,0.00008867763,0.00017604615,0.000010555257,0.000008527633,0.000012903644,0.000010266923,0.99356693,0.0023429263,0.0030933195,0.00067712535,0.000006562489],"about_ca_topic_score_codex":0.0018114711,"about_ca_topic_score_gemma":0.0019394201,"teacher_disagreement_score":0.003232446,"about_ca_system_score_codex":0.00073349243,"about_ca_system_score_gemma":0.00081662834,"threshold_uncertainty_score":0.010813594},"labels":[],"label_agreement":null},{"id":"W2386599773","doi":"","title":"STRUCTURAL RELIABILITY ANALYSIS USING STOCHASTIC RESPONSE SURFACE METHOD","year":2010,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Polynomial chaos; Hermite polynomials; Collocation (remote sensing); Mathematics; Random variable; Applied mathematics; Surface (topology); Polynomial; Stochastic process; Monte Carlo method; Mathematical analysis; Computer science; Statistics; Geometry","score_opus":0.09267051915847714,"score_gpt":0.4024171982675926,"score_spread":0.30974667910911546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2386599773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024702756,0.000046359695,0.99679714,0.000030436066,0.000006252927,0.000011628574,0.000009450377,0.000120002966,0.0005084649],"genre_scores_gemma":[0.66578287,0.00058684085,0.32946318,0.0000810741,0.00008221178,0.00037036889,0.00020667963,0.00022483265,0.0032019545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847025,0.0006711922,0.00004481082,0.00012895901,0.00060124666,0.000083610554],"domain_scores_gemma":[0.99856,0.00089188654,0.00011164116,0.00010077746,0.0003134089,0.00002223062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001455381,0.00083882303,0.0011532409,0.0011448768,0.00031688673,0.0006607361,0.0009353769,0.0008909489,0.0019198797],"category_scores_gemma":[0.0034376676,0.0003892239,0.0012981602,0.00073861994,0.0006745986,0.00078948366,0.00062686595,0.0008466197,0.000555914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012877602,0.000009392988,0.00019610314,0.000044848504,0.000021259353,0.00002580424,0.000023731567,0.96851736,0.0023928068,0.0109657515,0.00018233416,0.017607749],"study_design_scores_gemma":[0.0000014798514,0.0000066914904,0.000047289737,0.0000021111305,0.0000020418386,0.0000055502715,0.0000023029745,0.9976338,0.00032439537,0.0017614177,0.00020951331,0.0000033498686],"about_ca_topic_score_codex":0.0026193822,"about_ca_topic_score_gemma":0.0011443049,"teacher_disagreement_score":0.0026193822,"about_ca_system_score_codex":0.00059052743,"about_ca_system_score_gemma":0.0009359541,"threshold_uncertainty_score":0.007696867},"labels":[],"label_agreement":null},{"id":"W2408295797","doi":"10.1007/978-3-642-35275-1_21","title":"Domain Decomposition Methods of Stochastic PDEs","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computational science and engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Domain decomposition methods; Decomposition; Domain (mathematical analysis); Computer science; Applied mathematics; Mathematics; Mathematical optimization; Chemistry; Engineering; Mathematical analysis; Structural engineering","score_opus":0.04186285457208082,"score_gpt":0.34627272794870445,"score_spread":0.3044098733766236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2408295797","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009134387,0.0011708016,0.99491763,0.0001149603,0.00013845267,0.000011157502,0.000035992754,0.000059564787,0.0026381363],"genre_scores_gemma":[0.07931737,0.0058560246,0.89058536,0.00018704898,0.0005602767,0.00023182691,0.00048475421,0.0005016792,0.022275632],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996307,0.00015779305,0.00001849132,0.000042047483,0.00012686745,0.000024070885],"domain_scores_gemma":[0.99921715,0.00047669225,0.000040479114,0.000068619986,0.00014863857,0.000048310816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089749286,0.0008687592,0.0009565131,0.0008717763,0.000271538,0.0010951207,0.0009402905,0.00088924804,0.00369139],"category_scores_gemma":[0.00234913,0.00053380296,0.0010636491,0.0008495706,0.0008199641,0.00084613176,0.001433778,0.002330681,0.001509586],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004730664,0.000055445376,0.00029506724,0.0004988951,0.00006570282,0.00006285697,0.00013042102,0.28289625,0.007310385,0.49783847,0.016200464,0.19459885],"study_design_scores_gemma":[0.0000061212154,0.000007258863,0.00006430037,0.000036023863,0.000005506596,0.00003063215,0.000008946172,0.9193825,0.00047555155,0.07167597,0.008300421,0.0000067711103],"about_ca_topic_score_codex":0.0011754015,"about_ca_topic_score_gemma":0.0011502391,"teacher_disagreement_score":0.00369139,"about_ca_system_score_codex":0.0004459084,"about_ca_system_score_gemma":0.00064472936,"threshold_uncertainty_score":0.01234889},"labels":[],"label_agreement":null},{"id":"W2410679624","doi":"","title":"A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees","year":2016,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Generalization; Column (typography); Computer science; Minification; Bayesian probability; Column generation; Mathematical optimization; Artificial intelligence; Mathematics; Computer network","score_opus":0.2157938118567386,"score_gpt":0.3608432622986248,"score_spread":0.1450494504418862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2410679624","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022170164,0.00027396265,0.99417275,0.00033780804,0.0000719062,0.000029623343,0.00008860739,0.00048910064,0.0023191683],"genre_scores_gemma":[0.1949602,0.0006244809,0.78706807,0.0010359078,0.0005659943,0.00030761867,0.0011409546,0.00094038,0.013356416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99784374,0.00070311024,0.00008791891,0.00035991258,0.0008000998,0.00020526384],"domain_scores_gemma":[0.9957967,0.0022954182,0.00020005331,0.0008335801,0.00070179696,0.00017247877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002095859,0.0019885227,0.0019875085,0.0013762638,0.00084623345,0.0022866228,0.0031954148,0.0025689919,0.011442192],"category_scores_gemma":[0.009336486,0.0011054155,0.0012901366,0.0025175654,0.0011526764,0.003225975,0.0033365407,0.0040815403,0.0029206213],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003267306,0.00032828588,0.0005043493,0.00024215473,0.00015411503,0.00014986697,0.00009652802,0.5726121,0.005083156,0.09917882,0.028572071,0.29275176],"study_design_scores_gemma":[0.000012407165,0.000024747533,0.00005629212,0.000010737302,0.000011340612,0.00003095035,0.0000056713798,0.9739942,0.0008770943,0.024063889,0.00090402795,0.000008688924],"about_ca_topic_score_codex":0.0029816662,"about_ca_topic_score_gemma":0.0042813052,"teacher_disagreement_score":0.011442192,"about_ca_system_score_codex":0.0009739437,"about_ca_system_score_gemma":0.0019257509,"threshold_uncertainty_score":0.038277924},"labels":[],"label_agreement":null},{"id":"W2439722346","doi":"10.1109/sapiw.2016.7496288","title":"Efficient time-domain variability analysis using parameterized model-order reduction","year":2016,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Parallelizable manifold; Model order reduction; Subspace topology; Algorithm; Parameterized complexity; Moment (physics); Applied mathematics; Dimensionality reduction; Computer science; Laplace transform; Time domain; Reduction (mathematics); Frequency domain; Inversion (geology); Krylov subspace; Numerical integration; Method of moments (probability theory); Set (abstract data type); Mathematical optimization; Mathematics; Iterative method; Mathematical analysis; Artificial intelligence","score_opus":0.0699316981815304,"score_gpt":0.3208489778518447,"score_spread":0.2509172796703143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2439722346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016361574,0.000022646618,0.997818,0.00001627874,0.0000037820464,0.000009947464,0.00002194136,0.00023582413,0.00023556003],"genre_scores_gemma":[0.27585852,0.00024245356,0.72059804,0.00006160627,0.000037885773,0.00027746853,0.0005511562,0.00040136758,0.0019715116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997732,0.000051688836,0.000010133322,0.000035489593,0.000109355264,0.000020076188],"domain_scores_gemma":[0.99943894,0.0003385139,0.000060386265,0.000075843986,0.000071421826,0.00001488974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004687048,0.00094719295,0.0008893014,0.0005352359,0.00040022252,0.00077376637,0.0009161745,0.00051880145,0.001720317],"category_scores_gemma":[0.0014390313,0.00038394512,0.0010291296,0.00052889937,0.00034182402,0.00075543515,0.00076762075,0.0013940644,0.00059900637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042639942,0.00004022779,0.0003387977,0.00006855363,0.000051734656,0.00007068747,0.000054732387,0.853089,0.01623218,0.021581646,0.0010682299,0.10736162],"study_design_scores_gemma":[0.0000017881839,0.000006272787,0.000034467015,0.0000013182627,0.0000023934251,0.000009799024,0.0000018585555,0.9958072,0.0007426945,0.0030213273,0.00036745204,0.000003441635],"about_ca_topic_score_codex":0.0023178237,"about_ca_topic_score_gemma":0.0026105584,"teacher_disagreement_score":0.0023178237,"about_ca_system_score_codex":0.00051638717,"about_ca_system_score_gemma":0.0010583779,"threshold_uncertainty_score":0.0057550073},"labels":[],"label_agreement":null},{"id":"W2473394720","doi":"10.13182/nt08-a3916","title":"Effect of the Surveillance Test Frequency of SDS1 on the Core Damage Probability","year":2008,"lang":"en","type":"article","venue":"Nuclear Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"University Network of Excellence in Nuclear Engineering","keywords":"Unavailability; Spurious relationship; Core (optical fiber); Nuclear engineering; Shutdown; Statistical power; Computer science; Statistics; Mathematics; Engineering; Telecommunications","score_opus":0.05495388858271906,"score_gpt":0.28506573397613766,"score_spread":0.2301118453934186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2473394720","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9323078,0.00037780934,0.065197065,0.00015558618,0.00001709053,0.00003938591,0.00009433108,0.00015476339,0.0016562215],"genre_scores_gemma":[0.99613416,0.000049631235,0.003688817,0.000012838561,0.000003594397,0.000007537661,0.000023169765,0.000006396788,0.000073926065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99756515,0.00075997534,0.00008864204,0.00038079428,0.0009292459,0.00027623723],"domain_scores_gemma":[0.9597284,0.033523943,0.003455694,0.0015180226,0.0014630223,0.00031085586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004016841,0.00048251433,0.00057347695,0.0007169235,0.00032346608,0.00050088274,0.00050946523,0.00066345226,0.0007669108],"category_scores_gemma":[0.021407802,0.00021673327,0.00058578944,0.00036397297,0.0008710847,0.00079927844,0.0004884539,0.00065800885,0.00006671379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011722859,0.0001436292,0.03801582,0.00012527058,0.00015747915,0.00048785287,0.00008462825,0.8940265,0.026870498,0.0031085021,0.00021577341,0.035591736],"study_design_scores_gemma":[0.000047171357,0.0014138714,0.038264032,0.000036039248,0.00025979555,0.000714022,0.00012689795,0.9074008,0.04836388,0.0029176471,0.0003881783,0.00006771946],"about_ca_topic_score_codex":0.0030434437,"about_ca_topic_score_gemma":0.0021170801,"teacher_disagreement_score":0.004016841,"about_ca_system_score_codex":0.0011208005,"about_ca_system_score_gemma":0.0008088159,"threshold_uncertainty_score":0.021243393},"labels":[],"label_agreement":null},{"id":"W2478387680","doi":"10.1017/cbo9781316219232.004","title":"Interpolation and approximation","year":2016,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Interpolation (computer graphics); Content (measure theory); Computer science; Mathematics; Applied mathematics; Computer graphics (images); Mathematical analysis","score_opus":0.060234685726949684,"score_gpt":0.24201454910169704,"score_spread":0.18177986337474736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2478387680","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021774962,0.089757554,0.7293217,0.0027937084,0.0031496026,0.000053170967,0.0007896232,0.0014485796,0.1705086],"genre_scores_gemma":[0.1344751,0.10850834,0.40418756,0.001875653,0.0044248197,0.00028879655,0.0033914754,0.0025911331,0.34025708],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992169,0.00020012417,0.00004370245,0.00015600825,0.000326399,0.000056872217],"domain_scores_gemma":[0.99930894,0.00033016258,0.000025442752,0.00017501455,0.00013105772,0.0000294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013221048,0.0009268558,0.0014206513,0.0013099599,0.0005023494,0.0022740262,0.0010585927,0.001231268,0.031067755],"category_scores_gemma":[0.0039978772,0.00055455894,0.0012955478,0.0023998853,0.0015641928,0.0023225525,0.0016817085,0.0030866677,0.014878151],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011078289,0.000036207286,0.00039157236,0.0009931035,0.00007851728,0.00010677209,0.0001467765,0.024241576,0.0023277742,0.48142487,0.12267127,0.36747077],"study_design_scores_gemma":[0.000015377269,0.00005396573,0.0006023402,0.0003306366,0.000032986638,0.00029387968,0.00006174045,0.056023505,0.0019940138,0.5385972,0.40195674,0.000037542955],"about_ca_topic_score_codex":0.002164571,"about_ca_topic_score_gemma":0.0020287905,"teacher_disagreement_score":0.031067755,"about_ca_system_score_codex":0.0012330519,"about_ca_system_score_gemma":0.00086976675,"threshold_uncertainty_score":0.10393202},"labels":[],"label_agreement":null},{"id":"W2491653901","doi":"10.1108/mmms-07-2015-0035","title":"Improved bat algorithm for structural reliability assessment: application and challenges","year":2016,"lang":"en","type":"article","venue":"Multidiscipline Modeling in Materials and Structures","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of British Columbia","keywords":"Algorithm; Computer science; Mathematical optimization; Reliability (semiconductor); Robustness (evolution); Benchmark (surveying); First-order reliability method; Reliability engineering; Mathematics; Engineering; Artificial intelligence","score_opus":0.08077897990687291,"score_gpt":0.3462573066991176,"score_spread":0.2654783267922447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2491653901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074715666,0.00078468374,0.9891679,0.00024797267,0.000057476973,0.000022379625,0.000026294205,0.000257032,0.0019646815],"genre_scores_gemma":[0.24151422,0.0011360366,0.7514633,0.00019311774,0.000102946826,0.00018851072,0.00021263787,0.00020293434,0.0049862987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992619,0.00029148714,0.000034206245,0.00009537186,0.0002763909,0.000040720835],"domain_scores_gemma":[0.99854195,0.0006387623,0.00009773165,0.00011117442,0.0005641037,0.00004625877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012289897,0.00059692276,0.00084380934,0.0006337868,0.00033361383,0.00078518427,0.0010462442,0.0010304644,0.0024870536],"category_scores_gemma":[0.00280919,0.00028701822,0.000705498,0.0007555874,0.0004321445,0.0010026423,0.0006431242,0.0012454872,0.0008003738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078744066,0.00005443311,0.0011914045,0.00020310328,0.00008795978,0.0000717685,0.00009164269,0.79426414,0.005989967,0.023070553,0.0034081878,0.17148815],"study_design_scores_gemma":[0.0000062659774,0.000030225003,0.00013081337,0.000010786948,0.0000069016914,0.00004076033,0.000008458154,0.9944027,0.0005715331,0.0029937471,0.0017928656,0.0000048751895],"about_ca_topic_score_codex":0.0023944972,"about_ca_topic_score_gemma":0.0020191618,"teacher_disagreement_score":0.0024870536,"about_ca_system_score_codex":0.00047240904,"about_ca_system_score_gemma":0.0011219179,"threshold_uncertainty_score":0.008320034},"labels":[],"label_agreement":null},{"id":"W2503400496","doi":"10.1007/978-3-7908-2598-5","title":"Recent Developments in Applied Probability and Statistics","year":2010,"lang":"en","type":"book","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Statistics; Probability and statistics; Computer science; Econometrics; Mathematics","score_opus":0.0935733799302833,"score_gpt":0.31139798410656805,"score_spread":0.21782460417628474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2503400496","genre_codex":"review","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009526667,0.70911634,0.06923231,0.011422666,0.013470695,0.00007782401,0.0004846417,0.00059394864,0.19464889],"genre_scores_gemma":[0.012372891,0.74573565,0.058439355,0.0059660124,0.0233321,0.00022203376,0.0010224794,0.0005960598,0.1523134],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99541926,0.0009721846,0.0002780631,0.00051864027,0.002654351,0.0001575409],"domain_scores_gemma":[0.9929766,0.0046879333,0.00021006119,0.00045229745,0.0014375655,0.00023551127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035479835,0.001436904,0.0018971348,0.0041499506,0.0008647145,0.0048268004,0.0011956191,0.0019215115,0.023352934],"category_scores_gemma":[0.008998609,0.0009192742,0.0011768793,0.008911396,0.0020477148,0.0049682762,0.0017544132,0.006409968,0.02057038],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041221618,0.00006505058,0.00039580185,0.0024405294,0.000096920936,0.0001684223,0.00028782527,0.0022934086,0.0006976803,0.18881881,0.34305176,0.46164268],"study_design_scores_gemma":[0.0000046826094,0.000024054165,0.00045941377,0.0006010176,0.000015319914,0.0003731702,0.000041188086,0.0014651231,0.00016002706,0.08100946,0.9158271,0.000019527366],"about_ca_topic_score_codex":0.0013499253,"about_ca_topic_score_gemma":0.0014728282,"teacher_disagreement_score":0.023352934,"about_ca_system_score_codex":0.0019823122,"about_ca_system_score_gemma":0.0023729617,"threshold_uncertainty_score":0.07812333},"labels":[],"label_agreement":null},{"id":"W2507205846","doi":"10.1615/int.j.uncertaintyquantification.2016015843","title":"A PRIORI ERROR ANALYSIS OF STOCHASTIC GALERKIN PROJECTION SCHEMES FOR RANDOMLY PARAMETRIZED ORDINARY DIFFERENTIAL EQUATIONS","year":2016,"lang":"en","type":"article","venue":"International Journal for Uncertainty Quantification","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Applied mathematics; A priori and a posteriori; Galerkin method; Discretization; Ordinary differential equation; Nonlinear system; Stochastic differential equation; Projection (relational algebra); Differential equation; Mathematical analysis; Algorithm","score_opus":0.19104342884589412,"score_gpt":0.4363292191118509,"score_spread":0.2452857902659568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2507205846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029444752,0.00043424778,0.96813345,0.0003400231,0.00003622193,0.000033973687,0.000045100936,0.00007289658,0.0014592913],"genre_scores_gemma":[0.8739293,0.0009618802,0.120281406,0.000116412186,0.0000856516,0.0002509664,0.00022853105,0.00013497267,0.004010874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998528,0.0006911308,0.00007498882,0.00015449697,0.00046955433,0.00008172426],"domain_scores_gemma":[0.9914773,0.0059762313,0.000970348,0.00036643256,0.0009956154,0.00021400777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050096493,0.0011924885,0.001126008,0.0009483962,0.00047972254,0.00133325,0.0010225743,0.0014749946,0.0008241039],"category_scores_gemma":[0.011922361,0.00055035745,0.00080844405,0.00047006184,0.0025602388,0.0013745229,0.0024723115,0.0017671199,0.00012598193],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059727197,0.00002527121,0.0005388218,0.0001144964,0.000032029053,0.000052452626,0.00010027792,0.8807316,0.0031927226,0.10978476,0.00026493793,0.0051029786],"study_design_scores_gemma":[0.0000014228807,0.000007695234,0.000046213427,0.000005513399,0.0000014481437,0.000003985213,0.0000031626448,0.9943955,0.00032516033,0.0051116156,0.000094174124,0.0000041373123],"about_ca_topic_score_codex":0.0030109314,"about_ca_topic_score_gemma":0.0011725092,"teacher_disagreement_score":0.0050096493,"about_ca_system_score_codex":0.0011298372,"about_ca_system_score_gemma":0.0016642951,"threshold_uncertainty_score":0.026493907},"labels":[],"label_agreement":null},{"id":"W2520458656","doi":"10.1115/1.4034690","title":"Imprecise Probabilities in Fatigue Reliability Assessment of Hydraulic Turbines","year":2016,"lang":"en","type":"article","venue":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Mitacs","keywords":"Reliability (semiconductor); Probabilistic logic; Computer science; Expert elicitation; Uncertainty quantification; Reliability engineering; Mathematics; Artificial intelligence; Statistics; Machine learning; Engineering; Power (physics)","score_opus":0.03523837020550188,"score_gpt":0.2971794667338323,"score_spread":0.2619410965283304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520458656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.127635,0.0008068137,0.86910576,0.00018215402,0.000021675174,0.00003124942,0.000042716398,0.000063210864,0.0021114221],"genre_scores_gemma":[0.96382606,0.0003426912,0.03544723,0.000015513155,0.000024709674,0.000038760678,0.000030544485,0.000015926142,0.00025853503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948041,0.002918534,0.00022520873,0.00048181813,0.0014451223,0.0001251495],"domain_scores_gemma":[0.9784887,0.017894669,0.0015826123,0.0008074659,0.0011113511,0.00011518675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007929936,0.000610218,0.00067747757,0.0029653837,0.00044736045,0.0015015161,0.00093307777,0.0010111689,0.00076030206],"category_scores_gemma":[0.037698913,0.0005663097,0.0006328455,0.0013447636,0.0015222323,0.00322012,0.0011940177,0.00088912895,0.00014957588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098454664,0.000022010903,0.003916714,0.00014041702,0.00005606713,0.0001348771,0.00037307042,0.91909647,0.0020120929,0.03728941,0.00017951905,0.036680903],"study_design_scores_gemma":[0.0000058442693,0.00006873894,0.0033800886,0.00006242002,0.000028112376,0.000100710015,0.00010224915,0.93094134,0.0018907996,0.06282135,0.00054400606,0.00005432903],"about_ca_topic_score_codex":0.0015049615,"about_ca_topic_score_gemma":0.0011667402,"teacher_disagreement_score":0.007929936,"about_ca_system_score_codex":0.0006963541,"about_ca_system_score_gemma":0.0005848107,"threshold_uncertainty_score":0.041938007},"labels":[],"label_agreement":null},{"id":"W2525575697","doi":"10.1088/1742-6596/744/1/012173","title":"Stability of a nonlinear second order equation under parametric bounded noise excitation","year":2016,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bounded function; Noise (video); Parametric statistics; Harmonic; Nonlinear system; Mathematics; Mathematical analysis; Work (physics); Forcing (mathematics); Control theory (sociology); Statistical physics; Acoustics; Physics; Computer science; Statistics; Quantum mechanics","score_opus":0.15359336761281425,"score_gpt":0.3272075188162674,"score_spread":0.17361415120345317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2525575697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4225395,0.0006373299,0.53555906,0.0025513154,0.00018184932,0.00010799312,0.00029210423,0.000309467,0.037821405],"genre_scores_gemma":[0.9820619,0.00017745097,0.004443663,0.00010756357,0.000041211686,0.000082590224,0.00008153427,0.00003388468,0.012970228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995591,0.000119445715,0.0000145964,0.00011335455,0.00012973615,0.00006375422],"domain_scores_gemma":[0.998679,0.0007465908,0.00020343941,0.00006016956,0.00024398416,0.00006685159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089665345,0.0006788067,0.00086497865,0.0004992868,0.00052420795,0.0018177433,0.0007194847,0.0019066201,0.0018676512],"category_scores_gemma":[0.0043092486,0.0003114678,0.00057127594,0.0002937756,0.0022730918,0.0009734529,0.0018515465,0.0011230957,0.00029053484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031812064,0.000057767185,0.0027494486,0.00019391786,0.0000812384,0.00066624826,0.00080908753,0.81749964,0.024427973,0.14427061,0.0008910945,0.008034856],"study_design_scores_gemma":[0.0000139464855,0.00003960647,0.0003409536,0.00000875873,0.000005725979,0.00003176078,0.000042895852,0.9928262,0.00064834923,0.005799443,0.00022792409,0.000014427105],"about_ca_topic_score_codex":0.008712332,"about_ca_topic_score_gemma":0.0028946593,"teacher_disagreement_score":0.008712332,"about_ca_system_score_codex":0.0009590956,"about_ca_system_score_gemma":0.0009968398,"threshold_uncertainty_score":0.017323196},"labels":[],"label_agreement":null},{"id":"W2534164515","doi":"10.1109/iesm.2015.7380207","title":"Reliability analysis of supply chain for contingency operations","year":2015,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Supply chain; Reliability (semiconductor); Population; Distribution center; Contingency; Reliability engineering; First-order reliability method; Probabilistic logic; Computer science; Distribution (mathematics); Operations research; Statistics; Engineering; Business; Mathematics; Marketing","score_opus":0.14309285060506233,"score_gpt":0.3720140141820011,"score_spread":0.22892116357693876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2534164515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07328028,0.00074649963,0.9226023,0.00026040207,0.000024523064,0.000064553926,0.00012749613,0.0001622423,0.0027318117],"genre_scores_gemma":[0.9717299,0.0005788825,0.026301544,0.000026822014,0.000039290608,0.000085741434,0.00019398125,0.000033703353,0.001010082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793786,0.00067147304,0.00009803387,0.00028979266,0.0007851169,0.0002176244],"domain_scores_gemma":[0.99611473,0.0020801993,0.00061019533,0.00018028464,0.00092585955,0.000088698136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002192297,0.00092211296,0.0007544937,0.0018754471,0.00053529255,0.0009845519,0.0007687942,0.0006848273,0.0018135987],"category_scores_gemma":[0.0074001336,0.00049084594,0.0011604495,0.0010650763,0.00080846983,0.0013363296,0.0007600533,0.0007342285,0.00019989983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033593653,0.0000078611365,0.0018509126,0.000055865487,0.00004601724,0.0001171331,0.000055475688,0.9813803,0.0012829374,0.008853365,0.00021252401,0.006103939],"study_design_scores_gemma":[0.0000017038977,0.00002480319,0.00048042616,0.000007368329,0.000013840145,0.000037360624,0.000016968836,0.9941841,0.00032463268,0.004734508,0.00016785215,0.00000648781],"about_ca_topic_score_codex":0.005875579,"about_ca_topic_score_gemma":0.0019467581,"teacher_disagreement_score":0.005875579,"about_ca_system_score_codex":0.001292599,"about_ca_system_score_gemma":0.0010097639,"threshold_uncertainty_score":0.011682749},"labels":[],"label_agreement":null},{"id":"W2536091692","doi":"10.1109/acssc.2006.354991","title":"Parameter estimation in linear models based on outage probability minimization","year":2006,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Probability density function; Estimation theory; Gaussian noise; Mean squared error; Noise (video); Mathematics; Gaussian; Algorithm; SIGNAL (programming language); Applied mathematics; Minification; Statistics; Multivariate random variable; Computer science; Random variable; Mathematical optimization; Artificial intelligence; Physics","score_opus":0.0861482836767076,"score_gpt":0.31415887813191035,"score_spread":0.22801059445520275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2536091692","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047789407,0.00024879267,0.99421614,0.00008638812,0.0000052877967,0.0000070538763,0.000018635637,0.00009602475,0.00054271246],"genre_scores_gemma":[0.7751406,0.001652813,0.21914867,0.00016959291,0.00015612772,0.0001960362,0.00031341842,0.00018973715,0.003032989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985189,0.00077550125,0.00006248689,0.00021598673,0.00034454267,0.00008251984],"domain_scores_gemma":[0.99717206,0.0021538143,0.00030408063,0.00015613504,0.00018252713,0.000031530206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018348449,0.0010485662,0.0016238001,0.0007224652,0.0002624094,0.0011856972,0.0009694748,0.0010382567,0.0010081985],"category_scores_gemma":[0.0074779647,0.0006827846,0.00067002955,0.0008514524,0.001178544,0.0016473832,0.0013597155,0.0009589576,0.0003778675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026526968,0.000013904061,0.00027552698,0.00006319805,0.000043939843,0.000046591544,0.000042361968,0.9639376,0.00077572936,0.016792651,0.00039589917,0.017586067],"study_design_scores_gemma":[0.0000020757473,0.000007638254,0.00006155097,0.0000056298395,0.0000048512634,0.000011555428,0.0000034058537,0.99096173,0.00027154872,0.008542725,0.00012229496,0.0000049544433],"about_ca_topic_score_codex":0.0019771757,"about_ca_topic_score_gemma":0.0014563653,"teacher_disagreement_score":0.0019771757,"about_ca_system_score_codex":0.0007999399,"about_ca_system_score_gemma":0.0005740463,"threshold_uncertainty_score":0.009703696},"labels":[],"label_agreement":null},{"id":"W2552930863","doi":"10.1016/j.gete.2016.11.001","title":"Uncertainty quantification for reservoir geomechanics","year":2016,"lang":"en","type":"article","venue":"Geomechanics for Energy and the Environment","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Petrobras; Conselho Nacional de Desenvolvimento Científico e Tecnológico; CMG Reservoir Simulation Foundation","keywords":"Geomechanics; Probabilistic logic; Reliability (semiconductor); Uncertainty quantification; Computer science; Petroleum engineering; Geology; Geotechnical engineering; Artificial intelligence; Machine learning","score_opus":0.05158137659027347,"score_gpt":0.26607148221309035,"score_spread":0.2144901056228169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552930863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003245809,0.00039047093,0.9947154,0.00022032825,0.000017652088,0.0000090325175,0.000042278658,0.000049659142,0.0013093703],"genre_scores_gemma":[0.82028586,0.0018465064,0.17209557,0.00023792524,0.00024224751,0.00017507223,0.0002844604,0.00018038378,0.0046520173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792504,0.00078153075,0.000119750744,0.0002702122,0.0007777248,0.00012563074],"domain_scores_gemma":[0.99378234,0.00436949,0.00063892687,0.00032888757,0.00073749176,0.00014295595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043588956,0.0011513798,0.0016638609,0.0016871482,0.00054114976,0.0023302077,0.0012571783,0.0012132614,0.001400665],"category_scores_gemma":[0.014211647,0.0007817924,0.0012104033,0.001076375,0.0023251483,0.00331695,0.0027255008,0.002046866,0.0001628842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022822023,0.000012210555,0.00018056677,0.000103264756,0.000048615853,0.000021262405,0.000034657478,0.82316905,0.00087956083,0.15735714,0.00047928482,0.017691487],"study_design_scores_gemma":[0.0000015771355,0.000009098876,0.00007017438,0.000014857975,0.0000058586857,0.0000055495375,0.0000038037379,0.9229072,0.00028116207,0.0762545,0.0004375594,0.0000086516],"about_ca_topic_score_codex":0.003900958,"about_ca_topic_score_gemma":0.0024665277,"teacher_disagreement_score":0.0043588956,"about_ca_system_score_codex":0.0017612358,"about_ca_system_score_gemma":0.0016538666,"threshold_uncertainty_score":0.023052335},"labels":[],"label_agreement":null},{"id":"W2557755265","doi":"10.1002/fut.21958","title":"Multivariate constrained robust M‐regression for shaping forward curves in electricity markets","year":2018,"lang":"en","type":"preprint","venue":"Journal of Futures Markets","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"KU Leuven; Fonds Wetenschappelijk Onderzoek","keywords":"Outlier; Multivariate statistics; Electricity; Arbitrage; Econometrics; Electricity market; Robust regression; Regression; Economics; Computer science; Mathematical optimization; Mathematics; Statistics; Financial economics; Engineering","score_opus":0.10933804631716573,"score_gpt":0.3644222952349074,"score_spread":0.2550842489177416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557755265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043268455,0.00008359049,0.9950807,0.00005545423,0.000008088092,0.000007796289,0.000023523384,0.00009213633,0.00032188196],"genre_scores_gemma":[0.553362,0.0008208453,0.4379582,0.0001469276,0.00013774273,0.00017863694,0.00039326018,0.00046349413,0.0065389215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917954,0.0004332454,0.000027762459,0.00014611753,0.00015785788,0.000055465884],"domain_scores_gemma":[0.997673,0.0015029812,0.00029780576,0.00017542629,0.00029430675,0.00005640061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022447137,0.0010738581,0.0010093984,0.00078938325,0.00027186493,0.00075853884,0.0011430989,0.0010195563,0.0025540956],"category_scores_gemma":[0.007747519,0.00048699268,0.0010034431,0.00081748166,0.0008238492,0.0009592573,0.0010709143,0.0016584211,0.00066272425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074209034,0.000028267781,0.0004612292,0.00007879677,0.000052171963,0.000056435776,0.00003308787,0.91408145,0.004235312,0.034489684,0.0008776552,0.045531735],"study_design_scores_gemma":[0.0000021892852,0.000006954668,0.00006173385,0.0000028521165,0.0000022021297,0.0000064126257,0.000001715997,0.99536383,0.0005051062,0.003791958,0.000249895,0.0000051271068],"about_ca_topic_score_codex":0.0025571613,"about_ca_topic_score_gemma":0.0016628965,"teacher_disagreement_score":0.0025571613,"about_ca_system_score_codex":0.00051922566,"about_ca_system_score_gemma":0.00080897886,"threshold_uncertainty_score":0.011871278},"labels":[],"label_agreement":null},{"id":"W2560434059","doi":"10.1115/1.4035429","title":"Stability and Robustness Analysis of Uncertain Nonlinear Systems Using Entropy Properties of Left and Right Singular Vectors","year":2016,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Singular value decomposition; Singular value; Robustness (evolution); Mathematics; Nonlinear system; Entropy (arrow of time); Eigenvalues and eigenvectors; Control theory (sociology); Computer science; Algorithm","score_opus":0.13304783215756533,"score_gpt":0.30667862477240737,"score_spread":0.17363079261484204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560434059","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020244239,0.00023412425,0.97788334,0.00005272055,0.000011820087,0.000018137838,0.00003392163,0.000058687732,0.0014629412],"genre_scores_gemma":[0.91988534,0.0005803074,0.077836804,0.000035205692,0.00008364913,0.00012899502,0.00014215767,0.000057228634,0.0012503376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991009,0.00023665305,0.00005391564,0.00013479135,0.00042769127,0.00004601874],"domain_scores_gemma":[0.9974775,0.0017490519,0.0003540457,0.00012511191,0.0002482099,0.000046173114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015921072,0.0008082892,0.0007098514,0.0013952904,0.0003119415,0.0007913011,0.00043043314,0.00035699958,0.0008275068],"category_scores_gemma":[0.0037077633,0.0002867152,0.0008782943,0.00043844688,0.0011604605,0.0010578366,0.0009166748,0.00060236844,0.00014561035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005494936,0.000020651847,0.00092154805,0.00009929132,0.000066723245,0.00006591358,0.00006286597,0.9374734,0.009510184,0.028529927,0.00013668033,0.023057817],"study_design_scores_gemma":[0.0000015965552,0.000026563413,0.00032572492,0.00000596164,0.0000053504814,0.000010943707,0.0000054101033,0.9903639,0.0014026692,0.0076953345,0.00014860004,0.000007937207],"about_ca_topic_score_codex":0.0010264931,"about_ca_topic_score_gemma":0.00055791653,"teacher_disagreement_score":0.0015921072,"about_ca_system_score_codex":0.00061682076,"about_ca_system_score_gemma":0.0004629097,"threshold_uncertainty_score":0.008419991},"labels":[],"label_agreement":null},{"id":"W2561645249","doi":"10.1142/s0218539317500103","title":"Efficient Stability and Robustness Analysis of Uncertain Nonlinear Systems using a New Response-based Method","year":2016,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Nonlinear system; Control theory (sociology); Linearization; Singular value decomposition; Singular value; Rendering (computer graphics); Stability (learning theory); Mathematics; Computer science; Algorithm","score_opus":0.11830010040307748,"score_gpt":0.395411743572008,"score_spread":0.2771116431689305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2561645249","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018666061,0.00005926514,0.9974511,0.000020726136,0.000010077769,0.000013638654,0.000009821287,0.00010734039,0.00046130613],"genre_scores_gemma":[0.4207666,0.0006053145,0.57324445,0.000089131616,0.00013123496,0.00040306244,0.0002440111,0.00021800034,0.004298179],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992198,0.0002784792,0.000038441798,0.000107310676,0.00031423807,0.00004166964],"domain_scores_gemma":[0.99864167,0.00078659167,0.00016238143,0.00010643751,0.00026541876,0.00003748371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016198016,0.000979605,0.0008843755,0.0012061151,0.00025417918,0.0006655513,0.00087980065,0.0007567742,0.0020209285],"category_scores_gemma":[0.0031106328,0.00039613555,0.0010918889,0.00050705625,0.0006193547,0.0006689388,0.00095090753,0.0010963931,0.00054206507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008749638,0.000037475576,0.00030405473,0.00012160862,0.00006449616,0.000098602606,0.00005085545,0.9174084,0.0145807285,0.009744464,0.00039979763,0.057102036],"study_design_scores_gemma":[0.0000027309131,0.000015789396,0.000027338096,0.000003097796,0.0000034800469,0.000009186707,0.0000015834974,0.9984987,0.00064909464,0.0005730817,0.00021211557,0.000003887371],"about_ca_topic_score_codex":0.0013647738,"about_ca_topic_score_gemma":0.000715912,"teacher_disagreement_score":0.0020209285,"about_ca_system_score_codex":0.00037310921,"about_ca_system_score_gemma":0.00051271927,"threshold_uncertainty_score":0.008566439},"labels":[],"label_agreement":null},{"id":"W2567585254","doi":"10.2514/1.j054983","title":"Fast Analysis of Unsteady Wing Aerodynamics via Stochastic Models","year":2016,"lang":"en","type":"article","venue":"AIAA Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Deutscher Akademischer Austauschdienst; Pacific Institute for Climate Solutions","keywords":"Inflow; Polynomial chaos; Randomness; Aerodynamics; Term (time); Stochastic process; Wing; Computer science; Applied mathematics; Mathematical optimization; Mathematics; Monte Carlo method; Engineering; Mechanics; Aerospace engineering; Physics","score_opus":0.06935354284912698,"score_gpt":0.31061704851726923,"score_spread":0.24126350566814225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567585254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02834718,0.00016222097,0.9691251,0.00013066022,0.000014011013,0.000021358843,0.00004626949,0.00015921159,0.0019939947],"genre_scores_gemma":[0.92038864,0.0005374036,0.07392635,0.00006130454,0.000044552857,0.00017194595,0.00014457293,0.00010855774,0.0046167914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957603,0.00012757778,0.000013841619,0.00004225074,0.00018806323,0.0000523157],"domain_scores_gemma":[0.99884593,0.0007308335,0.0001797581,0.00006219779,0.00013460602,0.000046604655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010971042,0.00068886956,0.0006541836,0.0005553241,0.00034016656,0.00081237016,0.00076653814,0.0007868854,0.0012844544],"category_scores_gemma":[0.0026831322,0.0006186371,0.0008690937,0.00032888324,0.0009123816,0.00068387965,0.00095343584,0.0010022656,0.0002193406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000065413424,0.000004586066,0.00015942722,0.000011761118,0.0000074024356,0.000013557906,0.000009639453,0.9899951,0.0006607035,0.007840389,0.00005091083,0.00124],"study_design_scores_gemma":[7.277996e-7,0.0000021883802,0.00002515417,0.000001146716,6.35222e-7,0.0000016702451,9.423997e-7,0.9987379,0.000074812764,0.0010916439,0.000062227846,9.969086e-7],"about_ca_topic_score_codex":0.0057813236,"about_ca_topic_score_gemma":0.0037407584,"teacher_disagreement_score":0.0057813236,"about_ca_system_score_codex":0.00078973727,"about_ca_system_score_gemma":0.0010394506,"threshold_uncertainty_score":0.011495352},"labels":[],"label_agreement":null},{"id":"W2571293944","doi":"10.1109/cdc.2016.7798760","title":"Decomposition-based global optimization for optimal design of power distribution systems","year":2016,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Global optimization; Mathematical optimization; Decomposition; Optimization problem; Computer science; Engineering optimization; Power flow; Constraint (computer-aided design); Power (physics); Constrained optimization problem; Electric power system; Mathematics","score_opus":0.059939674071755523,"score_gpt":0.33620997537561115,"score_spread":0.2762703013038556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2571293944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006247429,0.00018013052,0.9982309,0.000037176997,0.000011058678,0.000009095319,0.000009471589,0.000035291647,0.00086217275],"genre_scores_gemma":[0.31667855,0.001973614,0.6761848,0.00014488457,0.0001849525,0.00050323555,0.0003380854,0.00028841343,0.0037035241],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993056,0.00036550386,0.000023155939,0.00007968252,0.00018306391,0.000042962132],"domain_scores_gemma":[0.99944466,0.00033545733,0.00005093216,0.000050276893,0.00009466151,0.000023980949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017273222,0.0021980687,0.0015543714,0.0007409486,0.0003686334,0.0009575416,0.0006863902,0.00096793036,0.0020769443],"category_scores_gemma":[0.002371481,0.00060570904,0.001121011,0.0008232665,0.0011561795,0.0009909897,0.0013673479,0.0018196426,0.00048706605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019611536,0.0000141123965,0.00007997185,0.00009546317,0.00002506761,0.000015653375,0.000025034069,0.94234467,0.001194107,0.034925498,0.0010670141,0.020193819],"study_design_scores_gemma":[0.0000051981806,0.000022431643,0.000023698005,0.000011046945,0.000005672895,0.00000573646,0.000003941886,0.98371017,0.0002741112,0.014916564,0.001018152,0.0000032778823],"about_ca_topic_score_codex":0.0017002578,"about_ca_topic_score_gemma":0.0013228782,"teacher_disagreement_score":0.0021980687,"about_ca_system_score_codex":0.0007640695,"about_ca_system_score_gemma":0.0010962517,"threshold_uncertainty_score":0.009135067},"labels":[],"label_agreement":null},{"id":"W2571459559","doi":"10.1016/j.compstruc.2016.12.010","title":"Response sensitivity analysis for plastic plane problems based on direct differentiation method","year":2017,"lang":"en","type":"article","venue":"Computers & Structures","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Sensitivity (control systems); Finite element method; OpenSees; Plane stress; Context (archaeology); Plane (geometry); Mathematical optimization; Computer science; Structural engineering; Applied mathematics; Mathematics; Engineering; Geometry","score_opus":0.07154176526163956,"score_gpt":0.3467177712708087,"score_spread":0.27517600600916914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2571459559","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015477517,0.00052079256,0.9761027,0.00016683013,0.000053894088,0.00006729557,0.000038527538,0.000119001714,0.007453556],"genre_scores_gemma":[0.8617371,0.0010263253,0.1251772,0.00019925882,0.000078009,0.00023436906,0.00009733221,0.00017675084,0.011273627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949634,0.0002187823,0.000017481807,0.00005090274,0.0001793943,0.000037051468],"domain_scores_gemma":[0.9984174,0.001226062,0.00006916889,0.00007088526,0.00018983797,0.000026711117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012404451,0.000723084,0.00090221583,0.0010221594,0.00026645348,0.0008023917,0.00067607604,0.0007852643,0.0024660444],"category_scores_gemma":[0.0034396336,0.00042494212,0.0009022678,0.00038167377,0.0007837703,0.0006520409,0.0010784027,0.0010507316,0.00025964028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085367195,0.000057982328,0.00034390873,0.00028685533,0.00005579159,0.00013075207,0.00007279204,0.9244668,0.014365361,0.03411295,0.00063031784,0.025391128],"study_design_scores_gemma":[0.0000028025856,0.000011712842,0.000073773226,0.000008249231,0.0000058859428,0.000015000682,0.000004420382,0.9951807,0.0008979088,0.003517375,0.00027731567,0.000004835678],"about_ca_topic_score_codex":0.0020295794,"about_ca_topic_score_gemma":0.001034349,"teacher_disagreement_score":0.0024660444,"about_ca_system_score_codex":0.0005609246,"about_ca_system_score_gemma":0.000612667,"threshold_uncertainty_score":0.00824976},"labels":[],"label_agreement":null},{"id":"W2577379255","doi":"10.1109/tcpmt.2016.2642199","title":"Variability Analysis via Parameterized Model Order Reduction and Numerical Inversion of Laplace Transform","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Components Packaging and Manufacturing Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Laplace transform; Parameterized complexity; Model order reduction; Applied mathematics; Inversion (geology); Frequency domain; Algorithm; Subspace topology; Inverse Laplace transform; Numerical integration; Time domain; Mathematics; Frequency response; Dimensionality reduction; Representation (politics); Computer science; Numerical analysis; Mathematical optimization; Mathematical analysis","score_opus":0.045423512440772855,"score_gpt":0.2949294368384428,"score_spread":0.24950592439766994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577379255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014950723,0.000020856833,0.99803954,0.000016583681,0.0000033570718,0.00000795923,0.000013287775,0.00014858892,0.00025483567],"genre_scores_gemma":[0.30192193,0.000264499,0.69461256,0.00006705477,0.000045787376,0.0002898135,0.00036023612,0.00030341418,0.002134702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973804,0.000065368054,0.000012340565,0.000037338952,0.00012502851,0.000021861213],"domain_scores_gemma":[0.9992324,0.00046738682,0.00009646314,0.00008519583,0.00009941687,0.000019061006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067875435,0.00072598265,0.00069818547,0.00066624756,0.00035514176,0.00077855884,0.0008185838,0.00050555536,0.0014141189],"category_scores_gemma":[0.0021149002,0.00037424773,0.0010435072,0.0005998485,0.0005008653,0.0007358592,0.0008384391,0.0011565665,0.0004697655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000277301,0.00003086126,0.0006300577,0.000057236666,0.000039204402,0.0000910752,0.00006922414,0.8704708,0.010749396,0.034823578,0.0008008112,0.08221],"study_design_scores_gemma":[0.0000012090827,0.000005252787,0.000034940236,0.0000013397104,0.0000014923415,0.000012024117,0.0000021880176,0.9959912,0.00041788927,0.0032165535,0.00031287255,0.0000029970038],"about_ca_topic_score_codex":0.0025416478,"about_ca_topic_score_gemma":0.002258709,"teacher_disagreement_score":0.0025416478,"about_ca_system_score_codex":0.0005971154,"about_ca_system_score_gemma":0.0010863624,"threshold_uncertainty_score":0.0050537586},"labels":[],"label_agreement":null},{"id":"W2577874402","doi":"10.5555/3042094.3042459","title":"A Bayesian inference based simulation approach for estimating fraction nonconforming of pipe spool welding processes","year":2016,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Monte Carlo method; Fraction (chemistry); Process (computing); Welding; Computer science; Schedule; Bayesian inference; Reliability (semiconductor); Inference; Process variable; Engineering; Bayesian probability; Reliability engineering; Mechanical engineering; Artificial intelligence; Mathematics; Statistics","score_opus":0.1283665555365754,"score_gpt":0.3805438260227215,"score_spread":0.25217727048614613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577874402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051901404,0.00016575915,0.94503045,0.00013861178,0.000014813355,0.000063000814,0.00009643791,0.00023476903,0.0023547478],"genre_scores_gemma":[0.86910415,0.0003300904,0.12764615,0.00007498184,0.000028645964,0.00024747255,0.00037941392,0.00005261041,0.0021365823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871695,0.000590527,0.00008025497,0.00021452838,0.00028155878,0.00011621125],"domain_scores_gemma":[0.99423075,0.004285596,0.00058856205,0.0002217981,0.0005403875,0.00013293316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004072108,0.00070516934,0.0012171987,0.0016573889,0.0006589552,0.0011664256,0.0018727677,0.0014080523,0.0017889062],"category_scores_gemma":[0.011629775,0.0011091628,0.001121951,0.0012200355,0.000931604,0.0015764724,0.0009350976,0.0014431161,0.00022804723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020334783,0.000009567868,0.0005527182,0.0000064652368,0.000010410504,0.000013883975,0.0000170297,0.9929309,0.00012683762,0.003937195,0.00004229504,0.002332392],"study_design_scores_gemma":[0.0000029035907,0.000005035058,0.00010049012,0.0000022790155,0.000002702302,0.0000033114793,0.000001930646,0.9988438,0.00006332582,0.0009219166,0.000048440044,0.0000037826828],"about_ca_topic_score_codex":0.02671366,"about_ca_topic_score_gemma":0.014814609,"teacher_disagreement_score":0.02671366,"about_ca_system_score_codex":0.0021957867,"about_ca_system_score_gemma":0.0019548659,"threshold_uncertainty_score":0.05311638},"labels":[],"label_agreement":null},{"id":"W2580105310","doi":"10.1016/j.compstruc.2017.01.002","title":"Adaptive reduced basis strategy dedicated to the solution of nonstationary stochastic thermal problems","year":2017,"lang":"en","type":"article","venue":"Computers & Structures","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Discretization; Basis (linear algebra); Dimension (graph theory); Benchmark (surveying); Computer science; Monte Carlo method; Finite element method; Mathematical optimization; Key (lock); Section (typography); Basis function; Transient (computer programming); Numerical analysis; Modal; Stochastic modelling; Stochastic control; Applied mathematics; Mathematics; Optimal control; Engineering","score_opus":0.10269593084387084,"score_gpt":0.32849038918105034,"score_spread":0.22579445833717948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580105310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0099482285,0.00021763457,0.98592967,0.000101586025,0.000066732646,0.000021448175,0.000022619637,0.00006696431,0.0036251592],"genre_scores_gemma":[0.5438295,0.0011027695,0.4372244,0.00024854802,0.00022215053,0.00043365572,0.00030947497,0.00018664145,0.016442854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998166,0.000080212645,0.000006165576,0.000021252186,0.00005942909,0.000016278183],"domain_scores_gemma":[0.9997739,0.00009676951,0.000018412404,0.000021492433,0.00007123058,0.00001814753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004500437,0.0006173342,0.0009209211,0.00037521587,0.00022255637,0.0006077917,0.00085357216,0.0008520205,0.0019239535],"category_scores_gemma":[0.00085665134,0.00024578953,0.00061808457,0.0003825307,0.00055170694,0.0004014399,0.0007251218,0.0010883688,0.00043262303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007863478,0.00008973354,0.00021439213,0.00017337021,0.0000830674,0.00007942494,0.000067952475,0.8320395,0.0120969135,0.09971042,0.002288187,0.053078417],"study_design_scores_gemma":[0.0000029166472,0.000010268694,0.00002067693,0.0000018740565,0.0000032453909,0.0000041267895,0.0000019280176,0.99693716,0.00020749476,0.0024808813,0.0003275068,0.0000019225297],"about_ca_topic_score_codex":0.002031245,"about_ca_topic_score_gemma":0.0016589945,"teacher_disagreement_score":0.002031245,"about_ca_system_score_codex":0.00031051782,"about_ca_system_score_gemma":0.0007596043,"threshold_uncertainty_score":0.0064362288},"labels":[],"label_agreement":null},{"id":"W2580336769","doi":"10.1016/j.ress.2007.07.006","title":"Reliable design space and complete single-loop reliability-based design optimization","year":2007,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":197,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Simple (philosophy); Reliability (semiconductor); Limit (mathematics); Mathematical optimization; Point (geometry); Space (punctuation); Optimization problem; Loop (graph theory); State space; Mathematics; Conceptual design; Work (physics); Computer science; State (computer science); Algorithm; Engineering","score_opus":0.061236486584831815,"score_gpt":0.2632736667795107,"score_spread":0.20203718019467887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580336769","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01364129,0.00013227158,0.9827895,0.00008954887,0.000012458544,0.00002653421,0.00007106349,0.0001956649,0.003041601],"genre_scores_gemma":[0.8170588,0.00027872168,0.17813903,0.00009657152,0.000052493204,0.0003365193,0.00027134593,0.00022663888,0.0035398065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991159,0.00035499845,0.00002722806,0.000113276954,0.00031436665,0.00007420557],"domain_scores_gemma":[0.99861693,0.000811483,0.00017399185,0.00016064277,0.00020482314,0.000031970107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021347376,0.0015535946,0.001500427,0.0009811027,0.00040588362,0.001122562,0.001101822,0.001011922,0.0032712484],"category_scores_gemma":[0.0060880673,0.0008385066,0.000995671,0.0006985768,0.0011661402,0.0014260807,0.0012738372,0.0010128905,0.00048975466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004448761,0.000013618239,0.00008126919,0.000038459348,0.00001760678,0.000011478758,0.00001664546,0.9779416,0.0006413876,0.011817078,0.00023831596,0.009138125],"study_design_scores_gemma":[0.000009391538,0.000029457286,0.00004453201,0.0000042481784,0.0000073960427,0.0000055580595,0.0000031358475,0.9832244,0.00033908186,0.016117703,0.00021127022,0.0000037866903],"about_ca_topic_score_codex":0.0012897766,"about_ca_topic_score_gemma":0.00094585377,"teacher_disagreement_score":0.0032712484,"about_ca_system_score_codex":0.00058107864,"about_ca_system_score_gemma":0.0012035742,"threshold_uncertainty_score":0.011289716},"labels":[],"label_agreement":null},{"id":"W2580676852","doi":"","title":"Polynomial chaos solution to the Black Scholes equation with a random volatility","year":2012,"lang":"en","type":"article","venue":"ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mathematics; Applied mathematics; Volatility (finance); Black–Scholes model; Orthonormal basis; Polynomial chaos; SABR volatility model; Polynomial; Implied volatility; Mathematical analysis; Econometrics; Monte Carlo method; Physics; Statistics","score_opus":0.21965134060100744,"score_gpt":0.40895401255907543,"score_spread":0.189302671958068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580676852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1525939,0.00053466513,0.841435,0.0005642697,0.00008316366,0.000026652944,0.00005690317,0.000090004665,0.0046153874],"genre_scores_gemma":[0.95698327,0.00051618746,0.037718464,0.000054498294,0.000055052376,0.000036227535,0.000058009533,0.000028540211,0.004549816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996625,0.000102724174,0.0000149273965,0.00004809271,0.0001257409,0.00004606907],"domain_scores_gemma":[0.99939084,0.00032302408,0.00011488598,0.000038699338,0.0000934854,0.00003909488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060211617,0.0003561458,0.0005841311,0.0003680498,0.00037195286,0.0008353593,0.00043352856,0.0008413975,0.000977308],"category_scores_gemma":[0.0031249612,0.00020000049,0.0006145858,0.00048304262,0.0008089134,0.0014086807,0.00070789224,0.0009534291,0.00012263446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075354146,0.000022642102,0.001067597,0.00007110233,0.000031711257,0.00023663128,0.00009723274,0.5440424,0.007462576,0.43875477,0.00041610547,0.0077219214],"study_design_scores_gemma":[0.000009227684,0.000015786547,0.00012852483,0.000002361065,0.0000043290675,0.000029138377,0.000007141211,0.9762058,0.00058247533,0.022778949,0.0002286922,0.000007579028],"about_ca_topic_score_codex":0.0023350671,"about_ca_topic_score_gemma":0.00090731366,"teacher_disagreement_score":0.0023350671,"about_ca_system_score_codex":0.0004327336,"about_ca_system_score_gemma":0.00084662094,"threshold_uncertainty_score":0.0046429634},"labels":[],"label_agreement":null},{"id":"W2587831136","doi":"10.1016/j.cma.2017.01.042","title":"Bayesian model selection using automatic relevance determination for nonlinear dynamical systems","year":2017,"lang":"en","type":"article","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Ontario Innovation Trust","keywords":"Mathematics; Parameter space; Model selection; Nonlinear system; Maximum a posteriori estimation; Applied mathematics; Prior probability; Bayesian inference; Markov chain Monte Carlo; Mathematical optimization; Bayesian probability; Algorithm; Statistics","score_opus":0.09805260451440018,"score_gpt":0.39060187288548537,"score_spread":0.2925492683710852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587831136","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007942521,0.00025527077,0.9910086,0.00013587515,0.000019950223,0.000025647783,0.000026651169,0.00022381544,0.00036164172],"genre_scores_gemma":[0.6745859,0.00051012635,0.32061386,0.00019822715,0.00022368641,0.00025277492,0.00041695515,0.00027395578,0.0029245033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99710983,0.0016007246,0.00014526772,0.00044198797,0.0005639618,0.00013818937],"domain_scores_gemma":[0.9909814,0.0073412,0.00042862658,0.0004053339,0.0007023782,0.00014108235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040118536,0.00096646784,0.0024156866,0.0016973757,0.0009141284,0.0014688743,0.0016347669,0.001435664,0.0015161942],"category_scores_gemma":[0.019595131,0.0012140301,0.0014356504,0.0008756074,0.0011411286,0.0015333133,0.002316792,0.0020289903,0.0005683172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003468263,0.00016477604,0.0009922972,0.00031662275,0.0003048863,0.00021031626,0.00016579272,0.7831787,0.0058531356,0.029618964,0.0022185338,0.17662922],"study_design_scores_gemma":[0.00001272916,0.00001758513,0.00013754495,0.000005288868,0.00001300257,0.000017132683,0.000002989998,0.98659986,0.00045360066,0.012547407,0.00018113822,0.000011588948],"about_ca_topic_score_codex":0.0037509135,"about_ca_topic_score_gemma":0.0038597246,"teacher_disagreement_score":0.0040118536,"about_ca_system_score_codex":0.00079646276,"about_ca_system_score_gemma":0.0016744433,"threshold_uncertainty_score":0.021216989},"labels":[],"label_agreement":null},{"id":"W2589696949","doi":"10.1061/jtepbs.0000037","title":"Safety Evaluation and Adjustment of Superelevation Design Guides for Horizontal Curves Based on Reliability Analysis","year":2017,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Petro-Canada","funders":"","keywords":"Geometric design; Reliability (semiconductor); Design speed; Engineering; Margin (machine learning); Reliability engineering; Operating speed; Simulation; Transport engineering; Computer science; Civil engineering; Power (physics)","score_opus":0.10600063135388317,"score_gpt":0.3475165402233338,"score_spread":0.24151590886945062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589696949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24874666,0.0002551908,0.74271995,0.00006096549,0.000041411815,0.00048000514,0.00021310645,0.0014501372,0.006032493],"genre_scores_gemma":[0.762208,0.0001437593,0.23555525,0.000024376639,0.000009376063,0.00029284976,0.00027516801,0.00018945507,0.0013017901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9923683,0.0024568737,0.0004504075,0.0005395358,0.0039256276,0.00025930817],"domain_scores_gemma":[0.9741521,0.008384339,0.0032128044,0.002662081,0.01136608,0.00022260695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072638853,0.0014041569,0.0005881094,0.0032072254,0.00040975393,0.001224217,0.0011211919,0.0006453567,0.0022228041],"category_scores_gemma":[0.024621565,0.00044876218,0.00061101525,0.001260383,0.0006721238,0.0011649022,0.0007528799,0.00073653064,0.00044396246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064112176,0.0001873189,0.015454834,0.00031092603,0.00006661448,0.00015799247,0.0007194607,0.6982703,0.038359463,0.011518015,0.0018928404,0.23242106],"study_design_scores_gemma":[0.00007041042,0.0024865617,0.017290266,0.00009592396,0.00010508244,0.00019945651,0.00037838286,0.8977129,0.066394806,0.0059170183,0.009155124,0.00019404413],"about_ca_topic_score_codex":0.0023469408,"about_ca_topic_score_gemma":0.0030166095,"teacher_disagreement_score":0.0072638853,"about_ca_system_score_codex":0.0011345525,"about_ca_system_score_gemma":0.0015604129,"threshold_uncertainty_score":0.03841555},"labels":[],"label_agreement":null},{"id":"W2590241631","doi":"10.1007/978-3-319-52425-2_13","title":"Using FORM for Minimizing the Uncertain Cost of Structural Designs","year":2017,"lang":"en","type":"book-chapter","venue":"Springer series in reliability engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Differentiable function; Mathematical optimization; Reliability (semiconductor); Random variable; Function (biology); Distribution (mathematics); Computer science; Mathematics; Statistics","score_opus":0.2031390037217352,"score_gpt":0.3632661383023304,"score_spread":0.1601271345805952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2590241631","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021973008,0.00033625498,0.9922708,0.00018951665,0.00006708458,0.00003111326,0.000045645033,0.00006040166,0.0048018186],"genre_scores_gemma":[0.15251125,0.0018044589,0.8244624,0.00022600146,0.00037103685,0.00040993534,0.00027612274,0.0007550529,0.019183775],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998606,0.0006041059,0.00006929256,0.00019135574,0.0004699781,0.000059287326],"domain_scores_gemma":[0.9985499,0.0008280262,0.00010418864,0.0002310922,0.00025476766,0.00003203883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021946942,0.0018125786,0.0013091911,0.0009680294,0.00044493456,0.0018964623,0.0016870977,0.0018100223,0.0067657027],"category_scores_gemma":[0.008510925,0.00076658785,0.0012519272,0.0013499124,0.0012273086,0.0026973716,0.0019210466,0.0021624947,0.0013697696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007251324,0.00007097703,0.0002699017,0.00044715713,0.00006493632,0.000076671684,0.00014655497,0.38213912,0.005900516,0.4242289,0.0072712996,0.17931141],"study_design_scores_gemma":[0.000010708467,0.00009888504,0.00014055993,0.0000730652,0.000036861766,0.00009860986,0.000025860609,0.6923392,0.0020015896,0.29394552,0.011213396,0.000015723203],"about_ca_topic_score_codex":0.0007314629,"about_ca_topic_score_gemma":0.00088174065,"teacher_disagreement_score":0.0067657027,"about_ca_system_score_codex":0.0012238456,"about_ca_system_score_gemma":0.0008931499,"threshold_uncertainty_score":0.022633493},"labels":[],"label_agreement":null},{"id":"W2591333604","doi":"10.1201/9781003078166-6","title":"Dynamic simulation of multi-phase mining venture risks resolution","year":2020,"lang":"en","type":"book-chapter","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Phase (matter); Business; Resolution (logic); Computer science; Artificial intelligence; Physics","score_opus":0.23316913836303507,"score_gpt":0.40734010114624974,"score_spread":0.17417096278321467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591333604","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8740777,0.00014179913,0.114807054,0.000355726,0.000037067606,0.00008111651,0.00031950616,0.00020617414,0.00997389],"genre_scores_gemma":[0.9916468,0.00004061621,0.007033481,0.000019234767,0.0000035386636,0.00004491357,0.00010264614,0.000010492766,0.0010983256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968314,0.000102185666,0.000014155307,0.00004342257,0.00008124002,0.0000757928],"domain_scores_gemma":[0.99771786,0.0016474599,0.00023911963,0.00009729219,0.00018285743,0.000115488096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009120322,0.0003125908,0.000512665,0.0004942922,0.0003647135,0.00093815895,0.00074902485,0.0012714996,0.0019409623],"category_scores_gemma":[0.0033420331,0.00033704902,0.00060241355,0.00047487873,0.0005927727,0.00066046795,0.0006353741,0.0007080437,0.00012480108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021487163,0.0000148786,0.0010818588,0.0000035930577,0.000005313767,0.000033211138,0.000012988613,0.99638903,0.00021349563,0.0014804611,0.000050533163,0.0006931764],"study_design_scores_gemma":[0.0000036597232,0.0000072084194,0.00017041204,9.731664e-7,0.0000014156893,0.0000049558616,0.000004288139,0.9993362,0.00008159011,0.0003357059,0.000051203682,0.0000023768714],"about_ca_topic_score_codex":0.017902425,"about_ca_topic_score_gemma":0.007864233,"teacher_disagreement_score":0.017902425,"about_ca_system_score_codex":0.0011701611,"about_ca_system_score_gemma":0.0010712516,"threshold_uncertainty_score":0.03559649},"labels":[],"label_agreement":null},{"id":"W2592176073","doi":"10.1109/edaps.2016.7874399","title":"Statistical analysis via generalized decoupled polynomial chaos","year":2016,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Polynomial chaos; Polynomial; Decoupling (probability); Polynomial matrix; Computer science; Matrix polynomial; Projection (relational algebra); Mathematics; Applied mathematics; Galerkin method; Algorithm; Algebra over a field; Nonlinear system; Pure mathematics; Monte Carlo method; Mathematical analysis; Control engineering; Engineering","score_opus":0.06280650588615722,"score_gpt":0.34240434588474244,"score_spread":0.27959783999858523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592176073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013325504,0.00019533059,0.98453146,0.00011887192,0.000020431668,0.000012632454,0.000039546412,0.00009818205,0.0016580655],"genre_scores_gemma":[0.86244756,0.0008304392,0.13117447,0.00015622089,0.00021095904,0.000106899155,0.00016686054,0.00011407214,0.004792618],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99925023,0.0002151916,0.000023998107,0.00011209313,0.00034058213,0.00005800355],"domain_scores_gemma":[0.99889565,0.00054080714,0.00018651006,0.00015141196,0.00017812978,0.000047433346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058537,0.0004931728,0.0005472289,0.0010374513,0.00032343587,0.00072338624,0.00064837665,0.00046926024,0.0010381147],"category_scores_gemma":[0.0027255933,0.00023893412,0.00062542746,0.00064767845,0.001168176,0.0012893577,0.0010083939,0.0009619247,0.00022499805],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033516095,0.000017122822,0.0005083754,0.00006209728,0.000047229856,0.000110123125,0.000065986686,0.41402867,0.0106120305,0.5517414,0.0005990607,0.022174422],"study_design_scores_gemma":[0.0000041605786,0.000019265164,0.00023623483,0.0000034879695,0.0000045701427,0.00003325549,0.0000056694435,0.90650064,0.0010590315,0.091071434,0.0010521162,0.000010171167],"about_ca_topic_score_codex":0.00097409444,"about_ca_topic_score_gemma":0.00059184624,"teacher_disagreement_score":0.0010381147,"about_ca_system_score_codex":0.00064831984,"about_ca_system_score_gemma":0.000659631,"threshold_uncertainty_score":0.004703939},"labels":[],"label_agreement":null},{"id":"W2592649075","doi":"10.1002/mats.201600095","title":"Parameter Estimation for an Inverse Nonlinear Stochastic Problem: Reactivity Ratio Studies in Copolymerization","year":2017,"lang":"en","type":"article","venue":"Macromolecular Theory and Simulations","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inverse; Monte Carlo method; Reactivity (psychology); Nonlinear system; Mathematics; Applied mathematics; Estimation theory; Inverse problem; Markov chain; Polynomial chaos; Markov chain Monte Carlo; Mathematical optimization; Computer science; Algorithm; Statistics; Physics; Mathematical analysis","score_opus":0.14168875852116788,"score_gpt":0.41479787914832644,"score_spread":0.27310912062715853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592649075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015635507,0.00009004741,0.9836818,0.00010282796,0.000005532494,0.00001946346,0.000011393937,0.000057005174,0.00039651067],"genre_scores_gemma":[0.69926244,0.00043016483,0.29851848,0.00007582223,0.000042241492,0.000200133,0.00009693049,0.000113020375,0.0012607445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957687,0.00018480481,0.000020283767,0.00007452916,0.00011803371,0.000025442465],"domain_scores_gemma":[0.9978631,0.0016660703,0.00022485109,0.000082189355,0.00013404273,0.000029782574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018468825,0.00056227454,0.00071896403,0.0005882944,0.00029887856,0.00048721395,0.00063656695,0.00081151904,0.0005535357],"category_scores_gemma":[0.00567505,0.00039890019,0.00051810563,0.00038818136,0.0010443506,0.00088659313,0.00097054127,0.0008747965,0.00012730692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049601535,0.000025790134,0.00063839316,0.00010049107,0.000028876333,0.000061731225,0.0000729135,0.9372554,0.013965628,0.021673566,0.00021922824,0.025908332],"study_design_scores_gemma":[0.0000016391211,0.000006402851,0.000047473684,0.0000014842215,0.0000017709473,0.000008088082,0.0000018245775,0.9968671,0.0012585395,0.0017265176,0.0000754091,0.0000036493202],"about_ca_topic_score_codex":0.0014340737,"about_ca_topic_score_gemma":0.00079664227,"teacher_disagreement_score":0.0018468825,"about_ca_system_score_codex":0.00051318354,"about_ca_system_score_gemma":0.00079046865,"threshold_uncertainty_score":0.009767354},"labels":[],"label_agreement":null},{"id":"W2592831100","doi":"10.5194/wes-2-507-2017","title":"An engineering model for 3-D turbulent wind inflow based on a limited set of random variables","year":2017,"lang":"en","type":"article","venue":"Wind energy science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Deutscher Akademischer Austauschdienst; Pacific Institute for Climate Solutions","keywords":"Stochastic process; Inflow; Random variable; Turbine; Covariance; Wind power; Stochastic modelling; Wind speed; Random field; Computer science; Intermittency; Mathematics; Turbulence; Mathematical optimization; Statistics; Engineering; Meteorology; Aerospace engineering; Physics","score_opus":0.07489504114108637,"score_gpt":0.3150148846261164,"score_spread":0.24011984348503002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592831100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07909579,0.00014426191,0.9151571,0.00023771482,0.00005524998,0.00006832814,0.00028796704,0.00026429573,0.004689311],"genre_scores_gemma":[0.9520249,0.00019721556,0.040774055,0.000078113,0.000025164774,0.0002223317,0.00023955431,0.00003497737,0.0064036627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997745,0.0000688794,0.000012387024,0.000049308583,0.00007212018,0.000022808224],"domain_scores_gemma":[0.99964976,0.00017639353,0.00007535051,0.000020819261,0.000057956775,0.000019742132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042934908,0.0004647769,0.000598814,0.00032587204,0.00030801224,0.0007536695,0.0007393682,0.0010934783,0.0013323925],"category_scores_gemma":[0.00077902037,0.000374449,0.00063236465,0.00035573778,0.00073056534,0.000531161,0.0005092038,0.00077687757,0.00024853536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010726609,0.000010578582,0.00024535792,0.000009776333,0.0000055188048,0.000029347537,0.000006088561,0.9948324,0.0008349957,0.002948611,0.000076692704,0.000989842],"study_design_scores_gemma":[0.0000017668463,0.000003914238,0.000051580282,7.3081674e-7,0.0000010494856,0.000003282545,7.214509e-7,0.9995659,0.00007551568,0.00023141319,0.000062781975,0.0000014039091],"about_ca_topic_score_codex":0.0061796834,"about_ca_topic_score_gemma":0.0052052625,"teacher_disagreement_score":0.0061796834,"about_ca_system_score_codex":0.0005994227,"about_ca_system_score_gemma":0.0008910749,"threshold_uncertainty_score":0.012287438},"labels":[],"label_agreement":null},{"id":"W2593467507","doi":"10.1002/aic.15702","title":"A comparison of efficient uncertainty quantification techniques for stochastic multiscale systems","year":2017,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polynomial chaos; Uncertainty quantification; Propagation of uncertainty; Multivariate statistics; Mathematical optimization; Computer science; Applied mathematics; Scale (ratio); Algorithm; Biological system; Mathematics; Monte Carlo method; Statistics; Physics; Machine learning","score_opus":0.21847170002142627,"score_gpt":0.4538826030049917,"score_spread":0.23541090298356546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593467507","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013332854,0.00061796233,0.9843002,0.00008286629,0.000021579888,0.000038102226,0.000042381354,0.0001900486,0.001374031],"genre_scores_gemma":[0.4244196,0.0013693444,0.57269776,0.00006914037,0.00006599333,0.00016056647,0.00020066553,0.00017445676,0.0008423929],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797124,0.00077364774,0.00012834185,0.0001836124,0.0008508299,0.000092276015],"domain_scores_gemma":[0.99453896,0.0037106252,0.00040491557,0.00051216903,0.00076901156,0.00006435435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003620875,0.0010982775,0.0010767684,0.001504263,0.0004103921,0.0010931627,0.0009943316,0.0010306691,0.0011753936],"category_scores_gemma":[0.010246319,0.00045004673,0.0010864814,0.00091589557,0.000811032,0.0021976805,0.001661023,0.0012965506,0.0002199018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016516962,0.00006280963,0.0006619291,0.00030848832,0.00012356248,0.00005982949,0.00011939184,0.78314483,0.010523209,0.06251729,0.000486273,0.14182724],"study_design_scores_gemma":[0.0000066858047,0.00003887369,0.00017312584,0.00001529436,0.000009593921,0.000026616406,0.000011842742,0.9899888,0.0034023884,0.00576131,0.00055039325,0.000015039231],"about_ca_topic_score_codex":0.0015748753,"about_ca_topic_score_gemma":0.001031682,"teacher_disagreement_score":0.003620875,"about_ca_system_score_codex":0.00076905877,"about_ca_system_score_gemma":0.001078734,"threshold_uncertainty_score":0.019149244},"labels":[],"label_agreement":null},{"id":"W2597150170","doi":"10.1080/15732479.2017.1299771","title":"A probabilistic framework based on statistical learning theory for structural reliability analysis of transmission line systems","year":2017,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probabilistic logic; Monte Carlo method; Reliability (semiconductor); Structural system; Reliability engineering; Computer science; Component (thermodynamics); Transmission line; Solver; Engineering; Structural engineering; Mathematics; Statistics; Artificial intelligence","score_opus":0.019558289458092203,"score_gpt":0.3090610648234834,"score_spread":0.2895027753653912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597150170","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00054346054,0.00016742805,0.99867624,0.00007354133,0.00001126909,0.000010373961,0.000019960951,0.000030952382,0.0004667437],"genre_scores_gemma":[0.37536386,0.0033027246,0.6137791,0.0003269315,0.0007405555,0.0007785841,0.00044099297,0.00014957455,0.0051176357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980173,0.00085015054,0.00008892969,0.00024070674,0.00069922535,0.0001037637],"domain_scores_gemma":[0.9953803,0.00336068,0.000385327,0.00026320232,0.0005257146,0.00008491654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036510746,0.001301672,0.0013422379,0.0017976844,0.00049952837,0.0015838803,0.0017406283,0.0011665346,0.002000402],"category_scores_gemma":[0.008813229,0.0007111897,0.0017012496,0.0016753745,0.0018138239,0.0018480881,0.0012833555,0.0021452499,0.0005496078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009030341,0.00002764452,0.00029548406,0.000065974746,0.000054275864,0.000049150593,0.000028643652,0.79598355,0.00055805,0.18346614,0.00046985803,0.018992173],"study_design_scores_gemma":[0.0000033274314,0.000018916537,0.00007742596,0.000010560295,0.00000844921,0.000021281176,0.0000034464917,0.94202644,0.00012394102,0.05680195,0.00089648424,0.0000077461145],"about_ca_topic_score_codex":0.0026527669,"about_ca_topic_score_gemma":0.0018669998,"teacher_disagreement_score":0.0036510746,"about_ca_system_score_codex":0.0013097342,"about_ca_system_score_gemma":0.0016162605,"threshold_uncertainty_score":0.019308925},"labels":[],"label_agreement":null},{"id":"W2604943677","doi":"10.1109/edaps.2016.7893162","title":"Efficient time-domain variability analysis of active circuits","year":2016,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Electronic circuit; Laplace transform; Time domain; Stability (learning theory); Domain analysis; Inversion (geology); Algorithm; Reduction (mathematics); Model order reduction; Electronic engineering; Mathematics; Engineering; Software; Machine learning; Electrical engineering","score_opus":0.04101701049752651,"score_gpt":0.2992035217443095,"score_spread":0.25818651124678293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604943677","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046694805,0.000047863796,0.9943721,0.000026882028,0.0000055521937,0.000007736127,0.000019256824,0.000116741474,0.00073429063],"genre_scores_gemma":[0.7164256,0.00042657965,0.2797973,0.000058693233,0.000056338893,0.00012200808,0.00023454579,0.00015744753,0.0027214885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979645,0.000038544626,0.000006372409,0.000022966535,0.0001214793,0.000014288671],"domain_scores_gemma":[0.99958783,0.0002422443,0.00004238046,0.000042097756,0.00007491965,0.000010418555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039744625,0.00042754237,0.0003827359,0.00046789856,0.00021368105,0.00055068824,0.00064708525,0.00032788268,0.0011241403],"category_scores_gemma":[0.0013826309,0.00017023885,0.0004773854,0.00032281945,0.00033005737,0.00072578515,0.00042306667,0.00068196515,0.00025726852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004284347,0.000029451838,0.000506325,0.00009048528,0.000034228487,0.00008321922,0.000059744238,0.8079867,0.036633752,0.060524113,0.0007722905,0.09323688],"study_design_scores_gemma":[0.0000011546277,0.0000055977544,0.000055692268,0.0000017473158,0.0000015640463,0.0000136725985,0.0000023625248,0.99442744,0.0014614917,0.0036557824,0.00037111682,0.0000024387516],"about_ca_topic_score_codex":0.0007831589,"about_ca_topic_score_gemma":0.0009500975,"teacher_disagreement_score":0.0011241403,"about_ca_system_score_codex":0.00035545995,"about_ca_system_score_gemma":0.0004404855,"threshold_uncertainty_score":0.0037606359},"labels":[],"label_agreement":null},{"id":"W2613460262","doi":"10.1049/iet-gtd.2016.1956","title":"Optimal transmission switching in the stochastic linearised SCUC for uncertainty management of the wind power generation and equipment failures","year":2017,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Etobicoke General Hospital","funders":"","keywords":"Upload; Transmission (telecommunications); Computer science; Power (physics); Reliability engineering; Operations research; Mathematical optimization; Engineering; Telecommunications; Mathematics","score_opus":0.07658372981144554,"score_gpt":0.33223709888012626,"score_spread":0.25565336906868075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613460262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06512343,0.0005956974,0.92001426,0.0009406862,0.00012642053,0.00011000819,0.0002838289,0.00036330102,0.012442414],"genre_scores_gemma":[0.9749518,0.00020881367,0.018892251,0.00012707668,0.000040443447,0.00012206728,0.00015444575,0.000111753594,0.005391352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991819,0.0003132421,0.000033545057,0.00015323963,0.00015083382,0.0001671643],"domain_scores_gemma":[0.9972115,0.001915306,0.00028058476,0.000094451534,0.00040223353,0.0000959349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022708306,0.001111078,0.0015889903,0.0005683204,0.00053920975,0.0020873197,0.0010489139,0.0015611432,0.0053728307],"category_scores_gemma":[0.0055464176,0.0009699584,0.0009340914,0.0007069524,0.0017088759,0.0011918725,0.0014772966,0.0023320941,0.000411592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050170103,0.000007410458,0.00009129726,0.000030260122,0.0000125365705,0.000025892088,0.000024332505,0.9940089,0.00013906705,0.0029008994,0.00031779773,0.0023914706],"study_design_scores_gemma":[0.0000068621976,0.000013142883,0.000055554043,0.0000074623545,0.0000060233137,0.00000358552,0.0000060952098,0.9983341,0.00007712732,0.0013988013,0.00008733338,0.0000039222045],"about_ca_topic_score_codex":0.019099114,"about_ca_topic_score_gemma":0.010462918,"teacher_disagreement_score":0.019099114,"about_ca_system_score_codex":0.0018255615,"about_ca_system_score_gemma":0.0019177473,"threshold_uncertainty_score":0.037975907},"labels":[],"label_agreement":null},{"id":"W2614044203","doi":"","title":"Time-dependent sensitivity and uncertainty analyses of an agro-climatic model for the water status management of vineyard","year":2013,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Inuit Tapiriit Kanatami","funders":"","keywords":"Vineyard; Sensitivity (control systems); Work (physics); Environmental science; Decision support system; Computer science; Environmental resource management; Geography; Engineering; Data mining","score_opus":0.06540048775665916,"score_gpt":0.30908982801926943,"score_spread":0.24368934026261027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2614044203","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9755039,0.0002272609,0.022011464,0.00031252936,0.00004080264,0.000026750578,0.0004744415,0.000093173716,0.0013096808],"genre_scores_gemma":[0.9985043,0.000048687667,0.0010494473,0.000014791361,0.0000044856106,0.000006887142,0.00012341622,0.000011550269,0.00023656971],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936765,0.00032967256,0.00003221735,0.000113348404,0.000080488426,0.000076600634],"domain_scores_gemma":[0.9947273,0.004439253,0.0002768802,0.00016814355,0.0002817626,0.00010669503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027848934,0.0006590208,0.00071005156,0.0008771707,0.00045810806,0.0010378555,0.0008172309,0.0013443405,0.00089007575],"category_scores_gemma":[0.0074572577,0.0005246291,0.0013773614,0.0006020703,0.0006979054,0.0008076987,0.00085328426,0.0010952882,0.00004558815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030527557,0.000013885804,0.0008744135,0.000009290573,0.000028869768,0.000023557113,0.000007565084,0.9978027,0.00054109684,0.00025160075,0.000032879343,0.0003835762],"study_design_scores_gemma":[0.000004561957,0.000022223612,0.0010340746,0.0000022685535,0.000016648954,0.0000059629274,0.000009628657,0.99773884,0.0007711544,0.0003430065,0.000042415657,0.000009145392],"about_ca_topic_score_codex":0.040103424,"about_ca_topic_score_gemma":0.013163684,"teacher_disagreement_score":0.040103424,"about_ca_system_score_codex":0.0019962785,"about_ca_system_score_gemma":0.0006932109,"threshold_uncertainty_score":0.07973999},"labels":[],"label_agreement":null},{"id":"W2620647458","doi":"10.2514/6.2017-4326","title":"Multi-Level MDO of a Long-Range Transport Aircraft Using a Distributed Analysis Framework","year":2017,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Partenariat Canadien Contre Le Cancer","keywords":"Sizing; Aerodynamics; Multidisciplinary design optimization; Range (aeronautics); Computer science; High fidelity; Aerospace engineering; Fidelity; Conceptual design; Work (physics); Multidisciplinary approach; Simulation; Engineering; Mechanical engineering","score_opus":0.2575152444585503,"score_gpt":0.40076641023194254,"score_spread":0.14325116577339225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620647458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08312793,0.00015192878,0.9086665,0.0003245601,0.000041092717,0.00010699775,0.00012379883,0.0002771079,0.007180027],"genre_scores_gemma":[0.85138106,0.00009405181,0.14480089,0.000087789325,0.000030588362,0.00031896518,0.00014009522,0.00008588771,0.0030607448],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980396,0.00008092905,0.00000679672,0.000030786123,0.000048998587,0.000028561113],"domain_scores_gemma":[0.9990995,0.0006011269,0.000078753634,0.000070416456,0.00008997602,0.000060280494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010288684,0.0005532802,0.00088616787,0.00042214635,0.0005914002,0.0008391033,0.0008534591,0.001062403,0.0022224796],"category_scores_gemma":[0.0017868248,0.00042594265,0.0007439616,0.0003920332,0.0008021359,0.0006574541,0.0012109131,0.0010151579,0.00017827282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011318568,0.000005565302,0.00009420638,0.0000055685086,0.0000048065112,0.000009573477,0.000004344873,0.99780303,0.00019014954,0.00084463326,0.00003195871,0.0009948406],"study_design_scores_gemma":[0.0000058210167,0.0000066403136,0.000028762735,8.958843e-7,0.0000014596394,0.00000134497,0.0000021779595,0.99937516,0.00006936312,0.0004205572,0.00008689463,0.0000010614262],"about_ca_topic_score_codex":0.008290814,"about_ca_topic_score_gemma":0.0068639205,"teacher_disagreement_score":0.008290814,"about_ca_system_score_codex":0.00091804273,"about_ca_system_score_gemma":0.0015297069,"threshold_uncertainty_score":0.016485155},"labels":[],"label_agreement":null},{"id":"W2622847897","doi":"10.1109/sapiw.2017.7944003","title":"Time-domain variability analysis of large circuits with stochastic linear terminations","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Parameterized complexity; Model order reduction; Computer science; Applied mathematics; Moment (physics); Stochastic process; Laplace transform; Electronic circuit; Inversion (geology); Algorithm; Mathematics; Mathematical optimization; Mathematical analysis; Statistics; Engineering","score_opus":0.06029589125991203,"score_gpt":0.3494834220182012,"score_spread":0.2891875307582892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622847897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014000869,0.000050633997,0.98530513,0.000031850817,0.0000058654487,0.000007589767,0.000011197143,0.00008092154,0.0005060252],"genre_scores_gemma":[0.9130709,0.00015721371,0.08521188,0.000029369328,0.000034565088,0.00006126665,0.000062341234,0.00006102616,0.0013113681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995864,0.000104922,0.000010982353,0.00006670502,0.00020652243,0.00002454186],"domain_scores_gemma":[0.99922264,0.0004538796,0.00014034015,0.00007485386,0.00008458507,0.000023787225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005706743,0.0005406208,0.00042932836,0.00035332676,0.00018046454,0.00058945117,0.000701965,0.0005325635,0.0004825157],"category_scores_gemma":[0.001802377,0.00020401942,0.00046435223,0.0002906947,0.0005513077,0.00060262915,0.0007478672,0.00073277426,0.00009199135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057452682,0.000028246195,0.0008152418,0.000057575104,0.00004120547,0.0001599034,0.00008155221,0.8822533,0.05785101,0.028799186,0.00031774928,0.029537633],"study_design_scores_gemma":[0.0000013603525,0.000011733253,0.00012241428,0.0000012935432,0.0000023050577,0.000019217876,0.000002162413,0.9956845,0.0017957998,0.0021948423,0.00016154724,0.000002818248],"about_ca_topic_score_codex":0.0004578156,"about_ca_topic_score_gemma":0.0004726566,"teacher_disagreement_score":0.000701965,"about_ca_system_score_codex":0.00034109014,"about_ca_system_score_gemma":0.00033134935,"threshold_uncertainty_score":0.0030180216},"labels":[],"label_agreement":null},{"id":"W2623564989","doi":"10.1002/cjce.22912","title":"Robust optimization of a multiscale heterogeneous catalytic reactor system with spatially‐varying uncertainty descriptions using polynomial chaos expansions","year":2017,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polynomial chaos; Parametric statistics; Uncertainty quantification; Probabilistic logic; Monte Carlo method; Propagation of uncertainty; Uncertainty analysis; Mathematical optimization; Robust optimization; Computer science; Sensitivity analysis; Biological system; Statistical physics; Mathematics; Algorithm; Simulation; Physics; Statistics","score_opus":0.08024621875203294,"score_gpt":0.25713309260777956,"score_spread":0.1768868738557466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2623564989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2599547,0.00018256572,0.73452616,0.00034561992,0.000020436382,0.00004853809,0.00009044273,0.00017817732,0.004653444],"genre_scores_gemma":[0.9875113,0.000043396503,0.011710355,0.000017201135,0.0000045861266,0.000033761058,0.000026151843,0.000013372987,0.0006399307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967563,0.00011016341,0.000012670382,0.00007103263,0.00008611136,0.00004443116],"domain_scores_gemma":[0.9991241,0.0005760384,0.00012893767,0.0000425049,0.00010076018,0.000027601813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009588005,0.0005925558,0.00087113905,0.00033869097,0.00034309045,0.0008777145,0.00062384503,0.000816986,0.00057427195],"category_scores_gemma":[0.0019080371,0.00040517247,0.00079866295,0.00030537162,0.0009621919,0.0004940562,0.0009217159,0.0006401098,0.00005980215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008316021,0.0000036636288,0.00007208467,0.0000050218146,0.000005354398,0.000012939362,0.0000040851405,0.9973163,0.0007483449,0.0012528051,0.00001552959,0.00055550813],"study_design_scores_gemma":[0.0000011875308,0.000004630056,0.000022688318,3.663974e-7,0.0000010381046,8.754231e-7,8.8181554e-7,0.9996228,0.000139682,0.00019204644,0.000012772711,9.662422e-7],"about_ca_topic_score_codex":0.009997067,"about_ca_topic_score_gemma":0.0039020756,"teacher_disagreement_score":0.009997067,"about_ca_system_score_codex":0.0011758063,"about_ca_system_score_gemma":0.0011213495,"threshold_uncertainty_score":0.019877732},"labels":[],"label_agreement":null},{"id":"W2638304165","doi":"10.1016/j.proeng.2017.05.247","title":"Calibrated Partial Factors for Support of Wedges Exposed in Tunnels","year":2017,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Limit state design; Eurocode; Wedge (geometry); Probabilistic logic; Context (archaeology); Engineering; Limit (mathematics); Code (set theory); Reliability (semiconductor); Computer science; Structural engineering; Civil engineering; Geology; Mathematics","score_opus":0.15244791857817358,"score_gpt":0.3437335438200714,"score_spread":0.1912856252418978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2638304165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08829941,0.00011144492,0.9075163,0.00005825988,0.00002049347,0.000062283274,0.000104220235,0.00030601487,0.0035215765],"genre_scores_gemma":[0.8881345,0.00009929638,0.11059424,0.000014108079,0.000006138983,0.00008453214,0.00010375235,0.000082332546,0.00088103785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989274,0.00031229266,0.00007638543,0.000140689,0.00046247515,0.00008079513],"domain_scores_gemma":[0.99782157,0.0010977716,0.000274545,0.00031682826,0.00041719116,0.00007219668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019778672,0.0006422997,0.0005367525,0.0007891892,0.00028268472,0.0011107324,0.0007194726,0.00086482166,0.0022721877],"category_scores_gemma":[0.007055128,0.00039743332,0.00065140636,0.00029160792,0.0009013901,0.0007639581,0.0011219372,0.00056054513,0.0003191342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013340879,0.000024636593,0.0023888994,0.00010608772,0.00001113658,0.00014079305,0.00012272273,0.93464917,0.012618604,0.020407299,0.00027897104,0.029118342],"study_design_scores_gemma":[0.000021517892,0.00029628768,0.0014917359,0.00005026777,0.000016674776,0.00020138753,0.0000682999,0.96378785,0.012261404,0.018924668,0.0028443602,0.00003550239],"about_ca_topic_score_codex":0.00086545513,"about_ca_topic_score_gemma":0.00076391303,"teacher_disagreement_score":0.0022721877,"about_ca_system_score_codex":0.0006492238,"about_ca_system_score_gemma":0.000614621,"threshold_uncertainty_score":0.010460079},"labels":[],"label_agreement":null},{"id":"W2652595529","doi":"10.1299/jsmedmc.2007._623-1_","title":"623 Natural Frequency Analysis for Models with Uncertain Parameters using Fuzzy Numbers","year":2007,"lang":"en","type":"article","venue":"The Proceedings of the Dynamics & Design Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cybernet Systems Corporation (Canada)","funders":"","keywords":"Fuzzy logic; Interval (graph theory); Fuzzy number; Mathematics; Frame (networking); Interval arithmetic; Computation; Mathematical optimization; Fuzzy set; Algorithm; Computer science; Artificial intelligence","score_opus":0.13474384504065373,"score_gpt":0.32030558482179416,"score_spread":0.18556173978114043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2652595529","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053147795,0.00008128561,0.9937198,0.000023506676,0.000005211433,0.000008424725,0.000010908812,0.000042550277,0.0007935373],"genre_scores_gemma":[0.6861246,0.00047036362,0.3109295,0.000033238444,0.000030433293,0.00017239596,0.000110740526,0.00006831523,0.0020604427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943155,0.00015854547,0.00002514808,0.00008491112,0.00026327945,0.000036537374],"domain_scores_gemma":[0.9992756,0.0004937923,0.00008706763,0.000051305567,0.00008052677,0.000011684869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010331868,0.0007655224,0.0009531163,0.001007405,0.00038136117,0.00083841593,0.0007673473,0.000644749,0.0017554858],"category_scores_gemma":[0.0028251626,0.00045437718,0.0010521229,0.0003758266,0.0007409585,0.0011739134,0.00072736177,0.0008362234,0.00023140074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022214745,0.000008725576,0.00023597947,0.000059431943,0.00001820093,0.000057784764,0.000055183482,0.9614448,0.0021207428,0.017607694,0.00011790239,0.018251436],"study_design_scores_gemma":[0.0000021100568,0.000011630252,0.00006375396,0.000008288376,0.000004341894,0.000017381177,0.000010457365,0.9895064,0.00042734397,0.009469908,0.00047192632,0.0000064223887],"about_ca_topic_score_codex":0.003161104,"about_ca_topic_score_gemma":0.0019012577,"teacher_disagreement_score":0.003161104,"about_ca_system_score_codex":0.0006130458,"about_ca_system_score_gemma":0.0006068939,"threshold_uncertainty_score":0.006285429},"labels":[],"label_agreement":null},{"id":"W2668123086","doi":"10.2991/jsta.2017.16.1.8","title":"Optimal Structure (ligkl/ig) Designs for Comparing Test Treatments with a Control","year":2017,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Test (biology); Statistics; Econometrics; Reliability engineering; Engineering","score_opus":0.07329674452216214,"score_gpt":0.36919011024548315,"score_spread":0.29589336572332103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2668123086","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017624483,0.00021099907,0.978639,0.00013360904,0.00006546677,0.0013044549,0.00023759081,0.00040459255,0.0013798091],"genre_scores_gemma":[0.15034525,0.00014327277,0.84147376,0.00026177437,0.00006578324,0.0066196714,0.00030390304,0.00009002657,0.00069651555],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8826257,0.09246326,0.002994326,0.011057089,0.008954995,0.0019045586],"domain_scores_gemma":[0.9413267,0.038004577,0.0051198467,0.011037317,0.0037180777,0.00079357077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.066258416,0.0022215396,0.0041832253,0.0039285123,0.0012627934,0.0019096833,0.0029753041,0.0040869303,0.008440118],"category_scores_gemma":[0.10478659,0.00135065,0.0035462137,0.0024756652,0.006031189,0.003083956,0.003125514,0.0033020661,0.0013338807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.012278197,0.0018166145,0.008559767,0.002239495,0.0016927011,0.0002913374,0.0013727769,0.06126083,0.017361196,0.5319445,0.0031083354,0.35807428],"study_design_scores_gemma":[0.0059109507,0.026969394,0.012662082,0.00053729175,0.001424053,0.00039668792,0.00038748054,0.34587184,0.02426155,0.569037,0.012026192,0.0005155355],"about_ca_topic_score_codex":0.0005568999,"about_ca_topic_score_gemma":0.00062559464,"teacher_disagreement_score":0.066258416,"about_ca_system_score_codex":0.0027823178,"about_ca_system_score_gemma":0.0032926118,"threshold_uncertainty_score":0.3504122},"labels":[],"label_agreement":null},{"id":"W26979418","doi":"10.1007/s10461-016-1356-3","title":"Increased Thermal Robustness of Turbine Structure","year":2014,"lang":"en","type":"dissertation","venue":"AIDS and Behavior","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; National Institute of Mental Health; National Natural Science Foundation of China; International Development Research Centre","keywords":"Robustness (evolution); Turbine; Thermal; Engineering; Computer science; Environmental science; Mechanical engineering; Physics; Meteorology; Chemistry","score_opus":0.029941319612612535,"score_gpt":0.3070146143812256,"score_spread":0.2770732947686131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W26979418","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83950496,0.00045985606,0.14927202,0.00035626133,0.00009133184,0.000059823473,0.00032679658,0.000309157,0.009619822],"genre_scores_gemma":[0.997148,0.000055634337,0.002035306,0.000021777476,0.00001079657,0.0000138268,0.00008148595,0.000018921754,0.0006141788],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955803,0.00008545718,0.0000317745,0.0001372862,0.000111844216,0.00007555168],"domain_scores_gemma":[0.99849164,0.0005834347,0.00036128543,0.00026094308,0.00023752704,0.00006515575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007991254,0.00030566467,0.00044707416,0.00068060955,0.00048258124,0.0011243133,0.00045805235,0.00054510025,0.0036326614],"category_scores_gemma":[0.0037164472,0.00043065334,0.000587941,0.0002693122,0.0006286971,0.0009639761,0.00066655426,0.0004784831,0.0004686782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039160848,0.0001624352,0.110352166,0.000281699,0.00026434884,0.00039416703,0.00028764634,0.71480674,0.08944751,0.010970261,0.001098893,0.07154255],"study_design_scores_gemma":[0.000021598562,0.00035423832,0.13274443,0.000038812763,0.00009191367,0.0002411911,0.00015288523,0.84373707,0.011910598,0.008418991,0.0022245103,0.00006373162],"about_ca_topic_score_codex":0.001526611,"about_ca_topic_score_gemma":0.001299563,"teacher_disagreement_score":0.0036326614,"about_ca_system_score_codex":0.0006100784,"about_ca_system_score_gemma":0.00035477322,"threshold_uncertainty_score":0.012152493},"labels":[],"label_agreement":null},{"id":"W2734798151","doi":"10.23919/acc.2017.7963636","title":"Performance sensitivity analysis of linear alarm filters","year":2017,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sensitivity (control systems); Filter (signal processing); Linear filter; ALARM; Computer science; False alarm; Gaussian; Algorithm; Control theory (sociology); Mathematics; Artificial intelligence; Engineering; Electronic engineering; Computer vision; Physics","score_opus":0.1269905160408275,"score_gpt":0.3644234547544741,"score_spread":0.23743293871364657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734798151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05075518,0.0011733952,0.9412155,0.00027276322,0.000040053605,0.00006419517,0.0000861002,0.00026591262,0.006126948],"genre_scores_gemma":[0.97784334,0.00071162573,0.01936585,0.00013096297,0.000048850092,0.00006761717,0.00008507713,0.00007884724,0.0016678848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994968,0.0021327243,0.00017036207,0.00084549637,0.0013062682,0.0005771685],"domain_scores_gemma":[0.97274345,0.02201127,0.0015852362,0.0010632466,0.0024445376,0.00015218578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006675448,0.0014016171,0.0010418434,0.0016621572,0.0005687079,0.002019182,0.000999355,0.0013715423,0.0022659516],"category_scores_gemma":[0.032421444,0.0004807184,0.0012449228,0.00086999766,0.0015081994,0.0020706907,0.0012894338,0.0012724216,0.0003257652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010042129,0.000028588176,0.0010866352,0.00013254487,0.000104569706,0.00011951008,0.000073376024,0.96806884,0.0038754153,0.012892887,0.00031756583,0.013199539],"study_design_scores_gemma":[0.0000050665435,0.0001202368,0.000747585,0.000026819733,0.00004984839,0.00011809216,0.00004009979,0.9849006,0.0046011833,0.008885949,0.0004710614,0.000033556495],"about_ca_topic_score_codex":0.003332533,"about_ca_topic_score_gemma":0.00089609664,"teacher_disagreement_score":0.006675448,"about_ca_system_score_codex":0.0025909522,"about_ca_system_score_gemma":0.0010014342,"threshold_uncertainty_score":0.035303593},"labels":[],"label_agreement":null},{"id":"W2737319167","doi":"10.1016/j.apm.2017.07.036","title":"An effective approach for probabilistic lifetime modelling based on the principle of maximum entropy with fractional moments","year":2017,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Probabilistic logic; Copula (linguistics); Principle of maximum entropy; Mathematics; Applied mathematics; Entropy (arrow of time); Maximization; Statistical physics; Mathematical optimization; Stochastic process; Computer science; Statistics; Econometrics; Physics","score_opus":0.08832919881277594,"score_gpt":0.32231007599596734,"score_spread":0.23398087718319138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737319167","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018972519,0.00040794388,0.9954006,0.00014536915,0.00005055371,0.000011616668,0.000020781874,0.00003559307,0.0020302946],"genre_scores_gemma":[0.61694694,0.0028837149,0.36754704,0.00039936675,0.00066219823,0.00033819678,0.0001532607,0.00025571004,0.010813642],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993006,0.00025524455,0.000037268623,0.00008783199,0.00026967682,0.00004937789],"domain_scores_gemma":[0.9992501,0.00046524798,0.000086613894,0.00007487948,0.00009049589,0.00003265183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013203984,0.0009070197,0.0013234036,0.0014538433,0.0007219394,0.0014348214,0.0016730982,0.001603051,0.0014545711],"category_scores_gemma":[0.0026661644,0.0004998544,0.0016535423,0.00086816505,0.0016706071,0.002944058,0.001774056,0.0016886243,0.0002910355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002039994,0.000024905494,0.00017990006,0.00014610478,0.000053701347,0.00012919081,0.000105588886,0.3472392,0.004554961,0.6290893,0.0007045898,0.017752135],"study_design_scores_gemma":[0.0000025181516,0.000012887803,0.000057640485,0.00001480542,0.000013432568,0.00005369959,0.00000741974,0.8202702,0.0005787268,0.1775862,0.00138486,0.000017685361],"about_ca_topic_score_codex":0.0007436533,"about_ca_topic_score_gemma":0.0006208618,"teacher_disagreement_score":0.0016730982,"about_ca_system_score_codex":0.00094572175,"about_ca_system_score_gemma":0.00076586823,"threshold_uncertainty_score":0.006982982},"labels":[],"label_agreement":null},{"id":"W2743712474","doi":"10.5170/cern-2011-006.148","title":"Setting Limits, Computing Intervals, and Detection","year":2011,"lang":"en","type":"article","venue":"CERN Document Server (European Organization for Nuclear Research)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Banff International Research Station for Mathematical Innovation and Discovery; CERN; High Energy Physics; National Science Foundation","keywords":"Sensitivity (control systems); Limit (mathematics); Computer science; Computation; Focus (optics); Detection theory; Data mining; Statistics; Algorithm; Mathematics; Detector; Engineering","score_opus":0.10402274383424846,"score_gpt":0.3110969152090946,"score_spread":0.20707417137484613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743712474","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005408074,0.00058996235,0.9919362,0.0002670293,0.00003939159,0.000041331437,0.000055439803,0.00032603648,0.001336546],"genre_scores_gemma":[0.19864367,0.0011357546,0.79741675,0.0003043373,0.00024259988,0.00040042915,0.0003758281,0.00049580034,0.0009848344],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9657218,0.016336063,0.0028342574,0.0046556857,0.009047317,0.0014049191],"domain_scores_gemma":[0.7836806,0.17582588,0.011926692,0.016598286,0.01019122,0.0017772488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040856462,0.0024038237,0.0033437063,0.006508616,0.0020326695,0.008785751,0.0052153342,0.003225569,0.0029497256],"category_scores_gemma":[0.23019642,0.0019821273,0.002183671,0.0042865975,0.0074817883,0.010931913,0.010407229,0.005291281,0.00086420175],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000235092,0.00008604817,0.005498639,0.0006707119,0.00018992722,0.00029132995,0.0010228297,0.24819411,0.002196478,0.583429,0.0019444004,0.15624152],"study_design_scores_gemma":[0.000035892095,0.00011088499,0.0007916764,0.00023389926,0.000046182413,0.00023993362,0.00023203845,0.33134076,0.005097386,0.6572358,0.0045100558,0.00012558818],"about_ca_topic_score_codex":0.0028609298,"about_ca_topic_score_gemma":0.0010580855,"teacher_disagreement_score":0.040856462,"about_ca_system_score_codex":0.0029994196,"about_ca_system_score_gemma":0.0032661234,"threshold_uncertainty_score":0.2160722},"labels":[],"label_agreement":null},{"id":"W2744527450","doi":"10.1007/s00158-017-1771-8","title":"Efficient strategies for reliability-based design optimization of variable stiffness composite structures","year":2017,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Fundação para a Ciência e a Tecnologia; MIT Portugal; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Reliability (semiconductor); Piecewise; Stiffness; Mathematical optimization; Optimization problem; Topology optimization; Computer science; Composite number; Reliability engineering; Structural engineering; Finite element method; Engineering; Mathematics; Algorithm","score_opus":0.0560290299895305,"score_gpt":0.33383050577209855,"score_spread":0.27780147578256803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2744527450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009422055,0.00021810012,0.9875695,0.00008135952,0.000016358084,0.000038291324,0.00002047264,0.00007259829,0.0025612265],"genre_scores_gemma":[0.59020364,0.00046609822,0.40389282,0.000103261715,0.000054604476,0.0004928681,0.00011038774,0.00020418949,0.004472143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994055,0.0002565908,0.000019292638,0.000056208613,0.0002055173,0.000056897225],"domain_scores_gemma":[0.99835336,0.0011893222,0.0001587045,0.000062485946,0.00019409475,0.000041996558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020858264,0.0014416148,0.0012901145,0.0015255938,0.0004189524,0.00093353883,0.0013734958,0.0013226564,0.0023266952],"category_scores_gemma":[0.004839876,0.0010538071,0.0008637626,0.00075580843,0.0009964074,0.00088043127,0.0013861775,0.0009779624,0.00040058378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019819629,0.00001814332,0.00006496698,0.0000357428,0.000014824298,0.000013407871,0.000021526032,0.9819542,0.00091795623,0.0072723427,0.00018592409,0.0094812205],"study_design_scores_gemma":[0.000004724282,0.000016506121,0.000021699578,0.0000041358876,0.000004131732,0.000003535986,0.000003739268,0.99655426,0.000206221,0.0030420674,0.00013675791,0.0000021068297],"about_ca_topic_score_codex":0.0017894113,"about_ca_topic_score_gemma":0.0023691177,"teacher_disagreement_score":0.0023266952,"about_ca_system_score_codex":0.0008909663,"about_ca_system_score_gemma":0.0011421535,"threshold_uncertainty_score":0.011030972},"labels":[],"label_agreement":null},{"id":"W2746967387","doi":"10.1007/978-3-319-66176-6_10","title":"Intertwined Global Optimization Based Reachability Analysis","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Reachability; Computer science; Bounding overwatch; Hybrid system; Parametric statistics; Mathematical optimization; Algorithm; Theoretical computer science; Mathematics; Artificial intelligence","score_opus":0.05246711387970485,"score_gpt":0.3199695496392576,"score_spread":0.26750243575955274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2746967387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030457643,0.00032502267,0.98635405,0.00009230491,0.000040114526,0.000021723929,0.000042577296,0.00017526101,0.009903065],"genre_scores_gemma":[0.39277524,0.0016980608,0.5791371,0.00026705774,0.00013123568,0.00035060753,0.0006090946,0.0008181925,0.024213433],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986565,0.00038517738,0.00006284742,0.00030062135,0.0004905661,0.000104261984],"domain_scores_gemma":[0.9985744,0.00089616334,0.00008016212,0.00026856904,0.00014524763,0.000035556328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013718565,0.0015912305,0.0012503935,0.0016445293,0.0006578151,0.0017642311,0.002135651,0.0013673758,0.005991432],"category_scores_gemma":[0.0031902518,0.00079040433,0.00207135,0.0013759873,0.002114062,0.0030964187,0.0042884257,0.0033668478,0.0012881665],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048949587,0.000047284026,0.0002147213,0.00022742679,0.000079385914,0.00006974291,0.00012451044,0.4261194,0.0037571976,0.4920862,0.0014216047,0.07580365],"study_design_scores_gemma":[0.00000410183,0.000025780037,0.0000748116,0.000036582267,0.000023634382,0.000021663056,0.000016062975,0.630037,0.0014417798,0.36538583,0.002920364,0.000012454088],"about_ca_topic_score_codex":0.001160722,"about_ca_topic_score_gemma":0.0015922079,"teacher_disagreement_score":0.005991432,"about_ca_system_score_codex":0.0012000285,"about_ca_system_score_gemma":0.0007618495,"threshold_uncertainty_score":0.020043314},"labels":[],"label_agreement":null},{"id":"W2747730049","doi":"10.1115/1.4037630","title":"The Transfer Matrix Metamodel for Dynamic Systems With Arbitrary Time-Variant Inputs","year":2017,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Singular value decomposition; Matrix (chemical analysis); Metamodeling; Computer science; Robustness (evolution); Algorithm; Computation; Transfer matrix; Control theory (sociology); Modal matrix; State-transition matrix; Artificial intelligence; Symmetric matrix; Eigenvalues and eigenvectors; Diagonalizable matrix","score_opus":0.08294563031666996,"score_gpt":0.34117559253787816,"score_spread":0.2582299622212082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2747730049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021362707,0.00004691636,0.996318,0.00005994665,0.000016040987,0.000023251345,0.00004619507,0.00018170693,0.0011716922],"genre_scores_gemma":[0.53084886,0.00068399974,0.45524263,0.000260897,0.00008444687,0.000679686,0.00058823434,0.00020583085,0.011405423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972445,0.000094983305,0.000012297081,0.000057265137,0.00009280107,0.000018186167],"domain_scores_gemma":[0.99967754,0.00016212117,0.00005112459,0.000055649976,0.000043719967,0.000009820312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007285717,0.0007664231,0.00062504073,0.00039607327,0.0002628669,0.00066504,0.00075644726,0.0011018902,0.002335036],"category_scores_gemma":[0.0016299558,0.00028266074,0.0007362454,0.00039591792,0.00053522695,0.0008628766,0.0006441855,0.0011364352,0.00071834045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030845287,0.000028598133,0.00026625255,0.000108446366,0.000030052814,0.000055771845,0.00004596408,0.9326214,0.008831998,0.031951644,0.0006102552,0.02541877],"study_design_scores_gemma":[0.000004035766,0.000029208823,0.00006795608,0.000011113147,0.00000497041,0.000021818289,0.0000072846005,0.9887601,0.0012168568,0.007554728,0.0023162882,0.0000055969376],"about_ca_topic_score_codex":0.001801569,"about_ca_topic_score_gemma":0.0016332857,"teacher_disagreement_score":0.002335036,"about_ca_system_score_codex":0.00039894832,"about_ca_system_score_gemma":0.0009429879,"threshold_uncertainty_score":0.0078114867},"labels":[],"label_agreement":null},{"id":"W2747743000","doi":"10.1016/j.ast.2017.08.011","title":"Zone-based reliability analysis on fatigue life of GH720Li turbine disk concerning uncertainty quantification","year":2017,"lang":"en","type":"article","venue":"Aerospace Science and Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Reliability (semiconductor); Turbine; Extrapolation; Probabilistic logic; Uncertainty quantification; Reliability engineering; Computer science; Empirical probability; Finite element method; Discretization; Bayesian inference; Bayesian probability; Engineering; Mathematics; Structural engineering; Statistics; Mechanical engineering; Artificial intelligence; Posterior probability; Machine learning; Power (physics); Physics","score_opus":0.0931616243856528,"score_gpt":0.3600443351542431,"score_spread":0.2668827107685903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2747743000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39548522,0.0018377111,0.5972099,0.00017654539,0.000026282429,0.000029455481,0.0001447062,0.00019210188,0.0048981495],"genre_scores_gemma":[0.9945069,0.00017105349,0.004748546,0.0000060603656,0.000007771503,0.000011042395,0.000042180483,0.000011216351,0.0004951863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997811,0.000080215075,0.000009150925,0.00003243072,0.00006894828,0.000028131055],"domain_scores_gemma":[0.9992925,0.0004276018,0.00006270754,0.000031683976,0.0001712774,0.000014257136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000848455,0.00047082777,0.0004881523,0.00076860667,0.0002580615,0.0005270728,0.0006277864,0.00047205103,0.0010663423],"category_scores_gemma":[0.0016004752,0.00020469166,0.00055826444,0.00037052276,0.0004001769,0.00080458715,0.00035832013,0.00032358145,0.00007817713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058079564,0.000007443903,0.0007867411,0.000077661694,0.000021686941,0.00005863169,0.00005829621,0.9820676,0.003728489,0.0067716106,0.00015239976,0.006211423],"study_design_scores_gemma":[5.929655e-7,0.000010095302,0.00040062887,0.0000029537252,0.0000052426253,0.000009322364,0.000008051808,0.9983815,0.00029614422,0.0008402048,0.000042869957,0.0000024140547],"about_ca_topic_score_codex":0.0030393477,"about_ca_topic_score_gemma":0.0017863741,"teacher_disagreement_score":0.0030393477,"about_ca_system_score_codex":0.00049400533,"about_ca_system_score_gemma":0.0003428011,"threshold_uncertainty_score":0.006043315},"labels":[],"label_agreement":null},{"id":"W2757101479","doi":"10.1016/j.ress.2017.09.008","title":"A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis","year":2017,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":312,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Surrogate model; Reliability (semiconductor); Adaptive sampling; Kriging; Importance sampling; Computer science; Monte Carlo method; Sampling (signal processing); Sample (material); Algorithm; Mathematical optimization; Limit (mathematics); Sample size determination; Data mining; Machine learning; Mathematics; Statistics","score_opus":0.09563866078808429,"score_gpt":0.3561772878898688,"score_spread":0.2605386271017845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757101479","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013474725,0.000030517014,0.9982337,0.000014736629,0.000017358296,0.000016189497,0.0000152121065,0.00006442628,0.00026033164],"genre_scores_gemma":[0.11194933,0.00014956895,0.88514495,0.00008066224,0.00007952522,0.00034564495,0.00026247342,0.00014018714,0.0018477582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998587,0.0007559199,0.00006106847,0.00012635172,0.00041268088,0.000056937602],"domain_scores_gemma":[0.99657816,0.0022581464,0.00019869811,0.0002719458,0.0005816007,0.000111312525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030121643,0.00093150587,0.0011551045,0.000910881,0.00042740547,0.0005896959,0.0016612903,0.0008904431,0.0029467747],"category_scores_gemma":[0.007402537,0.00074448006,0.0011314842,0.0009559668,0.0005840406,0.0012001513,0.0012620679,0.001450097,0.00049858313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001951012,0.00014487118,0.00074494013,0.00013594887,0.00011527547,0.00008888213,0.00005903782,0.8422033,0.006730773,0.037844542,0.002030944,0.10970629],"study_design_scores_gemma":[0.0000071124136,0.000014318884,0.000025024048,0.0000019399768,0.0000041150656,0.000008160249,9.632638e-7,0.99737835,0.00022111263,0.002038321,0.0002977283,0.000002840174],"about_ca_topic_score_codex":0.0026186397,"about_ca_topic_score_gemma":0.0036126047,"teacher_disagreement_score":0.0030121643,"about_ca_system_score_codex":0.00048465267,"about_ca_system_score_gemma":0.0010503503,"threshold_uncertainty_score":0.015930057},"labels":[],"label_agreement":null},{"id":"W2757568594","doi":"10.5539/ijsp.v6n6p60","title":"Interval Estimation of Stress-Strength Reliability for a General Exponential Form Distribution with Different Unknown Parameters","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Weibull distribution; Mathematics; Estimator; Interval estimation; Exponential distribution; Reliability (semiconductor); Interval (graph theory); Statistics; Exponential function; Applied mathematics; Stress (linguistics); Confidence interval; Mathematical analysis; Power (physics); Combinatorics","score_opus":0.04848762896142129,"score_gpt":0.3362981476893493,"score_spread":0.287810518727928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757568594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025487283,0.0004823694,0.9734946,0.000028612605,0.000009047088,0.000016405096,0.000038707276,0.000091522175,0.00035144217],"genre_scores_gemma":[0.7706753,0.0016396872,0.22628663,0.000034447006,0.00008102065,0.000115991206,0.00038677992,0.00006695589,0.00071317446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99693596,0.0015003927,0.00017616464,0.0004782301,0.0007383244,0.00017093217],"domain_scores_gemma":[0.9712006,0.024181461,0.0018012244,0.0012182372,0.0014401668,0.00015833264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007978151,0.00088498864,0.00096667325,0.0017051565,0.0001978221,0.00081293966,0.0012024181,0.0010527896,0.0008311254],"category_scores_gemma":[0.029889204,0.00029383862,0.00095494685,0.0014280513,0.0007432937,0.0018182712,0.0010180663,0.0011018378,0.00019915824],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025832164,0.000054353117,0.0041026915,0.00025062426,0.00012872287,0.00020285453,0.00016415054,0.8848691,0.0049683256,0.024454802,0.00031712136,0.08022899],"study_design_scores_gemma":[0.0000062519052,0.00007887243,0.0013495762,0.000023795332,0.000029917333,0.00013875942,0.000024350982,0.98580974,0.0016864032,0.010524556,0.0002994718,0.000028243863],"about_ca_topic_score_codex":0.00070481194,"about_ca_topic_score_gemma":0.0004084876,"teacher_disagreement_score":0.007978151,"about_ca_system_score_codex":0.00049302424,"about_ca_system_score_gemma":0.0004334391,"threshold_uncertainty_score":0.042193055},"labels":[],"label_agreement":null},{"id":"W2758232364","doi":"10.5539/mas.v11n10p123","title":"Sensitivity-Based Reliability Analysis of MEMS Acceleration Switch","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Defense Acquisition Program Administration; Agency for Defense Development","keywords":"Reliability (semiconductor); Sensitivity (control systems); Acceleration; Microelectromechanical systems; Finite element method; Computer science; Process (computing); Beam (structure); Reliability engineering; Materials science; Electronic engineering; Structural engineering; Engineering; Physics; Nanotechnology","score_opus":0.0987687901964254,"score_gpt":0.35337432506023436,"score_spread":0.254605534863809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2758232364","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6626402,0.0013221463,0.33080038,0.00031340285,0.00004975048,0.00007720797,0.0002630864,0.000371201,0.0041626357],"genre_scores_gemma":[0.99619603,0.0001246729,0.0033355148,0.000011877716,0.000007997932,0.000019307643,0.00004778615,0.000009121407,0.00024778044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922466,0.00029643564,0.000028383593,0.00011234625,0.0002528999,0.00008529525],"domain_scores_gemma":[0.997138,0.002110822,0.00023508811,0.00013613072,0.00034376036,0.000036162462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001648647,0.00067925965,0.00062838354,0.0013351509,0.0002710528,0.0003997085,0.00045273366,0.000612011,0.00067774236],"category_scores_gemma":[0.004534237,0.00037399182,0.00089973956,0.00039640057,0.00048951985,0.00050839025,0.0005232876,0.00044476517,0.00006441131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003293789,0.00001062831,0.0011029361,0.00003719578,0.000031350253,0.000058027577,0.00002342756,0.9899692,0.0044416846,0.001127228,0.00008738079,0.0030780486],"study_design_scores_gemma":[0.0000011076893,0.000020320967,0.0005996339,0.0000028105653,0.00000849501,0.000015078092,0.0000044948265,0.99774677,0.0011635385,0.00038809577,0.000045069424,0.000004607637],"about_ca_topic_score_codex":0.004281958,"about_ca_topic_score_gemma":0.001321977,"teacher_disagreement_score":0.004281958,"about_ca_system_score_codex":0.00086523173,"about_ca_system_score_gemma":0.00047566247,"threshold_uncertainty_score":0.008718967},"labels":[],"label_agreement":null},{"id":"W2759598203","doi":"10.1016/j.jfranklin.2017.09.019","title":"Higher-order stochastic averaging for a SDOF fractional viscoelastic system under bounded noise excitation","year":2017,"lang":"en","type":"article","venue":"Journal of the Franklin Institute","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Lyapunov exponent; Bounded function; Viscoelasticity; Mathematical analysis; Mathematics; Moment (physics); Noise (video); Second moment of area; Applied mathematics; Statistical physics; Physics; Classical mechanics; Nonlinear system; Geometry; Computer science","score_opus":0.09222700924308079,"score_gpt":0.34646625225108946,"score_spread":0.25423924300800865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2759598203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2302539,0.0005244284,0.7562104,0.00077326666,0.00016560289,0.000044996534,0.000085547355,0.00017875843,0.011763077],"genre_scores_gemma":[0.98863447,0.00021087826,0.0068691904,0.00007172876,0.000049407085,0.000021280834,0.000037787773,0.000019238285,0.0040860623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995939,0.00008390637,0.000027328857,0.00010696588,0.0001191952,0.00006874963],"domain_scores_gemma":[0.99901295,0.00042879916,0.00021363715,0.00007547354,0.00017772714,0.00009143881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009848502,0.00060560333,0.0012425001,0.0007560015,0.00074275734,0.0013899873,0.00062892516,0.0014816568,0.0015414021],"category_scores_gemma":[0.0020982698,0.00031159967,0.001047203,0.00042473376,0.0014783632,0.0011475781,0.0011728248,0.0008103201,0.00008891247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011359732,0.00010873378,0.0020476019,0.000163031,0.00017314752,0.0008427651,0.00024557786,0.79707867,0.022856792,0.16359967,0.00066306663,0.012107374],"study_design_scores_gemma":[0.0000018947592,0.000015108939,0.00027961764,0.000002654291,0.0000098832315,0.00003741867,0.000007475516,0.9944988,0.00027413407,0.0047713197,0.00009308015,0.000008715154],"about_ca_topic_score_codex":0.0048814015,"about_ca_topic_score_gemma":0.003499567,"teacher_disagreement_score":0.0048814015,"about_ca_system_score_codex":0.0008757067,"about_ca_system_score_gemma":0.0009413532,"threshold_uncertainty_score":0.00970602},"labels":[],"label_agreement":null},{"id":"W2765747723","doi":"10.1115/detc2017-67365","title":"Computational Modal Analysis of a Twin-Engine Rear Fuselage Mounted Aircraft Support Frame","year":2017,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"European Social Fund; Natural Sciences and Engineering Research Council of Canada","keywords":"Fuselage; Modal analysis; Modal; Diagonal; Frame (networking); Focus (optics); Structural engineering; Computer science; Acoustics; Engineering; Finite element method; Mechanical engineering; Mathematics; Materials science; Physics; Geometry","score_opus":0.06912548417541982,"score_gpt":0.3659680735208786,"score_spread":0.2968425893454588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765747723","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9475461,0.000045465702,0.046272878,0.00008922244,0.000014371715,0.000030834155,0.00017809863,0.000101041056,0.0057219015],"genre_scores_gemma":[0.9886701,0.000019409596,0.010154695,0.000007570306,0.0000028122095,0.000019793057,0.000080330625,0.000013582399,0.0010315664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999256,0.000014441991,0.000002648004,0.000010195222,0.000027798136,0.000019310006],"domain_scores_gemma":[0.99970466,0.00019436821,0.000024533694,0.000018448565,0.000044784865,0.000013155531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028946262,0.00021335534,0.00032477287,0.0002631387,0.00029868714,0.0004256013,0.0004984451,0.00047623183,0.0024592725],"category_scores_gemma":[0.0007864097,0.00017703997,0.000323937,0.00015445378,0.00034258398,0.00026610465,0.00028387064,0.00031227744,0.00018685292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001319404,0.000029707362,0.0009794491,0.000040425326,0.000008300463,0.00008591275,0.000046648165,0.9801651,0.011849556,0.0017805474,0.00013425812,0.004748113],"study_design_scores_gemma":[0.00000536649,0.00002129586,0.00041045327,0.0000016167367,0.0000019989068,0.000005046401,0.000016348156,0.9982862,0.0010555178,0.00010671597,0.00008690555,0.0000025351096],"about_ca_topic_score_codex":0.007091048,"about_ca_topic_score_gemma":0.0059432746,"teacher_disagreement_score":0.007091048,"about_ca_system_score_codex":0.0004226811,"about_ca_system_score_gemma":0.00054144126,"threshold_uncertainty_score":0.014099538},"labels":[],"label_agreement":null},{"id":"W2766571713","doi":"10.1016/j.advwatres.2017.10.023","title":"Efficient uncertainty quantification in fully-integrated surface and subsurface hydrologic simulations","year":2017,"lang":"en","type":"article","venue":"Advances in Water Resources","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Hydrological modelling; Uncertainty quantification; Environmental science; Hydrology (agriculture); Geology; Geotechnical engineering; Computer science; Climatology","score_opus":0.05495816498489564,"score_gpt":0.3404401051973619,"score_spread":0.28548194021246626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766571713","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07189787,0.00027615557,0.92476195,0.00036285596,0.000036096062,0.000039462557,0.00014414059,0.00031299266,0.0021684577],"genre_scores_gemma":[0.9133854,0.0001720875,0.08506354,0.00006188106,0.00003297881,0.000077757664,0.00016848725,0.00010816853,0.0009297862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992893,0.0002803038,0.00004733642,0.000074119635,0.00022872908,0.00008031811],"domain_scores_gemma":[0.9966337,0.0024448899,0.00022434672,0.00023864645,0.00035646223,0.000102006365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019688893,0.0007513769,0.0011843673,0.0005622877,0.00046329308,0.0016081518,0.0012557134,0.0011691209,0.000978722],"category_scores_gemma":[0.009151026,0.00094961235,0.00071215513,0.00059890875,0.001097125,0.0018712993,0.0020539635,0.0011935828,0.00012686616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018646115,0.0000070444057,0.0001287107,0.000009473898,0.000009450805,0.000007899185,0.00000725224,0.9932857,0.000291451,0.0033717032,0.000050797946,0.0028118836],"study_design_scores_gemma":[0.0000014926471,0.0000024943959,0.000020014793,8.376282e-7,0.0000010768158,0.0000010223146,8.7176176e-7,0.99831116,0.000116285184,0.001516796,0.000026908818,0.0000011200947],"about_ca_topic_score_codex":0.0089594,"about_ca_topic_score_gemma":0.006152266,"teacher_disagreement_score":0.0089594,"about_ca_system_score_codex":0.0011095204,"about_ca_system_score_gemma":0.0019840288,"threshold_uncertainty_score":0.017814517},"labels":[],"label_agreement":null},{"id":"W2767219300","doi":"10.1051/e3sconf/20172200024","title":"Comparison of power curve monitoring methods","year":2017,"lang":"en","type":"article","venue":"E3S Web of Conferences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Desjardins; École de Technologie Supérieure","funders":"","keywords":"Control chart; Metric (unit); Wind power; Computer science; Power (physics); Curve fitting; Chart; Reliability engineering; Data mining; Statistics; Engineering; Mathematics; Machine learning; Process (computing); Operations management","score_opus":0.3231365162638549,"score_gpt":0.5126238556671937,"score_spread":0.18948733940333878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767219300","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10423831,0.0059593655,0.8714656,0.0004323142,0.0004069707,0.0006075938,0.0016615944,0.00439189,0.010836289],"genre_scores_gemma":[0.69990337,0.003222069,0.28964117,0.00011245792,0.00016993486,0.0005002162,0.0025099134,0.00054796983,0.003392891],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9922305,0.0027445245,0.0006174052,0.0009662712,0.003194699,0.0002465953],"domain_scores_gemma":[0.98012745,0.010833915,0.0016216395,0.0023784114,0.0048063262,0.00023230596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007085571,0.0011870945,0.0013178217,0.0053415885,0.0002993331,0.0017089401,0.0018190771,0.0011809428,0.002904747],"category_scores_gemma":[0.031316936,0.00031986952,0.0011133484,0.003839517,0.00038742903,0.0020818193,0.0010151834,0.0010156763,0.0009014377],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012335656,0.00030591534,0.012566655,0.0012853332,0.00041726627,0.000117448006,0.0002970518,0.1903552,0.0039935373,0.006845931,0.0056380397,0.77694404],"study_design_scores_gemma":[0.00011280005,0.00068548776,0.018882979,0.00019353999,0.0001433771,0.00039856156,0.00028180427,0.95053333,0.011946702,0.0055360603,0.011151093,0.00013420706],"about_ca_topic_score_codex":0.002429522,"about_ca_topic_score_gemma":0.0010505364,"teacher_disagreement_score":0.007085571,"about_ca_system_score_codex":0.0008608137,"about_ca_system_score_gemma":0.00062732317,"threshold_uncertainty_score":0.037472546},"labels":[],"label_agreement":null},{"id":"W2767249151","doi":"10.1016/j.crma.2017.10.020","title":"An LP empirical quadrature procedure for parametrized functions","year":2017,"lang":"en","type":"article","venue":"Comptes Rendus Mathématique","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Quadrature (astronomy); Gauss–Jacobi quadrature; Applied mathematics; Gaussian quadrature; Gauss–Kronrod quadrature formula; Inverse; Manifold (fluid mechanics); A priori and a posteriori; Parametric statistics; Tanh-sinh quadrature; Gauss–Laguerre quadrature; Gauss–Hermite quadrature; Mathematical analysis; Integral equation; Nyström method; Geometry; Statistics; Physics","score_opus":0.1864795109275929,"score_gpt":0.44247672949029243,"score_spread":0.2559972185626995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767249151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007678877,0.000016679374,0.9984518,0.00003694729,0.0000053901454,0.000006966989,0.00001063306,0.00008799791,0.0006157037],"genre_scores_gemma":[0.08470752,0.00012662027,0.9083419,0.00014873201,0.00005020867,0.00020705044,0.0001971789,0.0005242447,0.00569662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992161,0.0002798855,0.00003078704,0.00009634508,0.00031238678,0.00006446011],"domain_scores_gemma":[0.9986523,0.0006594037,0.0000836278,0.00018815823,0.00036484154,0.000051748055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002296951,0.00071431114,0.0006493918,0.00071707857,0.00034988613,0.0012519354,0.0015471721,0.0010951901,0.006378615],"category_scores_gemma":[0.0065466356,0.00042859293,0.00061673403,0.0006212587,0.0011018525,0.0011787539,0.0019347216,0.0024940523,0.0018455295],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011305512,0.00010041349,0.0006553023,0.00015751018,0.00004269621,0.00013264002,0.00020972524,0.5177192,0.011925535,0.28708342,0.004707927,0.17715253],"study_design_scores_gemma":[0.0000058053597,0.000013061437,0.000030392073,0.000010487356,0.0000031119523,0.000015323541,0.0000084081385,0.97978836,0.0012041836,0.016878441,0.0020374786,0.0000049829873],"about_ca_topic_score_codex":0.002243536,"about_ca_topic_score_gemma":0.002384736,"teacher_disagreement_score":0.006378615,"about_ca_system_score_codex":0.00084713503,"about_ca_system_score_gemma":0.0011799645,"threshold_uncertainty_score":0.021338642},"labels":[],"label_agreement":null},{"id":"W2770533673","doi":"10.1002/we.2148","title":"A fast stochastic solution method for the Blade Element Momentum equations for long‐term load assessment","year":2017,"lang":"en","type":"article","venue":"Wind Energy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Deutscher Akademischer Austausch Dienst Kairo; Natural Sciences and Engineering Research Council of Canada; Pacific Institute for Climate Solutions","keywords":"Polynomial chaos; Turbine blade; Aerodynamics; Term (time); Turbine; Control theory (sociology); Applied mathematics; Stochastic optimization; Projection (relational algebra); Mathematical optimization; Stochastic process; Exponential function; Polynomial; Computer science; Momentum (technical analysis); Mathematics; Monte Carlo method; Engineering; Algorithm; Mathematical analysis; Mechanical engineering; Physics; Statistics","score_opus":0.12819803705767419,"score_gpt":0.4102921761499394,"score_spread":0.2820941390922652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770533673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023406295,0.00006641723,0.99696773,0.000058781327,0.000018457651,0.000015748823,0.000012696119,0.000031732594,0.00048782094],"genre_scores_gemma":[0.39750326,0.0005858235,0.59429413,0.000105421976,0.0001237314,0.0004977705,0.0001668283,0.00014899783,0.006574049],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996666,0.0001468756,0.00001289958,0.000030202471,0.00012653923,0.00001689223],"domain_scores_gemma":[0.9991499,0.00051323755,0.00008188496,0.000036160687,0.00018506391,0.000033759417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012228417,0.0006259382,0.0005207885,0.00046097112,0.00031788464,0.00045615068,0.0005361417,0.000715955,0.0021181954],"category_scores_gemma":[0.002674177,0.00032653104,0.0007107598,0.00039139538,0.00057591416,0.00043353764,0.0009889563,0.0011932378,0.0004542683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031133553,0.00003477378,0.00040321887,0.00008834504,0.000033672193,0.00004694078,0.000049434584,0.8889296,0.0041920575,0.07503895,0.00086887955,0.030283056],"study_design_scores_gemma":[0.0000022639815,0.0000062258214,0.000021559788,0.0000029781677,0.0000013775144,0.0000042327324,0.0000014823981,0.9972752,0.00014194737,0.002182111,0.00035861638,0.0000020499706],"about_ca_topic_score_codex":0.0025225156,"about_ca_topic_score_gemma":0.002529856,"teacher_disagreement_score":0.0025225156,"about_ca_system_score_codex":0.00044112088,"about_ca_system_score_gemma":0.0015011238,"threshold_uncertainty_score":0.007086098},"labels":[],"label_agreement":null},{"id":"W2770712475","doi":"10.1080/00949655.2017.1407936","title":"Variance-based importance analysis measure for mission reliability of phased mission system","year":2017,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Component (thermodynamics); Reliability (semiconductor); Variance (accounting); Measure (data warehouse); Monte Carlo method; Variance components; Reliability engineering; Component analysis; Computer science; Function (biology); Econometrics; Data mining; Mathematics; Statistics; Machine learning; Engineering","score_opus":0.10918403557686358,"score_gpt":0.4055676302277595,"score_spread":0.2963835946508959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770712475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027104395,0.00029642292,0.97100294,0.00009380242,0.00003450341,0.000053118798,0.000058404774,0.00020015705,0.0011562464],"genre_scores_gemma":[0.8539804,0.0003914864,0.14353576,0.000086596054,0.00010738559,0.0002508341,0.00033095173,0.00009919362,0.0012173367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778396,0.00083308545,0.00012122368,0.00034248043,0.00079642376,0.00012280395],"domain_scores_gemma":[0.99250245,0.0052572666,0.0005259502,0.00046713228,0.0011245793,0.00012268958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003388886,0.000882036,0.00070819317,0.0030425768,0.00043901728,0.0008068133,0.0008484158,0.0008403851,0.0014808669],"category_scores_gemma":[0.017381478,0.000245358,0.001056239,0.0015210094,0.0008961743,0.0016402949,0.0008875512,0.0011685526,0.00023414564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023105253,0.00014567202,0.014204061,0.0002800168,0.0002454693,0.00022593819,0.0002319898,0.74335474,0.014089099,0.09754444,0.0022948624,0.12715268],"study_design_scores_gemma":[0.000010537596,0.0000906048,0.0038116227,0.000016507616,0.0000400369,0.0001354945,0.000025549774,0.9757165,0.0021138021,0.017189246,0.00081245124,0.00003771866],"about_ca_topic_score_codex":0.0013042879,"about_ca_topic_score_gemma":0.0010541526,"teacher_disagreement_score":0.003388886,"about_ca_system_score_codex":0.000983299,"about_ca_system_score_gemma":0.00063087087,"threshold_uncertainty_score":0.017922342},"labels":[],"label_agreement":null},{"id":"W2771156384","doi":"10.3390/e19120687","title":"Choosing between Higher Moment Maximum Entropy Models and Its Application to Homogeneous Point Processes with Random Effects","year":2017,"lang":"en","type":"article","venue":"Entropy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; HEC Montréal","funders":"","keywords":"Poisson distribution; Principle of maximum entropy; Kullback–Leibler divergence; Divergence (linguistics); Mathematics; Entropy (arrow of time); Bayesian probability; Statistical physics; Homogeneous; Measure (data warehouse); Prior probability; Applied mathematics; Computer science; Statistics; Mathematical optimization; Data mining; Physics; Combinatorics","score_opus":0.04215903062800697,"score_gpt":0.29997239864485015,"score_spread":0.2578133680168432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771156384","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010281507,0.00023694,0.9885415,0.00019452404,0.00001134423,0.000024055458,0.000027991233,0.0000708792,0.0006112142],"genre_scores_gemma":[0.5519668,0.0008722195,0.44288078,0.00027332295,0.00017430968,0.0002678639,0.00024296611,0.00017910314,0.0031426505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99407,0.004228283,0.00020357732,0.00054749363,0.000737967,0.00021273343],"domain_scores_gemma":[0.9653937,0.030153912,0.0016543397,0.0015408808,0.0008135238,0.0004436124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017291512,0.0013133986,0.0019249758,0.0023142057,0.00079464744,0.0021913657,0.0023239828,0.002430078,0.0018311884],"category_scores_gemma":[0.03618831,0.0009591477,0.0020090607,0.0015071342,0.0025314847,0.004086736,0.0031623796,0.002230944,0.00037856086],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002560106,0.00013587948,0.0024012353,0.00020876412,0.0001951482,0.00025586292,0.00029175146,0.61080503,0.0021580337,0.3436178,0.00059852126,0.039076045],"study_design_scores_gemma":[0.000019136953,0.000066224,0.00051534246,0.000034380482,0.000026071964,0.00005638256,0.000016617643,0.7880227,0.0007622499,0.20986418,0.0005732972,0.000043404878],"about_ca_topic_score_codex":0.0015253889,"about_ca_topic_score_gemma":0.0014383113,"teacher_disagreement_score":0.017291512,"about_ca_system_score_codex":0.001529763,"about_ca_system_score_gemma":0.0013610558,"threshold_uncertainty_score":0.09144735},"labels":[],"label_agreement":null},{"id":"W2771960206","doi":"10.2495/safe-v8-n2-299-306","title":"Optimal tradeoffs between the security and cost of critical buildings and infrastructure systems","year":2018,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Engineer Research and Development Center; U.S. Air Force; Construction Engineering Research Laboratory; U.S. Department of Defense","keywords":"Critical infrastructure; Risk analysis (engineering); Critical infrastructure protection; Computer science; Computer security; Business","score_opus":0.02238443914938343,"score_gpt":0.3037579042129284,"score_spread":0.281373465063545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771960206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6360396,0.0005771011,0.33704677,0.0006414185,0.000043585515,0.00018388533,0.000273035,0.0001256533,0.025068933],"genre_scores_gemma":[0.97936857,0.00015887791,0.01821982,0.000021031497,0.0000049536143,0.00006866544,0.000049047147,0.000018840434,0.0020903386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959856,0.00014280973,0.000010241559,0.000048919144,0.00008789128,0.00011155314],"domain_scores_gemma":[0.999268,0.0004731576,0.000099306984,0.00003279547,0.00006817404,0.00005852852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009151904,0.00081261474,0.00060481316,0.0007945502,0.00051053765,0.001484335,0.00066127663,0.0010848603,0.002580741],"category_scores_gemma":[0.0026679235,0.0006977026,0.00053652463,0.0006565637,0.0008190353,0.0014067381,0.0007413794,0.00073337747,0.00016369711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002560605,0.000015146159,0.00033061454,0.000013209871,0.0000048374864,0.000018046008,0.0000072894322,0.99448544,0.00048287894,0.0025319417,0.00006742822,0.0020176198],"study_design_scores_gemma":[0.0000094005,0.000074583084,0.00053736806,0.000006866229,0.000013997037,0.000022949502,0.00005108393,0.9950584,0.0005974276,0.0033208807,0.00030036294,0.0000067125093],"about_ca_topic_score_codex":0.0044962917,"about_ca_topic_score_gemma":0.005649846,"teacher_disagreement_score":0.0044962917,"about_ca_system_score_codex":0.0018164295,"about_ca_system_score_gemma":0.0014430804,"threshold_uncertainty_score":0.013179243},"labels":[],"label_agreement":null},{"id":"W2772538237","doi":"10.1002/aic.16045","title":"Multilevel Monte Carlo applied to chemical engineering systems subject to uncertainty","year":2017,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo method; Uncertainty quantification; Sampling (signal processing); Polynomial chaos; Latin hypercube sampling; Engineering; Mathematics; Statistics","score_opus":0.09056344298251777,"score_gpt":0.3369848557918908,"score_spread":0.24642141280937302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772538237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045704667,0.00036178803,0.9516392,0.00017543627,0.000031817843,0.000053154825,0.000054336848,0.0002306416,0.0017490122],"genre_scores_gemma":[0.8167811,0.00030047676,0.18203889,0.00008000378,0.000042256845,0.00014747049,0.00007896335,0.000063606596,0.00046722146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845326,0.0008356079,0.000057480327,0.000111689704,0.00044951774,0.00009238647],"domain_scores_gemma":[0.9909537,0.007453865,0.00045747607,0.0004817442,0.00053383166,0.00011940102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002834579,0.00046059294,0.00095813023,0.0009862831,0.00048214628,0.00086372794,0.00080266275,0.0008663962,0.0011870316],"category_scores_gemma":[0.012690584,0.0003885627,0.00079827604,0.00085310015,0.0008305704,0.0008395507,0.0010658208,0.0011032579,0.00011963712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041149946,0.00001352872,0.000802607,0.000041542513,0.000039075294,0.00003046811,0.000023972809,0.97677785,0.0008187016,0.012970558,0.000104433784,0.008336015],"study_design_scores_gemma":[0.000001600509,0.0000069540256,0.00005699405,0.0000019440697,0.0000025155202,0.0000028938832,0.0000010758824,0.9980996,0.0002143414,0.0015397973,0.00007066254,0.00000167639],"about_ca_topic_score_codex":0.0069034095,"about_ca_topic_score_gemma":0.004088893,"teacher_disagreement_score":0.0069034095,"about_ca_system_score_codex":0.00097238034,"about_ca_system_score_gemma":0.0010246763,"threshold_uncertainty_score":0.014990807},"labels":[],"label_agreement":null},{"id":"W2775330503","doi":"10.13111/2066-8201.2017.9.4.4","title":"Cessna Citation X Business Aircraft Eigenvalue Stability– Part 1: a New GUI for the LFRs Generation","year":2017,"lang":"en","type":"article","venue":"INCAS BULLETIN","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Flight envelope; Robustness (evolution); Graphical user interface; Flight simulator; Computer science; Computation; Representation (politics); Stability (learning theory); Envelope (radar); Aerospace engineering; Engineering; Aerodynamics; Simulation; Algorithm","score_opus":0.3006139695411982,"score_gpt":0.3639262662231064,"score_spread":0.06331229668190824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775330503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014775627,0.00016976132,0.6699266,0.0002253444,0.00012368867,0.00021488572,0.005207873,0.282253,0.02710319],"genre_scores_gemma":[0.33924153,0.000585807,0.4849024,0.00075262313,0.00022060865,0.0017537429,0.019529693,0.07836611,0.07464752],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996853,0.000073443545,0.00002101674,0.00007209257,0.000118777265,0.00002942493],"domain_scores_gemma":[0.99890065,0.000661843,0.000048285063,0.00014527513,0.000196648,0.00004744634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005848815,0.0016613891,0.0007374177,0.0011004101,0.00026771906,0.0012655745,0.00105387,0.00096076383,0.08428426],"category_scores_gemma":[0.002570675,0.0004457312,0.0008073255,0.00037786784,0.00028481105,0.0011131209,0.0008648476,0.001063635,0.01879105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004398508,0.000543145,0.0074617947,0.0014446558,0.00022865261,0.0014982662,0.0016882338,0.04791728,0.10025581,0.04276369,0.33196512,0.45983484],"study_design_scores_gemma":[0.0005717744,0.00042587722,0.00854932,0.00024906278,0.00009248397,0.0011145343,0.00013128248,0.51824075,0.085128844,0.017211437,0.36805904,0.00022560134],"about_ca_topic_score_codex":0.000835567,"about_ca_topic_score_gemma":0.0004406155,"teacher_disagreement_score":0.08428426,"about_ca_system_score_codex":0.00030177014,"about_ca_system_score_gemma":0.00031295876,"threshold_uncertainty_score":0.28195894},"labels":[],"label_agreement":null},{"id":"W2777492178","doi":"10.2140/memocs.2017.5.261","title":"A variational formulation for fuzzy analysis in continuum mechanics","year":2017,"lang":"en","type":"article","venue":"Mathematics and Mechanics of Complex Systems","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Otto von Guericke University Magdeburg; Universität Duisburg-Essen; Freie Universität Berlin; Bilkent Üniversitesi; Centre National de la Recherche Scientifique; University of North Carolina at Chapel Hill; Universität zu Köln; Università degli Studi di Pavia; Akademie Věd České Republiky; Université de Lyon; Universität Wien; Deutsche Forschungsgemeinschaft; McGill University; Indian National Science Academy; Carnegie Mellon University; Universidad Rey Juan Carlos; University of Pittsburgh; Louisiana State University; Wayne State University; Vanderbilt University","keywords":"Mathematics; Discretization; Hyperelastic material; Applied mathematics; Fuzzy logic; Variational analysis; Continuum mechanics; Numerical analysis; Simple (philosophy); Mathematical optimization; Computational mechanics; Calculus (dental); Finite element method; Computer science; Mathematical analysis; Classical mechanics; Physics","score_opus":0.14075316196047094,"score_gpt":0.3479199123076605,"score_spread":0.20716675034718957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2777492178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002751046,0.0010288068,0.99022514,0.0005140123,0.00009440074,0.00001670509,0.000068987116,0.000020064335,0.0052808872],"genre_scores_gemma":[0.39981586,0.0036991804,0.5712915,0.0005812179,0.0006760598,0.00030414222,0.00029484968,0.0001493103,0.023187906],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996088,0.0001763964,0.000017847458,0.000062180865,0.00011036089,0.000024470077],"domain_scores_gemma":[0.9995876,0.00022822683,0.000036319954,0.000033450768,0.000083143386,0.00003125263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012745181,0.0005766728,0.00056865124,0.0006150731,0.00044983035,0.0010836353,0.0007694532,0.0010023877,0.0025393518],"category_scores_gemma":[0.001464317,0.00025362577,0.00091637176,0.000553925,0.0014759866,0.0009732396,0.0011698673,0.0016745278,0.00038252556],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000065406625,0.000008078335,0.00010125532,0.00007205843,0.000019811061,0.000044479595,0.000067786365,0.06860578,0.002430546,0.9175022,0.0008544009,0.010287076],"study_design_scores_gemma":[0.0000068036056,0.000031108997,0.00014092418,0.00003032831,0.000008987203,0.000059009282,0.000030127498,0.50777394,0.00045198642,0.4832151,0.008238298,0.000013426302],"about_ca_topic_score_codex":0.0020980162,"about_ca_topic_score_gemma":0.0017341148,"teacher_disagreement_score":0.0025393518,"about_ca_system_score_codex":0.0010788522,"about_ca_system_score_gemma":0.00085368723,"threshold_uncertainty_score":0.008495033},"labels":[],"label_agreement":null},{"id":"W2778827262","doi":"10.1007/978-3-319-69802-1_3","title":"Compressed Sensing Approaches for Polynomial Approximation of High-Dimensional Functions","year":2017,"lang":"en","type":"book-chapter","venue":"Applied and numerical harmonic analysis","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":85,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Curse of dimensionality; Polynomial; Truncation (statistics); Dimension (graph theory); Computation; Applied mathematics; Mathematics; Focus (optics); Compressed sensing; Multivariate statistics; Sparse approximation; Algorithm; Computer science; Mathematical optimization; Pure mathematics; Mathematical analysis; Artificial intelligence; Machine learning","score_opus":0.106563013848511,"score_gpt":0.28248300645713065,"score_spread":0.17591999260861965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2778827262","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012001328,0.002984075,0.9904918,0.00030912374,0.00014682305,0.000011249571,0.00005767203,0.00012353557,0.004675651],"genre_scores_gemma":[0.15805098,0.02110867,0.7944815,0.0005588918,0.0023855453,0.00016857902,0.00071024906,0.00033164537,0.0222039],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99927396,0.00019159607,0.00003220806,0.000112551694,0.00035268723,0.000037020585],"domain_scores_gemma":[0.9983078,0.0012194546,0.000062548075,0.00021166162,0.00015858228,0.00003976784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009929829,0.0012837163,0.00094931026,0.0009929995,0.0002905229,0.0014699397,0.0015128275,0.0012458118,0.0049435226],"category_scores_gemma":[0.0042253835,0.00043986348,0.00071809103,0.0018121394,0.0017291445,0.0019902352,0.0016974993,0.0032355634,0.0015569549],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007293428,0.00004541957,0.00013288672,0.000463,0.000053307973,0.00007503025,0.00016429457,0.1343032,0.009137741,0.6154418,0.012857145,0.22725326],"study_design_scores_gemma":[0.000008593965,0.000028959194,0.00010842246,0.000051130864,0.00001094435,0.00011967099,0.000023029304,0.67117774,0.00202061,0.31398463,0.012445442,0.00002070418],"about_ca_topic_score_codex":0.0013667366,"about_ca_topic_score_gemma":0.0013592242,"teacher_disagreement_score":0.0049435226,"about_ca_system_score_codex":0.00090425543,"about_ca_system_score_gemma":0.00046075392,"threshold_uncertainty_score":0.016537726},"labels":[],"label_agreement":null},{"id":"W2779593711","doi":"10.1142/s0218539318500110","title":"A Simple Explicit Meta-Model for Probabilistic Design of Dynamic Systems with Multiple Mixed Inputs","year":2017,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Component (thermodynamics); Computer science; Probabilistic logic; Simple (philosophy); Computation; Nonlinear system; Matrix (chemical analysis); Metamodeling; Control theory (sociology); Damper; Mathematical optimization; Algorithm; Control engineering; Mathematics; Engineering; Artificial intelligence","score_opus":0.1660533105819121,"score_gpt":0.3759705399250272,"score_spread":0.2099172293431151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2779593711","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023063573,0.00006762912,0.99558383,0.0000655004,0.000010832171,0.000025701502,0.00005930897,0.00013467637,0.0017461544],"genre_scores_gemma":[0.46697095,0.00040368733,0.52580315,0.0001731407,0.000050860326,0.000820772,0.00030307696,0.00016482659,0.0053095035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931085,0.00020028219,0.000040654533,0.00012897149,0.00026479363,0.00005449701],"domain_scores_gemma":[0.99906904,0.0005165482,0.00012896868,0.00011598711,0.00013897709,0.00003054296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015395749,0.0012816375,0.0010466679,0.0006490196,0.00034031397,0.001484398,0.0018670025,0.0013057379,0.0031475136],"category_scores_gemma":[0.0025010114,0.00094537623,0.0016147585,0.00056377525,0.0008442976,0.0013741526,0.0012631286,0.0017082895,0.00070622755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017337918,0.000012026472,0.00012759994,0.000049510443,0.000018847664,0.000020609246,0.000017815672,0.9780985,0.00082034426,0.01467489,0.00011376672,0.006028789],"study_design_scores_gemma":[0.0000051938323,0.000020237112,0.000030243467,0.000010627204,0.000008354946,0.000008118762,0.0000028192715,0.9922186,0.00037456452,0.0067221816,0.0005954346,0.00000353009],"about_ca_topic_score_codex":0.0014711546,"about_ca_topic_score_gemma":0.0024969974,"teacher_disagreement_score":0.0031475136,"about_ca_system_score_codex":0.0007944597,"about_ca_system_score_gemma":0.0012840837,"threshold_uncertainty_score":0.0105294585},"labels":[],"label_agreement":null},{"id":"W2783148942","doi":"10.14359/51714475","title":"Partial Material Strength Reduction Factors: for ACI 318?","year":2019,"lang":"en","type":"article","venue":"ACI Structural Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Materials science; Reduction (mathematics); Composite material; Structural engineering; Strength reduction; Engineering; Mathematics; Finite element method; Geometry","score_opus":0.06237375121631043,"score_gpt":0.33325031959952844,"score_spread":0.270876568383218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783148942","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10991111,0.0035181278,0.79679245,0.003385915,0.00039204158,0.0007853812,0.00097302365,0.0023861495,0.081855856],"genre_scores_gemma":[0.55880404,0.0009107264,0.42517334,0.00058828626,0.00023588103,0.00055745966,0.0011191852,0.00041942563,0.012191734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969229,0.0007441282,0.00013258366,0.00039809727,0.0016637245,0.00013854027],"domain_scores_gemma":[0.9958788,0.00094445207,0.00063828955,0.00096101855,0.0015075793,0.000069726186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033907,0.001039006,0.0007016727,0.0010871876,0.0004306055,0.0010289385,0.0011099825,0.0010438265,0.009036508],"category_scores_gemma":[0.008924297,0.0003734964,0.0007431917,0.0006894279,0.0006373148,0.0011256026,0.00050934945,0.0009544134,0.0029585625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063605595,0.00036263053,0.01063981,0.0006572179,0.00014348478,0.00014472108,0.0001789835,0.086298145,0.037770648,0.057669934,0.020879213,0.7846192],"study_design_scores_gemma":[0.00037676594,0.002987641,0.027865639,0.0007433938,0.0002979151,0.001626369,0.0003113076,0.49267396,0.099403426,0.1108329,0.2625606,0.00031998497],"about_ca_topic_score_codex":0.0018027142,"about_ca_topic_score_gemma":0.0027134586,"teacher_disagreement_score":0.009036508,"about_ca_system_score_codex":0.0008071274,"about_ca_system_score_gemma":0.0017149542,"threshold_uncertainty_score":0.030230165},"labels":[],"label_agreement":null},{"id":"W2784325789","doi":"10.1016/j.compgeo.2018.01.002","title":"Influence of model type, bias and input parameter variability on reliability analysis for simple limit states with two load terms","year":2018,"lang":"en","type":"article","venue":"Computers and Geotechnics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Geomechanica (Canada); Rocscience (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Probabilistic logic; Limit (mathematics); Sensitivity (control systems); Mathematics; Margin (machine learning); Range (aeronautics); Statistics; Term (time); Random variable; Function (biology); Index (typography); Reliability engineering; Econometrics; Engineering; Computer science; Power (physics); Mathematical analysis","score_opus":0.06979819159910182,"score_gpt":0.3197135420522088,"score_spread":0.24991535045310698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784325789","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9359993,0.000299414,0.062519744,0.00015687446,0.000022701675,0.0000127895455,0.00008921508,0.00020615943,0.0006938383],"genre_scores_gemma":[0.99776435,0.00004471082,0.0020149422,0.0000142910085,0.0000055793976,0.0000054236502,0.000034251792,0.000041296087,0.00007522869],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842596,0.00089318695,0.000088974586,0.00017898389,0.00026447966,0.00014842914],"domain_scores_gemma":[0.9297,0.06421518,0.0020202002,0.0024581216,0.0013898066,0.00021666072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00797443,0.00044499183,0.0007625765,0.000594606,0.00029661614,0.00095646473,0.0004955104,0.00091800024,0.00039616163],"category_scores_gemma":[0.054273576,0.00039666842,0.000819713,0.0004332906,0.0007315469,0.001067737,0.0005914098,0.0007606833,0.000068652014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010214319,0.00014695249,0.025678614,0.00012674768,0.00020630629,0.00023465035,0.00013964986,0.9489525,0.009742934,0.0024010371,0.00017536209,0.01117393],"study_design_scores_gemma":[0.000024770245,0.00019397077,0.0076605957,0.000027096376,0.00017224417,0.00008240001,0.000033113203,0.9810867,0.008115696,0.0024925366,0.000076860946,0.000034069646],"about_ca_topic_score_codex":0.0014860838,"about_ca_topic_score_gemma":0.0013478653,"teacher_disagreement_score":0.00797443,"about_ca_system_score_codex":0.0004606263,"about_ca_system_score_gemma":0.00045635045,"threshold_uncertainty_score":0.042173326},"labels":[],"label_agreement":null},{"id":"W2784995741","doi":"","title":"Central U.S. WRF Statistical Verification of Simulated Composite Radar","year":2018,"lang":"en","type":"article","venue":"98th American Meteorological Society Annual Meeting","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Weather Research and Forecasting Model; Meteorology; Composite number; Radar; Geology; Remote sensing; Geodesy; Climatology; Computer science; Geography; Algorithm; Telecommunications","score_opus":0.03963600303720463,"score_gpt":0.3248442885904707,"score_spread":0.28520828555326605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784995741","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9230971,0.00016127263,0.04685628,0.0006488604,0.00030606386,0.0000870125,0.0114420615,0.0029661146,0.014435273],"genre_scores_gemma":[0.97584766,0.000018069826,0.017984346,0.000062435654,0.000028851524,0.000030263547,0.0049678357,0.00017681895,0.00088374835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984269,0.00039316478,0.000057798174,0.00025087973,0.00069800764,0.00017317911],"domain_scores_gemma":[0.98851556,0.0027769841,0.0005998365,0.002030662,0.0057494645,0.00032755634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054248613,0.0003414934,0.0003457041,0.0009781516,0.0010067855,0.00079442974,0.0010597435,0.0006487951,0.002012719],"category_scores_gemma":[0.010721711,0.00022677249,0.0004902694,0.00077899965,0.00037692595,0.0007230114,0.0005377659,0.0005904861,0.0009930861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035726088,0.00088712625,0.18041752,0.00014660599,0.00048354128,0.00053664774,0.00038875686,0.57187265,0.064708,0.0084938025,0.04139693,0.12709582],"study_design_scores_gemma":[0.0005285899,0.00041591533,0.11324691,0.00003545319,0.000118809105,0.00016655297,0.00012676482,0.83877116,0.03434967,0.0017672785,0.010382783,0.00009015879],"about_ca_topic_score_codex":0.051194485,"about_ca_topic_score_gemma":0.079488285,"teacher_disagreement_score":0.051194485,"about_ca_system_score_codex":0.0013261555,"about_ca_system_score_gemma":0.0030302296,"threshold_uncertainty_score":0.10179299},"labels":[],"label_agreement":null},{"id":"W2785924033","doi":"10.1109/pesgm.2017.8273801","title":"A cross-entropy-based control variate method for power system reliability assessment","year":2017,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Hydro (Canada)","funders":"","keywords":"Control variates; Reliability (semiconductor); Monte Carlo method; Computer science; Random variate; Combing; Entropy (arrow of time); Variance reduction; Cross entropy; Convergence (economics); Reliability engineering; Variance (accounting); Electric power system; Algorithm; Mathematics; Statistics; Power (physics); Artificial intelligence; Random variable; Engineering; Markov chain Monte Carlo; Principle of maximum entropy; Hybrid Monte Carlo","score_opus":0.07015182276199583,"score_gpt":0.43386325188340735,"score_spread":0.3637114291214115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785924033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018551023,0.00008337636,0.9974118,0.000018912706,0.000014770599,0.000016727101,0.000013839337,0.0001059537,0.00047955083],"genre_scores_gemma":[0.39929748,0.00038410333,0.5966954,0.0001305295,0.0001391142,0.000374501,0.0002718062,0.00018438039,0.0025226038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986443,0.00067539944,0.000050049366,0.00014680308,0.00043915873,0.000044329397],"domain_scores_gemma":[0.9978834,0.0013521455,0.00015392179,0.00014852402,0.0003995665,0.00006242557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022124008,0.00068964704,0.00076515705,0.0014734098,0.00041215442,0.0007334932,0.00092775706,0.0006054054,0.0018755294],"category_scores_gemma":[0.0048462376,0.000329396,0.0006943667,0.0010759275,0.00073922984,0.0010145019,0.00080586434,0.00097353116,0.00026414968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009978959,0.00007071082,0.0016537083,0.00012050648,0.00011293439,0.00005177253,0.000060667495,0.8163207,0.005515506,0.04349174,0.0012159991,0.13128597],"study_design_scores_gemma":[0.0000023849182,0.000015274796,0.00013301783,0.000003941959,0.0000050600793,0.000010003748,0.0000014181068,0.99713135,0.0005231187,0.0017857368,0.00038250946,0.0000061514565],"about_ca_topic_score_codex":0.0029386156,"about_ca_topic_score_gemma":0.0020993385,"teacher_disagreement_score":0.0029386156,"about_ca_system_score_codex":0.0006853614,"about_ca_system_score_gemma":0.0009082625,"threshold_uncertainty_score":0.011700451},"labels":[],"label_agreement":null},{"id":"W2789392647","doi":"10.1002/cjce.23171","title":"A sensitivity analysis for tissue development by varying model parameters and input variables","year":2018,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dimensionless quantity; Sensitivity (control systems); Biological system; Volume fraction; Diffusion; Growth model; Porosity; Mechanics; Phenomenological model; Cell growth; Materials science; Chemistry; Mathematics; Biomedical engineering; Thermodynamics; Physics; Statistics; Engineering; Biology; Biochemistry; Composite material","score_opus":0.04742374344968395,"score_gpt":0.2646470300235449,"score_spread":0.21722328657386095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789392647","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6948359,0.00097362103,0.29490685,0.00050781923,0.00007926741,0.00027941368,0.0007207678,0.0004800021,0.007216386],"genre_scores_gemma":[0.99374855,0.00009736135,0.005495395,0.000020210799,0.0000034625568,0.000080330654,0.00008262918,0.000014634476,0.00045749254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929965,0.0003614878,0.000031079257,0.00008604368,0.00012007981,0.00010162072],"domain_scores_gemma":[0.9947901,0.004572075,0.00019336143,0.00014423399,0.000264792,0.00003544814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024434251,0.0011007898,0.0006653177,0.0010289247,0.00046635972,0.001065361,0.0005437191,0.0013851052,0.0017036898],"category_scores_gemma":[0.0055983765,0.0004300796,0.0016300684,0.00036300288,0.0006747491,0.0005061849,0.00085601606,0.0010725902,0.00009085402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051995383,0.000027949405,0.00047469352,0.00004842132,0.00003231547,0.000062160216,0.000020594432,0.99443984,0.0028817235,0.0005758618,0.00006034642,0.001324098],"study_design_scores_gemma":[0.0000060870098,0.000059737104,0.00036860866,0.000008808148,0.000024208055,0.000016863703,0.000017263354,0.99591345,0.0030085652,0.0004468124,0.000119652825,0.0000100041725],"about_ca_topic_score_codex":0.0071003134,"about_ca_topic_score_gemma":0.0020646546,"teacher_disagreement_score":0.0071003134,"about_ca_system_score_codex":0.0009506476,"about_ca_system_score_gemma":0.00066023535,"threshold_uncertainty_score":0.014117956},"labels":[],"label_agreement":null},{"id":"W2791131283","doi":"10.1016/j.apm.2018.02.012","title":"Efficient reliability analysis based on adaptive sequential sampling design and cross-validation","year":2018,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":106,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Reliability (semiconductor); Kriging; Surrogate model; Computer science; Adaptive sampling; Cluster analysis; Sampling (signal processing); Function (biology); Machine learning; Importance sampling; Limit (mathematics); Uncertainty quantification; Data mining; Reliability engineering; Mathematical optimization; Artificial intelligence; Monte Carlo method; Mathematics; Statistics; Engineering; Power (physics)","score_opus":0.1868313603449542,"score_gpt":0.3641460883175816,"score_spread":0.1773147279726274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791131283","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003195177,0.000048356007,0.996416,0.000014560941,0.000008014986,0.00003793071,0.000009493219,0.00013228582,0.00013815683],"genre_scores_gemma":[0.35563707,0.00018740975,0.6417738,0.00007114988,0.00006495406,0.0007599669,0.00028170517,0.0001781109,0.001045829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99046654,0.006018567,0.0004608102,0.00083943823,0.001949074,0.0002655934],"domain_scores_gemma":[0.9598343,0.029515151,0.001886638,0.0026784514,0.0057579917,0.00032745622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018641977,0.0021062554,0.0029435304,0.0022409202,0.0006945166,0.0010763495,0.0023172419,0.0012351401,0.0025905517],"category_scores_gemma":[0.03742563,0.0013089622,0.001852458,0.0010554001,0.001284277,0.0016782735,0.0018852664,0.002152873,0.00053171336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051307154,0.00017823795,0.0016294654,0.00027670822,0.00030418087,0.00005877731,0.00009540299,0.84987795,0.0057168915,0.01985455,0.0006435095,0.12085131],"study_design_scores_gemma":[0.000016055485,0.00008381762,0.00022810516,0.0000083851955,0.000022024353,0.000016564714,0.0000026847022,0.99512464,0.0010471394,0.0033207422,0.00012264615,0.000007287797],"about_ca_topic_score_codex":0.0021722266,"about_ca_topic_score_gemma":0.0023549923,"teacher_disagreement_score":0.018641977,"about_ca_system_score_codex":0.0010362413,"about_ca_system_score_gemma":0.0029345318,"threshold_uncertainty_score":0.09858936},"labels":[],"label_agreement":null},{"id":"W2792131322","doi":"10.1080/15732479.2018.1433692","title":"Value of information-based decision analysis of the optimal next inspection type for deteriorating structural systems","year":2018,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Value of information; Value (mathematics); Reliability engineering; Decision analysis; Computer science; Engineering; Operations research; Statistics; Mathematics; Artificial intelligence","score_opus":0.014365360598286055,"score_gpt":0.2699036431258836,"score_spread":0.2555382825275975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792131322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12227288,0.00041634732,0.8707521,0.00040081717,0.000022793083,0.00014240286,0.00013420497,0.00015346639,0.0057049105],"genre_scores_gemma":[0.8821028,0.00027800156,0.11613635,0.00005218635,0.000019552102,0.00011473123,0.00012259753,0.00003602948,0.0011376919],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979771,0.00095108314,0.00008819075,0.00020848995,0.000591213,0.00018391262],"domain_scores_gemma":[0.9849139,0.012645657,0.00079329364,0.00034425486,0.0011584564,0.0001443512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058771786,0.0010629938,0.0013344396,0.0018909583,0.00052603416,0.0019121342,0.0010067722,0.0009897428,0.002261791],"category_scores_gemma":[0.01923333,0.0005567924,0.00083170534,0.0008548366,0.0012383402,0.0019487304,0.00090460834,0.001020724,0.00019590787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010742675,0.000030183694,0.0008533317,0.00004286798,0.000022918393,0.00004154285,0.000034023284,0.97819066,0.0009462046,0.008031073,0.00012424792,0.011575554],"study_design_scores_gemma":[0.0000050407825,0.000036698028,0.00018972359,0.000011747171,0.0000111145755,0.000008433143,0.000010524474,0.99478394,0.0008192531,0.004007444,0.00010892717,0.000007130587],"about_ca_topic_score_codex":0.0036034537,"about_ca_topic_score_gemma":0.0032269552,"teacher_disagreement_score":0.0058771786,"about_ca_system_score_codex":0.0026137177,"about_ca_system_score_gemma":0.0020713757,"threshold_uncertainty_score":0.031081855},"labels":[],"label_agreement":null},{"id":"W2797453828","doi":"","title":"Optimal Sample Size for Managing Uncertainty in Hoek-Brown Strength Parameters","year":2016,"lang":"en","type":"article","venue":"50th U.S. Rock Mechanics/Geomechanics Symposium","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sample (material); Sample size determination; Geology; Statistics; Geotechnical engineering; Mathematics","score_opus":0.038390977695260155,"score_gpt":0.27627133531562964,"score_spread":0.2378803576203695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797453828","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04411024,0.000491523,0.952217,0.0008331108,0.000065642365,0.00023080828,0.00013111449,0.00024287133,0.0016777797],"genre_scores_gemma":[0.7369457,0.00033641298,0.2591111,0.0004741845,0.00025429265,0.00072800706,0.00037797645,0.00011603901,0.0016561394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99070203,0.0062041725,0.0003607475,0.0010034067,0.0013327276,0.0003968503],"domain_scores_gemma":[0.8827057,0.106499776,0.002056428,0.0040885448,0.0034709654,0.0011786177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024843536,0.000756816,0.0022461538,0.0012133433,0.0006084971,0.0012472388,0.0026425463,0.0020099634,0.0023529576],"category_scores_gemma":[0.10413114,0.00080203696,0.0007510472,0.00074524985,0.0018324064,0.0029097341,0.0024691077,0.0023020217,0.00030809123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024287116,0.00034693754,0.0055114487,0.00029195964,0.00031160095,0.00019909983,0.00026193826,0.7480935,0.0041318685,0.061436143,0.0031179697,0.17386876],"study_design_scores_gemma":[0.0002666553,0.0002821078,0.0010824049,0.000045985602,0.0000534961,0.000043285872,0.000032856125,0.954838,0.0012898832,0.04146271,0.0005815465,0.00002100683],"about_ca_topic_score_codex":0.0024272187,"about_ca_topic_score_gemma":0.0024948935,"teacher_disagreement_score":0.024843536,"about_ca_system_score_codex":0.001098528,"about_ca_system_score_gemma":0.0028704419,"threshold_uncertainty_score":0.1313867},"labels":[],"label_agreement":null},{"id":"W2797482998","doi":"10.48550/arxiv.1804.03225","title":"Applying Polynomial Chaos Expansion to Assess Probabilistic Available Delivery Capability for Distribution Networks with Renewables","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polynomial chaos; Randomness; Probabilistic logic; Computer science; Mathematical optimization; Monte Carlo method; Renewable energy; CHAOS (operating system); Probability distribution; Reliability engineering; Mathematics; Engineering; Statistics","score_opus":0.19981447579051448,"score_gpt":0.24734972128641708,"score_spread":0.0475352454959026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797482998","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052419394,0.0001553624,0.9452821,0.000102760976,0.000011493255,0.000038233695,0.000044251265,0.00008523231,0.0018611522],"genre_scores_gemma":[0.947538,0.00022304025,0.051309094,0.00002074916,0.000017808108,0.000053617387,0.000057548677,0.000024570782,0.00075552845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961424,0.00017374307,0.000017635824,0.0000330007,0.00013253903,0.000028825998],"domain_scores_gemma":[0.9976794,0.0017965824,0.00019114335,0.000098535274,0.00019201833,0.000042293494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012873987,0.0005192625,0.00042621946,0.000792737,0.00022275465,0.0005151925,0.00047023772,0.00045141045,0.0006714506],"category_scores_gemma":[0.0063330797,0.0002450618,0.0004680824,0.00053514435,0.0006289264,0.0008554372,0.0008277435,0.00062720093,0.00007963898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014985895,0.0000048481816,0.00039808123,0.000018477987,0.000007405295,0.00003154737,0.000014206192,0.9866688,0.0008310715,0.006988596,0.000059317746,0.0049626585],"study_design_scores_gemma":[6.74796e-7,0.0000034927834,0.000050287475,0.0000012473528,8.24379e-7,0.000004922299,0.0000017965991,0.99856216,0.00016346276,0.0011716884,0.000037877795,0.0000015326059],"about_ca_topic_score_codex":0.0029388277,"about_ca_topic_score_gemma":0.0016924217,"teacher_disagreement_score":0.0029388277,"about_ca_system_score_codex":0.0006489664,"about_ca_system_score_gemma":0.0006951669,"threshold_uncertainty_score":0.0068085194},"labels":[],"label_agreement":null},{"id":"W2800092417","doi":"10.1139/tcsme-2001-0017","title":"MODELING STATISTICAL UNCERTAINTIES IN ROBUST ENGINEERING DESIGN","year":2001,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Design of experiments; Monte Carlo method; Computer science; Probabilistic design; Engineering design process; Engineering; Mathematics; Statistics","score_opus":0.08585882073584594,"score_gpt":0.273633630318298,"score_spread":0.18777480958245207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800092417","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019011894,0.0005037482,0.9964277,0.00012234919,0.00001984408,0.000012596583,0.00001408211,0.000051023602,0.0009473742],"genre_scores_gemma":[0.6781712,0.0046472736,0.31298187,0.00028511634,0.00040398008,0.00048097628,0.00016161423,0.00013667149,0.0027312043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968977,0.0013572301,0.00016979805,0.00039201768,0.0010531461,0.00013005293],"domain_scores_gemma":[0.992573,0.0056252307,0.0007618675,0.00046008226,0.00051204686,0.00006762732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004218908,0.0011887097,0.0012004942,0.0010649124,0.00040996334,0.0017179954,0.0012082148,0.001746762,0.0010088052],"category_scores_gemma":[0.014070664,0.0007636997,0.0008197133,0.001253415,0.0025094182,0.0021456368,0.0015922694,0.0016186753,0.00033781657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001524521,0.000007833609,0.00017288822,0.00005910257,0.000024279481,0.000038368216,0.00003243134,0.9271542,0.00045076598,0.059389956,0.00016393069,0.012491037],"study_design_scores_gemma":[0.000009148381,0.000037801063,0.00009023516,0.00002355836,0.000011223903,0.000018629373,0.000009800806,0.9119781,0.00061003753,0.08558286,0.0016087224,0.000019888214],"about_ca_topic_score_codex":0.002255671,"about_ca_topic_score_gemma":0.0012682473,"teacher_disagreement_score":0.004218908,"about_ca_system_score_codex":0.0011573408,"about_ca_system_score_gemma":0.0011879241,"threshold_uncertainty_score":0.022311985},"labels":[],"label_agreement":null},{"id":"W2800127782","doi":"10.22215/etd/2014-10081","title":"Efficient Hermite-based Variability Analysis Using Decoupling Technique","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Hermite polynomials; Polynomial chaos; Decoupling (probability); Computer science; Algorithm; Random variable; Electronic circuit; Stochastic process; Polynomial; Process (computing); Mathematics; Theoretical computer science; Monte Carlo method; Engineering; Control engineering; Statistics; Electrical engineering","score_opus":0.055535960296582115,"score_gpt":0.3573147476333559,"score_spread":0.30177878733677377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800127782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005513812,0.000040480878,0.9934436,0.000024605986,0.0000070793435,0.00000716446,0.000025495348,0.00019261084,0.0007451945],"genre_scores_gemma":[0.55155265,0.0005225948,0.440271,0.00009992785,0.000078117235,0.00012916133,0.00052731374,0.00043617733,0.0063831904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994646,0.00010411981,0.000018034994,0.00006781741,0.0003015299,0.00004401094],"domain_scores_gemma":[0.99948597,0.00025468782,0.00005286833,0.00009843909,0.00009115327,0.000016833284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068733084,0.00067753735,0.00064514176,0.0006056799,0.00033476876,0.0007303102,0.00074487174,0.00039135118,0.0015784747],"category_scores_gemma":[0.0016616565,0.0004363011,0.00084002677,0.00062621024,0.0004319567,0.00090294547,0.00080384884,0.0014445364,0.0005275621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028325547,0.0001241487,0.0013362172,0.00016821384,0.00014259235,0.00025371785,0.00014458034,0.55207956,0.12016437,0.10415967,0.0028279328,0.2183158],"study_design_scores_gemma":[0.000004935289,0.000023122004,0.0002063485,0.000003832366,0.000009877674,0.00006477495,0.000006113973,0.97882766,0.009634877,0.010077322,0.0011297717,0.000011417527],"about_ca_topic_score_codex":0.0006021265,"about_ca_topic_score_gemma":0.0010260986,"teacher_disagreement_score":0.0015784747,"about_ca_system_score_codex":0.00037751536,"about_ca_system_score_gemma":0.0006167246,"threshold_uncertainty_score":0.0052805543},"labels":[],"label_agreement":null},{"id":"W2801390137","doi":"10.1080/1023697x.2002.10667885","title":"Probabilistic Analysis for the Reduction in Cross-section Area of Bored Piles with Defects","year":2002,"lang":"en","type":"article","venue":"HKIE Transactions","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fuzhou University; University of Waterloo; University of Hong Kong","keywords":"Reduction (mathematics); Pile; Cross section (physics); Section (typography); Event (particle physics); Probabilistic logic; Geotechnical engineering; Statistics; Probabilistic analysis of algorithms; Forensic engineering; Structural engineering; Geology; Environmental science; Mathematics; Engineering; Geometry; Computer science; Physics","score_opus":0.11180874963509649,"score_gpt":0.31502841946797366,"score_spread":0.20321966983287718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801390137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35776556,0.00047030888,0.63865006,0.0001471126,0.000011178216,0.00006362213,0.00020086077,0.00045661782,0.002234715],"genre_scores_gemma":[0.97929335,0.00017808362,0.019514214,0.000013913299,0.0000088660745,0.00005012245,0.00017645795,0.000022125767,0.00074284064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990677,0.00022949917,0.000032146,0.00012815282,0.00046224793,0.000080274884],"domain_scores_gemma":[0.9910458,0.0064919745,0.0011852856,0.0004626645,0.0007321348,0.00008207974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022207638,0.00044864244,0.00035395552,0.0012113929,0.00015884056,0.0003966803,0.00077648606,0.00047088004,0.0013745256],"category_scores_gemma":[0.008083872,0.00033605853,0.00066356355,0.0005313661,0.0008183308,0.00048000773,0.00046659334,0.00044572353,0.0001693053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008346859,0.000011486727,0.002503653,0.00004517929,0.000029675113,0.000055809734,0.000024339926,0.9816929,0.00586212,0.002878004,0.00008509286,0.0067282035],"study_design_scores_gemma":[0.000002066971,0.000055043558,0.0037721943,0.000004865554,0.000011000661,0.00006748794,0.0000074234717,0.9927781,0.0022311616,0.0009222696,0.0001361636,0.0000121230105],"about_ca_topic_score_codex":0.0029209005,"about_ca_topic_score_gemma":0.0023890622,"teacher_disagreement_score":0.0029209005,"about_ca_system_score_codex":0.0007556579,"about_ca_system_score_gemma":0.00048844924,"threshold_uncertainty_score":0.011744678},"labels":[],"label_agreement":null},{"id":"W2802346997","doi":"10.1038/s41598-020-70980-5","title":"Robust design from systems physics","year":2020,"lang":"en","type":"preprint","venue":"Scientific Reports","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; University of Michigan","keywords":"Dichotomy; Complex system; Mesoscale meteorology; Coupling (piping); Systems engineering; Computer science; Engineering; Mechanical engineering; Physics; Mathematics; Artificial intelligence","score_opus":0.2887373933838095,"score_gpt":0.32265499542168785,"score_spread":0.03391760203787836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802346997","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028187756,0.0007253682,0.98547226,0.00068344874,0.000058557813,0.000040293242,0.000094835574,0.0001017021,0.010004795],"genre_scores_gemma":[0.52250487,0.0058166813,0.4568656,0.0011399472,0.0007489051,0.0010085272,0.00061877514,0.0003957424,0.010900938],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970419,0.0010689808,0.0001397409,0.0004990094,0.0011311972,0.00011916558],"domain_scores_gemma":[0.99589807,0.00259354,0.00038604732,0.0005908786,0.000451002,0.000080425525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037170474,0.0015145693,0.0010861321,0.0018763301,0.0005313088,0.0025766152,0.0011004893,0.0013545081,0.004648575],"category_scores_gemma":[0.0105079785,0.0006950238,0.0015333564,0.0011512466,0.0038160218,0.0028021978,0.0027946716,0.0028468894,0.00080433226],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001489592,0.000012456836,0.00016429489,0.00016677937,0.000038691942,0.000032447027,0.000057193563,0.118409485,0.0011211543,0.85877705,0.0011984862,0.020007104],"study_design_scores_gemma":[0.0000113503,0.000034202676,0.00013387506,0.0000521957,0.000012159757,0.000024716594,0.000012941228,0.1431492,0.00059369375,0.8505042,0.0054567447,0.000014723974],"about_ca_topic_score_codex":0.0010395834,"about_ca_topic_score_gemma":0.0006214997,"teacher_disagreement_score":0.004648575,"about_ca_system_score_codex":0.0021271685,"about_ca_system_score_gemma":0.0013673538,"threshold_uncertainty_score":0.01965785},"labels":[],"label_agreement":null},{"id":"W2807885815","doi":"10.1016/j.rinam.2019.100068","title":"Stochastic Discontinuous Galerkin Methods (SDGM) based on fluctuation-dissipation balance","year":2019,"lang":"en","type":"article","venue":"Results in Applied Mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Lawrence Livermore National Laboratory; Advanced Scientific Computing Research; National Sleep Foundation; Sandia National Laboratories; National Defense Science and Engineering Graduate; U.S. Department of Energy; U.S. Department of Defense; Natural Sciences and Engineering Research Council of Canada; National Nuclear Security Administration; National Science Foundation","keywords":"Mathematics; Dissipation; Applied mathematics; Galerkin method; Partial differential equation; Dissipative system; Discontinuous Galerkin method; Stochastic differential equation; Boundary value problem; Stochastic partial differential equation; Mathematical optimization; Mathematical analysis; Nonlinear system; Finite element method; Physics","score_opus":0.058917391563883176,"score_gpt":0.359880838531119,"score_spread":0.30096344696723587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807885815","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023639882,0.00011480849,0.9965533,0.00008583945,0.000027003165,0.00002048902,0.000022316792,0.000042435215,0.00076976116],"genre_scores_gemma":[0.3383845,0.000818601,0.6557377,0.00020419298,0.00013868997,0.0003892326,0.00017365137,0.00012393545,0.0040295147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995235,0.00016580116,0.000034522378,0.00005726417,0.00019125192,0.000027623408],"domain_scores_gemma":[0.99941134,0.0003322363,0.00007729467,0.000061607294,0.000081257356,0.00003628378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011776806,0.0007890174,0.0007521499,0.00071965816,0.00037953485,0.00082795095,0.0010872621,0.0010652584,0.001039472],"category_scores_gemma":[0.002486888,0.00032427936,0.0009116321,0.0005335349,0.0012994342,0.0009087593,0.0014244729,0.0012713997,0.00021095968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024173014,0.000030116586,0.00046769425,0.0001705292,0.000033793724,0.00010798544,0.00010641453,0.6945387,0.010512509,0.2745849,0.00068923715,0.01873398],"study_design_scores_gemma":[0.0000042103115,0.00001051573,0.000034775643,0.0000067075575,0.0000026252146,0.000016878741,0.0000033839933,0.9799129,0.00075984915,0.018056372,0.0011872763,0.000004552897],"about_ca_topic_score_codex":0.0010041054,"about_ca_topic_score_gemma":0.0005923629,"teacher_disagreement_score":0.0011776806,"about_ca_system_score_codex":0.000588751,"about_ca_system_score_gemma":0.00085594103,"threshold_uncertainty_score":0.006228268},"labels":[],"label_agreement":null},{"id":"W2809729201","doi":"","title":"Optimal Air Pollution Control Strategies with Application to the Power Generation Sector","year":2006,"lang":"en","type":"article","venue":"12th Conference on Atmospheric Radiation/12th Conference on Cloud Physics (10-14 July 2006)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Air pollution; Environmental science; Control (management); Pollution; Natural resource economics; Business; Environmental planning; Computer science; Economics; Artificial intelligence","score_opus":0.03133057246540033,"score_gpt":0.26972051814886167,"score_spread":0.23838994568346134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809729201","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14081618,0.0027260594,0.82172376,0.0018113852,0.00022423845,0.00018949155,0.00013320145,0.000433411,0.031942226],"genre_scores_gemma":[0.98194915,0.00052374427,0.014989937,0.000078983576,0.00006210421,0.000055971646,0.000024635852,0.000021481052,0.002293844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944955,0.00023154296,0.000026293126,0.00007648376,0.000109057975,0.000107067695],"domain_scores_gemma":[0.9979042,0.0014776222,0.00019469897,0.000056704415,0.00029526735,0.00007153188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013626256,0.0011333331,0.0014087289,0.0008314664,0.0005926803,0.0018308685,0.00081473414,0.0015290837,0.0028708254],"category_scores_gemma":[0.0047063725,0.00056827004,0.0005671516,0.0008512606,0.00072585884,0.0008993273,0.0010380282,0.0009828082,0.00019726004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008823811,0.000058491547,0.00015142238,0.000050263803,0.000021596308,0.00004548649,0.00002966376,0.9750699,0.00036196187,0.008744321,0.00046664733,0.01491208],"study_design_scores_gemma":[0.000030763134,0.00005138562,0.000107128806,0.0000054635507,0.000013733544,0.0000063908888,0.00001691136,0.9912674,0.00016746845,0.008062159,0.000266703,0.00000459827],"about_ca_topic_score_codex":0.010307109,"about_ca_topic_score_gemma":0.007155031,"teacher_disagreement_score":0.010307109,"about_ca_system_score_codex":0.0012064835,"about_ca_system_score_gemma":0.0013789304,"threshold_uncertainty_score":0.020494223},"labels":[],"label_agreement":null},{"id":"W2810939921","doi":"10.1155/2018/4725148","title":"Stochastic Stability of Coupled Viscoelastic Systems Excited by Real Noise","year":2018,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lyapunov exponent; Mathematics; Moment (physics); Lyapunov function; Lyapunov equation; Mathematical analysis; Stability (learning theory); Parametric statistics; Noise (video); Viscoelasticity; Eigenvalues and eigenvectors; Statistical physics; Control theory (sociology); Applied mathematics; Nonlinear system; Physics; Classical mechanics; Statistics; Computer science; Quantum mechanics","score_opus":0.053052082540588616,"score_gpt":0.2905615334065,"score_spread":0.23750945086591135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810939921","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7091658,0.0003934941,0.28627503,0.00028960046,0.000031003172,0.000020108955,0.000038884726,0.00011813777,0.0036678736],"genre_scores_gemma":[0.99742883,0.000063506675,0.001907715,0.0000114593695,0.0000076462775,0.000010050219,0.000010076102,0.0000041752833,0.00055655953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960023,0.00012607539,0.000019495763,0.00007530281,0.00013049669,0.000048412974],"domain_scores_gemma":[0.998809,0.0006724318,0.00028904935,0.000055341767,0.00012608871,0.00004809112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007695158,0.00035649468,0.0005486437,0.0005158628,0.00036791773,0.00062952243,0.00037914177,0.0005356613,0.00038201286],"category_scores_gemma":[0.0025786515,0.00018975884,0.00038437505,0.00027636255,0.0011709381,0.00060944376,0.0007491562,0.00036090525,0.000044897326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010027015,0.000020335576,0.001422785,0.000046731315,0.000054001895,0.00027498358,0.000136351,0.92179424,0.02066837,0.05045426,0.00012071737,0.004906804],"study_design_scores_gemma":[0.000002558581,0.000014595696,0.0002425081,0.0000017336677,0.0000031273169,0.000015970754,0.0000067507026,0.9958676,0.00050121726,0.0032994202,0.0000384837,0.000006014423],"about_ca_topic_score_codex":0.0012971313,"about_ca_topic_score_gemma":0.00053827517,"teacher_disagreement_score":0.0012971313,"about_ca_system_score_codex":0.00045384327,"about_ca_system_score_gemma":0.00034809532,"threshold_uncertainty_score":0.0040696263},"labels":[],"label_agreement":null},{"id":"W2883765371","doi":"10.1080/0013791x.2018.1498961","title":"Postauditing and Cost Estimation Applications: An Illustration of MCMC Simulation for Bayesian Regression Analysis","year":2018,"lang":"en","type":"article","venue":"The Engineering Economist","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Federal Energy Management Program; Social Sciences and Humanities Research Council of Canada; World Bank Group","keywords":"Markov chain Monte Carlo; Bayesian probability; Computer science; Econometrics; Monte Carlo method; Bayesian inference; Bayesian statistics; Artificial intelligence; Statistics; Mathematics","score_opus":0.077380262791054,"score_gpt":0.358347737139369,"score_spread":0.28096747434831504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883765371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003268018,0.0005628861,0.9761247,0.0014221455,0.00007936169,0.000061197075,0.0000780607,0.00041865546,0.017984927],"genre_scores_gemma":[0.20521866,0.0018589915,0.7793273,0.0006396478,0.00022267249,0.00035582687,0.00018852767,0.00059311633,0.011595291],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946455,0.0038871865,0.00013765733,0.00028840007,0.00088283204,0.0001583967],"domain_scores_gemma":[0.9822832,0.015018778,0.00051451405,0.0011029836,0.0009222077,0.00015829684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074628405,0.0011478902,0.00088763784,0.001597656,0.0011772235,0.0030147173,0.002255759,0.0028341343,0.011603376],"category_scores_gemma":[0.032091092,0.0005402561,0.0010082364,0.0027322439,0.0018662091,0.002175343,0.0027507532,0.0031183076,0.0012169204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007007153,0.00006687797,0.0013103021,0.00013731015,0.00003512782,0.00024682286,0.00035946196,0.12834394,0.00056341203,0.76735014,0.0052151517,0.096301325],"study_design_scores_gemma":[0.000021453954,0.000028630779,0.00042863985,0.000096082484,0.000017713774,0.00017075663,0.00006817195,0.68345636,0.0008444489,0.29302907,0.021801863,0.000036779722],"about_ca_topic_score_codex":0.010732105,"about_ca_topic_score_gemma":0.010855245,"teacher_disagreement_score":0.011603376,"about_ca_system_score_codex":0.0020496198,"about_ca_system_score_gemma":0.0023620944,"threshold_uncertainty_score":0.039467752},"labels":[],"label_agreement":null},{"id":"W2884052941","doi":"10.1115/1.4040897","title":"Uncertainty Quantification of NOx Emission Due to Operating Conditions and Chemical Kinetic Parameters in a Premixed Burner","year":2018,"lang":"en","type":"article","venue":"Journal of Engineering for Gas Turbines and Power","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Siemens (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; University of Galway; Siemens Canada; Science Foundation Ireland; College of Engineering and Informatics, National University of Ireland, Galway","keywords":"Combustor; NOx; Uncertainty quantification; Uncertainty analysis; Combustion; Sensitivity (control systems); Nuclear engineering; Turbine; Environmental science; Process engineering; Computer science; Engineering; Aerospace engineering; Chemistry; Simulation","score_opus":0.03937342639310279,"score_gpt":0.3150997178922043,"score_spread":0.27572629149910155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884052941","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6493218,0.00014531893,0.3466412,0.00008373568,0.000013286057,0.000039255854,0.00018711569,0.00038373694,0.0031844468],"genre_scores_gemma":[0.98950934,0.000033915745,0.010113824,0.0000067020324,0.0000015387487,0.000016229542,0.00005982795,0.000015425872,0.00024315716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996455,0.00008878929,0.000015113271,0.00006745051,0.00015621335,0.000026971573],"domain_scores_gemma":[0.9994117,0.00034792387,0.000082384206,0.000063736916,0.00008186042,0.000012446342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008618499,0.0006058895,0.00030540134,0.0004583553,0.0003039368,0.00042724377,0.00048261802,0.00033561094,0.000418219],"category_scores_gemma":[0.0014213448,0.00028610352,0.000495057,0.00024336811,0.00038921632,0.00070940127,0.00061929337,0.0005336687,0.000033419954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003497833,0.000010946991,0.0015093988,0.000020006813,0.000011518984,0.000036877034,0.0000182701,0.9871867,0.005393308,0.0007703471,0.000026755639,0.0049809976],"study_design_scores_gemma":[0.0000017310801,0.00001564898,0.00060473353,0.0000018321867,0.000004291867,0.0000061035335,0.0000052659925,0.99456054,0.00428582,0.0004412569,0.00006880969,0.0000039227916],"about_ca_topic_score_codex":0.0050666644,"about_ca_topic_score_gemma":0.003605689,"teacher_disagreement_score":0.0050666644,"about_ca_system_score_codex":0.0006240044,"about_ca_system_score_gemma":0.0005240848,"threshold_uncertainty_score":0.010074317},"labels":[],"label_agreement":null},{"id":"W2884447341","doi":"10.1137/17m1132185","title":"Parallel Domain Decomposition Strategies for Stochastic Elliptic Equations. Part A: Local Karhunen--Loève Representations","year":2018,"lang":"en","type":"article","venue":"SIAM Journal on Scientific Computing","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Sandia National Laboratories; Office of Science; King Abdullah University of Science and Technology","keywords":"Domain decomposition methods; Mathematics; Discretization; Applied mathematics; Galerkin method; Projection (relational algebra); Monte Carlo method; Covariance; Stochastic process; Mathematical optimization; Algorithm; Mathematical analysis; Finite element method","score_opus":0.10681529834103323,"score_gpt":0.3918997272058532,"score_spread":0.28508442886482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884447341","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019291674,0.00015766207,0.99648607,0.00004562532,0.00001608202,0.000022088436,0.00001242964,0.000054626096,0.001276207],"genre_scores_gemma":[0.18804051,0.0008092526,0.8034802,0.00012517386,0.00007381787,0.0004243034,0.0001667635,0.0001767694,0.0067032925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967825,0.00011913002,0.000015236431,0.000031840904,0.00012848721,0.00002710063],"domain_scores_gemma":[0.9996811,0.00014185178,0.00003711356,0.000045895526,0.00006990955,0.00002415576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082285586,0.0007638213,0.00066487206,0.0006110889,0.00032078262,0.0007005429,0.0006390218,0.000552427,0.0025819356],"category_scores_gemma":[0.0012963532,0.00029986468,0.00077146373,0.00039764048,0.00062561495,0.0008064516,0.0012836863,0.0010651966,0.0007834433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075736876,0.000102379374,0.000434363,0.00020174727,0.000045634097,0.00010783152,0.00013419312,0.69668967,0.012528793,0.1364533,0.0022629718,0.15096335],"study_design_scores_gemma":[0.000007676071,0.000018199058,0.00003061078,0.000008132295,0.0000030551764,0.000022757917,0.000008732004,0.9828774,0.0010787266,0.014151839,0.001788961,0.000003901006],"about_ca_topic_score_codex":0.0009358119,"about_ca_topic_score_gemma":0.0010977167,"teacher_disagreement_score":0.0025819356,"about_ca_system_score_codex":0.00044757602,"about_ca_system_score_gemma":0.0006417725,"threshold_uncertainty_score":0.008637369},"labels":[],"label_agreement":null},{"id":"W2888558195","doi":"10.1115/1.4041172","title":"Reliability-Based Design Optimization on Qualitative Objective With Limited Information","year":2018,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mathematical optimization; Reliability (semiconductor); Random variable; Computer science; Computation; Monte Carlo method; Constraint (computer-aided design); Optimal design; Optimization problem; Function (biology); Mathematics; Algorithm; Statistics; Machine learning","score_opus":0.11333213433251964,"score_gpt":0.35486733711457985,"score_spread":0.2415352027820602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888558195","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039536003,0.00009177549,0.99495476,0.00003330215,0.0000049599075,0.000019744046,0.000009658705,0.00007917255,0.0008530335],"genre_scores_gemma":[0.48059937,0.0004213333,0.5159964,0.00008970893,0.000029916993,0.00045477643,0.00012866728,0.00014828902,0.0021315592],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923575,0.00031067658,0.000032551376,0.00012354385,0.00024463292,0.00005280436],"domain_scores_gemma":[0.99785656,0.0016087601,0.00020727178,0.00009936249,0.00019924655,0.000028860482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018562726,0.0013975825,0.0015941823,0.0012044344,0.00038015048,0.00082107756,0.000893885,0.00094510434,0.0014118286],"category_scores_gemma":[0.004072545,0.0008729696,0.00096835825,0.00082839647,0.0010765027,0.0009764582,0.0009898858,0.0010923382,0.00028095572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015979307,0.000010105096,0.00008057358,0.00005391644,0.000013230559,0.000012346076,0.000022505508,0.9808841,0.0007372671,0.0059890053,0.000107374944,0.012073544],"study_design_scores_gemma":[0.0000045837205,0.000016148011,0.00002686129,0.00000603627,0.0000047426606,0.0000052519,0.0000029755981,0.99594826,0.00030860608,0.0035146687,0.00015957226,0.0000024522842],"about_ca_topic_score_codex":0.002120881,"about_ca_topic_score_gemma":0.0016220186,"teacher_disagreement_score":0.002120881,"about_ca_system_score_codex":0.0009832891,"about_ca_system_score_gemma":0.0013060202,"threshold_uncertainty_score":0.009817004},"labels":[],"label_agreement":null},{"id":"W2891713809","doi":"10.1007/978-3-319-95040-2_5","title":"Gaussian 1-Capacity to Gaussian ∞-Capacity","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Mathematics; Gaussian; Gaussian random field; Gaussian process; Statistical physics; Applied mathematics; Econometrics; Physics","score_opus":0.1059488882521656,"score_gpt":0.3086370776910309,"score_spread":0.2026881894388653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891713809","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051445577,0.018150117,0.4166126,0.01323794,0.0037794225,0.0000971872,0.0010996581,0.00069206604,0.49488539],"genre_scores_gemma":[0.85154146,0.015049258,0.037798885,0.0060065137,0.0069356565,0.0002961061,0.00057998206,0.00063704397,0.08115503],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998279,0.00050189503,0.00006802854,0.00029856723,0.00057254074,0.00027999876],"domain_scores_gemma":[0.9951994,0.003234034,0.00018845583,0.00047628407,0.0006310479,0.00027074874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019022967,0.001374235,0.0009963623,0.0016644527,0.0008957684,0.0029742985,0.0016023063,0.001598152,0.013246236],"category_scores_gemma":[0.009725945,0.0005097725,0.00080299785,0.0024994689,0.00605028,0.00604698,0.0031731653,0.0051189307,0.002345076],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001424164,0.000005497589,0.000020849577,0.00005091871,0.0000039571755,0.000015147516,0.000039903367,0.0019019902,0.00018508866,0.9874956,0.004192051,0.006074698],"study_design_scores_gemma":[0.00000322311,0.00000664646,0.000039646384,0.000028977796,0.0000036959482,0.000038341997,0.00001105222,0.003615978,0.00018141739,0.99064726,0.0054149535,0.00000877711],"about_ca_topic_score_codex":0.0011039979,"about_ca_topic_score_gemma":0.0005106469,"teacher_disagreement_score":0.013246236,"about_ca_system_score_codex":0.0031471981,"about_ca_system_score_gemma":0.0013035863,"threshold_uncertainty_score":0.044313073},"labels":[],"label_agreement":null},{"id":"W2898148020","doi":"10.1115/pvp2018-84767","title":"Effects of Non-Normal Input Distributions and Sampling Region on Monte Carlo Results","year":2018,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nuclear Safety Commission","funders":"","keywords":"Monte Carlo method; Probabilistic logic; Computer science; Bounding overwatch; Importance sampling; Probabilistic analysis of algorithms; Sampling (signal processing); Mathematical optimization; Algorithm; Mathematics; Statistics; Artificial intelligence","score_opus":0.062431746638186306,"score_gpt":0.32551034035116383,"score_spread":0.26307859371297754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898148020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29859188,0.0010885316,0.69345707,0.0006054161,0.00004531281,0.00015636953,0.00015846586,0.00061497756,0.005281956],"genre_scores_gemma":[0.95873386,0.00024024167,0.0404163,0.00008422547,0.000012253406,0.00006633397,0.00007886478,0.000094366645,0.0002735894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99012053,0.00650673,0.00047251047,0.0006058216,0.0018644573,0.00042999926],"domain_scores_gemma":[0.76960766,0.20928754,0.006589058,0.007666686,0.0063049165,0.0005441502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025278758,0.00070500735,0.001130213,0.0012646514,0.0006061089,0.0009886976,0.00129388,0.0010674456,0.0012158169],"category_scores_gemma":[0.11680309,0.00052296405,0.00093578786,0.0007353121,0.0018463993,0.0015736589,0.0012176231,0.001272876,0.00014725122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024171977,0.000037331814,0.005438102,0.00009265647,0.00004976272,0.00018103216,0.00016281378,0.970627,0.001527502,0.014763153,0.00013129624,0.0067476006],"study_design_scores_gemma":[0.000009369984,0.000048498427,0.0012578743,0.000026571272,0.000021567934,0.00007941596,0.000029686104,0.99191135,0.0027268457,0.0037242405,0.00014440222,0.000020093748],"about_ca_topic_score_codex":0.0071648536,"about_ca_topic_score_gemma":0.004433999,"teacher_disagreement_score":0.025278758,"about_ca_system_score_codex":0.0012988026,"about_ca_system_score_gemma":0.0009460353,"threshold_uncertainty_score":0.13368845},"labels":[],"label_agreement":null},{"id":"W2898332908","doi":"10.1061/(asce)cp.1943-5487.0000805","title":"Structural Deterioration Modeling Using Variational Inference","year":2018,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Bayesian inference; Uncertainty quantification; Divergence (linguistics); Mathematical optimization; Computer science; Fiducial inference; Posterior probability; Statistical inference; Stochastic process; Algorithm; Bayesian probability; Data mining; Mathematics; Machine learning; Artificial intelligence; Bayesian statistics; Statistics","score_opus":0.11767903746454714,"score_gpt":0.35526826791143634,"score_spread":0.2375892304468892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898332908","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008248175,0.00043395968,0.9882682,0.00034707782,0.000027657421,0.000033657492,0.00014225523,0.00012224146,0.002376845],"genre_scores_gemma":[0.7423099,0.0015061125,0.24248657,0.00023761932,0.00018192099,0.00035174037,0.000827952,0.00021057489,0.011887519],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991941,0.00032844895,0.000036987527,0.0001552762,0.00020483053,0.000080321945],"domain_scores_gemma":[0.997875,0.0015954603,0.00017905135,0.00009091413,0.00019138714,0.000068215355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023835385,0.0008941486,0.0013972471,0.0012184399,0.0005493603,0.0013646036,0.002243406,0.0017318568,0.0019859078],"category_scores_gemma":[0.004756306,0.0009778967,0.0016151277,0.0011605374,0.001476466,0.0013896583,0.0015526393,0.0016543823,0.0002595843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008038597,0.000007899175,0.0003055149,0.000020313992,0.000024917153,0.000023127734,0.000023204573,0.96351063,0.00022374275,0.031764697,0.00027035514,0.0038175327],"study_design_scores_gemma":[0.0000017543771,0.0000019944564,0.000042164847,0.0000020914747,0.0000020641653,0.0000033718961,0.0000017371706,0.9918229,0.000026043163,0.007932161,0.00016106058,0.0000026637922],"about_ca_topic_score_codex":0.025187755,"about_ca_topic_score_gemma":0.01691121,"teacher_disagreement_score":0.025187755,"about_ca_system_score_codex":0.0018087537,"about_ca_system_score_gemma":0.001765335,"threshold_uncertainty_score":0.050082326},"labels":[],"label_agreement":null},{"id":"W2898336091","doi":"10.1016/j.envsoft.2018.10.005","title":"VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis","year":2018,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; Global Institute for Water Security","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Sensitivity (control systems); Computer science; Latin hypercube sampling; Robustness (evolution); Toolbox; Emulation; Sampling (signal processing); Variogram; Uncertainty analysis; Data mining; Software; Stability (learning theory); Visualization; Mathematical optimization; Machine learning; Simulation; Mathematics; Engineering; Statistics; Monte Carlo method; Kriging","score_opus":0.08929391861524157,"score_gpt":0.28894128538363617,"score_spread":0.19964736676839462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898336091","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007787803,0.00015709919,0.952349,0.00008952002,0.000053940006,0.000074410236,0.0031405222,0.039783057,0.0035736796],"genre_scores_gemma":[0.03592107,0.0005980758,0.9263698,0.00029283104,0.00010796018,0.0008865864,0.008386526,0.019446336,0.007990937],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984419,0.0004467188,0.00014504592,0.00018458159,0.0006745375,0.00010720443],"domain_scores_gemma":[0.995106,0.0032324262,0.00026375236,0.00040236884,0.0009034126,0.00009212391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003103433,0.0020469732,0.0013768977,0.002208042,0.00048570693,0.0018087305,0.0025662296,0.001136793,0.061611444],"category_scores_gemma":[0.009056488,0.0012799833,0.0017653839,0.0013367814,0.00062138337,0.0018781902,0.0021047054,0.0026993656,0.016950797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026839448,0.00023068594,0.0020612634,0.0022418322,0.00038339657,0.0005666159,0.00028557176,0.30500153,0.011873742,0.07784863,0.23075521,0.36848313],"study_design_scores_gemma":[0.00017493957,0.000073390045,0.0007878583,0.00025057315,0.000054446242,0.00034690672,0.00005176138,0.77609,0.009718695,0.06418569,0.14814821,0.0001174676],"about_ca_topic_score_codex":0.002504042,"about_ca_topic_score_gemma":0.003275868,"teacher_disagreement_score":0.061611444,"about_ca_system_score_codex":0.0005834442,"about_ca_system_score_gemma":0.001757797,"threshold_uncertainty_score":0.20611084},"labels":[],"label_agreement":null},{"id":"W2898341282","doi":"10.1061/ajrua6.0000994","title":"Probability Distribution of Maximum Load Generated by Stochastic Hazards Modeled as Shock, Pulse, and Alternating Renewal Processes","year":2018,"lang":"en","type":"article","venue":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University Network of Excellence in Nuclear Engineering","keywords":"Reliability (semiconductor); Shock (circulatory); Poisson distribution; Renewal theory; Hazard; Generalized extreme value distribution; Probabilistic logic; Extreme value theory; Stochastic process; Reliability theory; Mathematics; Distribution (mathematics); Stochastic modelling; Applied mathematics; Computer science; Statistics; Physics; Mathematical analysis; Failure rate","score_opus":0.023194711173295714,"score_gpt":0.2585809086913367,"score_spread":0.235386197518041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898341282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30919614,0.0003652123,0.6843254,0.00045732348,0.000033606004,0.000053130876,0.0002682389,0.00024464133,0.005056327],"genre_scores_gemma":[0.9910641,0.00020989508,0.0064167217,0.00002312611,0.00003152039,0.00003132291,0.00011667245,0.000023007298,0.0020837004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938977,0.00018806694,0.000025857687,0.0001126383,0.00017375973,0.00010995425],"domain_scores_gemma":[0.996024,0.0028190021,0.0005182837,0.00019831887,0.00031203064,0.0001283317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018926429,0.0004264875,0.00048815136,0.0008972842,0.00022796851,0.00097647496,0.0010553357,0.0008169596,0.0018658281],"category_scores_gemma":[0.006229771,0.00033657253,0.00050668884,0.0006625992,0.0013093319,0.0016278838,0.0009047424,0.00082088826,0.00024606808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011805603,0.000042651704,0.005492747,0.00006384608,0.000031253967,0.00047731094,0.00015989026,0.8973551,0.0028161497,0.08185196,0.00069588784,0.010895224],"study_design_scores_gemma":[0.000004468069,0.000019737787,0.0010069747,0.000006225655,0.0000049545247,0.00006296101,0.000029864485,0.98271215,0.00036125586,0.015598211,0.00018165338,0.000011530696],"about_ca_topic_score_codex":0.001395503,"about_ca_topic_score_gemma":0.0008775084,"teacher_disagreement_score":0.0018926429,"about_ca_system_score_codex":0.000622641,"about_ca_system_score_gemma":0.00036658256,"threshold_uncertainty_score":0.010009348},"labels":[],"label_agreement":null},{"id":"W2901339562","doi":"10.25071/10315/35270","title":"Improved Modal Contribution Factors As Response Tracking Mechanisms For Dynamic Systems During Design Optimization","year":2018,"lang":"en","type":"article","venue":"Progress in Canadian Mechanical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada); Carleton University","funders":"","keywords":"Computer science; Modal; Tracking (education); Control engineering; Control theory (sociology); Engineering; Control (management); Artificial intelligence; Materials science","score_opus":0.033949967250405715,"score_gpt":0.3018472131720031,"score_spread":0.26789724592159736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901339562","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0089240195,0.0002348511,0.988394,0.00003406299,0.000013880213,0.00003140062,0.00001228631,0.000108641856,0.0022468583],"genre_scores_gemma":[0.6458837,0.0010999735,0.34855518,0.00006174624,0.0000850889,0.0003042071,0.00011107648,0.0001990023,0.0037000661],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985668,0.00061218464,0.000056087738,0.00014303888,0.0005269142,0.00009508156],"domain_scores_gemma":[0.99666226,0.0022397796,0.00034960048,0.0002798019,0.00040565175,0.00006303759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004206474,0.002016244,0.00086013693,0.0014999376,0.00034551264,0.00096304074,0.0009942552,0.00083371525,0.0023865518],"category_scores_gemma":[0.009670463,0.0005363932,0.0010854267,0.00061289594,0.0010430063,0.00158794,0.0011048676,0.0010613075,0.00041436314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000124337,0.000048666607,0.0007574436,0.00026479707,0.000060782982,0.00007687771,0.00016205363,0.8686492,0.01292827,0.04284847,0.0003462783,0.073732734],"study_design_scores_gemma":[0.0000072345115,0.00012038721,0.00030335417,0.00004121218,0.000021175523,0.000031699194,0.000018438364,0.9866501,0.003543429,0.007764052,0.0014782808,0.00002054382],"about_ca_topic_score_codex":0.0009097128,"about_ca_topic_score_gemma":0.0008228347,"teacher_disagreement_score":0.004206474,"about_ca_system_score_codex":0.00054116064,"about_ca_system_score_gemma":0.000464052,"threshold_uncertainty_score":0.022246242},"labels":[],"label_agreement":null},{"id":"W2903258941","doi":"10.1111/sjos.12317","title":"Uncertainty Quantification in Case of Imperfect Models: A Non‐Bayesian Approach","year":2018,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Mathematics; Quantile; Point estimation; Uncertainty quantification; Confidence interval; Coverage probability; Prediction interval; Interval (graph theory); Bayesian probability; Bounded function; Imperfect; Sensitivity analysis; Stochastic modelling; Order statistic; Uncertainty analysis; Applied mathematics; Statistics","score_opus":0.09217781081737214,"score_gpt":0.34887557024930893,"score_spread":0.2566977594319368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903258941","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064261435,0.00043233426,0.991184,0.000488812,0.000028721677,0.000021510272,0.000044213724,0.000031670894,0.0013425925],"genre_scores_gemma":[0.7469022,0.0017512356,0.2473496,0.00041889638,0.0004625229,0.0003168726,0.00024017833,0.00011624226,0.0024421804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9898115,0.00574988,0.0004457204,0.0009653745,0.0026258735,0.00040170402],"domain_scores_gemma":[0.9363796,0.05459507,0.0035287722,0.0024910467,0.0024609792,0.0005445194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017831577,0.0011552894,0.0023703827,0.002776361,0.0009229432,0.0038435932,0.0032627424,0.0023293311,0.0016124318],"category_scores_gemma":[0.057547875,0.0012916299,0.0015923019,0.0016760811,0.004014693,0.0047048205,0.004812056,0.004096518,0.00020543973],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004782321,0.000049592345,0.0009240949,0.00019481718,0.00012348285,0.00027465858,0.00018127225,0.61029315,0.0006169049,0.37291244,0.0006371926,0.013744617],"study_design_scores_gemma":[0.000007199815,0.000020876696,0.00025123105,0.000052036557,0.000020112508,0.00006183325,0.000025388274,0.8060503,0.00027730022,0.19262086,0.00058556144,0.000027290605],"about_ca_topic_score_codex":0.0042094514,"about_ca_topic_score_gemma":0.0025092247,"teacher_disagreement_score":0.017831577,"about_ca_system_score_codex":0.002363343,"about_ca_system_score_gemma":0.0021709763,"threshold_uncertainty_score":0.09430355},"labels":[],"label_agreement":null},{"id":"W2903885922","doi":"10.1016/j.compfluid.2018.12.003","title":"On the influence of uncertainty in computational simulations of a high-speed jet flow from an aircraft exhaust","year":2018,"lang":"en","type":"article","venue":"Computers & Fluids","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Seventh Framework Programme","keywords":"Computational fluid dynamics; Reynolds-averaged Navier–Stokes equations; Turbulence; Jet (fluid); Uncertainty quantification; Computer science; Flow (mathematics); Mechanics; Computation; Statistical physics; Physics; Algorithm","score_opus":0.047093156490599766,"score_gpt":0.312776921980276,"score_spread":0.26568376548967626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903885922","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9633127,0.0006171703,0.02334014,0.0011891833,0.00012838551,0.00004888343,0.00018480814,0.00014292121,0.011035874],"genre_scores_gemma":[0.99795866,0.00007524369,0.0014588701,0.000047667745,0.00002043268,0.000011843541,0.0000429217,0.000029645684,0.00035460113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991665,0.0004040735,0.000043794196,0.00007205762,0.00018640324,0.00012711414],"domain_scores_gemma":[0.98170257,0.016155604,0.00058102264,0.00030420758,0.00088692765,0.0003696466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017727502,0.0008551641,0.000978221,0.0010043761,0.0012850876,0.0017171532,0.0010058015,0.002473063,0.0010816462],"category_scores_gemma":[0.017475333,0.0006427948,0.00092604244,0.0005459904,0.0019383489,0.001194069,0.0011530475,0.001760365,0.00008105447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010379504,0.00006649113,0.001225991,0.000026053229,0.00002137622,0.00006905746,0.000036218164,0.9959044,0.00066158327,0.00092269166,0.00009919894,0.00086313643],"study_design_scores_gemma":[0.0000056204353,0.000024689667,0.0003625799,0.000003572573,0.0000056167487,0.0000037285208,0.000013249676,0.99901104,0.00033435988,0.00020199924,0.000028765804,0.0000048108714],"about_ca_topic_score_codex":0.023084966,"about_ca_topic_score_gemma":0.009271643,"teacher_disagreement_score":0.023084966,"about_ca_system_score_codex":0.00094308716,"about_ca_system_score_gemma":0.0008288378,"threshold_uncertainty_score":0.04590118},"labels":[],"label_agreement":null},{"id":"W2904251429","doi":"10.1016/j.envsoft.2018.12.002","title":"A multi-method Generalized Global Sensitivity Matrix approach to accounting for the dynamical nature of earth and environmental systems models","year":2018,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Sensitivity (control systems); Earth (classical element); Matrix (chemical analysis); Dynamical systems theory; Environmental accounting; Environmental science; Econometrics; Computer science; Mathematics; Accounting; Economics; Physics; Engineering; Materials science","score_opus":0.04618148887534965,"score_gpt":0.3014373271078217,"score_spread":0.255255838232472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904251429","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029127328,0.00006049824,0.9962269,0.000054528155,0.000023820132,0.000015891557,0.000030335397,0.00009118224,0.0005841446],"genre_scores_gemma":[0.37037057,0.0002834154,0.6240199,0.00014489923,0.00012915743,0.0002399815,0.00021520657,0.00028347148,0.0043134387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990079,0.0006271001,0.000033321172,0.00012527354,0.00016166193,0.000044818662],"domain_scores_gemma":[0.9977397,0.0015519833,0.00014416639,0.00018867024,0.0003171765,0.000058271715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023251392,0.0009967582,0.0008204006,0.0009939679,0.00049720425,0.0008379139,0.0013823957,0.0009131338,0.0028405332],"category_scores_gemma":[0.0053535295,0.0006931105,0.0016429479,0.0006064848,0.0006347452,0.0014996078,0.00153138,0.0013967608,0.0003010167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022624206,0.00003196561,0.00034986463,0.000048400256,0.00010368559,0.000059545262,0.00003700078,0.94160813,0.001821191,0.032985874,0.00047181713,0.022459904],"study_design_scores_gemma":[0.0000024959097,0.00001259965,0.00007685902,0.0000025611464,0.0000078650955,0.000008969894,0.000002766575,0.9917996,0.00017848202,0.007506005,0.00039546264,0.0000063143298],"about_ca_topic_score_codex":0.00606776,"about_ca_topic_score_gemma":0.007966136,"teacher_disagreement_score":0.00606776,"about_ca_system_score_codex":0.0006770471,"about_ca_system_score_gemma":0.0009285366,"threshold_uncertainty_score":0.012296617},"labels":[],"label_agreement":null},{"id":"W2906356283","doi":"10.1109/access.2018.2888903","title":"Machine Learning and Uncertainty Quantification for Surrogate Models of Integrated Devices With a Large Number of Parameters","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":103,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Polynomial chaos; Support vector machine; Uncertainty quantification; Surrogate model; Computer science; Machine learning; Artificial intelligence; Least squares support vector machine; Random forest; Set (abstract data type); Polynomial; Algorithm; Mathematics; Statistics; Monte Carlo method","score_opus":0.12826336584120174,"score_gpt":0.3881523569953021,"score_spread":0.25988899115410036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906356283","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01697951,0.00019055087,0.9821301,0.00008102096,0.000008138443,0.000009498739,0.000027073691,0.00007121303,0.00050292833],"genre_scores_gemma":[0.88832456,0.0005223288,0.10994159,0.000044838045,0.000032710683,0.00010654851,0.0001232432,0.000040565094,0.0008636123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988164,0.0005892872,0.000056560486,0.00012017337,0.00037200472,0.00004572538],"domain_scores_gemma":[0.9967968,0.0023494277,0.00035690377,0.00026170583,0.00020450402,0.00003057876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021476534,0.0007299341,0.001031768,0.000579454,0.0002598919,0.0008459605,0.00062080025,0.00091889815,0.00044317878],"category_scores_gemma":[0.007514752,0.0004125786,0.0006089401,0.0005546586,0.00097322155,0.0017659108,0.0007801385,0.00096998207,0.00011076179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021315782,0.000010633216,0.00018443378,0.00003843393,0.000013847501,0.0000263979,0.000024840901,0.97762704,0.0018998707,0.010243307,0.00007768022,0.009832206],"study_design_scores_gemma":[7.00123e-7,0.000008431982,0.000039613504,0.0000029774517,0.0000012229357,0.0000086723885,0.0000022261409,0.9963947,0.0004924006,0.0029616167,0.00008499391,0.000002536235],"about_ca_topic_score_codex":0.00062579237,"about_ca_topic_score_gemma":0.00044689007,"teacher_disagreement_score":0.0021476534,"about_ca_system_score_codex":0.00050090754,"about_ca_system_score_gemma":0.00047020684,"threshold_uncertainty_score":0.011358023},"labels":[],"label_agreement":null},{"id":"W2910070996","doi":"10.1109/apusncursinrsm.2018.8608469","title":"Uncertainty Quantification of Vector Parabolic Equation based Wireless Channel Models Using Polynomial Chaos Expansion","year":2018,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Polynomial chaos; Polynomial expansion; Monte Carlo method; Uncertainty quantification; Polynomial; Channel (broadcasting); Applied mathematics; Computer science; Propagation of uncertainty; CHAOS (operating system); Wireless; Mathematical optimization; Algorithm; Mathematics; Mathematical analysis; Telecommunications; Machine learning","score_opus":0.24481050000504365,"score_gpt":0.3551160928930871,"score_spread":0.11030559288804345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910070996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014929054,0.000074588155,0.98404574,0.000059745915,0.000008038768,0.000015794672,0.000031842403,0.00005101496,0.0007842544],"genre_scores_gemma":[0.90246755,0.0004336574,0.094900936,0.000051987798,0.00003626648,0.00010502013,0.00013153057,0.0000508625,0.0018222156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993845,0.00023298578,0.00002812974,0.00006589922,0.0002437387,0.000044758104],"domain_scores_gemma":[0.9987135,0.00085352163,0.00015536736,0.00008540036,0.0001657267,0.000026538353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010279905,0.0006999109,0.0005385035,0.00056040706,0.0002935077,0.00073773327,0.0007929455,0.00065764895,0.00043214965],"category_scores_gemma":[0.0028329715,0.00031487594,0.0005995387,0.00043647253,0.0007667285,0.0013812325,0.0011979247,0.0010715971,0.0000835412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014001107,0.000010753382,0.00024057348,0.000020195963,0.00001174919,0.000027346148,0.000025868327,0.9738505,0.0027495506,0.01762577,0.000118053096,0.0053056707],"study_design_scores_gemma":[5.775543e-7,0.000004308795,0.000024417157,9.131428e-7,9.62166e-7,0.000005206408,0.0000015513083,0.9981521,0.00028087242,0.001479925,0.000046812074,0.0000024264077],"about_ca_topic_score_codex":0.0022419037,"about_ca_topic_score_gemma":0.0011459161,"teacher_disagreement_score":0.0022419037,"about_ca_system_score_codex":0.0005707578,"about_ca_system_score_gemma":0.000767064,"threshold_uncertainty_score":0.0054365993},"labels":[],"label_agreement":null},{"id":"W2911907565","doi":"10.1142/s0218539319500190","title":"Second-Moment-Based Design of Dynamic Systems with Both Uncertain Excitations and Parameters Via Differentiable Meta-Models","year":2019,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Singular value decomposition; Matrix (chemical analysis); Differentiable function; Moment (physics); Singular value; Applied mathematics; Nonlinear system; Design matrix; Computer science; Component (thermodynamics); Mathematics; Mathematical optimization; Algorithm; Linear model; Eigenvalues and eigenvectors; Mathematical analysis; Statistics; Physics","score_opus":0.09376547983008367,"score_gpt":0.32601212060825224,"score_spread":0.2322466407781686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911907565","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007073496,0.00021086978,0.9897484,0.00011775406,0.000018482953,0.000023364151,0.000037809143,0.00017391739,0.0025959748],"genre_scores_gemma":[0.8058265,0.0005006657,0.1880839,0.00013619818,0.000035777328,0.00033533378,0.0001556637,0.00014151313,0.0047844984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995764,0.00014326982,0.000020768117,0.000079946876,0.00014028465,0.00003942773],"domain_scores_gemma":[0.9993955,0.0003090927,0.000107012704,0.00006618419,0.000101296624,0.000021002252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010553312,0.0012653249,0.0011990326,0.00053714606,0.00032038317,0.0014091402,0.0009239607,0.0011630097,0.0017201541],"category_scores_gemma":[0.0018716591,0.0007710382,0.0014192951,0.00040113748,0.00085728127,0.0009782503,0.0010445297,0.0012717751,0.00040479895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024748257,0.000009347821,0.000111350426,0.000048742502,0.00002405184,0.000024534362,0.00002981801,0.98171264,0.0015929578,0.009575693,0.00013689038,0.006709129],"study_design_scores_gemma":[0.000004042976,0.000027224203,0.000029621822,0.0000108287095,0.0000062141066,0.0000072646876,0.0000040799073,0.99598926,0.00051176094,0.0029589834,0.0004467039,0.00000404986],"about_ca_topic_score_codex":0.0013389119,"about_ca_topic_score_gemma":0.0015995579,"teacher_disagreement_score":0.0017201541,"about_ca_system_score_codex":0.0008348604,"about_ca_system_score_gemma":0.00085594103,"threshold_uncertainty_score":0.0060574412},"labels":[],"label_agreement":null},{"id":"W2912668202","doi":"10.1080/10485252.2019.1567727","title":"Estimation of extreme quantiles in a simulation model","year":2019,"lang":"en","type":"article","venue":"Journal of nonparametric statistics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Quantile; Mathematics; Statistics; Rate of convergence; Estimation; Convergence (economics); Applied mathematics; Sampling (signal processing); Function (biology); Econometrics; Computer science; Key (lock)","score_opus":0.1557553884902597,"score_gpt":0.38629018029002926,"score_spread":0.23053479179976957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912668202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057955414,0.00014366538,0.94057477,0.00025608562,0.000011521342,0.00005208702,0.000098823344,0.0001419027,0.0007658465],"genre_scores_gemma":[0.89685667,0.0003422795,0.100352496,0.0000849718,0.000029832685,0.00025115616,0.00035533725,0.000045820227,0.0016814184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99470466,0.003952699,0.00016340236,0.0004579897,0.00044896008,0.00027229454],"domain_scores_gemma":[0.9686783,0.026556302,0.0019471362,0.0012739094,0.0009869224,0.000557314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010543318,0.00077873026,0.0017916332,0.001193647,0.00042728896,0.0018827135,0.0016748139,0.0019521138,0.0016394914],"category_scores_gemma":[0.034979697,0.0008460282,0.0009999122,0.0012282956,0.0022485703,0.0018365695,0.0020525448,0.0022221962,0.0002871745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008287665,0.000021320158,0.0011825616,0.000023758286,0.000025655194,0.00005513357,0.00004054469,0.9689154,0.00034077742,0.027247384,0.00009569651,0.0019689486],"study_design_scores_gemma":[0.000006911265,0.00001988527,0.00015411797,0.000006536868,0.000004312348,0.000013136194,0.000006618509,0.9890745,0.00012076121,0.01049673,0.00009008837,0.0000063105554],"about_ca_topic_score_codex":0.003696158,"about_ca_topic_score_gemma":0.0016303663,"teacher_disagreement_score":0.010543318,"about_ca_system_score_codex":0.0013919955,"about_ca_system_score_gemma":0.0012029433,"threshold_uncertainty_score":0.055759072},"labels":[],"label_agreement":null},{"id":"W2913645729","doi":"10.22215/etd/2018-13315","title":"Aeroelastic oscillations of a pitching cantilever wing with structural geometric nonlinearities: theory, numerical simulation and global sensitivity analysis","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Royal Military College of Canada","funders":"","keywords":"Aeroelasticity; Flutter; Aerodynamics; Nonlinear system; Parametric statistics; Cantilever; Wing; Sensitivity (control systems); Structural engineering; Aerodynamic force; Kinematics; Engineering; Classical mechanics; Physics; Mathematics; Mechanics","score_opus":0.03379298782390107,"score_gpt":0.34365246875420014,"score_spread":0.3098594809302991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913645729","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6547952,0.0011976053,0.3099276,0.0007207732,0.000064279564,0.00010005155,0.00016192398,0.00022871558,0.032803826],"genre_scores_gemma":[0.9905268,0.0004142277,0.005820871,0.000031676827,0.0000148585,0.000036048616,0.00004272693,0.00002874926,0.0030840677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998797,0.0000408326,0.000003500118,0.000019567871,0.000041473766,0.000014893651],"domain_scores_gemma":[0.99961615,0.0002657117,0.000036644527,0.000024540554,0.000043600117,0.000013226975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042106866,0.0004360446,0.00033453206,0.0003472867,0.00021394077,0.00048956653,0.0003475033,0.0006154698,0.0011531601],"category_scores_gemma":[0.0012728052,0.00022664064,0.0005466463,0.00017225624,0.0007635062,0.0003917552,0.00069536,0.0006119186,0.00011901495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003192955,0.000038637598,0.000778991,0.000057368055,0.000018078379,0.0000841433,0.00007109619,0.97243744,0.012001553,0.0083720805,0.00024896077,0.0058596972],"study_design_scores_gemma":[0.0000022207516,0.000015565276,0.00027725796,0.000005704017,0.0000029289663,0.00001514104,0.000012446867,0.997692,0.0007430812,0.0011128077,0.000117434014,0.0000035115097],"about_ca_topic_score_codex":0.0017791011,"about_ca_topic_score_gemma":0.0010232223,"teacher_disagreement_score":0.0017791011,"about_ca_system_score_codex":0.0003454295,"about_ca_system_score_gemma":0.0002864346,"threshold_uncertainty_score":0.0038577318},"labels":[],"label_agreement":null},{"id":"W2914475515","doi":"10.5075/epfl-mathicse-263557","title":"MATHICSE Technical Report : A continuation multi level Monte Carlo (C-MLMC) method for uncertainty quantification in compressible aerodynamics","year":2019,"lang":"en","type":"article","venue":"Infoscience (Ecole Polytechnique Fédérale de Lausanne)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"European Commission","keywords":"Continuation; Aerodynamics; Monte Carlo method; Compressibility; Computer science; Engineering; Aeronautics; Mathematics; Aerospace engineering; Statistics","score_opus":0.12292703842144492,"score_gpt":0.4036564563444347,"score_spread":0.2807294179229898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914475515","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020411024,0.00029865807,0.99146664,0.0003280749,0.0001836958,0.00006437901,0.00020551441,0.0009459791,0.0044659413],"genre_scores_gemma":[0.0682801,0.00045158304,0.9200754,0.00023147037,0.00021770237,0.00031864445,0.00072653254,0.0012402138,0.008458397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883586,0.00037828225,0.00004711358,0.00012958651,0.00055458414,0.00005453395],"domain_scores_gemma":[0.99751437,0.0010666343,0.00011754845,0.00043829533,0.00071283994,0.0001502384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027442023,0.00075721357,0.00086153136,0.0009246437,0.00056273723,0.0013374571,0.0011550499,0.0015496919,0.011217683],"category_scores_gemma":[0.008501977,0.00047258724,0.0010044896,0.0006908999,0.0009055569,0.0010181818,0.0017903637,0.0027072164,0.003940416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001914662,0.0001290066,0.0014239876,0.00023442002,0.00010715839,0.00032950056,0.00013989558,0.5517063,0.009297519,0.22681522,0.02163607,0.18798947],"study_design_scores_gemma":[0.00001663281,0.000019840249,0.00012393677,0.000018301205,0.0000055848373,0.000040517356,0.000003755212,0.96824384,0.0015643575,0.016132433,0.013818634,0.000012155034],"about_ca_topic_score_codex":0.0027236445,"about_ca_topic_score_gemma":0.0020237793,"teacher_disagreement_score":0.011217683,"about_ca_system_score_codex":0.00073618995,"about_ca_system_score_gemma":0.0015152777,"threshold_uncertainty_score":0.037526906},"labels":[],"label_agreement":null},{"id":"W2920829711","doi":"10.1029/2018wr023382","title":"Model Variable Augmentation (MVA) for Diagnostic Assessment of Sensitivity Analysis Results","year":2019,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sobol sequence; Bootstrapping (finance); Sensitivity (control systems); Variable (mathematics); Ranking (information retrieval); Reliability (semiconductor); Benchmark (surveying); Computer science; Variance (accounting); Sample (material); Statistics; Quality (philosophy); Econometrics; Variables; Mathematics; Machine learning; Engineering","score_opus":0.18933422743058698,"score_gpt":0.44498350169301665,"score_spread":0.25564927426242967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920829711","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08121775,0.00028654299,0.9108486,0.00019938024,0.000109178254,0.00059573015,0.0007661691,0.0036257925,0.0023508223],"genre_scores_gemma":[0.53295195,0.000059753613,0.46495733,0.00005407182,0.000030188581,0.0006055705,0.0007773618,0.0002217656,0.00034201978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9912224,0.005995974,0.0005252516,0.0007274063,0.0012983445,0.00023067584],"domain_scores_gemma":[0.95438933,0.03543335,0.0028936884,0.0035703005,0.003426003,0.0002873147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016514657,0.0019841876,0.0014463507,0.0046850946,0.00061937893,0.0016363598,0.0012576621,0.0008646614,0.0037671742],"category_scores_gemma":[0.055326365,0.00048192963,0.0017364094,0.0019024126,0.00085346436,0.0012882628,0.0021443865,0.0016485332,0.0003640762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010171647,0.00035730057,0.032556154,0.0008773459,0.0007798694,0.0005054787,0.00052724784,0.62294817,0.013593152,0.026054114,0.0045640315,0.29622],"study_design_scores_gemma":[0.00001657865,0.00020392274,0.0021259557,0.000050555354,0.00004977997,0.00006397399,0.000051179144,0.9878352,0.0035592837,0.005022866,0.0009909001,0.00002971925],"about_ca_topic_score_codex":0.001968698,"about_ca_topic_score_gemma":0.0016883486,"teacher_disagreement_score":0.016514657,"about_ca_system_score_codex":0.0007961008,"about_ca_system_score_gemma":0.0016115659,"threshold_uncertainty_score":0.087338924},"labels":[],"label_agreement":null},{"id":"W2921018371","doi":"10.3390/app9061106","title":"Development of a Vibroacoustic Stochastic Finite Element Prediction Tool for a CLT Floor","year":2019,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Cross laminated timber; Vibration; Finite element method; Computer science; Calibration; Structural engineering; Acoustics; Moduli; Soundproofing; Engineering; Mathematics; Physics; Statistics","score_opus":0.06562639390327185,"score_gpt":0.30236191784394173,"score_spread":0.23673552394066988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921018371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00979462,0.000029514536,0.98734283,0.000048583992,0.000024452687,0.00005254525,0.00009183774,0.0010456007,0.0015699932],"genre_scores_gemma":[0.3480918,0.00013940966,0.6475318,0.00008213476,0.000019024637,0.0004357716,0.0003714269,0.00023707893,0.0030915663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973875,0.000038522336,0.000015759557,0.00003575085,0.00015145827,0.000019728757],"domain_scores_gemma":[0.99951434,0.0002465606,0.000051612165,0.000044599732,0.00012291901,0.000019981953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005725357,0.0004846356,0.0004973981,0.00039638867,0.00026842346,0.00051868946,0.0011045702,0.001092075,0.0044544656],"category_scores_gemma":[0.0014202116,0.00043537296,0.00053320575,0.00025317617,0.00026888528,0.00033159743,0.0004927959,0.0008512354,0.00090443034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026597543,0.000037724905,0.00064448675,0.000083305444,0.000016073853,0.00008375153,0.000040735806,0.9509936,0.009686802,0.0024563742,0.00056322844,0.035367336],"study_design_scores_gemma":[0.0000027229667,0.0000094002535,0.000058801925,0.000005460206,0.0000016228238,0.0000119625465,0.0000042856745,0.997865,0.0012474209,0.00023208997,0.00055779837,0.0000033397184],"about_ca_topic_score_codex":0.0031244368,"about_ca_topic_score_gemma":0.0037691076,"teacher_disagreement_score":0.0044544656,"about_ca_system_score_codex":0.00028804128,"about_ca_system_score_gemma":0.0008903531,"threshold_uncertainty_score":0.014901638},"labels":[],"label_agreement":null},{"id":"W2921059766","doi":"10.1109/tr.2019.2898459","title":"Approximate Reliability Evaluation of Large-Scale Multistate Series-Parallel Systems","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canada First Research Excellence Fund; National Natural Science Foundation of China","keywords":"Discretization; Reliability (semiconductor); Series (stratigraphy); Computer science; Mathematical optimization; Scale (ratio); Process (computing); Limit (mathematics); Reliability theory; Algorithm; Gaussian process; Function (biology); Reliability engineering; Gaussian; Mathematics; Engineering; Failure rate","score_opus":0.05199833070200071,"score_gpt":0.3251776979862122,"score_spread":0.2731793672842115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921059766","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064695045,0.0004886802,0.93137795,0.00010599687,0.000023531362,0.000029660456,0.00005227855,0.00029379845,0.0029331555],"genre_scores_gemma":[0.9609406,0.0003034275,0.03766183,0.000018820894,0.000016087743,0.00004665979,0.00006043087,0.00004211222,0.0009100409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995945,0.00014710905,0.000015535106,0.000048216596,0.00016138212,0.000033327706],"domain_scores_gemma":[0.9992531,0.00041122222,0.00008733661,0.00006128557,0.00016287492,0.000024275223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008951681,0.0005549005,0.0006131636,0.00059206854,0.0003543727,0.0005431385,0.0006180053,0.000493189,0.000975858],"category_scores_gemma":[0.0021016602,0.00022023395,0.0005541278,0.00051929103,0.0005468756,0.00074391696,0.0005019435,0.00048081393,0.000101293714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023988128,0.000007190573,0.00052358734,0.000034983797,0.000011838406,0.00003256911,0.00002273983,0.985471,0.0009846211,0.0042848457,0.00016054104,0.008442213],"study_design_scores_gemma":[9.4651585e-7,0.000004079676,0.00006369812,0.000001355493,0.0000014201679,0.000005424217,0.0000038659728,0.99876827,0.00017672573,0.0009189196,0.00005391815,0.0000012689527],"about_ca_topic_score_codex":0.0051115025,"about_ca_topic_score_gemma":0.0024841512,"teacher_disagreement_score":0.0051115025,"about_ca_system_score_codex":0.00085083884,"about_ca_system_score_gemma":0.00074811885,"threshold_uncertainty_score":0.010163546},"labels":[],"label_agreement":null},{"id":"W2943670463","doi":"","title":"A Novel Method for Constructing Confidence Intervals for In Situ Stress","year":2018,"lang":"en","type":"article","venue":"ISRM International Symposium - 10th Asian Rock Mechanics Symposium","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Stress (linguistics); Computer science; Linguistics; Philosophy","score_opus":0.05202566950004682,"score_gpt":0.35315213751608726,"score_spread":0.3011264680160404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943670463","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00058273977,0.00003837672,0.99899334,0.0000098152,0.000012512824,0.000011274065,0.00002908126,0.00021759504,0.00010539201],"genre_scores_gemma":[0.060321357,0.00014044934,0.9376749,0.000060864084,0.00014284502,0.00018379904,0.00047598666,0.0002748821,0.00072507793],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9928139,0.0023274475,0.00050283584,0.0014287794,0.002655181,0.0002719193],"domain_scores_gemma":[0.95019364,0.037919544,0.0022919425,0.0038645193,0.005183968,0.00054627657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008839727,0.0017011216,0.0022673544,0.0041460725,0.00084567524,0.0029357097,0.0042932685,0.001949908,0.004048885],"category_scores_gemma":[0.055532064,0.0011298201,0.0020761676,0.0028555875,0.0012203482,0.00220963,0.0028814797,0.0037405218,0.0013821583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005588026,0.00026814238,0.0057327463,0.0006093634,0.0007810819,0.00045836956,0.00047820492,0.25957474,0.020034121,0.059430435,0.0042880406,0.6477859],"study_design_scores_gemma":[0.000058975987,0.00011288923,0.0013152403,0.00005555069,0.00010624043,0.00034235494,0.000037805657,0.95966804,0.0059972582,0.02875327,0.003472844,0.00007959644],"about_ca_topic_score_codex":0.0027830417,"about_ca_topic_score_gemma":0.0023108292,"teacher_disagreement_score":0.008839727,"about_ca_system_score_codex":0.00058058347,"about_ca_system_score_gemma":0.0014040787,"threshold_uncertainty_score":0.046749473},"labels":[],"label_agreement":null},{"id":"W2944103097","doi":"","title":"Investigating the Effect of Sample Size on Uncertainty in Stress Estimations","year":2018,"lang":"en","type":"article","venue":"52nd U.S. Rock Mechanics/Geomechanics Symposium","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sample size determination; Stress (linguistics); Econometrics; Sample (material); Statistics; Mathematics; Environmental science; Chemistry; Chromatography","score_opus":0.03286322828733785,"score_gpt":0.29372964755223,"score_spread":0.2608664192648922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944103097","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77928257,0.0040729837,0.20567282,0.0038635302,0.00058341044,0.0005678954,0.0007044129,0.00045233217,0.0048001064],"genre_scores_gemma":[0.9831172,0.00018303185,0.015482732,0.00033853776,0.00009794281,0.00015385087,0.00021378521,0.00006465517,0.00034819273],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8190298,0.15628435,0.0051587247,0.009256043,0.008913873,0.0013572227],"domain_scores_gemma":[0.059223417,0.9172235,0.005655933,0.013330842,0.004107782,0.00045856796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.17898676,0.00080605864,0.0011574168,0.0009627838,0.0009553655,0.0019471276,0.0017649141,0.0028878958,0.0015872854],"category_scores_gemma":[0.65871835,0.000824756,0.0018471516,0.000985317,0.003198975,0.0033917732,0.002208436,0.0023859877,0.00020052982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.04477398,0.0016907929,0.39973217,0.00152416,0.012518199,0.0017768345,0.0035299559,0.2192488,0.0127951065,0.025248053,0.0053429296,0.27181908],"study_design_scores_gemma":[0.004315239,0.016018026,0.22027777,0.00077959604,0.009018037,0.0017830145,0.0013576825,0.66011864,0.026614951,0.052933913,0.0063162735,0.00046683673],"about_ca_topic_score_codex":0.0033187745,"about_ca_topic_score_gemma":0.002781749,"teacher_disagreement_score":0.17898676,"about_ca_system_score_codex":0.0009346054,"about_ca_system_score_gemma":0.0016712165,"threshold_uncertainty_score":0.94658375},"labels":[],"label_agreement":null},{"id":"W2946173218","doi":"10.1109/sampta45681.2019.9030842","title":"Reconstructing high-dimensional Hilbert-valued functions via compressed sensing","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Oak Ridge National Laboratory; Office of Science; UT-Battelle; Battelle; Advanced Scientific Computing Research; U.S. Department of Energy","keywords":"Hilbert space; Reproducing kernel Hilbert space; Parameterized complexity; Mathematics; Applied mathematics; Norm (philosophy); Compressed sensing; Algorithm; Pure mathematics","score_opus":0.08693105610771029,"score_gpt":0.3072872394297375,"score_spread":0.22035618332202722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946173218","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035909284,0.000091628324,0.995777,0.00010655165,0.000011542915,0.000007905099,0.000016221662,0.00003989298,0.0003582809],"genre_scores_gemma":[0.3192913,0.0009794543,0.67656064,0.00019342077,0.00014450133,0.00010586896,0.0002163041,0.000060656555,0.0024478335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994955,0.00016475513,0.000023223087,0.0000703167,0.00021151992,0.000034673914],"domain_scores_gemma":[0.9985228,0.0009587161,0.00015656961,0.00018985078,0.00012666208,0.00004540204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001251101,0.00079323835,0.0006295362,0.00056616054,0.00027483172,0.0006051049,0.00094899104,0.0011587847,0.00084867456],"category_scores_gemma":[0.003655169,0.00026781252,0.00065997994,0.0007052634,0.001254371,0.0015892333,0.0013696287,0.0014254594,0.00026144445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012350104,0.00008956328,0.00075032044,0.00023517318,0.000077084835,0.00020721744,0.00015446747,0.6843742,0.03110623,0.1682691,0.0016349815,0.11297815],"study_design_scores_gemma":[0.000003550764,0.00001871745,0.000049186485,0.0000036543456,0.000003042359,0.000040094525,0.000005350864,0.9881892,0.0022242176,0.008986021,0.00047167492,0.0000053205104],"about_ca_topic_score_codex":0.0011435002,"about_ca_topic_score_gemma":0.0011141786,"teacher_disagreement_score":0.001251101,"about_ca_system_score_codex":0.00040835017,"about_ca_system_score_gemma":0.0006430452,"threshold_uncertainty_score":0.006616533},"labels":[],"label_agreement":null},{"id":"W2946884658","doi":"10.1016/j.compstruct.2019.111031","title":"Stochastic characterization of textile reinforcements in composites based on X-ray microtomographic scans","year":2019,"lang":"en","type":"article","venue":"Composite Structures","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Microscale chemistry; Materials science; Glass fiber; Hyperparameter; Characterization (materials science); Composite material; Crimp; Fiber; Gaussian process; Stochastic process; Volume fraction; Textile; Gaussian; Algorithm; Mathematics","score_opus":0.020633277965166567,"score_gpt":0.2759320216035969,"score_spread":0.25529874363843036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946884658","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63825476,0.0002587178,0.35914516,0.000109357614,0.000006791059,0.000029686835,0.00015887679,0.0002914546,0.0017451933],"genre_scores_gemma":[0.98648757,0.00006731797,0.013081354,0.000007692203,0.000004449388,0.000010064694,0.00006381407,0.000012725199,0.00026498974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973696,0.00004832309,0.000011306974,0.000048486505,0.00013674077,0.000018157609],"domain_scores_gemma":[0.9991253,0.00036402346,0.0002688141,0.000075132775,0.0001366975,0.000030001522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003928173,0.00027784202,0.00023188996,0.00088576775,0.00016683005,0.0005678737,0.00032903222,0.00043347589,0.00057414145],"category_scores_gemma":[0.0014325557,0.00029535446,0.00020215854,0.00042162032,0.00058902404,0.00043756107,0.00029571934,0.00023099426,0.00009314309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004971735,0.00011252678,0.02588144,0.00019342905,0.00006464982,0.00043811457,0.00021089498,0.5129204,0.4121147,0.0074271294,0.0003203988,0.039819095],"study_design_scores_gemma":[0.000004485855,0.000045276414,0.017548352,0.0000075195244,0.000009945985,0.00022387614,0.000039386796,0.94579864,0.035025507,0.0010802991,0.00019643185,0.000020229218],"about_ca_topic_score_codex":0.00080633105,"about_ca_topic_score_gemma":0.001645636,"teacher_disagreement_score":0.00088576775,"about_ca_system_score_codex":0.00035563132,"about_ca_system_score_gemma":0.0002496192,"threshold_uncertainty_score":0.002580285},"labels":[],"label_agreement":null},{"id":"W2950219488","doi":"10.48550/arxiv.1702.04781","title":"Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Arrow Energy; CMG Reservoir Simulation Foundation","keywords":"Polynomial chaos; Solver; Uncertainty quantification; Polynomial; Sensitivity (control systems); Coal; Computer science; Extraction (chemistry); Algorithm; Applied mathematics; Mathematical optimization; Mathematics; Statistics; Monte Carlo method; Engineering; Machine learning; Chemistry","score_opus":0.30094925166743114,"score_gpt":0.2742331604949803,"score_spread":0.02671609117245083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950219488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09252299,0.000118553966,0.90517443,0.00020309824,0.000013075139,0.000022168544,0.00006451234,0.00017734438,0.0017038469],"genre_scores_gemma":[0.9561574,0.00008470089,0.043079667,0.000021252912,0.000008789957,0.000031463012,0.00007338533,0.000028522734,0.0005147629],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940777,0.00023558592,0.000026402735,0.000079893834,0.00020895258,0.000041364026],"domain_scores_gemma":[0.9980849,0.0014139984,0.00016877032,0.000113260256,0.00018548385,0.000033632892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013788857,0.00044153817,0.00053268427,0.00042135757,0.00026751647,0.000662389,0.00048104866,0.0005336964,0.00052467885],"category_scores_gemma":[0.005244458,0.00028902304,0.00043391433,0.0004038273,0.00058057986,0.0007352308,0.00080615084,0.00070796884,0.000080385566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024996987,0.000007987977,0.0004729312,0.000015211781,0.000009545817,0.000016035645,0.0000142968265,0.986548,0.0014035432,0.0054310993,0.000083946215,0.0059723645],"study_design_scores_gemma":[7.369126e-7,0.0000035366406,0.000045364206,7.955412e-7,5.569334e-7,0.0000017569342,8.229326e-7,0.9985607,0.00037963828,0.0009731005,0.000031860196,0.0000010642066],"about_ca_topic_score_codex":0.0030886286,"about_ca_topic_score_gemma":0.0018653407,"teacher_disagreement_score":0.0030886286,"about_ca_system_score_codex":0.0006635196,"about_ca_system_score_gemma":0.0009742132,"threshold_uncertainty_score":0.0072922707},"labels":[],"label_agreement":null},{"id":"W2951133449","doi":"10.1007/s10260-019-00476-8","title":"Planning step-stress test plans under Type-I hybrid censoring for the log-location-scale distribution","year":2019,"lang":"en","type":"article","venue":"Statistical Methods & Applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Censoring (clinical trials); Scale (ratio); Computer science; Statistics; Test (biology); Mathematics; Geography; Cartography; Geology","score_opus":0.10674632335095684,"score_gpt":0.44308220262527076,"score_spread":0.3363358792743139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951133449","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15362844,0.00022927613,0.83961165,0.00050836045,0.000045516732,0.00039715983,0.00067865994,0.0010518135,0.0038490547],"genre_scores_gemma":[0.8791703,0.00007948808,0.117673464,0.000091335394,0.000023629009,0.00034105062,0.00067965616,0.00011204261,0.0018291066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999047,0.00039892228,0.000038057427,0.00016120022,0.0001733364,0.00018150035],"domain_scores_gemma":[0.98947656,0.008043159,0.00061929744,0.000567387,0.00078635215,0.00050725054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023357638,0.00081061013,0.0008156135,0.00069237576,0.0003843626,0.0008181019,0.0010652925,0.0010100689,0.0048237937],"category_scores_gemma":[0.012707688,0.000480706,0.0008087515,0.0005171145,0.00091815775,0.0010714,0.0010683195,0.001068608,0.0005109977],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005605295,0.000104210754,0.0043139094,0.00008723691,0.0000541247,0.00019169082,0.00009840202,0.930991,0.0014955365,0.011710698,0.0017384135,0.04865427],"study_design_scores_gemma":[0.00003163228,0.0001618769,0.0010193225,0.000010394219,0.000018074155,0.000025776946,0.00004978925,0.9839048,0.0010432046,0.0134748025,0.00024767438,0.000012648009],"about_ca_topic_score_codex":0.006454099,"about_ca_topic_score_gemma":0.00746278,"teacher_disagreement_score":0.006454099,"about_ca_system_score_codex":0.0009407668,"about_ca_system_score_gemma":0.0018229202,"threshold_uncertainty_score":0.016137242},"labels":[],"label_agreement":null},{"id":"W2951165782","doi":"10.1016/j.engstruct.2019.06.012","title":"Predicting reinforcing bar development length using polynomial chaos expansions","year":2019,"lang":"en","type":"article","venue":"Engineering Structures","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Bar (unit); Rebar; Parametric statistics; Structural engineering; Polynomial chaos; Sensitivity (control systems); Artificial neural network; Polynomial; Test data; Code (set theory); Stress (linguistics); Algorithm; Engineering; Computer science; Mathematics; Artificial intelligence; Statistics; Mathematical analysis; Monte Carlo method","score_opus":0.04196430485576509,"score_gpt":0.2852974106510975,"score_spread":0.2433331057953324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951165782","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5987921,0.0002012597,0.39661336,0.00013439351,0.000024636107,0.000024098417,0.0001662621,0.00071447267,0.0033294403],"genre_scores_gemma":[0.9797192,0.00005061461,0.019231247,0.0000067592873,0.0000061139885,0.000008870742,0.000063490275,0.00003889586,0.00087488326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998994,0.000019252993,0.0000035028218,0.000023997874,0.000041091113,0.000012816957],"domain_scores_gemma":[0.99883145,0.0007147211,0.00016977573,0.000079040656,0.00016034188,0.0000445512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028792623,0.00032256902,0.00029790078,0.00082677393,0.00022619334,0.0004167626,0.0003617935,0.0005578306,0.0010978214],"category_scores_gemma":[0.0019030493,0.00030027577,0.0002730851,0.00040093216,0.00026425574,0.0006719688,0.00033452822,0.0006033085,0.00035713453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007819677,0.000045871762,0.006464312,0.00003099922,0.000015666968,0.00006059081,0.000031647516,0.9427242,0.01497622,0.0035881295,0.00035285778,0.03163135],"study_design_scores_gemma":[7.1182e-7,0.000005700539,0.0005809099,8.379518e-7,0.0000014164847,0.000005068245,0.00000142203,0.99773335,0.0012091773,0.0004156387,0.000043537962,0.0000023110965],"about_ca_topic_score_codex":0.0017254287,"about_ca_topic_score_gemma":0.0033336591,"teacher_disagreement_score":0.0017254287,"about_ca_system_score_codex":0.0004887261,"about_ca_system_score_gemma":0.00036262308,"threshold_uncertainty_score":0.0036725998},"labels":[],"label_agreement":null},{"id":"W2952903859","doi":"10.1007/s00603-019-01891-9","title":"Uncertainty in In Situ Stress Estimations: A Statistical Simulation to Study the Effect of Numbers of Stress Measurements","year":2019,"lang":"en","type":"article","venue":"Rock Mechanics and Rock Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; University of Toronto","funders":"","keywords":"Stress (linguistics); In situ; Statistics; Mathematics; Physics","score_opus":0.03946273159092799,"score_gpt":0.31794757918601857,"score_spread":0.27848484759509057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952903859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8859603,0.00016856173,0.11219948,0.0002595423,0.000032103326,0.00003718628,0.00024027191,0.00013767976,0.0009648939],"genre_scores_gemma":[0.9917822,0.000032380067,0.007853599,0.000022258335,0.000007880552,0.00002650841,0.00009637905,0.00002026602,0.00015837087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754626,0.0013700395,0.00013494685,0.0003542613,0.00043083078,0.00016364022],"domain_scores_gemma":[0.90012455,0.09057737,0.0040980685,0.0027762442,0.0019555925,0.00046823217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074798763,0.00078527187,0.0008845402,0.0007419685,0.00059493625,0.00097397657,0.0011991732,0.0017888969,0.0006827524],"category_scores_gemma":[0.032868937,0.0008525833,0.0008432395,0.0008487287,0.0014902608,0.0017210363,0.0010519736,0.001591823,0.000082059196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015694933,0.000092704904,0.003394609,0.000022450984,0.000044402568,0.00003933107,0.000048501708,0.9919018,0.0011045551,0.0012249402,0.00007445399,0.0018952343],"study_design_scores_gemma":[0.000008090076,0.00006668671,0.00083459937,0.0000030475358,0.00001233799,0.000011669671,0.000010493728,0.9973109,0.0011068127,0.00059509673,0.000030996318,0.0000092854625],"about_ca_topic_score_codex":0.0058764876,"about_ca_topic_score_gemma":0.0047735414,"teacher_disagreement_score":0.0074798763,"about_ca_system_score_codex":0.0012233026,"about_ca_system_score_gemma":0.00072853453,"threshold_uncertainty_score":0.039557815},"labels":[],"label_agreement":null},{"id":"W2953457782","doi":"10.1139/cjce-2018-0720","title":"Reliability-based design of truck escape ramps","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Truck; Reliability engineering; Computer science; Moment (physics); Component (thermodynamics); Engineering; Simulation; Automotive engineering","score_opus":0.034097551854500203,"score_gpt":0.24170411077355544,"score_spread":0.20760655891905525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953457782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05033329,0.00014804202,0.9447406,0.000042125514,0.000021104748,0.00009382693,0.00006915046,0.00044527175,0.004106578],"genre_scores_gemma":[0.8644175,0.00012408,0.133269,0.000016962465,0.000013098355,0.00014298991,0.000105230225,0.00008616289,0.0018249723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883014,0.00042620787,0.000049109745,0.00014018593,0.00045956168,0.00009475003],"domain_scores_gemma":[0.99871457,0.00047286396,0.00021307633,0.000092302405,0.00045994823,0.000047287365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015361601,0.00066999195,0.00062951405,0.0012196271,0.00026014601,0.0005786821,0.00086224126,0.0004362911,0.0015336359],"category_scores_gemma":[0.0030546451,0.00051100284,0.000740156,0.000331369,0.00042522335,0.00044465263,0.00049395824,0.0005437536,0.00030373028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041287854,0.000012909705,0.0004908072,0.000049201604,0.000009362623,0.00004115663,0.000033302713,0.9732142,0.0042479145,0.0034233325,0.00018681708,0.018249804],"study_design_scores_gemma":[0.0000075386993,0.00009546596,0.00043323982,0.000007944704,0.000008702863,0.000030647603,0.000009464416,0.995821,0.0016529771,0.0011602959,0.00076410064,0.0000086519685],"about_ca_topic_score_codex":0.0020804894,"about_ca_topic_score_gemma":0.002795598,"teacher_disagreement_score":0.0020804894,"about_ca_system_score_codex":0.00069171714,"about_ca_system_score_gemma":0.0009393668,"threshold_uncertainty_score":0.008124113},"labels":[],"label_agreement":null},{"id":"W2954419084","doi":"10.3390/e21070649","title":"An Effective Approach for Reliability-Based Sensitivity Analysis with the Principle of Maximum Entropy and Fractional Moments","year":2019,"lang":"en","type":"article","venue":"Entropy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Principle of maximum entropy; Multiplicative function; Multivariate statistics; Applied mathematics; Sensitivity (control systems); Monte Carlo method; Maximum entropy probability distribution; Entropy (arrow of time); Mathematics; Computer science; Mathematical optimization; Algorithm; Statistical physics; Statistics; Mathematical analysis; Physics","score_opus":0.017951624970057443,"score_gpt":0.29399802943908043,"score_spread":0.276046404469023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954419084","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010519484,0.000106619635,0.99793965,0.000036483772,0.00001527389,0.000015392392,0.000007954109,0.00003555176,0.00079111016],"genre_scores_gemma":[0.3955413,0.0012973476,0.598793,0.00018879367,0.00029740384,0.00043764175,0.00010255737,0.0001662126,0.0031757962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99842215,0.00066281634,0.00006661855,0.0001363215,0.000658004,0.00005412043],"domain_scores_gemma":[0.9987557,0.0009105822,0.000089674664,0.00012343326,0.000102335136,0.000018240973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018546317,0.0014165969,0.0009703786,0.0018523036,0.00043799696,0.0009493424,0.00097502244,0.00086784415,0.001469317],"category_scores_gemma":[0.004565377,0.00049583905,0.0016704386,0.0008904732,0.0011515351,0.001329289,0.0016788713,0.0016984037,0.00022346771],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002512639,0.00005042952,0.0002823696,0.00021349854,0.00010899043,0.00012139459,0.00010770823,0.7210008,0.0120848175,0.21703716,0.0005600529,0.04840766],"study_design_scores_gemma":[0.0000022487175,0.000028067221,0.00012319183,0.000012062689,0.000016641477,0.00006455538,0.0000053300364,0.9596754,0.002153244,0.036771387,0.0011310783,0.000016740583],"about_ca_topic_score_codex":0.0006190989,"about_ca_topic_score_gemma":0.00046941324,"teacher_disagreement_score":0.0018546317,"about_ca_system_score_codex":0.00069464033,"about_ca_system_score_gemma":0.00062701653,"threshold_uncertainty_score":0.009808302},"labels":[],"label_agreement":null},{"id":"W2954898658","doi":"10.1016/j.crma.2019.05.009","title":"Sparse approximate solutions to stochastic Galerkin equations","year":2019,"lang":"fr","type":"article","venue":"Comptes Rendus Mathématique","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Mathematics; Krylov subspace; Conjugate gradient method; Discretization; Convergence (economics); Applied mathematics; Sparse approximation; Polynomial; Algorithm; Iterative method; Mathematical analysis","score_opus":0.09538316689213663,"score_gpt":0.3147640800241182,"score_spread":0.21938091313198158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954898658","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055990955,0.00012714886,0.9931855,0.00011593056,0.000024385265,0.000016955202,0.000019908899,0.00004421707,0.0008668175],"genre_scores_gemma":[0.47096232,0.00090554886,0.52087593,0.00018923027,0.0001764839,0.00031189786,0.00022969508,0.000111608715,0.0062372754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968934,0.00011300738,0.000012687168,0.000035951794,0.00012631953,0.000022765516],"domain_scores_gemma":[0.99946326,0.0003355202,0.000059755555,0.000035916557,0.0000842817,0.00002127856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006583165,0.0005352494,0.00077217753,0.00034261582,0.00024007521,0.0007030707,0.0006372572,0.0009963608,0.0011006166],"category_scores_gemma":[0.0019911276,0.00028437638,0.00047142032,0.00043148422,0.0008474822,0.0006687651,0.0009767319,0.001027175,0.00026074046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022139413,0.000015276522,0.00018377768,0.0000752317,0.000015635529,0.00003703357,0.000041121166,0.92234033,0.0021149938,0.061126992,0.00042519678,0.013602236],"study_design_scores_gemma":[0.0000026593018,0.0000036556546,0.000011078375,0.0000018587576,6.8173136e-7,0.0000041589497,0.0000021880264,0.9949083,0.00016990275,0.004625429,0.00026880763,0.0000013404955],"about_ca_topic_score_codex":0.0017572348,"about_ca_topic_score_gemma":0.0013633481,"teacher_disagreement_score":0.0017572348,"about_ca_system_score_codex":0.00038023072,"about_ca_system_score_gemma":0.0007942167,"threshold_uncertainty_score":0.0036819577},"labels":[],"label_agreement":null},{"id":"W2955302986","doi":"10.1007/978-3-030-21503-3_25","title":"A Framework to Implement Probabilistic Fatigue Design of Safe-Life Components","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in mechanical engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Probabilistic logic; Probabilistic design; Component (thermodynamics); Aerospace; Reliability engineering; Computer science; Process (computing); Probabilistic analysis of algorithms; Statistical model; Engineering; Engineering design process; Risk analysis (engineering); Systems engineering; Machine learning; Artificial intelligence","score_opus":0.12595275025084202,"score_gpt":0.32769233058375663,"score_spread":0.20173958033291461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955302986","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004454074,0.000020446607,0.9984017,0.000011997734,0.000007262448,0.000015289883,0.000010665317,0.00022928102,0.0008579302],"genre_scores_gemma":[0.07170107,0.00015449424,0.924718,0.00006180633,0.000029756047,0.00025570486,0.00012760266,0.00031214018,0.0026394287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99941766,0.00012408415,0.000032575503,0.00006867094,0.00029960103,0.00005745661],"domain_scores_gemma":[0.99950874,0.0001802729,0.00004447805,0.00008429019,0.00015753445,0.000024763458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013724137,0.0011077969,0.0007571522,0.00079207926,0.0005317045,0.0010743036,0.0026282542,0.001423521,0.0063120043],"category_scores_gemma":[0.0022887243,0.0007145937,0.0011915938,0.00047747185,0.00083406316,0.0010125012,0.0015446953,0.0016091971,0.0014789194],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004064269,0.000056177065,0.00021149566,0.0001061809,0.00002976388,0.00008309869,0.000077376615,0.76085246,0.008122355,0.15126483,0.0018458453,0.07730982],"study_design_scores_gemma":[0.000008530344,0.00002108405,0.000025403737,0.000014042862,0.0000072989505,0.000021533097,0.000004939998,0.96089107,0.0013070832,0.03335682,0.0043353685,0.000006798661],"about_ca_topic_score_codex":0.0022325462,"about_ca_topic_score_gemma":0.0029846716,"teacher_disagreement_score":0.0063120043,"about_ca_system_score_codex":0.00059821934,"about_ca_system_score_gemma":0.00097338704,"threshold_uncertainty_score":0.02111578},"labels":[],"label_agreement":null},{"id":"W2955391078","doi":"10.22215/etd/2018-13284","title":"Development and Design Optimization of High Fidelity Reduced Order Models for Dynamic Aeroelasticity Loads Analyses of Complex Airframes","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Mitacs; Bombardier","keywords":"Airframe; Aeroelasticity; Engineering; Finite element method; Flight envelope; Process (computing); Computer science; Structural engineering; Aerospace engineering; Aerodynamics","score_opus":0.1939939416103409,"score_gpt":0.3944833911086585,"score_spread":0.2004894494983176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955391078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027785989,0.00020959678,0.9652314,0.00007852305,0.000020020712,0.00009988471,0.00014272009,0.00035994503,0.0060719475],"genre_scores_gemma":[0.6360356,0.0009014311,0.35295418,0.000053186257,0.000027028002,0.00076354656,0.00050538,0.00024875923,0.008510946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978596,0.000048531583,0.000009134229,0.000026306641,0.00010647384,0.000023631865],"domain_scores_gemma":[0.999684,0.00015562844,0.000051812385,0.00003807396,0.000057489186,0.0000130844655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048778206,0.0006279417,0.0007448892,0.00044571442,0.00026879762,0.0009039784,0.0008053036,0.00082452066,0.002031079],"category_scores_gemma":[0.0010484024,0.0005613709,0.0011605474,0.00029661416,0.0003782571,0.00048803876,0.00059057784,0.0008646086,0.0006131521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007697322,0.00001389697,0.00017855343,0.000047546517,0.0000068805884,0.000018366198,0.000025324476,0.9872062,0.0025012365,0.002470973,0.00013628992,0.0073870555],"study_design_scores_gemma":[0.000002154381,0.00001389531,0.000052194177,0.0000051958345,0.0000024869264,0.000004411027,0.000005901864,0.9983657,0.00051291735,0.00040076338,0.0006322109,0.000002125518],"about_ca_topic_score_codex":0.0032963317,"about_ca_topic_score_gemma":0.0037282933,"teacher_disagreement_score":0.0032963317,"about_ca_system_score_codex":0.000480343,"about_ca_system_score_gemma":0.0010473147,"threshold_uncertainty_score":0.006794691},"labels":[],"label_agreement":null},{"id":"W2957201795","doi":"10.1115/1.4044204","title":"Uncertainty Quantification of NOx and CO Emissions in a Swirl-Stabilized Burner","year":2019,"lang":"en","type":"article","venue":"Journal of Engineering for Gas Turbines and Power","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Siemens (Canada)","funders":"","keywords":"Uncertainty quantification; Sobol sequence; Sensitivity (control systems); Combustor; Reliability (semiconductor); Combustion; Probabilistic logic; NOx; Surrogate model; Computer science; Uncertainty analysis; Flue gas; Propagation of uncertainty; Polynomial chaos; Process engineering; Reliability engineering; Engineering; Simulation; Algorithm; Mathematics; Monte Carlo method; Machine learning; Chemistry","score_opus":0.036949450989553156,"score_gpt":0.3162233362829764,"score_spread":0.27927388529342323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2957201795","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8944943,0.00006355581,0.10323768,0.000054375898,0.000010336052,0.000021216312,0.00009921167,0.00024462212,0.0017746937],"genre_scores_gemma":[0.996633,0.000011367037,0.0030526672,0.0000028110233,6.9212126e-7,0.000007527856,0.000028858814,0.000005451634,0.000257558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998122,0.000046447447,0.000006497006,0.000041516032,0.00007788363,0.000015539943],"domain_scores_gemma":[0.9997582,0.00011847706,0.000039192026,0.000023929633,0.000047862213,0.000012264047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043884452,0.0003512279,0.00034829302,0.00026448714,0.00030251563,0.00037188586,0.00045607064,0.00034748294,0.0005345629],"category_scores_gemma":[0.0006406515,0.00021219913,0.00032252813,0.00017163716,0.00032656494,0.00047367276,0.0004051246,0.00039232368,0.00005566967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089884,0.000026276046,0.0018477666,0.000018292942,0.000007966195,0.000037225887,0.000020026437,0.976393,0.015960617,0.0003668801,0.000039680715,0.0051923282],"study_design_scores_gemma":[0.0000021898209,0.000025776584,0.00039411552,6.700036e-7,0.000001675983,0.0000019958595,0.0000028498232,0.99555606,0.003891888,0.00008769259,0.000032938457,0.0000022171894],"about_ca_topic_score_codex":0.0070281858,"about_ca_topic_score_gemma":0.0050398135,"teacher_disagreement_score":0.0070281858,"about_ca_system_score_codex":0.0007005285,"about_ca_system_score_gemma":0.00044064637,"threshold_uncertainty_score":0.013974547},"labels":[],"label_agreement":null},{"id":"W2963080021","doi":"10.1109/tpwrs.2018.2825657","title":"Applying Polynomial Chaos Expansion to Assess Probabilistic Available Delivery Capability for Distribution Networks With Renewables","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Polynomial chaos; Probabilistic logic; Randomness; Mathematical optimization; Monte Carlo method; Renewable energy; Computer science; Probability distribution; CHAOS (operating system); Random variable; Reliability engineering; Engineering; Mathematics; Electrical engineering","score_opus":0.08336794606529971,"score_gpt":0.2984859823716327,"score_spread":0.215118036306333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963080021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085717976,0.00019282533,0.9116082,0.00009763725,0.000012695585,0.000053277683,0.00006603191,0.00012483755,0.0021263864],"genre_scores_gemma":[0.9599125,0.00017330123,0.039288506,0.000014078149,0.000013368401,0.000047646347,0.000058632173,0.000019253084,0.00047271547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996371,0.0001480938,0.000018326364,0.0000299238,0.00013916436,0.000027330272],"domain_scores_gemma":[0.9976967,0.0017659499,0.00020572252,0.00009750311,0.00019496988,0.000039185285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010584722,0.000494814,0.00038979165,0.00086320273,0.00021338728,0.00046059646,0.00042655782,0.0004213459,0.0005940319],"category_scores_gemma":[0.0059183696,0.00022063791,0.00042064418,0.00059633306,0.0005217452,0.00091111107,0.000717987,0.00051704154,0.00007561903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018751012,0.000005699766,0.00058991683,0.000021470523,0.000007686041,0.000042771204,0.000016000702,0.9860029,0.0011382902,0.0055337944,0.000060188813,0.006562603],"study_design_scores_gemma":[8.326766e-7,0.000004560275,0.00008619317,0.0000013060649,9.406362e-7,0.000007268448,0.0000022487932,0.99881303,0.00019282111,0.0008509623,0.000038027545,0.000001917447],"about_ca_topic_score_codex":0.0027537462,"about_ca_topic_score_gemma":0.0016281946,"teacher_disagreement_score":0.0027537462,"about_ca_system_score_codex":0.00055050274,"about_ca_system_score_gemma":0.0006166991,"threshold_uncertainty_score":0.00559783},"labels":[],"label_agreement":null},{"id":"W2963727569","doi":"10.1137/140961894","title":"On Discrete Least-Squares Projection in Unbounded Domain with Random Evaluations and its Application to Parametric Uncertainty Quantification","year":2014,"lang":"en","type":"article","venue":"SIAM Journal on Scientific Computing","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mathematics; Laguerre polynomials; Applied mathematics; Projection (relational algebra); Hermite polynomials; Parametric statistics; Bounded function; Scaling; Mathematical analysis; Mathematical optimization; Algorithm","score_opus":0.06176393128735426,"score_gpt":0.36566424231306144,"score_spread":0.3039003110257072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963727569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017723963,0.00012576157,0.9976113,0.000055875586,0.000008308246,0.000011327633,0.000005522522,0.00002157796,0.00038801253],"genre_scores_gemma":[0.2851068,0.0022068261,0.7085847,0.00015151036,0.00014102063,0.00030683674,0.00009819052,0.000116347845,0.0032877976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980805,0.0011285904,0.000075772965,0.00020167661,0.00044846136,0.00006498287],"domain_scores_gemma":[0.99636096,0.0028245936,0.00020020486,0.00021698896,0.00033651496,0.00006077159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039067455,0.0012281482,0.0010129595,0.000655121,0.00035370252,0.0009308139,0.0007330107,0.0010617044,0.0008965035],"category_scores_gemma":[0.008347533,0.0005145474,0.0009520176,0.0010151326,0.0023118167,0.0014349819,0.0023434767,0.0020548296,0.0002835577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091273265,0.000036696703,0.00031942467,0.00025221554,0.000055224937,0.0001622207,0.00013726093,0.7997508,0.007933655,0.13833801,0.00048078163,0.052442405],"study_design_scores_gemma":[0.0000033786416,0.00003572846,0.000048160957,0.000013339639,0.000004807842,0.000029521681,0.0000070238807,0.9794526,0.0013691443,0.01845485,0.0005680661,0.0000133450085],"about_ca_topic_score_codex":0.0013615686,"about_ca_topic_score_gemma":0.0006289569,"teacher_disagreement_score":0.0039067455,"about_ca_system_score_codex":0.00055814226,"about_ca_system_score_gemma":0.0008512528,"threshold_uncertainty_score":0.020661056},"labels":[],"label_agreement":null},{"id":"W2964150140","doi":"","title":"Interpolation of periodic hidden signal measured at steady-operating conditions on hydroelectric turbine runners","year":2019,"lang":"en","type":"preprint","venue":"Espace ÉTS (ETS)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; École de Technologie Supérieure","funders":"","keywords":"Hydroelectricity; Interpolation (computer graphics); SIGNAL (programming language); Turbine; Environmental science; Marine engineering; Control theory (sociology); Computer science; Meteorology; Electrical engineering; Engineering; Physics; Motion (physics); Mechanical engineering; Artificial intelligence","score_opus":0.04803752019422176,"score_gpt":0.3015224389400838,"score_spread":0.25348491874586204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964150140","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96518564,0.00007385063,0.03330278,0.000034856035,0.000030215737,0.000007663722,0.0003610723,0.00016393153,0.00083994353],"genre_scores_gemma":[0.99703884,0.000025054329,0.0022113246,0.000002564682,0.000007600127,0.0000026623622,0.00028058645,0.000014536996,0.0004168001],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989486,0.000019477271,0.000003934305,0.000033882483,0.000024484365,0.000023407525],"domain_scores_gemma":[0.99964285,0.00017902456,0.000036655885,0.000046479097,0.000060637638,0.000034252982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025879702,0.00033006084,0.00029574864,0.0004166295,0.00013522015,0.00029874183,0.00041554528,0.0005783596,0.0018321179],"category_scores_gemma":[0.0011432901,0.00018656041,0.00034121,0.00041752408,0.00026506826,0.00028963268,0.00028150377,0.00037730217,0.0002691097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033082662,0.00043401937,0.059136055,0.00037820407,0.00013472735,0.0010073676,0.0005503188,0.69521964,0.09957532,0.0031556361,0.0021983373,0.13490205],"study_design_scores_gemma":[0.000010319438,0.00012841677,0.060274832,0.000015131055,0.000016056729,0.000063838896,0.000068096015,0.9302486,0.008184466,0.0007177022,0.0002523835,0.000020133311],"about_ca_topic_score_codex":0.001825413,"about_ca_topic_score_gemma":0.0022495184,"teacher_disagreement_score":0.0018321179,"about_ca_system_score_codex":0.00017368485,"about_ca_system_score_gemma":0.00017253272,"threshold_uncertainty_score":0.006129086},"labels":[],"label_agreement":null},{"id":"W2965002381","doi":"10.1115/1.4044407","title":"Estimation of Flow-Accelerated Corrosion Rate in Nuclear Piping System","year":2019,"lang":"en","type":"article","venue":"Journal of Nuclear Engineering and Radiation Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Markov chain Monte Carlo; Piping; Computer science; Bayesian probability; Bayesian inference; Approximate Bayesian computation; Computation; Algorithm; Mathematical optimization; Inference; Artificial intelligence; Mathematics; Engineering","score_opus":0.026271185820248708,"score_gpt":0.27192943704819417,"score_spread":0.24565825122794546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965002381","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7760298,0.0001564221,0.22138484,0.0000589222,0.000009242578,0.00005022997,0.00024699385,0.0006803128,0.0013831066],"genre_scores_gemma":[0.9918281,0.000019668414,0.0079408195,0.0000021338615,0.0000010313898,0.0000082602155,0.00007741473,0.0000065693594,0.000116033974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996964,0.00008328069,0.000017118957,0.000073453746,0.00009985661,0.000029902778],"domain_scores_gemma":[0.99898785,0.00054904143,0.0001369894,0.000057720008,0.00023930827,0.000029224195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070395134,0.00046406683,0.00040284454,0.0008779328,0.00017913026,0.0003682091,0.00033188492,0.0004245939,0.00053447223],"category_scores_gemma":[0.0024759185,0.0002520269,0.00029120484,0.00039703402,0.00019916394,0.00030915902,0.00023980094,0.00036534615,0.00010126388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088906534,0.000032012886,0.019275546,0.000055195953,0.000023308958,0.000072441086,0.00003607639,0.951996,0.008842587,0.00035197387,0.00012580135,0.01910007],"study_design_scores_gemma":[0.0000016904331,0.000014157752,0.0028915824,0.0000017164526,0.00000258935,0.0000068059194,0.0000047619938,0.9953101,0.0016665133,0.000074753865,0.000021056861,0.000004253105],"about_ca_topic_score_codex":0.011928323,"about_ca_topic_score_gemma":0.004920381,"teacher_disagreement_score":0.011928323,"about_ca_system_score_codex":0.00052465335,"about_ca_system_score_gemma":0.0004367193,"threshold_uncertainty_score":0.023717761},"labels":[],"label_agreement":null},{"id":"W2967121770","doi":"10.1080/07350015.2023.2203768","title":"Simple Inference on Functionals of Set-Identified Parameters Defined by Linear Moments","year":2023,"lang":"en","type":"article","venue":"Journal of Business and Economic Statistics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Inference; Mathematics; Bootstrapping (finance); Simple (philosophy); Applied mathematics; Moment (physics); Linear programming; Parameter space; Scalar (mathematics); Set (abstract data type); Mathematical optimization; Computer science; Statistics","score_opus":0.12321060770635811,"score_gpt":0.34344005302439967,"score_spread":0.22022944531804156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967121770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050204196,0.000051350828,0.9942741,0.00007787165,0.0000059809836,0.000020132049,0.000043301778,0.00008402165,0.00042292298],"genre_scores_gemma":[0.554058,0.0002721672,0.44316146,0.00030268385,0.00012279728,0.00034931645,0.00050208793,0.00018823789,0.0010432887],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.989567,0.0057001356,0.00050997664,0.0017457965,0.0020765704,0.00040050177],"domain_scores_gemma":[0.9412728,0.04893258,0.0030224456,0.0048844786,0.0016031243,0.00028461916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012080252,0.0013923933,0.0019898894,0.0019784763,0.0006742223,0.0022719102,0.002792938,0.0018899532,0.002126866],"category_scores_gemma":[0.080917805,0.0010574225,0.0016993377,0.0011623107,0.0041029053,0.0061230566,0.0038503378,0.0034950867,0.00040881638],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024924637,0.00008373054,0.0037003893,0.00031298326,0.00028044093,0.000283061,0.0002455843,0.43558937,0.005937719,0.5083642,0.00079278834,0.04416043],"study_design_scores_gemma":[0.000018812987,0.00007005983,0.0005091804,0.00004678997,0.000030012394,0.00008392248,0.000025736092,0.7536918,0.0049476624,0.23988986,0.0006540781,0.00003203999],"about_ca_topic_score_codex":0.0009048976,"about_ca_topic_score_gemma":0.0005974526,"teacher_disagreement_score":0.012080252,"about_ca_system_score_codex":0.0014549632,"about_ca_system_score_gemma":0.0010880699,"threshold_uncertainty_score":0.06388724},"labels":[],"label_agreement":null},{"id":"W2968718572","doi":"10.2140/memocs.2019.7.99","title":"A polynomial chaos expanded hybrid fuzzy-stochastic model for transversely fiber reinforced plastics","year":2019,"lang":"en","type":"article","venue":"Mathematics and Mechanics of Complex Systems","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Otto von Guericke University Magdeburg; College of Engineering, Michigan State University; Universität Duisburg-Essen; Freie Universität Berlin; Bilkent Üniversitesi; Centre National de la Recherche Scientifique; University of North Carolina at Chapel Hill; Universität zu Köln; Università degli Studi di Pavia; Akademie Věd České Republiky; Université de Lyon; Universität Wien; Deutsche Forschungsgemeinschaft; McGill University; Universidad Rey Juan Carlos; Louisiana State University; University of Pittsburgh; Indian National Science Academy; Michigan State University; Carnegie Mellon University; Vanderbilt University; Wayne State University","keywords":"CHAOS (operating system); Polynomial; Fiber; Fuzzy logic; Mathematics; Materials science; Computer science; Mathematical analysis; Composite material; Artificial intelligence","score_opus":0.09751453856079552,"score_gpt":0.292725311694364,"score_spread":0.1952107731335685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968718572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04052238,0.0002654392,0.95370394,0.0002548075,0.00003319615,0.000020894382,0.00009395324,0.00007974651,0.005025733],"genre_scores_gemma":[0.96754307,0.0003560656,0.023844585,0.000052584328,0.00003195941,0.00007177159,0.000076699405,0.000022942157,0.008000287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976176,0.000056156576,0.000011183411,0.00005590692,0.0000877572,0.000027295153],"domain_scores_gemma":[0.9997954,0.000068991554,0.000059188693,0.000013866512,0.000046633344,0.000015941789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045328398,0.00053210324,0.0007605967,0.0005043403,0.00039562912,0.0009368812,0.0009479581,0.0009630385,0.0011376874],"category_scores_gemma":[0.00052554125,0.0002849012,0.0008057038,0.00043984983,0.0008015392,0.00074237166,0.0007400926,0.00072577613,0.00014639834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012255824,0.000007693101,0.00015696479,0.000016590071,0.000011051337,0.000055255685,0.000023512888,0.96967894,0.001884404,0.026122296,0.000093506096,0.0019375181],"study_design_scores_gemma":[0.0000011471159,0.0000060006805,0.000038326074,0.0000012200601,0.0000021545375,0.000007904289,0.0000023827167,0.9976883,0.00010070332,0.0020400318,0.00010877955,0.000003010396],"about_ca_topic_score_codex":0.00621524,"about_ca_topic_score_gemma":0.004476397,"teacher_disagreement_score":0.00621524,"about_ca_system_score_codex":0.0008521338,"about_ca_system_score_gemma":0.00082855154,"threshold_uncertainty_score":0.012358129},"labels":[],"label_agreement":null},{"id":"W2976033080","doi":"10.1007/s11222-019-09899-5","title":"Adaptive step-size selection for state-space probabilistic differential equation solvers","year":2019,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Mathematics; Selection (genetic algorithm); State space; Probabilistic logic; Applied mathematics; Space (punctuation); Differential equation; Computer science; Mathematical optimization; Algorithm; Theoretical computer science; Mathematical analysis; Artificial intelligence; Statistics","score_opus":0.04945765908105304,"score_gpt":0.29717843372261815,"score_spread":0.2477207746415651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976033080","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036878863,0.00007550629,0.99517316,0.000112045556,0.000029125542,0.000025701316,0.00001723434,0.0002035779,0.00067575474],"genre_scores_gemma":[0.4250778,0.0002646442,0.5688258,0.00024518292,0.00010463842,0.00048271718,0.00019145219,0.00037266826,0.004435189],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993356,0.0002836619,0.00004201591,0.00007603362,0.00022033897,0.000042224787],"domain_scores_gemma":[0.99576193,0.0033276714,0.00015509017,0.00019993895,0.0004583764,0.00009694101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020345566,0.00060988247,0.0010603114,0.0005754204,0.00037213217,0.00081034447,0.0014986356,0.0012825747,0.0028864888],"category_scores_gemma":[0.010241833,0.00071266363,0.00057279656,0.00047499008,0.0007341246,0.0009877933,0.001442686,0.0017523093,0.00057853793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020525964,0.00012326935,0.00078787643,0.00014478405,0.00007968938,0.00007740897,0.00007441566,0.8726348,0.0041760746,0.025793483,0.002121544,0.09378142],"study_design_scores_gemma":[0.0000058111186,0.0000053721005,0.000022848559,0.0000027008111,0.0000016633328,0.000003119945,0.0000012266917,0.9979547,0.00027236636,0.0015982888,0.00013006845,0.000001800702],"about_ca_topic_score_codex":0.002447761,"about_ca_topic_score_gemma":0.0033668398,"teacher_disagreement_score":0.0028864888,"about_ca_system_score_codex":0.00045974003,"about_ca_system_score_gemma":0.0011610106,"threshold_uncertainty_score":0.01075989},"labels":[],"label_agreement":null},{"id":"W2979628251","doi":"10.1063/1.5120035","title":"Quasi-Monte Carlo technique in global sensitivity analysis of wind resource assessment with a study on UAE","year":2019,"lang":"en","type":"article","venue":"Journal of Renewable and Sustainable Energy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Sobol sequence; Latin hypercube sampling; Sensitivity (control systems); Variance (accounting); Sampling (signal processing); Monte Carlo method; Computer science; Quasi-Monte Carlo method; Importance sampling; Mathematical optimization; Mathematics; Statistics; Applied mathematics; Engineering; Hybrid Monte Carlo; Markov chain Monte Carlo; Electronic engineering","score_opus":0.01477907889922024,"score_gpt":0.29536209946513076,"score_spread":0.2805830205659105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979628251","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08020776,0.001720053,0.90965307,0.00032280502,0.00009838244,0.00016741562,0.00012131267,0.00013893242,0.0075701647],"genre_scores_gemma":[0.85998696,0.0012382069,0.13642351,0.00012803504,0.00006053372,0.00032108402,0.00011363783,0.00006144442,0.0016666128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998212,0.0014031465,0.00003773589,0.00011382533,0.00016537182,0.00006795907],"domain_scores_gemma":[0.9957158,0.0036301918,0.00015974618,0.00018600642,0.0002712129,0.00003703226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036776718,0.00064676336,0.00070818845,0.0012058653,0.00040868018,0.0008042672,0.00055789016,0.00070286065,0.0016519522],"category_scores_gemma":[0.006143502,0.00037187486,0.0011109144,0.0009262361,0.0007246151,0.00079875847,0.0007901241,0.0007473397,0.00012212031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037310743,0.000041379848,0.0020569158,0.00013330829,0.00009708213,0.0001246174,0.00006012631,0.9515294,0.0007067891,0.02732356,0.00036670954,0.017522799],"study_design_scores_gemma":[0.0000060862153,0.000063600855,0.000992279,0.00002295103,0.000021426871,0.000034351604,0.000039890612,0.98633957,0.00031533948,0.011043112,0.0011083267,0.000013108042],"about_ca_topic_score_codex":0.0068549905,"about_ca_topic_score_gemma":0.004809807,"teacher_disagreement_score":0.0068549905,"about_ca_system_score_codex":0.0006310862,"about_ca_system_score_gemma":0.000726669,"threshold_uncertainty_score":0.019449592},"labels":[],"label_agreement":null},{"id":"W2981941954","doi":"10.1007/s12356-019-00053-4","title":"2D dynamic and earthquake response analysis of base isolation systems using a convex optimization framework","year":2019,"lang":"en","type":"article","venue":"Annals of Solid and Structural Mechanics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Base isolation; Shearing (physics); Base (topology); Computation; Response analysis; Solid mechanics; Structural engineering; Penalty method; Convex optimization; Computer science; Regular polygon; Mathematical optimization; Geology; Mathematics; Engineering; Algorithm; Materials science; Mathematical analysis; Geotechnical engineering; Geometry; Reduction (mathematics)","score_opus":0.07432592144824654,"score_gpt":0.35706534777027515,"score_spread":0.28273942632202864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981941954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016956443,0.00026301426,0.97531974,0.00036193928,0.00003680991,0.00003969873,0.00012959709,0.000078033896,0.006814603],"genre_scores_gemma":[0.90367895,0.0007368296,0.0816733,0.00025004084,0.00015412462,0.00027458236,0.00032515422,0.00025121227,0.012655688],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995652,0.00018194936,0.000014884549,0.000064072105,0.00011538853,0.000058587702],"domain_scores_gemma":[0.9991104,0.0005909151,0.000105196086,0.000035561163,0.000107868764,0.000050017265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010159226,0.0011919338,0.0013031363,0.0007681333,0.0004036262,0.0012746801,0.0009286033,0.0012552787,0.0035972036],"category_scores_gemma":[0.002364246,0.00082958245,0.00083663105,0.00050478586,0.00097389496,0.0011214184,0.0017403648,0.000975938,0.00039257715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000136267745,0.000014167564,0.00007323513,0.00002173237,0.00001236068,0.00002080367,0.000011237154,0.9912581,0.00050657697,0.00557976,0.00025909024,0.00222933],"study_design_scores_gemma":[8.279789e-7,0.000003987621,0.0000347674,0.0000011015115,0.0000012086816,0.0000025745744,0.0000025349348,0.99874014,0.000039204464,0.0011014533,0.00007049189,0.0000017211332],"about_ca_topic_score_codex":0.004225925,"about_ca_topic_score_gemma":0.0028786806,"teacher_disagreement_score":0.004225925,"about_ca_system_score_codex":0.0007675604,"about_ca_system_score_gemma":0.00080072886,"threshold_uncertainty_score":0.01203382},"labels":[],"label_agreement":null},{"id":"W2986384160","doi":"10.1109/jstqe.2019.2950761","title":"Efficient Variability Analysis of Photonic Circuits by Stochastic Parametric Building Blocks","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Quantum Electronics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Parametric statistics; Monte Carlo method; Polynomial chaos; Computer science; Macro; Stochastic process; Photonics; Tolerance analysis; Parametric design; Stochastic simulation; Parametric model; Electronic engineering; Algorithm; Mathematical optimization; Mathematics; Engineering; Physics; Optics; Engineering drawing","score_opus":0.024673114056413023,"score_gpt":0.2974661787232605,"score_spread":0.27279306466684744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986384160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02454545,0.000083584266,0.97335523,0.000050213373,0.000006302172,0.00001985988,0.000045478442,0.00016452683,0.0017294224],"genre_scores_gemma":[0.8632646,0.00025004768,0.13461554,0.000036040412,0.000020924777,0.00018932133,0.0001351792,0.00012566725,0.0013627562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995453,0.00012108264,0.000013404397,0.000040769577,0.00023195788,0.00004756175],"domain_scores_gemma":[0.9992454,0.00050438225,0.0000768513,0.00008567178,0.00007077565,0.000016934848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008102279,0.00045606587,0.0005111217,0.0005456224,0.0002925115,0.0006327568,0.00069980155,0.00046664051,0.00091123214],"category_scores_gemma":[0.0018201016,0.00041736037,0.00084060576,0.00031158837,0.0006085975,0.000702654,0.0006539265,0.000762283,0.00017561424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012540515,0.000008247992,0.00019826523,0.000016174108,0.000013316758,0.000021321763,0.00001575637,0.96856326,0.0035420337,0.022306453,0.00007579499,0.0052268375],"study_design_scores_gemma":[8.846518e-7,0.000005056562,0.000032020936,0.0000011384857,0.0000015735903,0.000004468328,9.538093e-7,0.99650097,0.00061408355,0.0027094248,0.00012745548,0.0000019649426],"about_ca_topic_score_codex":0.0012106438,"about_ca_topic_score_gemma":0.0009873568,"teacher_disagreement_score":0.0012106438,"about_ca_system_score_codex":0.00067318924,"about_ca_system_score_gemma":0.0007858067,"threshold_uncertainty_score":0.004884362},"labels":[],"label_agreement":null},{"id":"W2986801453","doi":"10.1007/s00170-019-04506-3","title":"A new ensemble modeling approach for reliability-based design optimization of flexure-based bridge-type amplification mechanisms","year":2019,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; National Office for Philosophy and Social Sciences; National Natural Science Foundation of China","keywords":"Bridge (graph theory); Reliability (semiconductor); Ensemble forecasting; Computer science; Process (computing); Surrogate model; Model selection; Selection (genetic algorithm); Ensemble learning; Data mining; Mathematical optimization; Machine learning; Mathematics","score_opus":0.06290486589909511,"score_gpt":0.3124923017691075,"score_spread":0.24958743587001236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986801453","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01024733,0.00021052531,0.98685735,0.000080652855,0.000037047823,0.00002807052,0.00007204827,0.00013138328,0.002335649],"genre_scores_gemma":[0.6393018,0.0009089251,0.35110858,0.0002528364,0.00018629977,0.0005857173,0.0005690724,0.00027249008,0.006814342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993987,0.00020066592,0.00003078715,0.000095939045,0.00020611277,0.00006781695],"domain_scores_gemma":[0.9989925,0.0004958379,0.000127573,0.00009605072,0.00023496962,0.000053122054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016529678,0.0014089093,0.002091547,0.001264796,0.00068315945,0.0012556632,0.0019573728,0.001872528,0.002574578],"category_scores_gemma":[0.002236581,0.0010751981,0.0023593856,0.0010872813,0.0005258291,0.0014505873,0.0014177428,0.0015163074,0.00044949216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053992567,0.00001316477,0.00010860481,0.000009835915,0.000024472543,0.000010563563,0.0000058077244,0.9935743,0.00037105245,0.0018070807,0.00010317574,0.0039664833],"study_design_scores_gemma":[6.291982e-7,0.0000041795383,0.000017630799,0.000001239542,0.0000034663508,0.0000017688641,7.7531655e-7,0.99930274,0.000038865535,0.00055553275,0.00007193019,0.0000012803342],"about_ca_topic_score_codex":0.00491096,"about_ca_topic_score_gemma":0.0058331266,"teacher_disagreement_score":0.00491096,"about_ca_system_score_codex":0.00067242206,"about_ca_system_score_gemma":0.001110266,"threshold_uncertainty_score":0.009764731},"labels":[],"label_agreement":null},{"id":"W2990993108","doi":"10.1177/1748006x19888421","title":"AK-PDF: An active learning method combining kriging and probability density function for efficient reliability analysis","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Kriging; Probability density function; Function (biology); Monte Carlo method; Reliability (semiconductor); Computer science; Mathematical optimization; Algorithm; Limit (mathematics); Mathematics; Statistics; Machine learning","score_opus":0.022115700084773,"score_gpt":0.2855558154777289,"score_spread":0.2634401153929559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990993108","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007850585,0.00011498148,0.9985128,0.00002659628,0.000016397968,0.0000104638275,0.0000129130585,0.000210175,0.00031060283],"genre_scores_gemma":[0.2064486,0.0007454392,0.7874584,0.00013751484,0.00013560796,0.00028001255,0.00020821884,0.0002879938,0.004298138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999201,0.00027671267,0.00004222188,0.0001161202,0.0003185794,0.000045337012],"domain_scores_gemma":[0.99819916,0.0010946152,0.000118820426,0.00011944018,0.00042376123,0.0000442282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021068386,0.0011602488,0.0014591988,0.0015673551,0.0004820029,0.000970711,0.0020262636,0.0014562072,0.0020672774],"category_scores_gemma":[0.00362983,0.000669436,0.0011320512,0.0012762339,0.000806532,0.0019571837,0.0011923579,0.001697985,0.00079405215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066514396,0.000057869674,0.00059054565,0.0001792812,0.000074803975,0.00006866317,0.00006906569,0.78075624,0.0033765465,0.010108645,0.0019962506,0.20265561],"study_design_scores_gemma":[0.0000031038242,0.000008546133,0.00003667595,0.000005222782,0.0000041990456,0.000014132855,0.0000019105353,0.9973762,0.00042103996,0.0014600515,0.000663531,0.0000053252134],"about_ca_topic_score_codex":0.0038661319,"about_ca_topic_score_gemma":0.0036003992,"teacher_disagreement_score":0.0038661319,"about_ca_system_score_codex":0.00064904,"about_ca_system_score_gemma":0.0012653816,"threshold_uncertainty_score":0.011142194},"labels":[],"label_agreement":null},{"id":"W2991299394","doi":"10.3150/19-bej1130","title":"Construction results for strong orthogonal arrays of strength three","year":2019,"lang":"en","type":"article","venue":"Bernoulli","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Orthogonal array; Mathematics; Space (punctuation); Class (philosophy); Algorithm; Computer science; Statistics; Artificial intelligence; Taguchi methods","score_opus":0.07257682961729396,"score_gpt":0.31570920459284896,"score_spread":0.243132374975555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991299394","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02870164,0.00043098384,0.9583291,0.0002077674,0.00007321504,0.000070651084,0.00019189631,0.00026580648,0.011729007],"genre_scores_gemma":[0.49469528,0.001180124,0.49272573,0.0006095251,0.00021852729,0.0007901645,0.00055789674,0.00025472025,0.008968004],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973023,0.0010632671,0.00018243819,0.00046327733,0.000730718,0.00025793284],"domain_scores_gemma":[0.9918624,0.0052267346,0.0008936317,0.00087479164,0.00078623544,0.00035609456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028002812,0.0007647196,0.0007244387,0.0008227602,0.0006135204,0.0012484489,0.00069125916,0.0006318553,0.00613646],"category_scores_gemma":[0.010626897,0.00059576327,0.0009259145,0.00084477,0.0014254446,0.0017287947,0.002416907,0.0014278799,0.0012725804],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002554561,0.00007483527,0.0014891538,0.00038932127,0.000048598024,0.00009816761,0.00019670754,0.035860125,0.026949724,0.8319422,0.0024630702,0.10023256],"study_design_scores_gemma":[0.00010166089,0.0011820608,0.0014116659,0.00013722079,0.00008185354,0.0006227508,0.00015779232,0.18428148,0.040608898,0.7322409,0.039047305,0.00012644436],"about_ca_topic_score_codex":0.00014150672,"about_ca_topic_score_gemma":0.00014087302,"teacher_disagreement_score":0.00613646,"about_ca_system_score_codex":0.0006370583,"about_ca_system_score_gemma":0.00080793304,"threshold_uncertainty_score":0.020528436},"labels":[],"label_agreement":null},{"id":"W2991417000","doi":"10.1016/j.compchemeng.2019.106663","title":"Uncertainty quantification of the factor of safety in a steam-assisted gravity drainage process through polynomial chaos expansion","year":2019,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Caprock; Geomechanics; Petroleum engineering; Polynomial chaos; Nonlinear system; Geology; Mathematical optimization; Geotechnical engineering; Mathematics; Statistics; Monte Carlo method","score_opus":0.03897718681397904,"score_gpt":0.2884408659541955,"score_spread":0.24946367914021644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991417000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14279081,0.00020108809,0.8542268,0.00025687445,0.000020770183,0.000022973927,0.000049979128,0.000066213164,0.0023644757],"genre_scores_gemma":[0.99010897,0.00008991936,0.008884458,0.0000148098725,0.000011940196,0.000014352645,0.000023848472,0.000018897279,0.00083277933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955875,0.00013955933,0.000016728434,0.00007744798,0.00014452665,0.00006296998],"domain_scores_gemma":[0.9980028,0.0014303317,0.00018685141,0.0000628836,0.0002658986,0.00005111357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013248988,0.00053782715,0.00090758083,0.0007299741,0.0005086312,0.0009606141,0.00056255783,0.00065503584,0.0005156634],"category_scores_gemma":[0.0036292989,0.00031607508,0.0007115514,0.00043684422,0.0016062786,0.0012401284,0.0011327497,0.000982489,0.000044854874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007660061,0.000017586042,0.00046371316,0.000046043042,0.000022114817,0.000053606458,0.000060412527,0.960559,0.004146477,0.029510885,0.000098656725,0.0049448353],"study_design_scores_gemma":[0.000001130448,0.000006887451,0.000076462085,0.0000012870465,0.0000021402827,0.0000027113153,0.000002425834,0.99724245,0.00031417122,0.0023219804,0.000025277554,0.000003094772],"about_ca_topic_score_codex":0.0050431956,"about_ca_topic_score_gemma":0.0023582242,"teacher_disagreement_score":0.0050431956,"about_ca_system_score_codex":0.0011633043,"about_ca_system_score_gemma":0.0011639628,"threshold_uncertainty_score":0.010027707},"labels":[],"label_agreement":null},{"id":"W2997360806","doi":"10.2514/6.2020-1215","title":"Crippling Failure Prediction in Composites Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"AIAA Scitech 2020 Forum","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Finite element method; Structural engineering; Composite number; Computer science; Test data; Point (geometry); Software; Engineering; Reliability engineering; Algorithm; Mathematics","score_opus":0.07154266708184337,"score_gpt":0.30061487777522317,"score_spread":0.2290722106933798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997360806","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5645443,0.00048329672,0.43199953,0.00023076673,0.000027440368,0.00006888482,0.00017063553,0.00071757723,0.0017575456],"genre_scores_gemma":[0.9547116,0.00011408858,0.044297624,0.000028365355,0.0000116623205,0.000038069207,0.0001635433,0.000013077357,0.0006220269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970275,0.00009202633,0.000020407968,0.00006464717,0.00008952784,0.000030709733],"domain_scores_gemma":[0.99743515,0.0017374103,0.00027549756,0.0001318773,0.00036081971,0.000059178856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013703007,0.0006568982,0.00045252068,0.0011190744,0.00021460046,0.00040518463,0.00047612382,0.0006235532,0.0005831598],"category_scores_gemma":[0.0035955873,0.00026156014,0.00040724882,0.00043541947,0.00039879832,0.00047835446,0.00037193304,0.00069542596,0.00022790224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051224266,0.000068326415,0.004381532,0.000021559652,0.00001814871,0.000025298697,0.0000132953155,0.95491964,0.0015066782,0.0003181526,0.00020509178,0.038471065],"study_design_scores_gemma":[7.7939666e-7,0.000011971909,0.0004590228,0.0000015846285,8.4772637e-7,0.0000030645351,0.0000019171466,0.99881554,0.00046519225,0.00021172829,0.000026781852,0.0000016695213],"about_ca_topic_score_codex":0.003129879,"about_ca_topic_score_gemma":0.0032224183,"teacher_disagreement_score":0.003129879,"about_ca_system_score_codex":0.00048788733,"about_ca_system_score_gemma":0.00052329816,"threshold_uncertainty_score":0.0072469115},"labels":[],"label_agreement":null},{"id":"W2997776632","doi":"10.1109/phm-qingdao46334.2019.8942835","title":"Machine Learning Based Dynamic Failure Criteria for Reliability Analysis of Bearings","year":2019,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Reliability (semiconductor); Estimator; Computer science; Reliability engineering; Kriging; Mechanical system; Machine learning; Engineering; Artificial intelligence; Mathematics; Statistics","score_opus":0.03597208959935279,"score_gpt":0.3323056240044332,"score_spread":0.2963335344050804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997776632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012212446,0.0004926807,0.98627543,0.000048588525,0.00002026497,0.00002617727,0.00005076537,0.00014067402,0.00073287985],"genre_scores_gemma":[0.8028896,0.0006919188,0.19374657,0.00004267604,0.000106429994,0.00022432455,0.0003272964,0.000083493025,0.0018877657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985806,0.0005564421,0.00008757081,0.00013933118,0.0005698958,0.00006623897],"domain_scores_gemma":[0.9969849,0.0017812303,0.00027465532,0.00015320908,0.0007570525,0.000048874783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023022834,0.00077354,0.0009272961,0.0016181938,0.00025538792,0.00077797286,0.00084079435,0.00082233234,0.0008900766],"category_scores_gemma":[0.0069800345,0.0002445595,0.0005717518,0.00085511367,0.00063850556,0.000945284,0.0005707766,0.0007769324,0.00034976823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044707744,0.000031969077,0.00081200077,0.00013076756,0.000025654259,0.000044924695,0.0000431435,0.9335098,0.006683038,0.014093304,0.00057453755,0.04400608],"study_design_scores_gemma":[0.0000012435248,0.0000162777,0.00020244956,0.000005739252,0.0000021903356,0.000011301845,0.0000024602102,0.9966491,0.0005571825,0.0023004655,0.0002456206,0.000005916959],"about_ca_topic_score_codex":0.0016472756,"about_ca_topic_score_gemma":0.0010042082,"teacher_disagreement_score":0.0023022834,"about_ca_system_score_codex":0.00073376653,"about_ca_system_score_gemma":0.0006620311,"threshold_uncertainty_score":0.012175798},"labels":[],"label_agreement":null},{"id":"W3003576863","doi":"10.1007/s10543-020-00825-0","title":"Error estimation and uncertainty quantification for first time to a threshold value","year":2020,"lang":"en","type":"preprint","venue":"BIT Numerical Mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Science Foundation","keywords":"A priori and a posteriori; Bounded function; Representation (politics); Mathematics; Applied mathematics; Function (biology); Value (mathematics); Maximum a posteriori estimation; Mathematical optimization; Statistics; Mathematical analysis; Maximum likelihood","score_opus":0.1631738410868106,"score_gpt":0.36721305909936097,"score_spread":0.20403921801255037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003576863","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047017005,0.00056480535,0.9897606,0.0004449409,0.00021466592,0.000027285074,0.000036927548,0.00018012461,0.0040688915],"genre_scores_gemma":[0.43992347,0.0019946517,0.5246345,0.0012886375,0.00085463736,0.00026700273,0.00027127596,0.0012906779,0.029475184],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99377924,0.0015209488,0.00037394685,0.0016786784,0.002096461,0.00055075245],"domain_scores_gemma":[0.98846054,0.0057313773,0.0010315732,0.0019010156,0.0024373853,0.00043816757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063502816,0.0018106048,0.0020885118,0.0037374774,0.0017590349,0.0050662267,0.0032591328,0.0035679042,0.008092058],"category_scores_gemma":[0.03468367,0.00101782,0.0023896329,0.0032882409,0.0055754213,0.010034036,0.007572004,0.008082281,0.0019358221],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014171879,0.000044675013,0.00046207124,0.0004590043,0.000074732845,0.00017275466,0.00045556473,0.063869536,0.0076071806,0.8659513,0.0024514794,0.058309928],"study_design_scores_gemma":[0.000010000888,0.000086626584,0.00026430198,0.00017259558,0.000055671742,0.00023331388,0.00006798704,0.40004516,0.011912104,0.57986826,0.007229454,0.000054476735],"about_ca_topic_score_codex":0.0021793148,"about_ca_topic_score_gemma":0.0008191601,"teacher_disagreement_score":0.008092058,"about_ca_system_score_codex":0.0031325773,"about_ca_system_score_gemma":0.0024400267,"threshold_uncertainty_score":0.03358388},"labels":[],"label_agreement":null},{"id":"W3005714504","doi":"10.1007/s00366-020-00961-9","title":"Subset simulation method including fitness-based seed selection for reliability analysis","year":2020,"lang":"en","type":"article","venue":"Engineering With Computers","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Markov chain Monte Carlo; Selection (genetic algorithm); Reliability (semiconductor); Markov chain; Monte Carlo method; Importance sampling; Tournament selection; Sampling (signal processing); Sample (material); Particle filter; Computer science; Mathematics; Event (particle physics); Statistics; Algorithm; Mathematical optimization; Filter (signal processing); Artificial intelligence","score_opus":0.09385581638543318,"score_gpt":0.35409475119869555,"score_spread":0.2602389348132624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005714504","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020899735,0.00009901891,0.9770139,0.000050146376,0.000028000479,0.00006039366,0.000048367805,0.0003754399,0.0014249772],"genre_scores_gemma":[0.62155044,0.00018940285,0.37362674,0.00010543244,0.00004904794,0.0004414605,0.00041308458,0.00024060656,0.003383782],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945146,0.000260402,0.000025056124,0.0000775844,0.00014912998,0.000036361445],"domain_scores_gemma":[0.998259,0.0011357695,0.00007546853,0.00017346154,0.00030368258,0.00005258648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011649055,0.00070438883,0.0013538342,0.0011237571,0.00074713177,0.00061408,0.0012853384,0.0007967967,0.0035664248],"category_scores_gemma":[0.0034285937,0.00042255927,0.001079087,0.0007752495,0.00038707833,0.00096119,0.00068363786,0.0006809692,0.00050480367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012003572,0.00008897392,0.0018410534,0.00008717831,0.000108824584,0.000117511,0.000075937474,0.915622,0.0051662554,0.01319904,0.0012030544,0.06237016],"study_design_scores_gemma":[0.00000463401,0.0000149562,0.00007687754,0.000002489374,0.0000110634155,0.000012190167,0.0000027199003,0.9980673,0.0003541029,0.0012804185,0.00017080689,0.000002418982],"about_ca_topic_score_codex":0.0028000602,"about_ca_topic_score_gemma":0.00284807,"teacher_disagreement_score":0.0035664248,"about_ca_system_score_codex":0.0004393464,"about_ca_system_score_gemma":0.0010700312,"threshold_uncertainty_score":0.011930823},"labels":[],"label_agreement":null},{"id":"W3007400702","doi":"10.1029/2019wr025436","title":"Correlation Effects? A Major but Often Neglected Component in Sensitivity and Uncertainty Analysis","year":2020,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"Canada First Research Excellence Fund","keywords":"Variogram; Sensitivity (control systems); Markov chain Monte Carlo; Econometrics; Multivariate normal distribution; Multivariate statistics; Bayesian probability; Statistics; Variance (accounting); Monte Carlo method; Computer science; Mathematics; Mathematical optimization; Kriging; Engineering","score_opus":0.09372949867446487,"score_gpt":0.34843134341289816,"score_spread":0.25470184473843327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007400702","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036253326,0.00067584397,0.95838904,0.0009757394,0.000052225598,0.000055114528,0.000079476486,0.00016325568,0.0033558914],"genre_scores_gemma":[0.90490896,0.00050400663,0.09298852,0.00031320282,0.00013451789,0.000108176224,0.00008662693,0.00015685573,0.00079911365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9879782,0.008372093,0.00037750785,0.0010051596,0.0019539676,0.000313008],"domain_scores_gemma":[0.9002718,0.08979385,0.0034457152,0.0042259376,0.0019562326,0.0003064982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01987234,0.0008717652,0.0010416568,0.0016289032,0.000586471,0.0020013447,0.0011371186,0.0011649977,0.0016563067],"category_scores_gemma":[0.07112069,0.0007368055,0.0016183356,0.0018544103,0.0027835537,0.0031676001,0.0022893776,0.0026279944,0.00012370583],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082187566,0.000058065234,0.018556563,0.00029688046,0.00056099164,0.0003705231,0.00033023456,0.65185225,0.0018412673,0.24174018,0.0015424131,0.082768455],"study_design_scores_gemma":[0.000011292814,0.00006526091,0.005643312,0.00009161527,0.00007421679,0.00011740053,0.000105761545,0.812128,0.0021363646,0.17726843,0.002294397,0.00006404762],"about_ca_topic_score_codex":0.0045031034,"about_ca_topic_score_gemma":0.0033586768,"teacher_disagreement_score":0.01987234,"about_ca_system_score_codex":0.001263501,"about_ca_system_score_gemma":0.0016032158,"threshold_uncertainty_score":0.10509622},"labels":[],"label_agreement":null},{"id":"W3007789654","doi":"10.1137/19m1304738","title":"Computing Shapley Effects for Sensitivity Analysis","year":2021,"lang":"en","type":"preprint","venue":"SIAM/ASA Journal on Uncertainty Quantification","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua","funders":"","keywords":"Computation; Sensitivity (control systems); Shapley value; Variance (accounting); Implementation; Function (biology); Computer science; Mathematical optimization; Order (exchange); Algorithm; Mathematics; Mathematical economics; Economics; Game theory; Engineering","score_opus":0.12297943157613138,"score_gpt":0.38476076234421064,"score_spread":0.26178133076807925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007789654","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008382913,0.0001809745,0.98847103,0.000106009014,0.000032803247,0.0000784735,0.00013980079,0.00029159224,0.002316479],"genre_scores_gemma":[0.32730478,0.0004817659,0.66735864,0.00023675249,0.00015332882,0.0004937641,0.00076294184,0.0003833524,0.0028247011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971354,0.0013364914,0.00015170676,0.00043046178,0.0007673957,0.00017858947],"domain_scores_gemma":[0.986528,0.011073577,0.00048173132,0.0009932764,0.0006474668,0.0002759286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006582135,0.0019117403,0.0019515026,0.0042159464,0.0009357272,0.0030840102,0.0014827697,0.0014598005,0.011304807],"category_scores_gemma":[0.026278341,0.00077697047,0.0022477126,0.0023432986,0.0016509058,0.004356895,0.003231025,0.0033340033,0.0008596609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000114744944,0.00010702801,0.0010747617,0.00024967483,0.00021585489,0.00007880117,0.000121280136,0.63169676,0.0013116306,0.2552288,0.0023310895,0.10746965],"study_design_scores_gemma":[0.000014344468,0.000025377689,0.000161429,0.000026377402,0.00002156058,0.000020397947,0.00001453249,0.687085,0.00072364364,0.3109893,0.0009022844,0.00001572332],"about_ca_topic_score_codex":0.0014820526,"about_ca_topic_score_gemma":0.0020571023,"teacher_disagreement_score":0.011304807,"about_ca_system_score_codex":0.0023460747,"about_ca_system_score_gemma":0.0017893644,"threshold_uncertainty_score":0.037818313},"labels":[],"label_agreement":null},{"id":"W3013826162","doi":"10.5194/wes-2020-24","title":"Surrogate models for unsteady aerodynamics using non-intrusive Polynomial Chaos Expansions","year":2020,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polynomial chaos; Aerodynamics; Turbine; Computational fluid dynamics; Wind power; Uncertainty quantification; Turbulence; Computer science; Computation; Flow (mathematics); Wind speed; Mathematics; Engineering; Algorithm; Statistics; Meteorology; Aerospace engineering; Monte Carlo method; Physics","score_opus":0.24174722957202197,"score_gpt":0.3590344280787769,"score_spread":0.11728719850675492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013826162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019452188,0.000108492626,0.97868484,0.000088160064,0.000024581133,0.000026498765,0.00006538754,0.00009734421,0.0014525778],"genre_scores_gemma":[0.89367133,0.00043758436,0.100091994,0.000073164825,0.00005005588,0.00018171214,0.00032413576,0.00009523646,0.0050747246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994006,0.00022818305,0.000027604792,0.000057013563,0.00023647744,0.00005007027],"domain_scores_gemma":[0.99830675,0.0010125582,0.00023598218,0.0001236071,0.00026264277,0.000058436483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011678955,0.0006473683,0.00064524694,0.0007416105,0.00025536068,0.0008714852,0.00080260925,0.0008071285,0.0010712071],"category_scores_gemma":[0.004406731,0.00038495133,0.0007832297,0.0005300728,0.0007558427,0.0010387973,0.0007704569,0.0010575459,0.00032398396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016693153,0.000012677132,0.00033506463,0.000020074565,0.000009287622,0.000027832524,0.000020436482,0.97866666,0.0009522959,0.016489197,0.0001690481,0.0032807665],"study_design_scores_gemma":[5.487991e-7,0.0000038671233,0.00002666514,0.0000012793763,5.6393867e-7,0.000003428909,9.169574e-7,0.99879503,0.000089649635,0.0010001492,0.0000764164,0.0000014114484],"about_ca_topic_score_codex":0.0019216066,"about_ca_topic_score_gemma":0.001606615,"teacher_disagreement_score":0.0019216066,"about_ca_system_score_codex":0.00053672894,"about_ca_system_score_gemma":0.00074210786,"threshold_uncertainty_score":0.0061764717},"labels":[],"label_agreement":null},{"id":"W3016091689","doi":"10.1007/s12206-020-0312-3","title":"Reliability evaluation of the servo turret with accurate failure data and interval censored data based on EM algorithm","year":2020,"lang":"en","type":"article","venue":"Journal of Mechanical Science and Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Reliability (semiconductor); Weibull distribution; Interval (graph theory); Algorithm; Confidence interval; Turret; Reliability engineering; Statistics; Estimator; Mathematics; Computer science; Engineering; Power (physics)","score_opus":0.132836406849016,"score_gpt":0.3580349157713702,"score_spread":0.2251985089223542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016091689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18206291,0.00063069025,0.8159576,0.000112209455,0.000024141747,0.000028739003,0.00005905775,0.00031751968,0.00080717914],"genre_scores_gemma":[0.96589124,0.00015177684,0.03317094,0.00001736828,0.000016395885,0.000028478396,0.00011834366,0.00002514166,0.0005803325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992304,0.00033418718,0.00005488347,0.00014508767,0.00017752779,0.000057962086],"domain_scores_gemma":[0.99647826,0.0021970333,0.00027204043,0.00022326488,0.00077551743,0.000053808017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027536978,0.0006689704,0.0010864509,0.0009341736,0.00024087404,0.00049386924,0.00082257844,0.000713879,0.0007122488],"category_scores_gemma":[0.006622617,0.0003453964,0.00071425736,0.0005036202,0.00042245007,0.00067951065,0.00049875333,0.0004273806,0.00012962674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001804099,0.000019602041,0.0042175157,0.00009228648,0.00006699222,0.0000782457,0.000041878942,0.96800774,0.0021850918,0.0016069319,0.00020002587,0.02330339],"study_design_scores_gemma":[0.0000025453567,0.000024280287,0.000725545,0.000003328454,0.000009122038,0.000019140107,0.000004568875,0.9984536,0.00039908476,0.0003215528,0.00003434135,0.000002835778],"about_ca_topic_score_codex":0.0033373763,"about_ca_topic_score_gemma":0.0014277535,"teacher_disagreement_score":0.0033373763,"about_ca_system_score_codex":0.00054428796,"about_ca_system_score_gemma":0.00056676334,"threshold_uncertainty_score":0.014563143},"labels":[],"label_agreement":null},{"id":"W3016640620","doi":"10.1016/j.physd.2020.132748","title":"Heat transport bounds for a truncated model of Rayleigh–Bénard convection via polynomial optimization","year":2020,"lang":"en","type":"article","venue":"Physica D Nonlinear Phenomena","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Ode; Mathematics; Rayleigh number; Rayleigh scattering; Galerkin method; Natural convection; Prandtl number; Polynomial; Mathematical analysis; Applied mathematics; Convection; Physics; Thermodynamics; Finite element method","score_opus":0.09222365941737289,"score_gpt":0.297422478800352,"score_spread":0.20519881938297913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016640620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04613634,0.0012834454,0.9408368,0.001583965,0.00012917505,0.000058592952,0.00017344188,0.00020865371,0.009589695],"genre_scores_gemma":[0.93698496,0.0011498262,0.04844815,0.00036652037,0.00029422343,0.00019124206,0.0003155097,0.000270942,0.011978624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988182,0.00053454743,0.00003681047,0.00019059695,0.00023441164,0.00018537686],"domain_scores_gemma":[0.9912026,0.0064428784,0.0007277672,0.0003770141,0.00075908226,0.00049072405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037214167,0.0021323399,0.0031463425,0.0013644276,0.0008757965,0.002765679,0.0025131076,0.0028678852,0.002790396],"category_scores_gemma":[0.013719725,0.0007631101,0.0015252364,0.0008967056,0.004132021,0.0033077095,0.0043038465,0.004363739,0.00033004227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008182529,0.00003305391,0.00012683202,0.00008820851,0.00002608431,0.000035111123,0.000048704078,0.91256094,0.0012393368,0.08294105,0.0007129755,0.0021059443],"study_design_scores_gemma":[0.0000020816594,0.000003854237,0.00001675495,0.0000031465859,0.0000022936588,0.0000019098918,0.000002342471,0.99109334,0.0000871742,0.008716091,0.00006788648,0.0000031038144],"about_ca_topic_score_codex":0.011574483,"about_ca_topic_score_gemma":0.0042407694,"teacher_disagreement_score":0.011574483,"about_ca_system_score_codex":0.0038098835,"about_ca_system_score_gemma":0.0024125676,"threshold_uncertainty_score":0.027642787},"labels":[],"label_agreement":null},{"id":"W3017502207","doi":"10.1109/epeps47316.2019.193229","title":"Efficient Regression-Based Polynomial Chaos using Adjoint Sensitivity","year":2019,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Polynomial chaos; Sensitivity (control systems); CHAOS (operating system); Polynomial regression; Polynomial; Applied mathematics; Regression; Mathematics; Computer science; Mathematical optimization; Algorithm; Statistics; Mathematical analysis; Engineering","score_opus":0.08429317940719297,"score_gpt":0.3323045534672467,"score_spread":0.24801137406005372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017502207","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042995163,0.000042920416,0.99502087,0.000024400617,0.000007057903,0.000008500111,0.000013802555,0.0001663872,0.00041654322],"genre_scores_gemma":[0.6454502,0.00024612414,0.35089996,0.000066650806,0.00005364386,0.00009919517,0.00015409723,0.00026509067,0.0027651482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950457,0.00013985806,0.000018987992,0.00007404798,0.00022896453,0.000033522334],"domain_scores_gemma":[0.9992182,0.0005320608,0.000060351915,0.0000724784,0.00009505126,0.000021809874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074561994,0.0005742449,0.00083845784,0.0006875359,0.0003061908,0.00063653174,0.0006781666,0.00053260586,0.0011842743],"category_scores_gemma":[0.0022344496,0.00029821624,0.00080191396,0.00048251616,0.0005600434,0.0007596402,0.0007292333,0.0011003661,0.00033461626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007305617,0.00003480955,0.000502926,0.00009055729,0.000052769035,0.000090296155,0.00006919742,0.8550326,0.028225467,0.049986266,0.0004331249,0.06540891],"study_design_scores_gemma":[0.0000015977122,0.0000094885145,0.000054541273,0.0000018457075,0.0000027018111,0.00001383101,0.0000010879346,0.99471766,0.0017522679,0.0031949324,0.00024499057,0.000005020082],"about_ca_topic_score_codex":0.0016942434,"about_ca_topic_score_gemma":0.0013094585,"teacher_disagreement_score":0.0016942434,"about_ca_system_score_codex":0.0004936346,"about_ca_system_score_gemma":0.0006284437,"threshold_uncertainty_score":0.0039618015},"labels":[],"label_agreement":null},{"id":"W3021291081","doi":"10.1002/aic.16262","title":"Multilevel Monte Carlo applied for uncertainty quantification in stochastic multiscale systems","year":2020,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo method; Polynomial chaos; Uncertainty quantification; Sampling (signal processing); Observable; Mathematical optimization; Projection (relational algebra); Heuristic; Algorithm; Applied mathematics; Computer science; Mathematics; Statistical physics; Statistics; Physics","score_opus":0.2170647122042458,"score_gpt":0.35551028871022694,"score_spread":0.13844557650598113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021291081","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046739507,0.00019975673,0.9517923,0.000094566494,0.000019072293,0.000042418134,0.000049843973,0.00017195112,0.0008907152],"genre_scores_gemma":[0.8213953,0.00014426908,0.1779473,0.00004436879,0.000022116554,0.00010098882,0.00006954861,0.000040231305,0.00023577339],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987576,0.00061945786,0.000048205915,0.00010330373,0.00039374194,0.00007770486],"domain_scores_gemma":[0.99433595,0.0044736043,0.00027986505,0.00036304683,0.00043755575,0.000109977525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026147484,0.00042559337,0.0008967372,0.00096370804,0.00045363477,0.0007112642,0.0007341715,0.00077000505,0.0010855638],"category_scores_gemma":[0.008370647,0.00031576486,0.00075041404,0.0006690195,0.000736809,0.00069377536,0.0010428161,0.0010159948,0.00009003141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086949,0.000023385453,0.0013390165,0.00006030454,0.0000570836,0.000041769224,0.000030457653,0.9649635,0.0024126188,0.01969118,0.00011826902,0.011175445],"study_design_scores_gemma":[0.0000012898024,0.00000590507,0.000060384755,0.0000014695712,0.0000017514097,0.000002541493,0.0000010190922,0.99882036,0.00030223807,0.0007623135,0.00003894233,0.0000016865597],"about_ca_topic_score_codex":0.006194657,"about_ca_topic_score_gemma":0.003457854,"teacher_disagreement_score":0.006194657,"about_ca_system_score_codex":0.0007674112,"about_ca_system_score_gemma":0.00085599325,"threshold_uncertainty_score":0.013828278},"labels":[],"label_agreement":null},{"id":"W3024182072","doi":"10.1093/imanum/drab015","title":"On the rate of convergence of the Gaver–Stehfest algorithm","year":2021,"lang":"en","type":"preprint","venue":"IMA Journal of Numerical Analysis","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Laplace transform; Mellin transform; Two-sided Laplace transform; Laplace–Stieltjes transform; Inverse Laplace transform; Mathematics; Laplace transform applied to differential equations; Rate of convergence; Differentiable function; Applied mathematics; Convergence (economics); Function (biology); Post's inversion formula; Mathematical optimization; Algorithm; Computer science; Mathematical analysis; Green's function for the three-variable Laplace equation; Fourier transform","score_opus":0.06416654015902439,"score_gpt":0.3230433876493471,"score_spread":0.2588768474903227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024182072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030276015,0.0017682542,0.95773023,0.0010466552,0.00027439857,0.00011245883,0.00010011133,0.00032847567,0.008363362],"genre_scores_gemma":[0.59202135,0.0022123654,0.38981318,0.00069130614,0.0002949435,0.00061476167,0.00043305536,0.00080613495,0.01311297],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99378407,0.0032996526,0.00026391543,0.0005680299,0.0015430467,0.0005412323],"domain_scores_gemma":[0.9347197,0.049742445,0.0017357866,0.003745216,0.009088853,0.00096793944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022527095,0.001431749,0.0016842072,0.0021844828,0.0013130065,0.0025406845,0.0028845323,0.0029079348,0.0040812357],"category_scores_gemma":[0.09543155,0.0006240347,0.0014152188,0.00095462525,0.005055053,0.004016852,0.00397603,0.004718112,0.0012004697],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011389469,0.00011771428,0.004955618,0.0005188955,0.00022963133,0.0002974508,0.0005905522,0.40783823,0.009292484,0.51256746,0.004917297,0.05753572],"study_design_scores_gemma":[0.00003272317,0.000090983194,0.00036871433,0.00012765756,0.000023358356,0.00013097234,0.000057800244,0.942988,0.0041364483,0.049902055,0.0020977028,0.000043597636],"about_ca_topic_score_codex":0.0038418793,"about_ca_topic_score_gemma":0.0014207636,"teacher_disagreement_score":0.022527095,"about_ca_system_score_codex":0.002158034,"about_ca_system_score_gemma":0.0017050836,"threshold_uncertainty_score":0.119136095},"labels":[],"label_agreement":null},{"id":"W3027933700","doi":"10.5194/hess-25-831-2021","title":"Objective functions for information-theoretical monitoring network design: what is “optimal”?","year":2021,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Greedy algorithm; Joint entropy; Mathematical optimization; Entropy (arrow of time); Redundancy (engineering); Information theory; Principle of maximum entropy; Data mining; Algorithm; Mathematics; Statistics; Artificial intelligence","score_opus":0.05686486194956853,"score_gpt":0.2998838395000223,"score_spread":0.24301897755045376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027933700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011422702,0.000967967,0.9819416,0.0015710597,0.000045609286,0.00007934716,0.00010372921,0.00007399087,0.003794027],"genre_scores_gemma":[0.56169873,0.0023324944,0.43036214,0.0008811585,0.00028660454,0.0006116904,0.00034631035,0.00026045207,0.003220489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99649733,0.002323188,0.00010841339,0.00041173914,0.00049364753,0.00016558886],"domain_scores_gemma":[0.9881719,0.009596839,0.00078206795,0.00038770112,0.0007906825,0.0002707894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076702684,0.0018618495,0.0019667381,0.0017429572,0.0005570878,0.002048277,0.0017443532,0.0021393623,0.0026404322],"category_scores_gemma":[0.02553113,0.00092035916,0.0007567767,0.001352521,0.002607046,0.004633356,0.0016899173,0.0019788286,0.0003916572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000104695704,0.00009387959,0.0007091186,0.00034086828,0.0001032459,0.00004730062,0.00007112341,0.84248537,0.0008434857,0.10865373,0.002461619,0.04408554],"study_design_scores_gemma":[0.000023910454,0.00008787876,0.0002187024,0.0001235428,0.000029558772,0.000026344578,0.00004252065,0.9016401,0.00072793843,0.09557508,0.0014843536,0.000020107795],"about_ca_topic_score_codex":0.0013668161,"about_ca_topic_score_gemma":0.0009791688,"teacher_disagreement_score":0.0076702684,"about_ca_system_score_codex":0.00221152,"about_ca_system_score_gemma":0.0017353287,"threshold_uncertainty_score":0.040564716},"labels":[],"label_agreement":null},{"id":"W3034054763","doi":"10.1177/0844562120932054","title":"Multivariate Outliers: A Conceptual and Practical Overview for the Nurse and Health Researcher","year":2020,"lang":"en","type":"review","venue":"Canadian Journal of Nursing Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Windsor; McMaster University; Impact","funders":"","keywords":"Outlier; Multivariate statistics; Mahalanobis distance; Leverage (statistics); Multivariate analysis; Computer science; Data mining; Identification (biology); Statistics; Econometrics; Data science; Artificial intelligence; Machine learning; Mathematics","score_opus":0.8334423278852698,"score_gpt":0.6376491203259785,"score_spread":0.1957932075592913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034054763","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00067296234,0.86868,0.068907924,0.05185768,0.0022905148,0.00016258741,0.0000959846,0.000107576714,0.007224873],"genre_scores_gemma":[0.011863007,0.93772423,0.03793467,0.007150476,0.0030825618,0.00041303225,0.00008497475,0.000054180233,0.0016928031],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98570824,0.008269175,0.0014163697,0.00056696264,0.003732338,0.0003068459],"domain_scores_gemma":[0.9596678,0.032105863,0.0019366943,0.0007096707,0.004850612,0.0007292987],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.023557052,0.0017465784,0.0026172043,0.009359325,0.0018191282,0.006436263,0.0033624186,0.00661091,0.0028250378],"category_scores_gemma":[0.032662276,0.0008197137,0.0015084307,0.010806335,0.009723681,0.011114496,0.0039358726,0.009769104,0.0013400894],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000735943,0.00012208307,0.001437428,0.01656746,0.00013814628,0.00055864686,0.0030576824,0.0026491364,0.00041342416,0.34739193,0.0514747,0.5761157],"study_design_scores_gemma":[0.000018516528,0.00021753325,0.0019480512,0.02020117,0.00011911452,0.00214468,0.0044474695,0.0021167556,0.00034838245,0.25108543,0.7172091,0.00014383363],"about_ca_topic_score_codex":0.004884003,"about_ca_topic_score_gemma":0.0062360885,"teacher_disagreement_score":0.97644293,"about_ca_system_score_codex":0.0058133444,"about_ca_system_score_gemma":0.011112976,"threshold_uncertainty_score":0.124583066},"labels":[],"label_agreement":null},{"id":"W3035190103","doi":"10.2139/ssrn.3270839","title":"Cascade Sensitivity Measures","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cascade; Sensitivity (control systems); Chemistry; Chromatography; Engineering; Electronic engineering","score_opus":0.05166773522449398,"score_gpt":0.31585414641306103,"score_spread":0.26418641118856706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035190103","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021287665,0.0008172044,0.91159713,0.0006140931,0.00026191506,0.00018619576,0.0005865285,0.0005236581,0.06412568],"genre_scores_gemma":[0.8295793,0.0015704603,0.11195955,0.0008121751,0.00063945085,0.00049359666,0.0013281048,0.00045455174,0.053162858],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99746454,0.0006510431,0.00011737612,0.00081753393,0.0007304787,0.0002190494],"domain_scores_gemma":[0.99535024,0.002022425,0.0005384323,0.00087319163,0.00091482955,0.00030085674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030415533,0.0020925188,0.0014640802,0.003976447,0.0009669279,0.0033156592,0.0014122361,0.0023646194,0.021579482],"category_scores_gemma":[0.012471852,0.00075488276,0.0018391624,0.0016294863,0.0017521714,0.0047433353,0.0031994588,0.0025852383,0.0032930076],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015369816,0.00009680983,0.0012428525,0.00022885675,0.00017139141,0.00019860393,0.0000921835,0.102507174,0.0072378265,0.80614275,0.00851147,0.07341638],"study_design_scores_gemma":[0.000014652708,0.00014286283,0.0015353514,0.00007237569,0.00007835379,0.00029220956,0.00003828979,0.42168608,0.0062710415,0.5615009,0.008306639,0.00006129132],"about_ca_topic_score_codex":0.00073723216,"about_ca_topic_score_gemma":0.0005881119,"teacher_disagreement_score":0.021579482,"about_ca_system_score_codex":0.0016443039,"about_ca_system_score_gemma":0.0007757299,"threshold_uncertainty_score":0.07219052},"labels":[],"label_agreement":null},{"id":"W3036117780","doi":"10.1142/s0218539320500199","title":"Efficient Reliability-Based Design Optimization of Degrading Systems Using a Meta-Model of the System Reliability","year":2020,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Computer science; Monte Carlo method; Mathematical optimization; Point (geometry); MATLAB; Particle swarm optimization; Process (computing); Engineering design process; Set (abstract data type); Engineering; Mathematics; Algorithm; Statistics; Power (physics)","score_opus":0.1995121326307128,"score_gpt":0.33828951000785507,"score_spread":0.13877737737714227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036117780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026936682,0.0014785809,0.96322685,0.00047027913,0.00006124843,0.00012554317,0.00020201624,0.0002957328,0.0072030975],"genre_scores_gemma":[0.5410736,0.0020890583,0.4495053,0.00026812468,0.00006344664,0.0009978204,0.0005163962,0.00018893201,0.005297305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939966,0.00025069562,0.000026642278,0.00009973733,0.00015846333,0.000064772445],"domain_scores_gemma":[0.99872893,0.0008530543,0.0001221448,0.000091582624,0.00015777283,0.000046483747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001910095,0.0015398873,0.0017075926,0.0015008672,0.0004213017,0.0019687712,0.0016489931,0.001564026,0.0016998493],"category_scores_gemma":[0.003052781,0.0010858332,0.0026095107,0.0011075177,0.0011332845,0.0010920522,0.0010580443,0.001643447,0.0003080416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008108706,0.0000075345265,0.00010944896,0.000039628525,0.000027493183,0.000020314916,0.000011589294,0.9937125,0.0003028928,0.0032440056,0.00008350366,0.002432966],"study_design_scores_gemma":[0.0000061093465,0.000024072793,0.000056506935,0.000019667157,0.00002529338,0.000009559383,0.000009586177,0.9943217,0.00018828677,0.00487599,0.000458888,0.000004370448],"about_ca_topic_score_codex":0.0030615649,"about_ca_topic_score_gemma":0.0034310059,"teacher_disagreement_score":0.0030615649,"about_ca_system_score_codex":0.0016483605,"about_ca_system_score_gemma":0.002491379,"threshold_uncertainty_score":0.011959791},"labels":[],"label_agreement":null},{"id":"W3037748341","doi":"10.1016/j.ymssp.2020.106980","title":"A novel approach for reliability analysis with correlated variables based on the concepts of entropy and polynomial chaos expansion","year":2020,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; China Scholarship Council; University of Waterloo; National Natural Science Foundation of China","keywords":"Random variable; Mathematics; Marginal distribution; Polynomial chaos; Entropy (arrow of time); Probability distribution; Applied mathematics; Principle of maximum entropy; Random field; Moment (physics); Multivariate random variable; Monte Carlo method; Mathematical optimization; Algorithm; Statistics","score_opus":0.0638101007184458,"score_gpt":0.2806672186688856,"score_spread":0.21685711795043983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037748341","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016883085,0.00023643867,0.99714065,0.000054905657,0.000040983647,0.0000145327,0.00002047515,0.000035589997,0.0007681522],"genre_scores_gemma":[0.43771333,0.0024728111,0.5520765,0.00031073185,0.0011386079,0.00034960816,0.00023601524,0.00021354719,0.0054888325],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989838,0.0003263935,0.000049811108,0.00014322434,0.00044454072,0.000052276122],"domain_scores_gemma":[0.9982634,0.0010136892,0.00017350035,0.00019154072,0.000294686,0.000063255415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013064693,0.0011192631,0.0013690373,0.0022241445,0.0004843338,0.0010885799,0.0013620354,0.0007173428,0.0011342587],"category_scores_gemma":[0.0043229884,0.0004042733,0.0013016069,0.0011671275,0.0013007796,0.0023162717,0.0017562363,0.0017544592,0.00028348414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051210365,0.00005376298,0.000813392,0.00019675531,0.0001894286,0.0002138457,0.00014814029,0.18449369,0.010202095,0.74622875,0.0014848023,0.0559242],"study_design_scores_gemma":[0.0000071896816,0.00005658888,0.00036291027,0.000018602697,0.000040139315,0.00013926793,0.000011419265,0.80662704,0.0012359682,0.18958166,0.0018851067,0.000034175908],"about_ca_topic_score_codex":0.00057637383,"about_ca_topic_score_gemma":0.000601507,"teacher_disagreement_score":0.0022241445,"about_ca_system_score_codex":0.0005508557,"about_ca_system_score_gemma":0.00087043707,"threshold_uncertainty_score":0.0069093704},"labels":[],"label_agreement":null},{"id":"W3040992378","doi":"10.1007/s11269-020-02608-2","title":"Robust Subsampling ANOVA Methods for Sensitivity Analysis of Water Resource and Environmental Models","year":2020,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"National Key Scientific Instrument and Equipment Development Projects of China","keywords":"Sobol sequence; Analysis of variance; Variance-based sensitivity analysis; Sensitivity (control systems); Statistics; Variance (accounting); Estimator; Analysis of covariance; One-way analysis of variance; Computer science; Mathematics; Engineering; Monte Carlo method","score_opus":0.14291948233882412,"score_gpt":0.31839800984716465,"score_spread":0.17547852750834053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040992378","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029705784,0.000089079076,0.99617344,0.000024916755,0.000027401402,0.00004815002,0.000081619146,0.00032308412,0.00026179475],"genre_scores_gemma":[0.24636894,0.0003507667,0.74826354,0.00013671751,0.0001490096,0.0013375772,0.0009626038,0.000944295,0.0014865309],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9865643,0.010615008,0.00035538262,0.00081454514,0.0013904546,0.0002602867],"domain_scores_gemma":[0.94790256,0.045681324,0.0011240104,0.0031929421,0.00191232,0.00018687006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018131731,0.001691469,0.0025392468,0.0021761472,0.0007017245,0.0010447188,0.002065368,0.001201141,0.00448971],"category_scores_gemma":[0.05131001,0.0011936579,0.0038643875,0.0010401426,0.0010831589,0.0012262039,0.0016476113,0.0027788708,0.00046587738],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051426,0.00046845325,0.0014483224,0.00061985623,0.0015094754,0.00015315515,0.00021961593,0.69981337,0.010080685,0.10626084,0.0036577955,0.17525426],"study_design_scores_gemma":[0.000023584247,0.00014840883,0.0004198671,0.000017350872,0.0000789199,0.000017163633,0.000012510947,0.970426,0.0017777963,0.025973491,0.0010809809,0.000023847077],"about_ca_topic_score_codex":0.004468588,"about_ca_topic_score_gemma":0.0042839,"teacher_disagreement_score":0.018131731,"about_ca_system_score_codex":0.001246077,"about_ca_system_score_gemma":0.0017089556,"threshold_uncertainty_score":0.09589094},"labels":[],"label_agreement":null},{"id":"W3041724873","doi":"10.1016/j.jsv.2020.115560","title":"Reliability-based optimization of nonlinear energy sink with negative stiffness and sliding friction","year":2020,"lang":"en","type":"article","venue":"Journal of Sound and Vibration","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Guangdong Province","keywords":"Nonlinear system; Stiffness; Control theory (sociology); Sensitivity (control systems); Moment (physics); Acceleration; Polynomial chaos; Computer science; Reliability (semiconductor); Engineering; Structural engineering; Mathematics; Control (management); Physics","score_opus":0.043908564826705,"score_gpt":0.2715211414708399,"score_spread":0.2276125766441349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041724873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09805907,0.0008735484,0.8875787,0.0006843935,0.00011231627,0.00012113563,0.00018130601,0.00026360154,0.012125839],"genre_scores_gemma":[0.9643138,0.00022850162,0.029928159,0.00006338463,0.0000313985,0.00013715544,0.000109060435,0.00014646995,0.0050421995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943,0.00024368796,0.000023863697,0.00009054844,0.000112489106,0.000099398705],"domain_scores_gemma":[0.99682355,0.002282314,0.00027774167,0.0000971908,0.00038782042,0.00013140737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024229805,0.0016542336,0.0025513612,0.0013944104,0.00049608934,0.0014716388,0.0014276869,0.0024041862,0.0033185584],"category_scores_gemma":[0.006573466,0.0015600504,0.0010318574,0.00063957897,0.0014334925,0.0013772354,0.0018289037,0.0011484585,0.00038714343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026231855,0.000009737979,0.0001045731,0.000031614632,0.0000129170985,0.000022667837,0.000012981588,0.99639064,0.00023959036,0.0017239026,0.000116490286,0.0013086675],"study_design_scores_gemma":[0.0000041691123,0.000015202092,0.000043457865,0.0000036457573,0.0000037277605,0.000003498429,0.0000041820385,0.9991541,0.00006159237,0.00065136334,0.00005271803,0.0000022888923],"about_ca_topic_score_codex":0.0052069393,"about_ca_topic_score_gemma":0.0031965736,"teacher_disagreement_score":0.0052069393,"about_ca_system_score_codex":0.0011950613,"about_ca_system_score_gemma":0.0014038472,"threshold_uncertainty_score":0.012814045},"labels":[],"label_agreement":null},{"id":"W3041830750","doi":"10.32920/ryerson.14648304.v1","title":"Multidisciplinary Aircraft Conceptual Design Optimization Considering Fidelity Uncertainties","year":2021,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Conceptual design; Multidisciplinary design optimization; Computer science; Conceptual framework; Engineering design process; Engineering optimization; Systems engineering; Optimization problem; Multidisciplinary approach; Engineering; Industrial engineering; Reliability engineering; Operations research; Mechanical engineering","score_opus":0.15301685393197112,"score_gpt":0.34129680192365963,"score_spread":0.1882799479916885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041830750","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051254697,0.0003802641,0.9438139,0.0001627034,0.000022310005,0.000057022724,0.000051173465,0.000072669696,0.00418515],"genre_scores_gemma":[0.8524396,0.0003757862,0.14512403,0.00006199683,0.000023582812,0.00017025627,0.00008777609,0.000048192047,0.001668833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992279,0.00033386267,0.000027542541,0.00011592615,0.00021639687,0.000078391924],"domain_scores_gemma":[0.9980578,0.0013822322,0.00020386252,0.00012263213,0.0001807597,0.000052763746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022054834,0.00077264936,0.0010267422,0.0007276107,0.00038723866,0.0012789529,0.000758811,0.0011222872,0.0010109345],"category_scores_gemma":[0.003949141,0.00057671976,0.0009174918,0.0004680264,0.00075548154,0.00086137373,0.0014084867,0.00096385676,0.00011969259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070334995,0.000004629592,0.00011776587,0.00001513518,0.0000064488386,0.000011461211,0.000008006162,0.99440914,0.00031031936,0.0020668358,0.00003080159,0.0030124914],"study_design_scores_gemma":[0.0000027876965,0.000017701681,0.0000966757,0.000004844819,0.0000037330728,0.0000058008627,0.0000073253723,0.99785256,0.00021477917,0.0015922827,0.00019901693,0.0000025857398],"about_ca_topic_score_codex":0.0027349489,"about_ca_topic_score_gemma":0.002033573,"teacher_disagreement_score":0.0027349489,"about_ca_system_score_codex":0.0009650205,"about_ca_system_score_gemma":0.0011165114,"threshold_uncertainty_score":0.0116637945},"labels":[],"label_agreement":null},{"id":"W3043414797","doi":"10.1137/19m1279459","title":"Near-Optimal Sampling Strategies for Multivariate Function Approximation on General Domains","year":2020,"lang":"en","type":"article","venue":"SIAM Journal on Mathematics of Data Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Pacific Institute for the Mathematical Sciences","keywords":"Mathematics; Measure (data warehouse); Sampling (signal processing); Multivariate statistics; Function (biology); Polynomial; Applied mathematics; Sample (material); Domain (mathematical analysis); Sample space; Function approximation; Space (punctuation); Sample complexity; Mathematical optimization; Mathematical analysis; Statistics; Computer science; Artificial intelligence","score_opus":0.32724891237695886,"score_gpt":0.41415230070744985,"score_spread":0.086903388330491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043414797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0079966225,0.000165473,0.9914336,0.000062310035,0.000007031687,0.00002060047,0.000013643493,0.000076846416,0.00022391164],"genre_scores_gemma":[0.34835118,0.0005217971,0.6488971,0.00016557939,0.00006391292,0.00024631523,0.00024025212,0.00013532251,0.0013784945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771583,0.0013062368,0.000101391655,0.00029991375,0.00045438763,0.00012234309],"domain_scores_gemma":[0.9926872,0.0055808863,0.00036930948,0.0007023846,0.0004635466,0.00019665627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047537126,0.0011584581,0.0017144554,0.0010518824,0.00043414126,0.0010076737,0.0019025384,0.0012426381,0.0011507318],"category_scores_gemma":[0.016235001,0.00069791806,0.0009889364,0.0009209557,0.0016294958,0.0023456952,0.0025617029,0.001998968,0.00035253575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026157816,0.00010601075,0.0011141615,0.00018255856,0.00008626837,0.00008149875,0.00013619284,0.87439924,0.0047826786,0.06014764,0.00074742513,0.0579547],"study_design_scores_gemma":[0.00000881092,0.000022094971,0.000058053774,0.0000047835206,0.0000035184623,0.000011897051,0.000008058066,0.9890428,0.0006746978,0.009978603,0.00018298512,0.0000037427271],"about_ca_topic_score_codex":0.002501856,"about_ca_topic_score_gemma":0.0020240385,"teacher_disagreement_score":0.0047537126,"about_ca_system_score_codex":0.0011852911,"about_ca_system_score_gemma":0.0008738451,"threshold_uncertainty_score":0.025140345},"labels":[],"label_agreement":null},{"id":"W3043670985","doi":"10.1017/9781316535547.006","title":"Conditional Moment Functions","year":2018,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Content (measure theory); Computer science; Moment (physics); Information retrieval; Mathematics; Physics; Mathematical analysis","score_opus":0.08213818228100359,"score_gpt":0.2529841197287638,"score_spread":0.1708459374477602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043670985","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029371548,0.008437913,0.7789002,0.0021133756,0.00086811866,0.000042488635,0.0030671,0.0014251198,0.20220856],"genre_scores_gemma":[0.26032162,0.029178744,0.21167117,0.0018768114,0.002887453,0.00037563316,0.009448854,0.0031909018,0.48104882],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994986,0.000115173614,0.000021438702,0.00012542173,0.00018354211,0.000055821038],"domain_scores_gemma":[0.9986834,0.00075062533,0.00007785753,0.00021127691,0.00021943862,0.000057426936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011999849,0.0011068227,0.000791805,0.0019457522,0.00041882563,0.0019025283,0.0011124606,0.0012120075,0.066433735],"category_scores_gemma":[0.005174909,0.00043627806,0.0011534024,0.0016109133,0.0010154084,0.0032403234,0.0010332735,0.0022014084,0.022064682],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002968792,0.000014888697,0.00019096283,0.00014625049,0.00002760539,0.00006956296,0.00005536498,0.009791831,0.0008990344,0.8607675,0.054029465,0.073977895],"study_design_scores_gemma":[0.000008906069,0.000028459059,0.00081596506,0.0001724919,0.00003775077,0.0004480034,0.000028244269,0.05874013,0.0021207337,0.78861326,0.14891964,0.000066426626],"about_ca_topic_score_codex":0.0011098963,"about_ca_topic_score_gemma":0.0010314387,"teacher_disagreement_score":0.066433735,"about_ca_system_score_codex":0.0010014337,"about_ca_system_score_gemma":0.00064101734,"threshold_uncertainty_score":0.22224295},"labels":[],"label_agreement":null},{"id":"W3045721162","doi":"10.1134/s1995080220040198","title":"Comparison of Accuracy Properties of Point Estimators for the Ratio of Binomial Proportions with the Inverse-Direct Sampling Scheme","year":2020,"lang":"en","type":"article","venue":"Lobachevskii Journal of Mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina; University of Calgary","funders":"","keywords":"Estimator; Mathematics; Statistics; Mean squared error; Inverse; Sampling (signal processing); Ratio estimator; Binomial (polynomial); Monte Carlo method; Bernoulli's principle; Point (geometry); Negative binomial distribution; Sample size determination; Binomial distribution; Point estimation; Bias of an estimator; Poisson distribution; Minimum-variance unbiased estimator; Computer science","score_opus":0.30316689768632754,"score_gpt":0.38366904354786213,"score_spread":0.08050214586153459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045721162","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059006333,0.0032129518,0.93426794,0.00029309004,0.00008558357,0.00011340793,0.00015424138,0.00043096198,0.002435421],"genre_scores_gemma":[0.59486,0.0020662334,0.40014657,0.00013370531,0.00014753143,0.00029322616,0.0005893372,0.00026047984,0.0015029189],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97019,0.01674228,0.0014493333,0.0026331027,0.0081764385,0.00080892845],"domain_scores_gemma":[0.81389797,0.15341586,0.005014958,0.014086059,0.013053257,0.00053185207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039586693,0.0008614874,0.0016693654,0.0035435539,0.00049820996,0.0018633566,0.0023472335,0.0023461874,0.0016054804],"category_scores_gemma":[0.19550318,0.00057704415,0.0014596225,0.0016432311,0.002011996,0.0027096944,0.002543688,0.0016331145,0.00061017286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026438334,0.00014539719,0.038840994,0.0014410097,0.00084899255,0.00033293478,0.0012173776,0.4864014,0.011440986,0.08856013,0.0018123416,0.36631465],"study_design_scores_gemma":[0.00008174738,0.0005506932,0.012412667,0.0002691138,0.00021103294,0.0007176813,0.000200205,0.94022864,0.012630589,0.02990647,0.0026393726,0.00015178071],"about_ca_topic_score_codex":0.002396163,"about_ca_topic_score_gemma":0.0008877419,"teacher_disagreement_score":0.039586693,"about_ca_system_score_codex":0.0011169949,"about_ca_system_score_gemma":0.0008682544,"threshold_uncertainty_score":0.20935696},"labels":[],"label_agreement":null},{"id":"W3046296964","doi":"","title":"VARS-TOOL: A Novel Toolbox for Comprehensive and Efficient Global Sensitivity Analysis","year":2018,"lang":"en","type":"article","venue":"AGU Fall Meeting 2018","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"","keywords":"Toolbox; Sensitivity (control systems); Computer science; Engineering","score_opus":0.09284421058415628,"score_gpt":0.33964090878784636,"score_spread":0.2467966982036901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046296964","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043895657,0.000037383343,0.98666686,0.000026835309,0.000023027387,0.000022328235,0.000321455,0.011153184,0.0013099225],"genre_scores_gemma":[0.062235743,0.00025376034,0.91845554,0.00019869252,0.0000766723,0.00049484544,0.0015887377,0.010600973,0.006094998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903166,0.00031055004,0.00007977182,0.0001382446,0.0003657443,0.000074016265],"domain_scores_gemma":[0.9975216,0.001568387,0.000119097924,0.0003313966,0.0003851809,0.00007427797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020243234,0.0025469325,0.0013595395,0.0016722869,0.0005198303,0.00166953,0.0025959872,0.0014901083,0.039374955],"category_scores_gemma":[0.006705435,0.001334,0.0018367943,0.0009387173,0.0006787821,0.0018360628,0.002785736,0.0030781429,0.0115011325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003060302,0.00027219762,0.0010031813,0.0012312845,0.00037270732,0.0005251917,0.00024623002,0.45177385,0.02039252,0.09024251,0.06527704,0.36835724],"study_design_scores_gemma":[0.000055378856,0.000033509306,0.0001080946,0.000053542102,0.000028383516,0.00011910416,0.000015739233,0.9354195,0.0052095465,0.038792536,0.020125642,0.00003914102],"about_ca_topic_score_codex":0.0014254438,"about_ca_topic_score_gemma":0.0025818802,"teacher_disagreement_score":0.039374955,"about_ca_system_score_codex":0.0003935643,"about_ca_system_score_gemma":0.0012365315,"threshold_uncertainty_score":0.13172233},"labels":[],"label_agreement":null},{"id":"W3047104034","doi":"10.3934/bdia.2020001","title":"Modeling portfolio loss by interval distributions","year":2020,"lang":"en","type":"article","venue":"Big Data and Information Analytics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Bank of Canada","funders":"","keywords":"Portfolio; Interval (graph theory); Outcome (game theory); Mathematics; Statistics; Econometrics; Capital allocation line; Regression analysis; Regression; Computer science; Economics; Mathematical economics; Combinatorics","score_opus":0.21734204926902354,"score_gpt":0.3329600770183434,"score_spread":0.11561802774931987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047104034","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01654789,0.0006463157,0.9785139,0.00043786198,0.000046271463,0.00005876502,0.00029323722,0.00027013558,0.0031855851],"genre_scores_gemma":[0.82061434,0.0031321466,0.15650697,0.00045868062,0.00030206866,0.00088268175,0.0010436577,0.00031696534,0.016742429],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99544626,0.0023270152,0.00014502509,0.0007462834,0.0009423732,0.00039304706],"domain_scores_gemma":[0.9780563,0.017536458,0.0019733692,0.0010595322,0.00104407,0.00033024556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011254372,0.001509218,0.0014712261,0.0017873411,0.00033635966,0.0029907096,0.0028725937,0.0019596647,0.0058698137],"category_scores_gemma":[0.038115226,0.0007337756,0.0015491333,0.001957395,0.0017142989,0.004167546,0.0017526721,0.0034122812,0.0011536931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016797936,0.00011770098,0.0036183372,0.00011580008,0.00011171776,0.00020128729,0.00019574474,0.6939335,0.00053314085,0.26481298,0.002264294,0.033927582],"study_design_scores_gemma":[0.00002492377,0.000071213464,0.0006598167,0.000035427736,0.000028144192,0.000069830676,0.000024953095,0.8426508,0.00019185983,0.15490767,0.0013128413,0.000022480574],"about_ca_topic_score_codex":0.0031543765,"about_ca_topic_score_gemma":0.0013125082,"teacher_disagreement_score":0.011254372,"about_ca_system_score_codex":0.001465563,"about_ca_system_score_gemma":0.0008410677,"threshold_uncertainty_score":0.05951953},"labels":[],"label_agreement":null},{"id":"W3048397425","doi":"10.1145/3386569.3392436","title":"Continuous multiple importance sampling","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematical optimization; Estimator; Computer science; Importance sampling; Sampling (signal processing); Generalization; Heuristic; Variance (accounting); Set (abstract data type); Range (aeronautics); Monte Carlo method; Algorithm; Mathematics; Statistics","score_opus":0.18607852931020075,"score_gpt":0.3359091232907236,"score_spread":0.14983059398052284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048397425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017594829,0.00016390216,0.99705756,0.00007472786,0.000027874434,0.000025134636,0.000017830282,0.00009486816,0.00077873457],"genre_scores_gemma":[0.32815114,0.0006376271,0.6660393,0.00031109384,0.00035515125,0.00032057805,0.00024520807,0.00018216201,0.003757769],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9960757,0.0016985208,0.00013407522,0.00057859294,0.0012833758,0.00022970917],"domain_scores_gemma":[0.9909977,0.006153355,0.00049972517,0.0010033175,0.0010733099,0.00027269032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054036416,0.0010853527,0.001628345,0.0012556644,0.0006738288,0.0016793078,0.0023778058,0.0013199685,0.003627851],"category_scores_gemma":[0.017545547,0.00070231996,0.0009794184,0.0012380211,0.0019167528,0.002340835,0.0024421215,0.0028275105,0.00053595804],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021932212,0.00010443469,0.0015875313,0.00035438355,0.000117973555,0.00019997417,0.00014196141,0.52745557,0.004259135,0.33321187,0.0032444866,0.1291034],"study_design_scores_gemma":[0.000020709384,0.00003854971,0.00015395317,0.000022118074,0.0000133489275,0.000042568056,0.000010306382,0.939853,0.0011783696,0.056759883,0.0018964015,0.000010801407],"about_ca_topic_score_codex":0.0019254677,"about_ca_topic_score_gemma":0.0017346446,"teacher_disagreement_score":0.0054036416,"about_ca_system_score_codex":0.0013353094,"about_ca_system_score_gemma":0.0012290587,"threshold_uncertainty_score":0.028577507},"labels":[],"label_agreement":null},{"id":"W3081106270","doi":"","title":"Variogram-based global sensitivity analysis of environmental models with dependent variables","year":2018,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Variogram; Sensitivity (control systems); Environmental science; Econometrics; Statistics; Mathematics; Kriging; Engineering","score_opus":0.03751799879305191,"score_gpt":0.2714385415369199,"score_spread":0.23392054274386798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081106270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14991863,0.00028666575,0.84637135,0.00021753728,0.000030963798,0.000046723315,0.00027439903,0.0004665326,0.0023872075],"genre_scores_gemma":[0.9581235,0.0001829021,0.04005854,0.000070932336,0.000023019533,0.000082677194,0.00034209708,0.0001878238,0.00092840544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983158,0.0012086135,0.00003787647,0.0001585512,0.00017134252,0.00010778564],"domain_scores_gemma":[0.9954242,0.003981062,0.00016936188,0.00018435765,0.0001929987,0.00004810307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049663316,0.001133243,0.0011146144,0.0017835016,0.0003196558,0.0011603638,0.001027154,0.00092201855,0.0011926794],"category_scores_gemma":[0.010319963,0.000676701,0.0020238967,0.0010597233,0.00093356596,0.0011046628,0.0021069932,0.0011204829,0.00009734413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039739585,0.000010978652,0.000361449,0.000016005684,0.00006414325,0.000016811897,0.000011059347,0.9944259,0.00054447487,0.0018015569,0.00006587437,0.002641971],"study_design_scores_gemma":[0.000003553778,0.000014439544,0.0002621369,0.0000020668044,0.000012910634,0.000004336038,0.0000037550699,0.99698657,0.0002973365,0.0023487753,0.00005901993,0.0000051747666],"about_ca_topic_score_codex":0.0057995785,"about_ca_topic_score_gemma":0.0032881894,"teacher_disagreement_score":0.0057995785,"about_ca_system_score_codex":0.0010668229,"about_ca_system_score_gemma":0.00079915783,"threshold_uncertainty_score":0.026264787},"labels":[],"label_agreement":null},{"id":"W3081577637","doi":"10.1002/asmb.2566","title":"On the information properties of working used systems using dynamic signature","year":2020,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Predictability; Entropy (arrow of time); Closeness; Information theory; Kullback–Leibler divergence; Divergence (linguistics); Residual; Computer science; Joint entropy; Mathematics; Signature (topology); Applied mathematics; Statistical physics; Algorithm; Principle of maximum entropy; Statistics; Artificial intelligence; Physics; Mathematical analysis","score_opus":0.17373649486875317,"score_gpt":0.2661978014313534,"score_spread":0.09246130656260024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081577637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43486336,0.00042229297,0.55840343,0.00034973907,0.000033213597,0.000034817676,0.00017625307,0.00014948279,0.005567337],"genre_scores_gemma":[0.99281365,0.00006471349,0.006555282,0.00002070473,0.000024137264,0.000017673572,0.00005477255,0.00001931902,0.0004297087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793965,0.0005766205,0.00013185458,0.000234681,0.0008752194,0.00024201747],"domain_scores_gemma":[0.98560286,0.009337735,0.0021539382,0.0010012869,0.0013406874,0.00056345545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027685342,0.00047465513,0.00071668945,0.001809981,0.0005306223,0.0015533742,0.000810507,0.0006945821,0.001309612],"category_scores_gemma":[0.014907485,0.00025830595,0.00045808227,0.0009684542,0.0023144314,0.0028584118,0.0018491353,0.0009668137,0.0001248525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025555718,0.0000671127,0.005445985,0.00013078682,0.00007988648,0.00043235187,0.00041051253,0.6375294,0.01732075,0.31927708,0.00054263807,0.018507898],"study_design_scores_gemma":[0.0000061222213,0.00007321817,0.0017546716,0.00001888878,0.0000098403225,0.00012238395,0.00005897745,0.89431953,0.002265594,0.101072244,0.0002651669,0.00003336942],"about_ca_topic_score_codex":0.0007439609,"about_ca_topic_score_gemma":0.00038134298,"teacher_disagreement_score":0.0027685342,"about_ca_system_score_codex":0.0012762218,"about_ca_system_score_gemma":0.00064719224,"threshold_uncertainty_score":0.014641583},"labels":[],"label_agreement":null},{"id":"W3081882803","doi":"10.1007/s11071-020-05895-x","title":"A polynomial chaos expansion approach for nonlinear dynamic systems with interval uncertainty","year":2020,"lang":"en","type":"article","venue":"Nonlinear Dynamics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Mathematics; Polynomial chaos; Interval (graph theory); Interval arithmetic; Applied mathematics; Polynomial; Nonlinear system; Taylor series; Infimum and supremum; Function (biology); Bounded function; Mathematical optimization; Mathematical analysis","score_opus":0.05760939555318855,"score_gpt":0.3011485301443579,"score_spread":0.24353913459116933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081882803","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004958258,0.00033067394,0.99221694,0.00009989008,0.00003715763,0.000010846435,0.000024111834,0.000036966205,0.002285203],"genre_scores_gemma":[0.7696508,0.0021085755,0.21453863,0.0001258931,0.00037557332,0.000124052,0.00014446193,0.00010214735,0.012829976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998336,0.000048540744,0.000008326896,0.000023672397,0.00007162182,0.000014209948],"domain_scores_gemma":[0.9996507,0.0002100342,0.000027791477,0.000027707903,0.00006841571,0.000015328413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004386289,0.00045369356,0.0006189405,0.0005885081,0.00026390702,0.0005172584,0.00057840673,0.0004701416,0.0016588585],"category_scores_gemma":[0.0013390387,0.00019651213,0.00052328664,0.0006079603,0.00051857205,0.00091335137,0.00071036024,0.00092722854,0.0003292205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054235064,0.000032356744,0.00024084745,0.00015500087,0.000045157973,0.00013533233,0.00010930706,0.6866118,0.0065092975,0.24986228,0.0015687593,0.054675695],"study_design_scores_gemma":[0.0000016426324,0.000013846402,0.000042954307,0.0000036327078,0.000003956885,0.0000134003385,0.0000038220696,0.9817902,0.0002066957,0.01731359,0.00060222566,0.0000039861434],"about_ca_topic_score_codex":0.0013521804,"about_ca_topic_score_gemma":0.0011627886,"teacher_disagreement_score":0.0016588585,"about_ca_system_score_codex":0.00032348646,"about_ca_system_score_gemma":0.000370031,"threshold_uncertainty_score":0.005549431},"labels":[],"label_agreement":null},{"id":"W3084304375","doi":"10.1002/mop.32929","title":"A partial element equivalent circuit‐metamodel combination for fast tolerance analysis of electromagnetic systems","year":2021,"lang":"en","type":"article","venue":"Microwave and Optical Technology Letters","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Metamodeling; Partial element equivalent circuit; Equivalent circuit; Monte Carlo method; Finite element method; Computer science; Element (criminal law); Electronic engineering; Engineering; Mathematics; Electrical engineering; Structural engineering; Voltage","score_opus":0.04357800196664423,"score_gpt":0.2885980246656431,"score_spread":0.24502002269899886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084304375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057482496,0.000035711455,0.99307173,0.00002456302,0.000010379066,0.000012730992,0.000010863556,0.00016569,0.0009200879],"genre_scores_gemma":[0.4970863,0.00016413516,0.4996211,0.00008147168,0.000030313025,0.00014700595,0.00010203569,0.00015652136,0.0026112455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997234,0.000095870324,0.000010761729,0.00002609043,0.00012195363,0.000021951499],"domain_scores_gemma":[0.99981064,0.00007812095,0.000019783442,0.000044425935,0.000036309288,0.000010714168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004620791,0.00049856224,0.000581137,0.00041793124,0.0002156408,0.00046346957,0.00066323,0.00061516167,0.00229151],"category_scores_gemma":[0.00065341155,0.00029692086,0.0006240734,0.00030131103,0.00023003227,0.00074582995,0.000611391,0.0006724967,0.00040919456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006780167,0.00009029302,0.00048172034,0.00010899137,0.00008136868,0.00013751232,0.00006140695,0.78596747,0.06269043,0.04347446,0.00085882196,0.105979726],"study_design_scores_gemma":[0.0000024986318,0.000020118367,0.000031197997,0.0000023894443,0.0000046722366,0.000017941868,0.0000020439384,0.9952997,0.0021762147,0.0015759863,0.000864279,0.000002874542],"about_ca_topic_score_codex":0.00059375033,"about_ca_topic_score_gemma":0.0008233151,"teacher_disagreement_score":0.00229151,"about_ca_system_score_codex":0.00022146136,"about_ca_system_score_gemma":0.00041346863,"threshold_uncertainty_score":0.007665813},"labels":[],"label_agreement":null},{"id":"W3087210325","doi":"10.1080/23249935.2020.1826596","title":"Investigating the impact of correlation on system multimode reliability-based analysis of highway geometric design","year":2020,"lang":"en","type":"article","venue":"Transportmetrica A Transport Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Reliability (semiconductor); Failure mode and effects analysis; Truck; Random variable; Variable (mathematics); Reliability engineering; Engineering; Series (stratigraphy); Computer science; Statistics; Mathematics; Automotive engineering","score_opus":0.12138690440578849,"score_gpt":0.3321383196306403,"score_spread":0.2107514152248518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087210325","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7004162,0.00018752205,0.29749027,0.00008734226,0.0000096812255,0.000029133544,0.000074769385,0.0001420883,0.0015630602],"genre_scores_gemma":[0.993385,0.000034580717,0.0063964208,0.0000068777836,0.0000039029132,0.000010122258,0.000035397872,0.000011684361,0.000116033836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830365,0.00092552655,0.000047612564,0.00018158303,0.00041554624,0.00012609392],"domain_scores_gemma":[0.9813897,0.014555183,0.0020680903,0.0007560811,0.0011171767,0.00011383036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004064718,0.00059528305,0.00058933743,0.00086578337,0.00020379566,0.00054656423,0.0004154292,0.00037727578,0.00049704174],"category_scores_gemma":[0.014985805,0.00034533237,0.0005899657,0.0006176382,0.00048998435,0.00066557096,0.00056657457,0.00061110756,0.00006956163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042881496,0.000020951675,0.009567285,0.000025722386,0.000047794125,0.000050007726,0.000025194768,0.98136246,0.001003853,0.0020679748,0.00005187929,0.0057340814],"study_design_scores_gemma":[8.9262426e-7,0.000028709474,0.0030941954,0.0000027856936,0.000010879183,0.000014973906,0.0000066562434,0.9953399,0.00090071297,0.0005653241,0.00003097979,0.0000039219217],"about_ca_topic_score_codex":0.002325728,"about_ca_topic_score_gemma":0.0020580196,"teacher_disagreement_score":0.004064718,"about_ca_system_score_codex":0.0008623785,"about_ca_system_score_gemma":0.00075016456,"threshold_uncertainty_score":0.021496594},"labels":[],"label_agreement":null},{"id":"W3090785602","doi":"10.1029/2020gl089829","title":"A Fresh Look at Variography: Measuring Dependence and Possible Sensitivities Across Geophysical Systems From Any Given Data","year":2020,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"","keywords":"Robustness (evolution); Computer science; Sensitivity (control systems); Sampling (signal processing); Variogram; Data mining; Sample (material); Econometrics; Machine learning; Mathematics; Kriging","score_opus":0.24244019834115385,"score_gpt":0.37340346494878923,"score_spread":0.13096326660763538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090785602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2702274,0.00038470875,0.7254251,0.0010523698,0.00003466522,0.000050473205,0.00033082688,0.00019065508,0.0023037826],"genre_scores_gemma":[0.95068127,0.00016223671,0.04852926,0.00012608779,0.000030923933,0.000040803752,0.00016522258,0.000041379335,0.00022277105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958973,0.0025384128,0.00020506166,0.0005009237,0.00073723594,0.000121144214],"domain_scores_gemma":[0.9504891,0.04176033,0.002413234,0.003790564,0.001259702,0.00028709645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010169192,0.00044691612,0.0007307638,0.002531941,0.00039102897,0.0019517611,0.00070762954,0.00081220287,0.0011706939],"category_scores_gemma":[0.047863703,0.00042256727,0.0008543684,0.0018609609,0.0021736843,0.00269761,0.0017416312,0.0020621477,0.00012482845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041410438,0.00017884524,0.08908285,0.00023673383,0.0006944298,0.0003723002,0.0006162607,0.7118018,0.0178982,0.06904729,0.0009847245,0.108672395],"study_design_scores_gemma":[0.00001648583,0.0002118437,0.044136066,0.00007092127,0.00007909471,0.00019621433,0.0002991184,0.8029264,0.010735118,0.13993849,0.0012721391,0.000118128024],"about_ca_topic_score_codex":0.0014392355,"about_ca_topic_score_gemma":0.0009583164,"teacher_disagreement_score":0.010169192,"about_ca_system_score_codex":0.0006127213,"about_ca_system_score_gemma":0.0004813135,"threshold_uncertainty_score":0.053780496},"labels":[],"label_agreement":null},{"id":"W3093576320","doi":"10.1186/s10086-020-01920-0","title":"Comparison of reliability evaluation methods between China and Canada standards for wood structures featuring duration of load","year":2020,"lang":"en","type":"article","venue":"Journal of Wood Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Harbin Institute of Technology; National Natural Science Foundation of China","keywords":"Reliability (semiconductor); Duration (music); Reliability engineering; Structural engineering; Term (time); China; Computer science; Engineering; Statistics; Mathematics; Power (physics)","score_opus":0.14374558746519872,"score_gpt":0.4643153839760686,"score_spread":0.32056979651086986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093576320","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.779331,0.0014994808,0.18867159,0.0002476673,0.00007966785,0.0003783642,0.0012661865,0.0009865566,0.027539415],"genre_scores_gemma":[0.91021425,0.00046585294,0.08372528,0.000030814866,0.000010290746,0.00017237975,0.0010350451,0.00011373206,0.004232376],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99326974,0.000987405,0.00036986446,0.00034209384,0.004749281,0.00028154242],"domain_scores_gemma":[0.9820847,0.0030395254,0.00094689516,0.001161008,0.012538091,0.00022971752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00561386,0.00046165028,0.00032793925,0.0035069447,0.00071717426,0.00064369023,0.0012465958,0.00031722712,0.001553707],"category_scores_gemma":[0.011494727,0.0002192854,0.00061154773,0.0024167083,0.00055389304,0.00036900505,0.0005811595,0.00036248882,0.00022670957],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009722918,0.0002696586,0.13729712,0.000994004,0.00022990016,0.00024917687,0.0020874687,0.18578644,0.03451719,0.01451133,0.008121894,0.6149636],"study_design_scores_gemma":[0.00013342874,0.0012303155,0.39242443,0.00036026858,0.0003930596,0.0003851834,0.0022887392,0.49014035,0.0789115,0.0040120385,0.02946816,0.0002525244],"about_ca_topic_score_codex":0.24122047,"about_ca_topic_score_gemma":0.43076327,"teacher_disagreement_score":0.7587795,"about_ca_system_score_codex":0.005916751,"about_ca_system_score_gemma":0.0060286913,"threshold_uncertainty_score":0.47963285},"labels":[],"label_agreement":null},{"id":"W3095573571","doi":"10.1109/emceurope48519.2020.9245716","title":"Design-oriented EMC analysis of wiring systems","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"European Commission","keywords":"Point (geometry); Electromagnetic compatibility; Computer science; Statistical analysis; Reliability engineering; Engineering; Electronic engineering; Mathematics","score_opus":0.2361283771213583,"score_gpt":0.3652666157371913,"score_spread":0.129138238615833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095573571","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012327738,0.00025511385,0.9815446,0.000059735346,0.000012939068,0.000024824336,0.00004275791,0.0001049045,0.0056274077],"genre_scores_gemma":[0.8590964,0.0013589235,0.12992407,0.00011160874,0.00011589435,0.00026452084,0.0003246369,0.00031095743,0.008492956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917847,0.0002682427,0.000025109794,0.00009204633,0.0003783178,0.00005778825],"domain_scores_gemma":[0.99925584,0.0003664217,0.00010650277,0.000051704228,0.00019547243,0.000024057932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090975827,0.0009984404,0.00052463607,0.0010768601,0.0002626209,0.0009331244,0.00050665234,0.00050967047,0.002603537],"category_scores_gemma":[0.0020789732,0.00033210177,0.0006504621,0.0005720701,0.000815823,0.00047529922,0.0007213606,0.0005893582,0.00046211216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001504965,0.000016493457,0.00035914106,0.000070258924,0.000024604,0.00004320987,0.000035207155,0.9281488,0.0037038208,0.05177074,0.0004437539,0.015369041],"study_design_scores_gemma":[0.0000021062558,0.000016084521,0.00015362962,0.00000725195,0.0000063564785,0.000016686638,0.0000073899982,0.9789818,0.0009857363,0.018673401,0.0011458434,0.0000037459722],"about_ca_topic_score_codex":0.0012423452,"about_ca_topic_score_gemma":0.00067087234,"teacher_disagreement_score":0.002603537,"about_ca_system_score_codex":0.00087338686,"about_ca_system_score_gemma":0.0005029464,"threshold_uncertainty_score":0.008709669},"labels":[],"label_agreement":null},{"id":"W3097279769","doi":"10.1038/s41598-020-75648-8","title":"Publisher Correction: Robust design from systems physics","year":2020,"lang":"en","type":"erratum","venue":"Scientific Reports","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Data science; Computational biology; Biology","score_opus":0.13532948521042637,"score_gpt":0.28992336664217466,"score_spread":0.1545938814317483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097279769","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017488249,0.0017856319,0.008746427,0.038436946,0.9185267,0.00006113014,0.0025190872,0.0013385649,0.02841062],"genre_scores_gemma":[0.012825199,0.0069235433,0.021816414,0.036861517,0.15023404,0.00028686196,0.0047932244,0.0045744353,0.7616848],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9933293,0.0009144442,0.00085443555,0.0008911004,0.0037375223,0.00027315886],"domain_scores_gemma":[0.9655294,0.0070547094,0.0010719348,0.0042030346,0.021359973,0.00078105973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004858676,0.0023174414,0.0019072664,0.0040560844,0.0029877296,0.0051844106,0.0032488743,0.0051349364,0.14585952],"category_scores_gemma":[0.060324207,0.0010709392,0.0018376425,0.003104346,0.002420276,0.0029730927,0.0018649003,0.00908287,0.089264795],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021116422,0.0000050900767,0.000022192016,0.00008817669,0.0000073179763,0.000054401335,0.000012037615,0.00015055382,0.00004529244,0.0043395576,0.98491675,0.010337411],"study_design_scores_gemma":[0.000026150126,0.000016625328,0.00016172469,0.00016646675,0.000019739244,0.00017688653,0.000024374784,0.0005347703,0.00034313495,0.008337675,0.99016786,0.000024528501],"about_ca_topic_score_codex":0.010121904,"about_ca_topic_score_gemma":0.014051567,"teacher_disagreement_score":0.14585952,"about_ca_system_score_codex":0.0036112713,"about_ca_system_score_gemma":0.0053519076,"threshold_uncertainty_score":0.48794872},"labels":[],"label_agreement":null},{"id":"W3101832170","doi":"10.1016/j.compgeo.2020.103870","title":"Application of binomial system-based reliability in optimizing resistance factor calibration of redundant pile groups","year":2020,"lang":"en","type":"article","venue":"Computers and Geotechnics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Western University","funders":"","keywords":"Pile; Reliability (semiconductor); Binomial theorem; Resistance Factors; Calibration; Binomial distribution; Range (aeronautics); Reliability engineering; Structural engineering; Engineering; Geotechnical engineering; Computer science; Mathematics; Statistics","score_opus":0.03720047527248673,"score_gpt":0.2613790919975012,"score_spread":0.22417861672501446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101832170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042179216,0.00010365336,0.9559238,0.00002944445,0.000012732028,0.000029364639,0.000015118374,0.00027448582,0.0014321526],"genre_scores_gemma":[0.86692077,0.000077563214,0.13234977,0.00002113491,0.00000882675,0.000060521157,0.000036501904,0.00006385898,0.00046100267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896526,0.00048400983,0.00004153312,0.00016903634,0.0002814164,0.000058742986],"domain_scores_gemma":[0.99778974,0.0012535122,0.00025731107,0.00016091095,0.0005073726,0.00003107358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021081795,0.00062498054,0.00089560024,0.0011194033,0.0004754143,0.0005732444,0.00091550295,0.00067152164,0.0012791466],"category_scores_gemma":[0.007290058,0.00049668155,0.00051062694,0.00077044225,0.00056020357,0.0010413096,0.0007900421,0.00047650613,0.00024775648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008300671,0.000022457263,0.0010493096,0.000069471076,0.000020066906,0.00003349271,0.00006467701,0.9544945,0.0059806053,0.004363162,0.00017347565,0.03364569],"study_design_scores_gemma":[0.0000028191782,0.00003055705,0.00019251287,0.0000045241068,0.0000052742266,0.000014757145,0.0000044244375,0.997378,0.0014835051,0.000780691,0.00009795775,0.0000049352757],"about_ca_topic_score_codex":0.001736068,"about_ca_topic_score_gemma":0.0020500205,"teacher_disagreement_score":0.0021081795,"about_ca_system_score_codex":0.0006388933,"about_ca_system_score_gemma":0.0006506144,"threshold_uncertainty_score":0.011149287},"labels":[],"label_agreement":null},{"id":"W3102503716","doi":"10.1063/5.0007143","title":"Erratum: “Quasi-Monte Carlo technique in global sensitivity analysis of wind resource assessment with a study on UAE” [J. Renewable Sustainable Energy <b>11</b>, 053303 (2019)]","year":2020,"lang":"en","type":"erratum","venue":"Journal of Renewable and Sustainable Energy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Renewable energy; Monte Carlo method; Wind power; Sustainable energy; Resource (disambiguation); Sensitivity (control systems); Environmental economics; Environmental science; Engineering; Computer science; Electrical engineering; Economics; Electronic engineering; Statistics; Mathematics","score_opus":0.016302309208040832,"score_gpt":0.2834074134544578,"score_spread":0.267105104246417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3102503716","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022137002,0.0063305614,0.036336232,0.08672797,0.79551864,0.00020539234,0.008214836,0.0021027531,0.062349968],"genre_scores_gemma":[0.03647174,0.013676495,0.07550726,0.08355595,0.064272515,0.00060928654,0.012984487,0.0065412796,0.7063809],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99839646,0.00036431217,0.00023879192,0.00016889177,0.0007647779,0.00006680891],"domain_scores_gemma":[0.9897188,0.003448345,0.00032231372,0.0004361017,0.005864045,0.00021042775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022127314,0.0015363371,0.0010454645,0.0019334567,0.0019087183,0.0023825543,0.0018375157,0.0031946811,0.046795502],"category_scores_gemma":[0.019946193,0.0007036937,0.0011845558,0.0018935618,0.001121481,0.0024646842,0.0009567847,0.0041444483,0.025661705],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062998035,0.000014511105,0.00010028297,0.00012337345,0.000012468954,0.00012503516,0.000016455113,0.0012294288,0.00018751036,0.0043696994,0.9816428,0.012115358],"study_design_scores_gemma":[0.00006267854,0.00006786239,0.0012838444,0.00047597676,0.00004453123,0.0003425866,0.00008190468,0.008537661,0.0011936722,0.012136151,0.9756822,0.00009098251],"about_ca_topic_score_codex":0.017527392,"about_ca_topic_score_gemma":0.027938278,"teacher_disagreement_score":0.046795502,"about_ca_system_score_codex":0.0016580943,"about_ca_system_score_gemma":0.002463681,"threshold_uncertainty_score":0.1565466},"labels":[],"label_agreement":null},{"id":"W3103857526","doi":"10.1080/03610926.2020.1843680","title":"The generalized Pearson family of distributions and explicit representation of the associated density functions","year":2020,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalization; Mathematics; Logarithm; Representation (politics); Simple (philosophy); Moment (physics); Function (biology); Moment-generating function; Applied mathematics; Probability density function; Mathematical analysis; Statistics","score_opus":0.16111298969476806,"score_gpt":0.4372012157763082,"score_spread":0.27608822608154016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3103857526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013844615,0.0007357663,0.9944079,0.00018521055,0.00004709944,0.000019427109,0.00012694167,0.00011144296,0.0029817556],"genre_scores_gemma":[0.2303951,0.010343625,0.7436244,0.0006997889,0.00091471575,0.00047008964,0.0008714621,0.00040101327,0.012279851],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99782133,0.000932193,0.00009455418,0.00036734718,0.0006375986,0.00014700054],"domain_scores_gemma":[0.9954181,0.0027977766,0.00040684277,0.0005600323,0.00071414985,0.000103133236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034475275,0.0010869239,0.0009642389,0.0030769445,0.00054540916,0.0021096587,0.0016772441,0.0014713086,0.0044380315],"category_scores_gemma":[0.014109767,0.0004971699,0.0011738362,0.0034085454,0.0020614022,0.0031382232,0.0014681442,0.0026102767,0.0020825625],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019903202,0.0000148568815,0.00044462137,0.00016040797,0.000025874191,0.00020056867,0.00017695887,0.034615517,0.0015367535,0.91686344,0.00406288,0.04187832],"study_design_scores_gemma":[0.000010479918,0.000054982323,0.00066254684,0.00009999021,0.0000240724,0.0011671922,0.00009260866,0.33802247,0.0010634417,0.6337687,0.024971852,0.00006162704],"about_ca_topic_score_codex":0.0017700335,"about_ca_topic_score_gemma":0.0009941956,"teacher_disagreement_score":0.0044380315,"about_ca_system_score_codex":0.0009558922,"about_ca_system_score_gemma":0.0013222458,"threshold_uncertainty_score":0.018232524},"labels":[],"label_agreement":null},{"id":"W3104568502","doi":"10.22215/etd/2006-08239","title":"Uncertainty simulation using domain decomposition and stratified sampling","year":2006,"lang":"en","type":"dissertation","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Sampling (signal processing); Domain (mathematical analysis); Computer science; Stratified sampling; Humanities; Mathematics; Statistics; Telecommunications; Art; Mathematical analysis","score_opus":0.1195816809656601,"score_gpt":0.41670642343049336,"score_spread":0.2971247424648332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104568502","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010721463,0.000097244636,0.98764634,0.00007482467,0.000017252712,0.000041822357,0.00006847249,0.00012990934,0.0012026096],"genre_scores_gemma":[0.5682302,0.00034382413,0.42693397,0.00012767036,0.000069908965,0.00032331405,0.0006625472,0.00014620471,0.0031622942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844164,0.00096269906,0.00005511972,0.00013895513,0.0002744265,0.00012715743],"domain_scores_gemma":[0.9930016,0.0051715844,0.00036448895,0.00066642836,0.0005891219,0.0002067596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034146316,0.00055780937,0.0011179141,0.001110979,0.00040999093,0.0012268458,0.0011005433,0.00079086464,0.002706699],"category_scores_gemma":[0.01135901,0.00078353245,0.0014345387,0.0009250089,0.0008762284,0.0013412794,0.0017196883,0.0011021822,0.00037988924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089081936,0.000027696908,0.00093221257,0.00003359252,0.000053329026,0.00003170156,0.0000384689,0.9412654,0.00046835426,0.043156654,0.00054338056,0.013360087],"study_design_scores_gemma":[0.0000050250437,0.0000057980546,0.00004436132,0.0000030187969,0.0000027119015,0.0000029520065,0.0000026509201,0.99256927,0.00010146827,0.0071025067,0.00015823136,0.000001939209],"about_ca_topic_score_codex":0.01018054,"about_ca_topic_score_gemma":0.008443602,"teacher_disagreement_score":0.01018054,"about_ca_system_score_codex":0.0012293642,"about_ca_system_score_gemma":0.0014870748,"threshold_uncertainty_score":0.020242572},"labels":[],"label_agreement":null},{"id":"W3107220894","doi":"10.3390/a13120325","title":"Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects","year":2020,"lang":"en","type":"article","venue":"Algorithms","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Natural Resources; University of Alberta","funders":"","keywords":"Monte Carlo method; Computer science; Fuzzy logic; Bivariate analysis; Multivariate statistics; Schedule; Sensitivity (control systems); Multivariate normal distribution; Copula (linguistics); Data mining; Econometrics; Machine learning; Statistics; Artificial intelligence; Mathematics; Engineering","score_opus":0.1288904907946143,"score_gpt":0.3623020091042172,"score_spread":0.23341151830960294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107220894","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010187553,0.00004856095,0.98873234,0.000037701084,0.0000052873047,0.000021413402,0.000034159533,0.00008599779,0.0008469932],"genre_scores_gemma":[0.772667,0.00027209445,0.22552207,0.000034737925,0.000025335676,0.00017930535,0.00011312648,0.000056393343,0.0011300087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987907,0.00068930414,0.000048013524,0.00011114083,0.0002880861,0.000072776245],"domain_scores_gemma":[0.99803406,0.0013751396,0.00020032295,0.00009777021,0.00024493012,0.000047750975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026644988,0.0008569367,0.00080073037,0.0013753702,0.00045141325,0.0014464236,0.0008409843,0.00067071157,0.0022390874],"category_scores_gemma":[0.005120368,0.00039109198,0.0012015977,0.000987614,0.00053087424,0.00090436445,0.0008249592,0.0011224722,0.00018319357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015953212,0.000015457075,0.00043227765,0.000022174816,0.000022332608,0.000016544669,0.000024929985,0.9789039,0.00045479272,0.0100479815,0.00008426823,0.009959573],"study_design_scores_gemma":[7.901961e-7,0.0000065190998,0.000087542096,0.0000036810334,0.000003341827,0.0000033068081,0.0000037633345,0.997334,0.0001376998,0.0023147862,0.00010187944,0.000002689834],"about_ca_topic_score_codex":0.006170655,"about_ca_topic_score_gemma":0.0052834284,"teacher_disagreement_score":0.006170655,"about_ca_system_score_codex":0.0011608509,"about_ca_system_score_gemma":0.0011774802,"threshold_uncertainty_score":0.0140913725},"labels":[],"label_agreement":null},{"id":"W3107824015","doi":"10.1016/j.cma.2020.113570","title":"Spatio-stochastic adaptive discontinuous Galerkin methods","year":2020,"lang":"en","type":"article","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto; Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; Molson Foundation; Government of Ontario","keywords":"Polynomial chaos; Uncertainty quantification; Mathematics; Nonlinear system; Galerkin method; Degree of a polynomial; Applied mathematics; Discontinuous Galerkin method; Discretization; Parametric statistics; Aerodynamics; Finite element method; Mathematical optimization; Propagation of uncertainty; Polynomial; Monte Carlo method; Mathematical analysis; Algorithm","score_opus":0.11480754069539022,"score_gpt":0.36544057896051796,"score_spread":0.25063303826512773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107824015","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004116483,0.00018658351,0.9939295,0.00011105057,0.00006659537,0.000016360942,0.000030899686,0.000060530852,0.0014819923],"genre_scores_gemma":[0.58757,0.0007447628,0.40072557,0.00023428175,0.0002489278,0.00024564753,0.00020894324,0.00018040046,0.009841431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995455,0.00016527374,0.000025956055,0.0000582645,0.00017493365,0.000029992509],"domain_scores_gemma":[0.99896514,0.0005453653,0.00013226892,0.00009597007,0.00020649408,0.000054672088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009261615,0.00061229104,0.00079910824,0.0006697413,0.00036276903,0.0009295492,0.0014213854,0.0014229444,0.0021007203],"category_scores_gemma":[0.0021032928,0.00042956093,0.0007391435,0.0005147466,0.0010630029,0.00071511185,0.0017156303,0.0009519487,0.00042230092],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007260985,0.000053125746,0.0008204006,0.00014360622,0.00007097833,0.00007126986,0.000050238617,0.8453069,0.0052825883,0.11652641,0.0009406119,0.030661337],"study_design_scores_gemma":[0.000004819999,0.0000064644037,0.000055732733,0.000003819753,0.0000037736534,0.000013546438,0.0000028139693,0.9934621,0.00032103615,0.005548317,0.00057418883,0.0000033950269],"about_ca_topic_score_codex":0.0013000724,"about_ca_topic_score_gemma":0.0014464305,"teacher_disagreement_score":0.0021007203,"about_ca_system_score_codex":0.00044176765,"about_ca_system_score_gemma":0.0007209703,"threshold_uncertainty_score":0.0070275664},"labels":[],"label_agreement":null},{"id":"W3110056219","doi":"10.1139/cjce-2019-0686","title":"Uncertainty quantification of wall thickness of onshore gas transmission pipelines","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pipeline transport; Corrosion; Reliability (semiconductor); Structural engineering; Welding; Pipeline (software); Cracking; Range (aeronautics); Stress (linguistics); Materials science; Geotechnical engineering; Environmental science; Engineering; Composite material; Mechanical engineering","score_opus":0.07243564660877215,"score_gpt":0.2728932499543465,"score_spread":0.20045760334557433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110056219","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98101,0.00007084936,0.018058587,0.000011376066,0.0000016181352,0.000007810457,0.00023329786,0.00006209845,0.0005443094],"genre_scores_gemma":[0.9977405,0.000026765301,0.0019616745,0.0000014828975,9.039115e-7,0.0000032856124,0.00014566559,0.000005155485,0.00011457141],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995511,0.00006352115,0.0000225699,0.00010188192,0.00021294956,0.00004802099],"domain_scores_gemma":[0.99867046,0.00044997394,0.00041652276,0.00008979572,0.000339442,0.000033846994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006399314,0.0004143319,0.00022629429,0.0012194773,0.00023466282,0.00047444797,0.00040166228,0.00023190257,0.00029099028],"category_scores_gemma":[0.0023865711,0.00021240521,0.00020485019,0.0007881502,0.0004275495,0.00042667595,0.00045927154,0.00016197984,0.000057336125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036575118,0.000047333524,0.41230887,0.00018124662,0.00015752597,0.00078384054,0.0011759548,0.42272997,0.095829606,0.0010499884,0.00032733407,0.06504255],"study_design_scores_gemma":[0.0000056881436,0.0001353699,0.58146197,0.00003764195,0.00006156356,0.0003783112,0.0008254418,0.3739172,0.041177753,0.0008428655,0.0010851038,0.00007112068],"about_ca_topic_score_codex":0.02142966,"about_ca_topic_score_gemma":0.028770369,"teacher_disagreement_score":0.02142966,"about_ca_system_score_codex":0.0007138436,"about_ca_system_score_gemma":0.00047890015,"threshold_uncertainty_score":0.04260987},"labels":[],"label_agreement":null},{"id":"W3112134271","doi":"10.1016/j.envsoft.2020.104954","title":"The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support","year":2020,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":592,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Global Institute for Water Security; University of Saskatchewan","funders":"Office of Science; Universitat Oberta de Catalunya; Universitetet i Bergen; Sandia National Laboratories; Joint Research Centre; National Nuclear Security Administration; Advanced Scientific Computing Research; U.S. Department of Energy; European Commission; Global Water Futures; Australian Government; National Socio-Environmental Synthesis Center; National Science Foundation","keywords":"Warrant; Multidisciplinary approach; Variety (cybernetics); Structuring; Management science; Context (archaeology); Risk analysis (engineering); Computer science; Perspective (graphical); Data science; Engineering ethics; Knowledge management; Engineering; Political science; Artificial intelligence; Sociology; Business; Social science","score_opus":0.044575074106422384,"score_gpt":0.2903875998439404,"score_spread":0.24581252573751802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112134271","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020894671,0.027265977,0.90464103,0.047620595,0.0012117185,0.00005940915,0.00013793599,0.00023289834,0.016741011],"genre_scores_gemma":[0.37863955,0.09725516,0.5004411,0.008953288,0.008174019,0.0006058782,0.0002550193,0.00047555903,0.005200511],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9790822,0.01551984,0.0007850585,0.0013192786,0.0029579834,0.00033566947],"domain_scores_gemma":[0.93078816,0.057056915,0.0023470444,0.004633332,0.004152997,0.0010216245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.034980968,0.0022218018,0.003494215,0.0044315513,0.002373044,0.011116957,0.0029456934,0.0058713057,0.0035254196],"category_scores_gemma":[0.042424355,0.00093418296,0.0025795114,0.0037290019,0.018237626,0.013549919,0.006013885,0.012968567,0.00072236016],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011910252,0.000020054296,0.00024609602,0.00030252896,0.00007182473,0.000029709472,0.00025387743,0.029625038,0.0001694469,0.9451058,0.0027544752,0.021409187],"study_design_scores_gemma":[0.0000025019401,0.000013221172,0.00005968289,0.00025700784,0.000010225243,0.000015783038,0.00010767414,0.023327217,0.000089730165,0.96226966,0.01382286,0.000024389035],"about_ca_topic_score_codex":0.0038362665,"about_ca_topic_score_gemma":0.0023171185,"teacher_disagreement_score":0.034980968,"about_ca_system_score_codex":0.004951826,"about_ca_system_score_gemma":0.007626896,"threshold_uncertainty_score":0.18499923},"labels":[],"label_agreement":null},{"id":"W3114316599","doi":"10.1115/detc2000/cie-14661","title":"Non-Linear and Non-Stationary Random Responses of Discretized Plate Structures by Stochastic Direct Integration With Correction Factor","year":2000,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Discretization; White noise; Stochastic process; Mathematics; Applied mathematics; Mathematical optimization; Stability (learning theory); Noise (video); Algorithm; Computer science; Mathematical analysis; Statistics","score_opus":0.024284675869719725,"score_gpt":0.2904826528863364,"score_spread":0.2661979770166167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114316599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087641105,0.000046043566,0.9109476,0.000042950167,0.000013304629,0.000022387945,0.000013892641,0.00011621908,0.0011564655],"genre_scores_gemma":[0.88739884,0.00006813694,0.11054014,0.000027723328,0.000007637481,0.000043603606,0.00003611,0.000024580677,0.0018531515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998024,0.000057681467,0.000007442618,0.000022381013,0.00009543273,0.000014653477],"domain_scores_gemma":[0.99949,0.0002934332,0.000049093385,0.000051108567,0.00009990867,0.000016372896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005834275,0.0003217051,0.00033176917,0.00022910879,0.000106876156,0.00032915676,0.00048478966,0.0003779037,0.0007526876],"category_scores_gemma":[0.0016264647,0.00019024056,0.0002727154,0.00019455288,0.00041947915,0.0003418167,0.00038837251,0.0003829722,0.000110350644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003817392,0.000025303856,0.00052171014,0.000032845866,0.000012778994,0.000048705155,0.000034687648,0.96479005,0.00881883,0.007709789,0.00008654591,0.017880589],"study_design_scores_gemma":[0.0000010921519,0.0000042994516,0.00003535933,6.0123693e-7,6.598122e-7,0.0000031253685,0.000001112593,0.9990976,0.00059528626,0.00022725012,0.000032573393,0.000001074034],"about_ca_topic_score_codex":0.0027186326,"about_ca_topic_score_gemma":0.0026371088,"teacher_disagreement_score":0.0027186326,"about_ca_system_score_codex":0.00037809755,"about_ca_system_score_gemma":0.00050271105,"threshold_uncertainty_score":0.005405605},"labels":[],"label_agreement":null},{"id":"W311875751","doi":"10.1016/j.compchemeng.2015.05.010","title":"Optimal scenario reduction framework based on distance of uncertainty distribution and output performance: II. Sequential reduction","year":2015,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Science Foundation of Sri Lanka; Foundation for the National Institutes of Health; National Science Foundation","keywords":"Reduction (mathematics); Mathematical optimization; Sensitivity (control systems); Computer science; Optimization problem; Robust optimization; Mathematics; Engineering","score_opus":0.04358956990617422,"score_gpt":0.2709376162897333,"score_spread":0.22734804638355907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W311875751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005083133,0.00014699732,0.99217063,0.00013189616,0.000018901228,0.00004070644,0.000060853752,0.000062023224,0.0022848186],"genre_scores_gemma":[0.6461239,0.000905387,0.34491462,0.00016034843,0.00020582178,0.000503531,0.00065029744,0.00022724606,0.006308925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998524,0.000531605,0.00005494215,0.00025257102,0.00049630296,0.00014059663],"domain_scores_gemma":[0.99914193,0.00047108042,0.000072890696,0.00009684513,0.00017548732,0.000041829713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017009085,0.001238641,0.0012863164,0.0008477636,0.0003767027,0.0012607421,0.0012844537,0.00068119215,0.0038426588],"category_scores_gemma":[0.002781335,0.00043752854,0.0012753238,0.0008127228,0.0009538807,0.0017235874,0.001761708,0.001345681,0.0004081221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010201107,0.000058321602,0.00024536124,0.00013423662,0.0000602408,0.00006683736,0.000050211678,0.88817966,0.0026709714,0.07061897,0.0010930448,0.036720205],"study_design_scores_gemma":[0.000009737293,0.00004684629,0.00011894796,0.00000812967,0.00001213173,0.00002799145,0.000011430666,0.9564449,0.0008769016,0.04173369,0.00069920527,0.000010077123],"about_ca_topic_score_codex":0.001933153,"about_ca_topic_score_gemma":0.0011778454,"teacher_disagreement_score":0.0038426588,"about_ca_system_score_codex":0.00088550063,"about_ca_system_score_gemma":0.0016182585,"threshold_uncertainty_score":0.012854934},"labels":[],"label_agreement":null},{"id":"W3121230781","doi":"10.1061/geosek.0000114","title":"Reliability-Based Design (RBD) For Everyone: (No Monte Carlo Simulation Required!)","year":2019,"lang":"en","type":"article","venue":"GEOSTRATA Magazine","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Limit state design; Reliability (semiconductor); Monte Carlo method; Probabilistic logic; Bridge (graph theory); Engineering; Margin (machine learning); Limit (mathematics); Reliability engineering; Resistance Factors; Probabilistic design; Point (geometry); Structural load; Structural engineering; Computer science; Mathematics; Engineering design process; Statistics; Power (physics)","score_opus":0.09345088426074027,"score_gpt":0.3376387915224535,"score_spread":0.24418790726171324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121230781","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001155574,0.000977338,0.8607698,0.0043820385,0.001397105,0.00030770068,0.0010782656,0.0160747,0.11385744],"genre_scores_gemma":[0.07695472,0.0029730932,0.6870593,0.0045062737,0.0008335482,0.0017032152,0.003458007,0.011121775,0.21139006],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996795,0.0008046907,0.00016277576,0.0003815441,0.001716661,0.00013932989],"domain_scores_gemma":[0.99297464,0.001523011,0.00031880598,0.002726956,0.0022448543,0.0002117721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004393767,0.0011469459,0.0009389203,0.001057618,0.0006373334,0.002596906,0.002453923,0.0018190182,0.10980028],"category_scores_gemma":[0.0150958225,0.0009769282,0.001060071,0.0007671575,0.0010572361,0.0037475547,0.0023151294,0.003173909,0.09444586],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001302095,0.00008445239,0.00076811557,0.0004352553,0.00006133604,0.00010066014,0.0002049253,0.021652615,0.0037920477,0.08009453,0.37612733,0.51654863],"study_design_scores_gemma":[0.00006481438,0.00013815008,0.0004818767,0.0003363047,0.000037053916,0.00032592012,0.00005387992,0.04878786,0.0033421451,0.05992978,0.8864309,0.00007128828],"about_ca_topic_score_codex":0.0016245904,"about_ca_topic_score_gemma":0.0022344592,"teacher_disagreement_score":0.10980028,"about_ca_system_score_codex":0.0010379881,"about_ca_system_score_gemma":0.001744232,"threshold_uncertainty_score":0.3673185},"labels":[],"label_agreement":null},{"id":"W3121277824","doi":"","title":"The Information Content of Implied Probabilities to Detect Structural Change","year":2008,"lang":"en","type":"article","venue":"Cahiers de recherche","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Estimator; Econometrics; Moment (physics); Statistics; Mathematics; Set (abstract data type); Computer science","score_opus":0.523421171638144,"score_gpt":0.39934033432516275,"score_spread":0.1240808373129812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121277824","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17978947,0.0012562446,0.80520594,0.003683396,0.00014396846,0.00014535396,0.001778788,0.00046795438,0.0075290264],"genre_scores_gemma":[0.93715304,0.0008383389,0.05775117,0.0004071245,0.00036475615,0.00026804992,0.0013964382,0.00008635779,0.0017346898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9920988,0.004732388,0.0004100523,0.0011611198,0.0012305237,0.00036711205],"domain_scores_gemma":[0.70756716,0.27686435,0.004817252,0.007096329,0.0028078305,0.0008470807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013322008,0.0009808864,0.0019743568,0.0044602887,0.00089464546,0.002545082,0.0020220387,0.002469028,0.006512101],"category_scores_gemma":[0.1684184,0.00073282316,0.0011011667,0.003369039,0.0026330873,0.0051050372,0.0022660182,0.0031265644,0.0006057035],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013375548,0.00027619067,0.022638436,0.000627619,0.0005110478,0.00041736074,0.00035979293,0.4915738,0.002211757,0.3080795,0.0050032614,0.1669637],"study_design_scores_gemma":[0.000062352316,0.00009497225,0.0032710647,0.00007314138,0.00006462441,0.00009225416,0.000029114783,0.72820544,0.0012872749,0.26615402,0.0006292444,0.000036371657],"about_ca_topic_score_codex":0.0013495743,"about_ca_topic_score_gemma":0.0011163077,"teacher_disagreement_score":0.013322008,"about_ca_system_score_codex":0.0016872399,"about_ca_system_score_gemma":0.0015098167,"threshold_uncertainty_score":0.07045436},"labels":[],"label_agreement":null},{"id":"W3122962021","doi":"10.3390/en14040821","title":"Probabilistic and Risk-Informed Life Extension Assessment of Wind Turbine Structural Components","year":2021,"lang":"en","type":"article","venue":"Energies","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Energistyrelsen","keywords":"Life extension; Probabilistic logic; Reliability engineering; Reliability (semiconductor); Turbine; Computer science; Risk assessment; Wind power; Calibration; Risk analysis (engineering); Engineering; Mathematics; Statistics; Artificial intelligence","score_opus":0.0664582131768938,"score_gpt":0.33840177701870716,"score_spread":0.2719435638418134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122962021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1865386,0.0005019368,0.8078293,0.00021702249,0.000017592933,0.000085261214,0.00017287135,0.00010966387,0.0045277053],"genre_scores_gemma":[0.97591066,0.00016339248,0.022888167,0.000015755668,0.0000080562995,0.000066542554,0.000082127175,0.000011022836,0.0008542616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986203,0.00063521235,0.00005968301,0.00013900716,0.00046081495,0.00008506581],"domain_scores_gemma":[0.99712795,0.0018595731,0.0004511879,0.00014554535,0.00037107017,0.00004467276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034376464,0.00069316966,0.0004826654,0.0009740522,0.00023467152,0.00074373407,0.0007519919,0.00093590777,0.0010760962],"category_scores_gemma":[0.006479282,0.00058576016,0.0007732899,0.00051131734,0.0006683185,0.0012438394,0.00085484394,0.000559069,0.00012515478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020960337,0.000005914035,0.00043087936,0.000011946645,0.000007376051,0.00001708504,0.000008260872,0.99345845,0.00037539538,0.001906866,0.000038625967,0.003718327],"study_design_scores_gemma":[0.0000035371465,0.000042841806,0.00069222215,0.000005966531,0.00001057556,0.00002307044,0.000008243766,0.99408734,0.0005292066,0.0043656845,0.00022154624,0.000009829775],"about_ca_topic_score_codex":0.0016415007,"about_ca_topic_score_gemma":0.0021367753,"teacher_disagreement_score":0.0034376464,"about_ca_system_score_codex":0.00086844014,"about_ca_system_score_gemma":0.0008021256,"threshold_uncertainty_score":0.018180251},"labels":[],"label_agreement":null},{"id":"W3123679449","doi":"10.1145/3429336","title":"Green Simulation with Database Monte Carlo","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Modeling and Computer Simulation","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control variates; Variance reduction; Computer science; Monte Carlo method; Variance (accounting); Convergence (economics); Reduction (mathematics); Idle; Database; Mathematical optimization; Simulation; Statistics; Mathematics; Monte Carlo molecular modeling","score_opus":0.11204015738904748,"score_gpt":0.32694047555853806,"score_spread":0.21490031816949057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123679449","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017709192,0.000098375895,0.97950065,0.0001275845,0.000029449511,0.000113084614,0.00006344609,0.0004204248,0.001937821],"genre_scores_gemma":[0.56305337,0.00015723566,0.43265128,0.00026616,0.000043017248,0.0010290615,0.0002579625,0.00022741806,0.0023145506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99472517,0.0034348823,0.00015307889,0.00057499093,0.0008713655,0.0002405643],"domain_scores_gemma":[0.9721352,0.021696439,0.0011468722,0.0033943388,0.0012743224,0.0003528916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008880114,0.0008038059,0.0011049012,0.0011279167,0.0006224553,0.0017462673,0.0022345867,0.0017791536,0.0031803665],"category_scores_gemma":[0.028393373,0.00063536566,0.0009781679,0.0012167686,0.001954024,0.0018384927,0.0017229215,0.0018883105,0.00045574494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028871393,0.00011018076,0.0012634605,0.000058470923,0.000053101074,0.0000432339,0.00008006683,0.8968866,0.0010270211,0.08499527,0.00047497783,0.014718977],"study_design_scores_gemma":[0.000018243341,0.000021423972,0.000056770823,0.0000032801595,0.000004181105,0.0000048098987,0.0000051703764,0.98592687,0.000437256,0.013239856,0.00027725883,0.0000049954488],"about_ca_topic_score_codex":0.004436161,"about_ca_topic_score_gemma":0.0029649509,"teacher_disagreement_score":0.008880114,"about_ca_system_score_codex":0.0015356606,"about_ca_system_score_gemma":0.0018459449,"threshold_uncertainty_score":0.046963036},"labels":[],"label_agreement":null},{"id":"W3124679924","doi":"10.20944/preprints202101.0569.v1","title":"Improved Method for Approximating the Hazard Rate for Convolution Poisson Random Variables","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Poisson distribution; Cumulative distribution function; Convolution (computer science); Random variable; Probability mass function; Mathematics; Applied mathematics; Hazard; Function (biology); Statistics; Probability density function; Computer science","score_opus":0.24624245961373245,"score_gpt":0.4146425003045163,"score_spread":0.16840004069078388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124679924","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028038225,0.00014288929,0.99662006,0.000050590497,0.000025642468,0.00001661136,0.0000144718515,0.000056677643,0.00026925295],"genre_scores_gemma":[0.27687594,0.001157096,0.71371895,0.00026410093,0.0001843589,0.00039518447,0.00025118218,0.0002908617,0.0068623503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988024,0.0006077633,0.00004622087,0.00012318467,0.0003618776,0.00005852469],"domain_scores_gemma":[0.9963181,0.002510224,0.00022278832,0.00026163433,0.0006019928,0.00008519472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005681469,0.00070597016,0.0010220099,0.0010404905,0.0003122825,0.0007685068,0.0019692632,0.0012387702,0.0027876853],"category_scores_gemma":[0.012438222,0.00038395374,0.0011298481,0.0008098535,0.0006811069,0.0012908396,0.00105321,0.001750406,0.0006353626],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019547864,0.000058461937,0.0017078511,0.00021252583,0.000101660284,0.00021582797,0.00017283254,0.79992956,0.005396646,0.12885097,0.0014369552,0.061721127],"study_design_scores_gemma":[0.000009919041,0.000021378371,0.000102127444,0.000009390023,0.000008604566,0.000053798292,0.0000036938316,0.9906126,0.00050070335,0.008098478,0.00057191326,0.0000073960414],"about_ca_topic_score_codex":0.0027083908,"about_ca_topic_score_gemma":0.0012105394,"teacher_disagreement_score":0.005681469,"about_ca_system_score_codex":0.00092887506,"about_ca_system_score_gemma":0.0014130069,"threshold_uncertainty_score":0.03004682},"labels":[],"label_agreement":null},{"id":"W3124880782","doi":"","title":"Point Decisions for Interval-Identified Parameters","year":2009,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Minimax; Interval (graph theory); Point (geometry); Mathematics; Optimal decision; Subclass; Sample (material); Decision maker; Object (grammar); Decision problem; Mathematical optimization; Mathematical economics; Computer science; Combinatorics; Algorithm; Artificial intelligence; Operations research; Decision tree","score_opus":0.18771116095657106,"score_gpt":0.4094045364724651,"score_spread":0.22169337551589405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124880782","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09876705,0.0006952538,0.89351714,0.00082612893,0.000047236514,0.00009539054,0.00010759407,0.00014465764,0.005799578],"genre_scores_gemma":[0.9059598,0.00046418916,0.08994518,0.00016150106,0.000104075814,0.00016061818,0.000110966204,0.00006510761,0.0030285162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99306023,0.003792909,0.00024136642,0.0015212666,0.00096805644,0.00041621612],"domain_scores_gemma":[0.9747834,0.019799039,0.0024604062,0.0013699055,0.0009701762,0.0006170239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011679175,0.0009778016,0.0018788644,0.0008242846,0.0005460789,0.0032646845,0.0022687216,0.0025298537,0.0042178286],"category_scores_gemma":[0.036871567,0.0006573382,0.0009859477,0.000680638,0.0025349811,0.005513053,0.0024813337,0.0035874287,0.00068500516],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015047925,0.00024499145,0.002468955,0.00027164078,0.00022309588,0.00058991124,0.0005707339,0.57262665,0.0033199512,0.34113428,0.0015131144,0.07553188],"study_design_scores_gemma":[0.00007906822,0.00043162846,0.001001833,0.000087357184,0.00005647728,0.00015136246,0.00016586906,0.66521645,0.0025267517,0.3289662,0.0012477743,0.000069287285],"about_ca_topic_score_codex":0.00043480704,"about_ca_topic_score_gemma":0.00028656758,"teacher_disagreement_score":0.011679175,"about_ca_system_score_codex":0.001643606,"about_ca_system_score_gemma":0.0007130342,"threshold_uncertainty_score":0.061766088},"labels":[],"label_agreement":null},{"id":"W3129856157","doi":"10.1115/1.4050160","title":"A Selection Strategy for Kriging Based Design of Experiments by Spectral Clustering and Learning Function","year":2021,"lang":"en","type":"article","venue":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Kriging; Computer science; Cluster analysis; Reliability (semiconductor); Monte Carlo method; Similarity (geometry); Function (biology); Machine learning; Artificial intelligence; Mathematics; Statistics","score_opus":0.0440512880511175,"score_gpt":0.2877007348884704,"score_spread":0.24364944683735293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129856157","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017332766,0.00004784676,0.9818652,0.000035460143,0.0000070544766,0.0001529272,0.00000750769,0.00015361012,0.00039756243],"genre_scores_gemma":[0.5583639,0.000058944042,0.43991283,0.00006719839,0.000013505215,0.0009201637,0.000046961944,0.000039732855,0.0005767132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967372,0.0018691181,0.0001473388,0.0004486568,0.00062903203,0.00016868264],"domain_scores_gemma":[0.99481636,0.0032616963,0.00049413345,0.00028896477,0.0009861707,0.0001526397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006454677,0.0011883288,0.0014253564,0.0010931456,0.0005168444,0.00068258384,0.001404734,0.00089804374,0.0009509709],"category_scores_gemma":[0.009314064,0.0006236141,0.0007110518,0.00045315223,0.00086701225,0.0007782358,0.00081116654,0.0007536619,0.00015440282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003029281,0.00022986018,0.001075374,0.00014604992,0.00008376318,0.000051726503,0.00012609552,0.9001061,0.011822533,0.008198231,0.00026912053,0.077588305],"study_design_scores_gemma":[0.000024639285,0.0001378042,0.00019663428,0.000005386076,0.000013447782,0.0000063977345,0.000007190484,0.9960097,0.0023302627,0.0010764055,0.00018628077,0.0000059100057],"about_ca_topic_score_codex":0.0018453354,"about_ca_topic_score_gemma":0.0017427215,"teacher_disagreement_score":0.006454677,"about_ca_system_score_codex":0.0011238342,"about_ca_system_score_gemma":0.0013036701,"threshold_uncertainty_score":0.034135997},"labels":[],"label_agreement":null},{"id":"W3130315730","doi":"10.48550/arxiv.2102.12735","title":"Random Forest based Qantile Oriented Sensitivity Analysis indices estimation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua","funders":"","keywords":"Sensitivity (control systems); Random forest; Estimation; Statistics; Computer science; Mathematics; Econometrics; Artificial intelligence; Economics; Engineering","score_opus":0.10737765279759641,"score_gpt":0.23591378128543866,"score_spread":0.12853612848784224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130315730","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033792325,0.0001093497,0.995782,0.000028252534,0.000011650793,0.000024814877,0.00008157222,0.00033253213,0.0002505356],"genre_scores_gemma":[0.3223919,0.00041796584,0.6738144,0.00017433365,0.000113223825,0.0003062173,0.001124186,0.00025427752,0.0014034759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977174,0.0010577078,0.00010751782,0.00045162084,0.0005117252,0.00015412735],"domain_scores_gemma":[0.99472886,0.0033387293,0.0004640342,0.00064081984,0.0007171105,0.00011056024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003910289,0.00090169976,0.0011662102,0.002433509,0.00036267168,0.0011095131,0.0013789311,0.0008299051,0.0020875193],"category_scores_gemma":[0.011873238,0.00035499196,0.0014446629,0.001443195,0.00052066066,0.0011023596,0.0011213858,0.0012271327,0.0009178848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014677554,0.00016698998,0.006283651,0.00018750117,0.00027229768,0.00015259678,0.000065117274,0.6604787,0.009657571,0.015844092,0.0027880915,0.30395657],"study_design_scores_gemma":[0.0000057565617,0.000026577309,0.00073782925,0.000012413865,0.000017834247,0.000039488896,0.0000045249444,0.98842806,0.0014201363,0.008715993,0.00057590107,0.00001547238],"about_ca_topic_score_codex":0.0022279178,"about_ca_topic_score_gemma":0.00223281,"teacher_disagreement_score":0.003910289,"about_ca_system_score_codex":0.00043099874,"about_ca_system_score_gemma":0.0009375642,"threshold_uncertainty_score":0.020679832},"labels":[],"label_agreement":null},{"id":"W3131684552","doi":"10.1007/s00158-020-02825-8","title":"An effective Kriging-based approximation for structural reliability analysis with random and interval variables","year":2021,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Kriging; Reliability (semiconductor); Benchmark (surveying); Interval (graph theory); Monte Carlo method; Truncation (statistics); Random variable; Interval arithmetic; Mathematical optimization; Surrogate model; Uncertainty quantification; Computer science; Function (biology); Variable (mathematics); First-order reliability method; Algorithm; Boundary (topology); Mathematics; Applied mathematics; Statistics; Machine learning","score_opus":0.018413317076859097,"score_gpt":0.3129931768797344,"score_spread":0.29457985980287527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131684552","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042252536,0.00013758679,0.9949325,0.000022908334,0.000012175241,0.000008933493,0.000024057072,0.000086748085,0.0005498727],"genre_scores_gemma":[0.35404256,0.00057630934,0.6423252,0.000065607914,0.000041519706,0.00013021362,0.00018343863,0.0001557728,0.0024793518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939466,0.00026286228,0.000023391976,0.000047270765,0.00022898281,0.00004288041],"domain_scores_gemma":[0.9990711,0.00062227395,0.0000548448,0.000081640144,0.00014574638,0.000024332217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011755857,0.000565616,0.0011344731,0.00069957913,0.0002887622,0.0006446123,0.0012699122,0.0007910437,0.0011684647],"category_scores_gemma":[0.0031498696,0.00046559438,0.0006992029,0.0009973662,0.0005172818,0.00080703746,0.00055388646,0.0010806071,0.0003966342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025678355,0.000020702479,0.00017881296,0.000044303146,0.000019198516,0.000021190595,0.000021303538,0.96334314,0.0011352426,0.008876361,0.0003211134,0.025992984],"study_design_scores_gemma":[0.0000015915334,0.0000054343272,0.000032801614,0.0000029152702,0.0000029297887,0.0000041063317,0.0000012352451,0.9984585,0.00012407388,0.0011724175,0.00019195928,0.0000020495727],"about_ca_topic_score_codex":0.008051935,"about_ca_topic_score_gemma":0.008551965,"teacher_disagreement_score":0.008051935,"about_ca_system_score_codex":0.00061205216,"about_ca_system_score_gemma":0.0011434376,"threshold_uncertainty_score":0.016010106},"labels":[],"label_agreement":null},{"id":"W3132485542","doi":"10.1007/s00366-021-01308-8","title":"HALK: A hybrid active-learning Kriging approach and its applications for structural reliability analysis","year":2021,"lang":"en","type":"article","venue":"Engineering With Computers","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Kriging; Reliability (semiconductor); Truncation (statistics); Computer science; Benchmark (surveying); Monte Carlo method; Surrogate model; Function (biology); Point (geometry); Boundary (topology); Structural reliability; Set (abstract data type); Mathematical optimization; Algorithm; Applied mathematics; Machine learning; Artificial intelligence; Mathematics; Statistics","score_opus":0.024170062812996527,"score_gpt":0.27162553673109024,"score_spread":0.24745547391809372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132485542","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021269722,0.00012830783,0.9957456,0.000046083544,0.000023288614,0.000016666256,0.00009101988,0.0014422721,0.0003797021],"genre_scores_gemma":[0.14082366,0.0003251055,0.85243803,0.00012006898,0.000052792857,0.00021175767,0.00067320716,0.0007886638,0.0045666653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991109,0.00030308956,0.000044001074,0.000111399764,0.0003821762,0.000048466816],"domain_scores_gemma":[0.9974752,0.0013728617,0.00015333925,0.0002774741,0.0006532465,0.00006775514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016028334,0.001007261,0.0014414185,0.001144939,0.0005076044,0.0011062124,0.0024262941,0.001521894,0.0030960874],"category_scores_gemma":[0.0036675837,0.00082888635,0.0009267159,0.0010039815,0.0007691581,0.0016599646,0.0014184801,0.0018972495,0.001773281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016831214,0.00012290855,0.00053529546,0.00027247838,0.00013467243,0.000058869053,0.000093468574,0.7193959,0.0051415544,0.0087096095,0.003241096,0.26212582],"study_design_scores_gemma":[0.0000046759133,0.0000093730805,0.00005230363,0.0000037280658,0.000004428657,0.0000073144174,0.00000318435,0.9972567,0.00076544425,0.0011544698,0.0007299644,0.0000085715055],"about_ca_topic_score_codex":0.007710048,"about_ca_topic_score_gemma":0.011430311,"teacher_disagreement_score":0.007710048,"about_ca_system_score_codex":0.0006160226,"about_ca_system_score_gemma":0.0014614181,"threshold_uncertainty_score":0.015330315},"labels":[],"label_agreement":null},{"id":"W3133891263","doi":"10.48550/arxiv.2102.13347","title":"MDA for random forests: inconsistency, and a practical solution via the\\n Sobol-MDA","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Sobol sequence; Random forest; Econometrics; Mathematics; Computer science; Environmental science; Statistics; Artificial intelligence; Monte Carlo method","score_opus":0.20519300803840892,"score_gpt":0.2670998338962951,"score_spread":0.06190682585788618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133891263","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00230902,0.0007907892,0.99481493,0.0010961768,0.00008748936,0.00003829625,0.000048432747,0.00019042795,0.0006244958],"genre_scores_gemma":[0.142937,0.0016014945,0.849686,0.0014538451,0.0007570828,0.00056976057,0.0002983911,0.00038262072,0.0023136814],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98416686,0.008184212,0.0010843734,0.0029086142,0.0031783609,0.0004775846],"domain_scores_gemma":[0.94289345,0.041263625,0.0027898946,0.006509039,0.005608085,0.0009359122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029505927,0.0014247728,0.002191857,0.0038780423,0.0016094544,0.004356996,0.0041673323,0.0037935402,0.0024179996],"category_scores_gemma":[0.11435389,0.0013461098,0.0022336706,0.002269079,0.0041914866,0.008339926,0.0074965926,0.009043278,0.000829358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100114856,0.00007798685,0.004070073,0.00042381618,0.00016628724,0.00018742878,0.0006059867,0.05274787,0.0015244749,0.77243054,0.0066772397,0.16098821],"study_design_scores_gemma":[0.00002124273,0.00005710344,0.0006156697,0.00018025593,0.00004078023,0.00023795258,0.000079375146,0.39836887,0.0011588348,0.5905123,0.008676238,0.000051376657],"about_ca_topic_score_codex":0.0033394517,"about_ca_topic_score_gemma":0.0033081262,"teacher_disagreement_score":0.029505927,"about_ca_system_score_codex":0.0034017863,"about_ca_system_score_gemma":0.0029053658,"threshold_uncertainty_score":0.15604413},"labels":[],"label_agreement":null},{"id":"W3134257808","doi":"10.1002/ffo2.71","title":"Scoping the future with theory‐driven models—Where’s the uncertainty? : A commentary on Lustick and Tetlock 2021","year":2021,"lang":"en","type":"article","venue":"Futures & Foresight Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Futures studies; Citation; Futures contract; Library science; Computer science; Operations research; Artificial intelligence; Mathematics; Economics","score_opus":0.027180972691838307,"score_gpt":0.2850403604236891,"score_spread":0.2578593877318508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134257808","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000075788426,0.007808363,0.00034718218,0.9819546,0.008717744,0.000004559199,0.00003155116,0.000005900616,0.001054405],"genre_scores_gemma":[0.0063366056,0.0046680267,0.0005991622,0.9698638,0.01721193,0.00006565668,0.000020493504,0.000049693725,0.0011846351],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9666621,0.01682022,0.0030832747,0.0045974934,0.006742148,0.0020947382],"domain_scores_gemma":[0.80742437,0.1710342,0.004450916,0.0029397514,0.01083373,0.0033170679],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05318841,0.00173076,0.0033352487,0.0029443328,0.008878443,0.013955377,0.009854815,0.08958822,0.007831153],"category_scores_gemma":[0.19036652,0.0017160632,0.0028836587,0.0040019825,0.04211209,0.028448194,0.012305109,0.096351966,0.0031645182],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006644333,0.000015493368,0.00008054862,0.0004677488,0.00004763944,0.00013834976,0.0037894484,0.0002727111,0.00006188423,0.15984572,0.8268732,0.008340807],"study_design_scores_gemma":[0.00014506673,0.000029704435,0.00020230845,0.005368752,0.0000837644,0.00016942625,0.0034670096,0.0005698549,0.00021389274,0.33632988,0.65324074,0.00017950682],"about_ca_topic_score_codex":0.047789987,"about_ca_topic_score_gemma":0.0649939,"teacher_disagreement_score":0.9468116,"about_ca_system_score_codex":0.01746535,"about_ca_system_score_gemma":0.02552867,"threshold_uncertainty_score":0.2812906},"labels":[],"label_agreement":null},{"id":"W3134272703","doi":"10.1101/2021.03.04.433888","title":"TRAIT2D: a Software for Quantitative Analysis of Single Particle Diffusion Data","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Biotechnology and Biological Sciences Research Council; Medical Research Council; Engineering and Physical Sciences Research Council; Fonds de recherche du Québec – Nature et technologies; Deutsche Forschungsgemeinschaft; International Seafood Sustainability Foundation; Wellcome Trust","keywords":"Python (programming language); Computer science; Software; Graphical user interface; Tracking (education); Computer graphics (images); Software engineering; Operating system","score_opus":0.13710488486254485,"score_gpt":0.3241046554140159,"score_spread":0.18699977055147105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134272703","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003407385,0.00023198537,0.7420854,0.00025578425,0.00017683358,0.00017719585,0.03147527,0.21957444,0.0026157172],"genre_scores_gemma":[0.044074725,0.00048862916,0.82177657,0.0005129504,0.00010189593,0.0031398688,0.043642335,0.080080286,0.0061826976],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755764,0.0003989891,0.00027870457,0.00046523646,0.0010941335,0.00020524766],"domain_scores_gemma":[0.9964874,0.001878442,0.00032253072,0.00052414095,0.0006353596,0.00015211926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049248654,0.0018355175,0.0020232622,0.0020495611,0.0009190067,0.0029138776,0.0032513398,0.0012514734,0.050173264],"category_scores_gemma":[0.009261597,0.0014521277,0.002158842,0.0016454966,0.0009822142,0.0018045229,0.0027417724,0.003167567,0.02406638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007635098,0.00023619678,0.0048828013,0.004322662,0.0007113488,0.00067293155,0.0008615496,0.057101127,0.04911794,0.05369925,0.62441295,0.20321783],"study_design_scores_gemma":[0.00029873516,0.00012836356,0.0041671246,0.0003836505,0.00014494815,0.0006943346,0.00013043499,0.55545324,0.050879374,0.05753611,0.32978827,0.0003954046],"about_ca_topic_score_codex":0.0029878772,"about_ca_topic_score_gemma":0.003371906,"teacher_disagreement_score":0.050173264,"about_ca_system_score_codex":0.0009436259,"about_ca_system_score_gemma":0.002928249,"threshold_uncertainty_score":0.16784632},"labels":[],"label_agreement":null},{"id":"W3134428001","doi":"10.1007/s00366-021-01349-z","title":"An improved Kriging-based approach for system reliability analysis with multiple failure modes","year":2021,"lang":"en","type":"article","venue":"Engineering With Computers","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Kriging; Reliability (semiconductor); Component (thermodynamics); Reliability engineering; Failure mode and effects analysis; Computer science; Sample (material); Function (biology); Sample size determination; Mode (computer interface); Algorithm; Mathematical optimization; Data mining; Mathematics; Statistics; Engineering; Machine learning","score_opus":0.01881214126238368,"score_gpt":0.24314807953814255,"score_spread":0.22433593827575887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134428001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061564706,0.000089103676,0.9928677,0.00001823534,0.000012457213,0.000016733784,0.00004851589,0.00038815584,0.00040257812],"genre_scores_gemma":[0.30953237,0.00022398219,0.6870809,0.000034761295,0.000033277123,0.00016741225,0.00026840158,0.00026953456,0.002389264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951136,0.00016787757,0.00002362107,0.000064693995,0.00019433175,0.00003819389],"domain_scores_gemma":[0.999218,0.00043751785,0.000047630365,0.00008277844,0.0001930329,0.000020914962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058904145,0.00087301945,0.0014722702,0.0008654597,0.0004840735,0.00046608906,0.0014554947,0.0006482867,0.0018584826],"category_scores_gemma":[0.0020676956,0.0006903168,0.0009948276,0.000980386,0.0003411384,0.00071597134,0.0005724295,0.0012039223,0.00060421193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035305715,0.00002344972,0.00025634788,0.000048271093,0.0000401878,0.000025019868,0.000024697927,0.95473576,0.0023154288,0.0014904228,0.00027567393,0.040729508],"study_design_scores_gemma":[0.0000019819086,0.000006322396,0.000070084774,0.0000013777305,0.0000044893895,0.0000045546108,0.0000011423639,0.9992035,0.00016362828,0.00040172384,0.00013781275,0.0000033562467],"about_ca_topic_score_codex":0.026397256,"about_ca_topic_score_gemma":0.035256505,"teacher_disagreement_score":0.026397256,"about_ca_system_score_codex":0.00055871025,"about_ca_system_score_gemma":0.0016121547,"threshold_uncertainty_score":0.052487195},"labels":[],"label_agreement":null},{"id":"W3134457856","doi":"","title":"A Practical Simulation Framework for Thermal Sensation Analysis of Fenestration Designs","year":2020,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fenestration; Thermal sensation; Sensation; Computer science; Engineering; Psychology; Medicine; Thermal comfort; Cognitive psychology; Geography; Surgery; Meteorology","score_opus":0.2868110897732901,"score_gpt":0.506289712680257,"score_spread":0.2194786229069669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134457856","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002746032,0.00010058017,0.992272,0.00006677897,0.000021025846,0.000047770678,0.000074756455,0.000164384,0.004506762],"genre_scores_gemma":[0.42598197,0.0011196614,0.5597403,0.00015791292,0.000103598766,0.0011216925,0.00056219817,0.0002911989,0.010921503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994363,0.00024187082,0.000026881045,0.00006390306,0.00017618675,0.00005474547],"domain_scores_gemma":[0.9990351,0.0006301387,0.00007680926,0.00005839864,0.0001697433,0.000029785011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001099209,0.0010404597,0.0009582443,0.00074204913,0.00047534998,0.0011125961,0.0014629225,0.0013678819,0.007553151],"category_scores_gemma":[0.0027744174,0.00048621482,0.0015421485,0.00063919876,0.0006370904,0.00058926747,0.0012593804,0.0012795476,0.0008105668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006722072,0.000010296198,0.00008805617,0.000026900421,0.0000062325485,0.000017750815,0.000013298512,0.98495126,0.0004985561,0.011302885,0.00019179276,0.0028862914],"study_design_scores_gemma":[0.0000026852513,0.0000071832965,0.000019696625,0.0000056837994,0.0000022389754,0.000006457896,0.000004161189,0.99631494,0.000103734994,0.0027001367,0.0008309974,0.0000021697024],"about_ca_topic_score_codex":0.00753197,"about_ca_topic_score_gemma":0.0046125487,"teacher_disagreement_score":0.007553151,"about_ca_system_score_codex":0.0007724115,"about_ca_system_score_gemma":0.001240644,"threshold_uncertainty_score":0.02526784},"labels":[],"label_agreement":null},{"id":"W3135213525","doi":"10.2749/newyork.2019.0570","title":"Reliability of Structures that Pass Imperfect Proof Load Tests","year":2019,"lang":"en","type":"article","venue":"Report","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Reliability (semiconductor); Mathematics; Repeatability; Reliability engineering; Imperfect; Statistics; Control theory (sociology); Computer science; Engineering; Power (physics); Physics","score_opus":0.05076417664273099,"score_gpt":0.32725532394587137,"score_spread":0.2764911473031404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135213525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88977873,0.0001365567,0.10197639,0.00017169783,0.000028007737,0.00004416248,0.00034846473,0.00052141206,0.0069946],"genre_scores_gemma":[0.9955154,0.000026740474,0.0031231719,0.000012780857,0.000008283789,0.000016383487,0.0001802824,0.000031670475,0.0010853088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976417,0.00036627578,0.00011771423,0.00044135746,0.0011362223,0.00029674353],"domain_scores_gemma":[0.9867791,0.0044371733,0.0026557464,0.0028102512,0.0030071102,0.00031078357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033947984,0.00046393368,0.00063786114,0.0013723283,0.00034858464,0.0010411162,0.001228329,0.0010111261,0.0023286552],"category_scores_gemma":[0.02168994,0.00046836174,0.00061871577,0.00051771564,0.0015093654,0.001536842,0.0009358141,0.00066433905,0.0010980055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000516594,0.00010066718,0.0856729,0.0001000302,0.000106479754,0.00093317346,0.0008476351,0.81025016,0.034309775,0.023755608,0.0015216048,0.04188538],"study_design_scores_gemma":[0.000023679351,0.00063769013,0.057947382,0.000036516987,0.00007589846,0.00047884975,0.0002302699,0.90092313,0.023695685,0.014144431,0.0017285022,0.00007800378],"about_ca_topic_score_codex":0.0026818553,"about_ca_topic_score_gemma":0.0015590184,"teacher_disagreement_score":0.0033947984,"about_ca_system_score_codex":0.0011423782,"about_ca_system_score_gemma":0.0005772521,"threshold_uncertainty_score":0.017953634},"labels":[],"label_agreement":null},{"id":"W3137149662","doi":"10.1016/j.strusafe.2021.102094","title":"A reformulation of the stochastic load combination problem","year":2021,"lang":"en","type":"article","venue":"Structural Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University Network of Excellence in Nuclear Engineering","keywords":"Mathematical optimization; Reliability (semiconductor); Mathematics; Process (computing); Poisson distribution; Renewal theory; Stochastic process; Shock (circulatory); Applied mathematics; Computer science; Statistics","score_opus":0.03576830310484669,"score_gpt":0.3018070989832834,"score_spread":0.2660387958784367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137149662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008069711,0.00017415894,0.97724336,0.00083133666,0.00008175629,0.000032231408,0.00024631163,0.00005612135,0.013265061],"genre_scores_gemma":[0.61743706,0.0010457814,0.34020656,0.0006164612,0.00080689474,0.00034110417,0.0011907977,0.00020923569,0.038146023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989165,0.0004270941,0.000041807114,0.00020253932,0.0003131332,0.00009895239],"domain_scores_gemma":[0.9991804,0.00042294094,0.00012564623,0.000078963756,0.00014822601,0.000043906293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001325444,0.0007535244,0.0010425787,0.00058896653,0.000387577,0.001225107,0.0013309239,0.0012108178,0.009620931],"category_scores_gemma":[0.0030849339,0.00041704992,0.00083187805,0.0008022525,0.00074864057,0.0017145608,0.0011980525,0.0013881682,0.00091444637],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039527178,0.000065638225,0.00023101218,0.00010403463,0.000038643877,0.0001285861,0.000047400103,0.5484008,0.0009804221,0.41285604,0.005993195,0.031114629],"study_design_scores_gemma":[0.000019282952,0.000044235112,0.00017770838,0.000017443723,0.000014255343,0.00007982309,0.000016995235,0.8189596,0.00026284193,0.17588165,0.0045146565,0.000011464785],"about_ca_topic_score_codex":0.0020045845,"about_ca_topic_score_gemma":0.0022311192,"teacher_disagreement_score":0.009620931,"about_ca_system_score_codex":0.0007298378,"about_ca_system_score_gemma":0.0012785041,"threshold_uncertainty_score":0.032185256},"labels":[],"label_agreement":null},{"id":"W3145302275","doi":"10.3390/mca26020026","title":"Investigation on the Mathematical Relation Model of Structural Reliability and Structural Robustness","year":2021,"lang":"en","type":"article","venue":"Mathematical and Computational Applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Henan University of Technology","keywords":"Robustness (evolution); Structural system; Truss; Computer science; Cantilever; Structural reliability; Reliability engineering; Structural engineering; Engineering; Artificial intelligence; Probabilistic logic","score_opus":0.09714453315611829,"score_gpt":0.30363230478067993,"score_spread":0.20648777162456164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145302275","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02382004,0.003771767,0.9560312,0.0009098289,0.0000836184,0.000047057336,0.00008899995,0.000098533514,0.015148862],"genre_scores_gemma":[0.8784396,0.010908928,0.09516556,0.00034869788,0.0004314562,0.0002971096,0.0003000705,0.000089403686,0.014019234],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99875,0.0003191872,0.000072597075,0.00031304042,0.00044337325,0.0001018258],"domain_scores_gemma":[0.99663615,0.0020348744,0.0004325603,0.00016277713,0.00068820035,0.000045354907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017769099,0.0008590729,0.0005878922,0.0020168321,0.00036426002,0.0010804079,0.0010094942,0.001100871,0.002712652],"category_scores_gemma":[0.007059589,0.00042893708,0.001292085,0.0011980092,0.0015047259,0.004162325,0.0007646085,0.001805835,0.0004754594],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002746287,0.000051746014,0.0025546483,0.00037040646,0.000086727465,0.00030620047,0.00036222843,0.31625473,0.00948687,0.6249058,0.0019168061,0.04367636],"study_design_scores_gemma":[0.000004859211,0.000089685156,0.0017235815,0.000048782644,0.00005070653,0.0003478814,0.00007441048,0.859992,0.0017406035,0.13184054,0.0040391744,0.000047818758],"about_ca_topic_score_codex":0.002849705,"about_ca_topic_score_gemma":0.001329165,"teacher_disagreement_score":0.002849705,"about_ca_system_score_codex":0.0016712681,"about_ca_system_score_gemma":0.0009355912,"threshold_uncertainty_score":0.012125969},"labels":[],"label_agreement":null},{"id":"W3153609404","doi":"10.1016/j.ejor.2021.04.004","title":"Global sensitivity analysis via a statistical tolerance approach","year":2021,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Institute of Dental and Craniofacial Research; National Institutes of Health; Foundation for the National Institutes of Health","keywords":"Sensitivity (control systems); Parametric statistics; Mathematical optimization; Computer science; Constraint (computer-aided design); Set (abstract data type); Geometric programming; Principal component analysis; Basis (linear algebra); Mathematics; Artificial intelligence; Statistics","score_opus":0.19529053660829812,"score_gpt":0.4290408487087178,"score_spread":0.23375031210041966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153609404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002334697,0.000055862627,0.9955895,0.000048270485,0.000018247354,0.000020327565,0.000021649768,0.00008188971,0.001829598],"genre_scores_gemma":[0.71127856,0.0005106549,0.27875173,0.00029894718,0.00017536314,0.00047448507,0.00019023346,0.00059262547,0.007727462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964474,0.0017532973,0.00014243477,0.0004120502,0.0010035334,0.00024139966],"domain_scores_gemma":[0.99524444,0.0032656167,0.00036085516,0.0005703182,0.0004945408,0.00006419055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051564276,0.001694558,0.0019222604,0.0024526792,0.0004985803,0.0017656427,0.0015797361,0.0011608038,0.0048219813],"category_scores_gemma":[0.01160982,0.00069809135,0.0028466661,0.00188975,0.0016519994,0.00233754,0.0035904779,0.0018289611,0.00038993344],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006561701,0.000040713086,0.00026879786,0.00013111698,0.00018436155,0.00009977508,0.000057751833,0.880849,0.005130129,0.078537546,0.0005814825,0.03405366],"study_design_scores_gemma":[0.000007232246,0.000083180785,0.00019981663,0.000014688372,0.000053938635,0.000038071426,0.00001518717,0.95347345,0.0017314118,0.043477893,0.00088581594,0.000019284169],"about_ca_topic_score_codex":0.0012186981,"about_ca_topic_score_gemma":0.0007279488,"teacher_disagreement_score":0.0051564276,"about_ca_system_score_codex":0.00077649124,"about_ca_system_score_gemma":0.0009928534,"threshold_uncertainty_score":0.027270138},"labels":[],"label_agreement":null},{"id":"W3155789586","doi":"10.1109/ieeeconf38699.2020.9389447","title":"Sensitivity analysis of plunger-type wavemakers with water current","year":2020,"lang":"en","type":"article","venue":"Global Oceans 2020: Singapore – U.S. Gulf Coast","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Plunger; Sensitivity (control systems); Amplitude; Wedge (geometry); Current (fluid); Control theory (sociology); Channel (broadcasting); Mechanics; Physics; Mathematics; Engineering; Computer science; Electronic engineering; Geometry; Optics; Electrical engineering","score_opus":0.05330803913491001,"score_gpt":0.3034379045132777,"score_spread":0.2501298653783677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155789586","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87195283,0.00026571314,0.12212148,0.00018445512,0.000047729605,0.00016288168,0.00045962218,0.0002802775,0.004525012],"genre_scores_gemma":[0.9966254,0.000047244554,0.0027915256,0.000021658907,0.0000027543879,0.000038954146,0.0001198517,0.000016442096,0.00033623687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989637,0.00041185325,0.00004735359,0.00020001718,0.00023504285,0.000141977],"domain_scores_gemma":[0.99494606,0.0041581714,0.00021864244,0.00023082411,0.0003927285,0.000053706073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024627151,0.0009657016,0.00063306233,0.00077821204,0.00037821473,0.00091453607,0.0006067308,0.00090567773,0.0010266105],"category_scores_gemma":[0.007486258,0.00045309518,0.0013272067,0.00041378225,0.00052200805,0.00090620905,0.00094480894,0.0009278149,0.000091893766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008444037,0.000039973667,0.00366645,0.00006124396,0.00006646289,0.00010488519,0.00003252912,0.98822355,0.004449035,0.0006142646,0.00009613155,0.0025609436],"study_design_scores_gemma":[0.000010034443,0.0001577942,0.0028624537,0.000009574523,0.000045231045,0.000033421715,0.00004592336,0.990694,0.0051127956,0.0008252959,0.00018127514,0.000022280654],"about_ca_topic_score_codex":0.009672363,"about_ca_topic_score_gemma":0.0036874972,"teacher_disagreement_score":0.009672363,"about_ca_system_score_codex":0.0009896165,"about_ca_system_score_gemma":0.00057742815,"threshold_uncertainty_score":0.019232154},"labels":[],"label_agreement":null},{"id":"W3162174057","doi":"10.1016/j.probengmech.2021.103139","title":"Robust design optimization of nonlinear energy sink under random system parameters","year":2021,"lang":"en","type":"article","venue":"Probabilistic Engineering Mechanics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Mathematical optimization; Optimization problem; Metric (unit); Nonlinear system; Mathematics; Nonlinear programming; Performance metric; Robust optimization; Computer science; Engineering","score_opus":0.07712406026780587,"score_gpt":0.2509812443426264,"score_spread":0.17385718407482054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162174057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011443858,0.00016516475,0.9837377,0.00021468982,0.000027136424,0.000047078145,0.000063972126,0.000098735414,0.004201569],"genre_scores_gemma":[0.92217606,0.00041705032,0.06752406,0.000102162136,0.00004559693,0.00029642563,0.000181526,0.00017299375,0.009084076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999171,0.0003262309,0.00002758866,0.00021366177,0.00018321477,0.00007830893],"domain_scores_gemma":[0.99853456,0.00082991313,0.00025386704,0.00008872108,0.00024934794,0.00004351043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023060697,0.0014437786,0.0017480969,0.0006929507,0.0004095124,0.0016307397,0.0011168417,0.0015486424,0.0025396456],"category_scores_gemma":[0.004869781,0.00093220826,0.0009329626,0.00052130624,0.0016205205,0.0015568185,0.0019739969,0.00094502006,0.00046321825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039336897,0.000009438893,0.000092893184,0.00005593759,0.00002497231,0.000022104165,0.000018004455,0.98641104,0.0011079753,0.00881671,0.00016020468,0.0032413553],"study_design_scores_gemma":[0.0000041639382,0.000019298786,0.00004445806,0.0000043870873,0.0000056427557,0.0000046164946,0.0000044328654,0.99650466,0.00031552181,0.0029486297,0.00013978675,0.0000043922073],"about_ca_topic_score_codex":0.0023293577,"about_ca_topic_score_gemma":0.0013061247,"teacher_disagreement_score":0.0025396456,"about_ca_system_score_codex":0.0014153558,"about_ca_system_score_gemma":0.0010988027,"threshold_uncertainty_score":0.012195826},"labels":[],"label_agreement":null},{"id":"W3163505835","doi":"10.1017/s1748499521000130","title":"Scenario Weights for Importance Measurement (<b>SWIM</b>) – an <tt>R</tt> package for sensitivity analysis","year":2021,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sensitivity (control systems); Computer science; Set (abstract data type); Scenario analysis; Model risk; Portfolio; Stress testing (software); Statistical model; Econometrics; Mathematics; Statistics; Engineering; Machine learning; Risk management; Economics","score_opus":0.3228062690453294,"score_gpt":0.42252517579013055,"score_spread":0.09971890674480116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163505835","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019590165,0.00007564383,0.93261707,0.00012599282,0.00007002833,0.00025337376,0.006118853,0.055424068,0.0033560458],"genre_scores_gemma":[0.04457715,0.00015335646,0.9053428,0.00024735884,0.000073013565,0.0024030157,0.007990825,0.034900382,0.00431203],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967458,0.001384184,0.0002794557,0.00030882467,0.0010751833,0.00020650074],"domain_scores_gemma":[0.9805604,0.01441929,0.0010595778,0.0018356484,0.0019590363,0.00016605151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008068786,0.0029362205,0.0016805081,0.0029309525,0.00055920356,0.0021884139,0.0027770335,0.0013734696,0.09494866],"category_scores_gemma":[0.031422302,0.001915023,0.0037352035,0.0019219184,0.0008259227,0.0022349493,0.003204886,0.003913396,0.018778814],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062329264,0.00044422538,0.0048961965,0.0024242068,0.0012497223,0.0006335103,0.0006001468,0.2622121,0.009934203,0.15260294,0.29286703,0.27151248],"study_design_scores_gemma":[0.00025104138,0.00017517224,0.001957157,0.00032174343,0.0001536883,0.0002835352,0.00006346739,0.7981958,0.00980272,0.09029072,0.09832618,0.00017879935],"about_ca_topic_score_codex":0.003255493,"about_ca_topic_score_gemma":0.0031562168,"teacher_disagreement_score":0.09494866,"about_ca_system_score_codex":0.0008486207,"about_ca_system_score_gemma":0.0017129285,"threshold_uncertainty_score":0.31763488},"labels":[],"label_agreement":null},{"id":"W3165748650","doi":"10.11159/iccste21.131","title":"A Reliability-Based Comparison of EC3 and SANS 10162-1","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Civil, Structural and Transportation Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Eurocode; Reliability (semiconductor); Reliability engineering; Structural engineering; Index (typography); Calibration; Monte Carlo method; Column (typography); Bending; Computer science; Engineering; Mathematics; Statistics","score_opus":0.04702426426080381,"score_gpt":0.2945359471489163,"score_spread":0.24751168288811248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165748650","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9291308,0.00043539086,0.028636023,0.00012261893,0.00006672216,0.000112063426,0.0032476597,0.00037738975,0.03787142],"genre_scores_gemma":[0.97929484,0.00013676801,0.011873946,0.000031476717,0.00000540233,0.00003824622,0.0034651316,0.00008471186,0.0050695953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978046,0.00028922583,0.00013611368,0.0002090069,0.0014319677,0.00012907623],"domain_scores_gemma":[0.9912417,0.0020388614,0.0006193354,0.0009128414,0.0050611463,0.00012614408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002567713,0.0004354369,0.00036577877,0.0016392533,0.00034240325,0.00063610694,0.00054001645,0.0005771335,0.0029455784],"category_scores_gemma":[0.0058487793,0.00018812576,0.00053743436,0.0017978307,0.0004368363,0.0004393003,0.00043034845,0.00038485447,0.00089149957],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002919276,0.00041421357,0.21471667,0.0005528236,0.0002967198,0.0007403116,0.00072966726,0.38693202,0.09409657,0.015854279,0.013825073,0.26892242],"study_design_scores_gemma":[0.00008452476,0.0034705559,0.53801775,0.0001625933,0.0002055205,0.0016006448,0.001091036,0.27341905,0.1178997,0.0038353978,0.059987266,0.00022602123],"about_ca_topic_score_codex":0.02183066,"about_ca_topic_score_gemma":0.03437202,"teacher_disagreement_score":0.02183066,"about_ca_system_score_codex":0.0013839199,"about_ca_system_score_gemma":0.0011255549,"threshold_uncertainty_score":0.043407142},"labels":[],"label_agreement":null},{"id":"W3169535623","doi":"10.1111/risa.13758","title":"Cascade Sensitivity Measures","year":2021,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sensitivity (control systems); Cascade; Measure (data warehouse); Computer science; Derivative (finance); Risk measure; Multivariate random variable; Econometrics; Random variable; Mathematics; Statistics; Data mining; Engineering; Electronic engineering; Economics","score_opus":0.06850112920414786,"score_gpt":0.3269921623677126,"score_spread":0.25849103316356475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169535623","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016158616,0.0008974676,0.9752001,0.0002975833,0.00008424253,0.00013692846,0.00022078963,0.00020506332,0.006799143],"genre_scores_gemma":[0.85867304,0.0016487313,0.13263834,0.00048821792,0.00032434482,0.0006130472,0.0004427999,0.00022348575,0.004947904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934714,0.0024708237,0.00036146748,0.0012047887,0.0021077995,0.00038368907],"domain_scores_gemma":[0.97988933,0.014797381,0.0018519522,0.0014779653,0.0016496505,0.0003337759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011697402,0.002963763,0.001856019,0.003999488,0.0007826556,0.0030508963,0.002078638,0.002376675,0.0053947745],"category_scores_gemma":[0.030028626,0.00081370963,0.002566901,0.0019061157,0.0029387777,0.0044632964,0.0032646582,0.0026727696,0.00054389436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010303308,0.000082531704,0.0030748853,0.00034809302,0.0003335899,0.00026964294,0.00017168684,0.6771448,0.0029879506,0.2731372,0.0018562197,0.04049038],"study_design_scores_gemma":[0.000013229258,0.00017775201,0.0014536974,0.0000894385,0.00009407624,0.00020977709,0.000053035154,0.7397706,0.0021503249,0.25304073,0.0028604208,0.00008684738],"about_ca_topic_score_codex":0.0015225498,"about_ca_topic_score_gemma":0.00077879924,"teacher_disagreement_score":0.011697402,"about_ca_system_score_codex":0.0023592487,"about_ca_system_score_gemma":0.0010894907,"threshold_uncertainty_score":0.06186247},"labels":[],"label_agreement":null},{"id":"W3170541248","doi":"10.1061/9780784483527.017","title":"Implications of Climate Variation in Flexible Airport Pavement Design and Performance","year":2021,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; University of Waterloo","funders":"","keywords":"Serviceability (structure); Subgrade; Environmental science; Climate change; Pavement engineering; Moisture; Rut; Stiffness; Asphalt pavement; Geotechnical engineering; Asphalt; Civil engineering; Engineering; Structural engineering; Geology; Materials science","score_opus":0.09774896906542332,"score_gpt":0.32639316525048473,"score_spread":0.2286441961850614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170541248","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91845304,0.0002708289,0.07861305,0.000072858275,0.000017330476,0.000021849146,0.00017838308,0.00011106457,0.002261653],"genre_scores_gemma":[0.9980406,0.000036678197,0.0017676413,0.000003025074,0.0000013853722,0.0000033264396,0.000029136127,0.0000060244606,0.00011220136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939823,0.00018981291,0.00002994111,0.00012445635,0.00015256232,0.00010493948],"domain_scores_gemma":[0.9990823,0.0003889777,0.00023421795,0.00011992187,0.0001325059,0.000041889813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011918991,0.00051637046,0.00027978414,0.00041086678,0.00027379685,0.0006259669,0.0003377607,0.0004909093,0.0006188399],"category_scores_gemma":[0.0022064857,0.00018420808,0.00038481414,0.00041237238,0.0003720091,0.00045335636,0.00036185025,0.00027901004,0.00013131948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089206595,0.000032575677,0.02444293,0.000025762502,0.000032385113,0.00009296299,0.00003928148,0.9477719,0.010802779,0.0004560757,0.000056979243,0.016157174],"study_design_scores_gemma":[0.0000068147597,0.00030190346,0.08553779,0.000010642306,0.000055971985,0.00014006662,0.00015638859,0.89995736,0.011768434,0.0013177448,0.00070720445,0.000039515064],"about_ca_topic_score_codex":0.004621275,"about_ca_topic_score_gemma":0.006507848,"teacher_disagreement_score":0.004621275,"about_ca_system_score_codex":0.0006390267,"about_ca_system_score_gemma":0.0003720112,"threshold_uncertainty_score":0.009188712},"labels":[],"label_agreement":null},{"id":"W3171706488","doi":"10.1007/s42797-021-00028-y","title":"Environmental load estimation for offshore structures considering parametric dependencies","year":2021,"lang":"en","type":"article","venue":"Safety in Extreme Environments","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Copula (linguistics); Joint probability distribution; Multivariate statistics; Parametric statistics; Econometrics; Bivariate analysis; Statistics; Marginal distribution; Mathematics; Computer science; Random variable","score_opus":0.11536673290606603,"score_gpt":0.295979808244132,"score_spread":0.18061307533806598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171706488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33724433,0.00008568913,0.65955216,0.0001250499,0.0000120420955,0.000016511412,0.00008766657,0.00012337163,0.0027531558],"genre_scores_gemma":[0.98777235,0.000053604257,0.011064555,0.000008121291,0.000009740188,0.000013707849,0.00008323779,0.000023329627,0.0009713288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997925,0.000052922034,0.000007835072,0.00004084123,0.0000690811,0.00003679272],"domain_scores_gemma":[0.9994081,0.00036895368,0.00008514916,0.000036639038,0.000077443474,0.000023647299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037512698,0.0006340469,0.00054306554,0.00060088217,0.000281503,0.00051493454,0.00066789606,0.00075542287,0.0009243221],"category_scores_gemma":[0.0018077983,0.0004884127,0.00045609806,0.00045237428,0.00045929322,0.0010446337,0.00094824855,0.00047359287,0.00019455586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026253487,0.000012737354,0.00096684915,0.000009453942,0.000008859897,0.00004059586,0.000014945368,0.99146664,0.000991664,0.000493914,0.000058882888,0.0059092664],"study_design_scores_gemma":[8.1942113e-7,0.0000062107474,0.0006212447,0.0000010741605,0.0000021941023,0.000004502735,0.0000072215844,0.9986314,0.00025911542,0.00043322006,0.000030559906,0.0000023498505],"about_ca_topic_score_codex":0.0051232576,"about_ca_topic_score_gemma":0.0055257264,"teacher_disagreement_score":0.0051232576,"about_ca_system_score_codex":0.00027374673,"about_ca_system_score_gemma":0.0004589711,"threshold_uncertainty_score":0.010186851},"labels":[],"label_agreement":null},{"id":"W3172877952","doi":"10.1139/cgj-2021-0004","title":"Uncertainty quantification of in situ horizontal stress with pressuremeter using a statistical inverse analysis method","year":2021,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta; Energi Simulation","keywords":"Geotechnical engineering; Uncertainty quantification; Inverse; Geology; Mathematics; Statistics; Geometry","score_opus":0.0883433225702943,"score_gpt":0.3438401497572297,"score_spread":0.2554968271869354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172877952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004286324,0.000018953448,0.9952924,0.000021321868,0.0000034287234,0.000011277928,0.000013173256,0.0000840422,0.00026901378],"genre_scores_gemma":[0.42484975,0.00015092466,0.5734583,0.000058355414,0.000026012976,0.00023512379,0.00012829434,0.000102645165,0.0009905243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986842,0.00045693511,0.00007685386,0.00019129754,0.0005320682,0.000058596557],"domain_scores_gemma":[0.99772316,0.0014072048,0.00030190722,0.0001454178,0.00039255954,0.000029744873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001983153,0.0007975967,0.0006468105,0.0009536657,0.00031365684,0.0009462306,0.00085920846,0.00065747945,0.00080601446],"category_scores_gemma":[0.004777711,0.00042533744,0.0007247239,0.00063193147,0.0009311767,0.0007335809,0.0010293856,0.0008179531,0.00014786159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042376883,0.000053256994,0.001102818,0.00010121053,0.00005323658,0.000049553615,0.000075372904,0.90670234,0.014342501,0.011223561,0.00029270686,0.06596105],"study_design_scores_gemma":[0.0000018393098,0.000015992788,0.00019238907,0.0000032414162,0.0000043195455,0.000010905,0.000004489176,0.9958444,0.0022509622,0.0014525857,0.00021220579,0.0000065905797],"about_ca_topic_score_codex":0.002884645,"about_ca_topic_score_gemma":0.0022523755,"teacher_disagreement_score":0.002884645,"about_ca_system_score_codex":0.000599084,"about_ca_system_score_gemma":0.0017816952,"threshold_uncertainty_score":0.010488033},"labels":[],"label_agreement":null},{"id":"W3174825660","doi":"10.1016/j.jsv.2021.116320","title":"Hybrid uncertainties-based analysis and optimization methods for axial friction force of drive-shaft systems","year":2021,"lang":"en","type":"article","venue":"Journal of Sound and Vibration","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Control theory (sociology); Torque; Engineering; Vibration; Drive shaft; Structural engineering; Computer science; Mechanical engineering; Physics","score_opus":0.055963579213531026,"score_gpt":0.36130639004268883,"score_spread":0.3053428108291578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174825660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074401177,0.00031080682,0.99067384,0.00007371152,0.000023684419,0.000020107973,0.000024736932,0.0000580262,0.0013749585],"genre_scores_gemma":[0.8740456,0.00065880903,0.119163245,0.00007815922,0.00010216288,0.0002627076,0.00011850479,0.00017412665,0.005396652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999556,0.00015010635,0.000029007757,0.000055637425,0.00016713138,0.000042085296],"domain_scores_gemma":[0.99868184,0.00094002666,0.000105217565,0.000035045356,0.000210632,0.000027255714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013577556,0.00094778254,0.0013290069,0.00083430443,0.00045449397,0.0011052918,0.00088979723,0.0010632484,0.0020071694],"category_scores_gemma":[0.002643758,0.00070367433,0.0010248725,0.00056161574,0.00081082946,0.000973906,0.0011747215,0.0009251857,0.00024964157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002811674,0.000010953719,0.00009034062,0.000052868927,0.000026336742,0.000014070797,0.000022965203,0.9831254,0.00046384308,0.004550594,0.00013763363,0.011476801],"study_design_scores_gemma":[0.0000011198754,0.0000045112215,0.000028217784,0.0000029650787,0.0000019039732,0.0000011974976,0.0000016496476,0.9990814,0.00006890351,0.0007271393,0.00007941469,0.0000017285273],"about_ca_topic_score_codex":0.0074414625,"about_ca_topic_score_gemma":0.0045927432,"teacher_disagreement_score":0.0074414625,"about_ca_system_score_codex":0.00075989426,"about_ca_system_score_gemma":0.0011150037,"threshold_uncertainty_score":0.014796317},"labels":[],"label_agreement":null},{"id":"W3175147250","doi":"10.1016/j.automatica.2021.109754","title":"Trajectory planning under environmental uncertainty with finite-sample safety guarantees","year":2021,"lang":"en","type":"article","venue":"Automatica","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"European Research Council; Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; European Commission; National Aeronautics and Space Administration","keywords":"Trajectory; Constraint (computer-aided design); Gaussian; Mathematical optimization; Sample (material); Finite set; Set (abstract data type); Computer science; Gaussian process; Trajectory optimization; Mathematics; Control theory (sociology); Control (management); Optimal control; Artificial intelligence","score_opus":0.05288281988581145,"score_gpt":0.29461471303344156,"score_spread":0.2417318931476301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175147250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018153714,0.00013728412,0.9799716,0.00024073257,0.000010128465,0.000024023066,0.000067268804,0.00018981993,0.0012054032],"genre_scores_gemma":[0.87468994,0.00034648948,0.12249357,0.00012485494,0.000049483355,0.00021533121,0.00029290013,0.00013235981,0.0016549798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978783,0.0006455378,0.00009739501,0.00043196813,0.0006401023,0.00030663787],"domain_scores_gemma":[0.9815398,0.01431633,0.0018656331,0.0006781584,0.0011091535,0.00049103785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027975568,0.0013745603,0.001319325,0.000862404,0.0007411329,0.001611942,0.0020231428,0.0014947569,0.0014473681],"category_scores_gemma":[0.02010887,0.00079190684,0.0009752686,0.0011316582,0.0024937452,0.0024526396,0.0029536744,0.002334747,0.00023941873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035286193,0.000012342719,0.00025442019,0.00003073279,0.000013603638,0.000057782858,0.00004463223,0.9818601,0.00041063805,0.013317761,0.00012564767,0.0038371212],"study_design_scores_gemma":[0.0000048655893,0.000025309058,0.0000618647,0.000006575425,0.000002795667,0.000011776462,0.000010479877,0.98333436,0.0004694322,0.015963044,0.000104944156,0.0000046192076],"about_ca_topic_score_codex":0.006429887,"about_ca_topic_score_gemma":0.0044276444,"teacher_disagreement_score":0.006429887,"about_ca_system_score_codex":0.001755494,"about_ca_system_score_gemma":0.0026576298,"threshold_uncertainty_score":0.014795065},"labels":[],"label_agreement":null},{"id":"W3177253845","doi":"10.2514/6.2001-4099","title":"Uncertainty models and robust complex-rational controller design for flexible structures","year":2001,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation, and Control Conference and Exhibit","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Robust control; Computer science; Robustness (evolution); Control theory (sociology); Controller (irrigation); Control engineering; Control system; Engineering; Control (management); Artificial intelligence","score_opus":0.1556977245075806,"score_gpt":0.32538048385471713,"score_spread":0.16968275934713653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177253845","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008525449,0.00043554837,0.98680913,0.00016739458,0.000030381987,0.000021574935,0.000035187164,0.00018242079,0.0037928063],"genre_scores_gemma":[0.9284189,0.00092859,0.06396733,0.00011389346,0.00006041773,0.00019666398,0.000113444556,0.000066604494,0.0061342497],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958044,0.00010406239,0.000019823774,0.00007967221,0.0001681563,0.000047948768],"domain_scores_gemma":[0.9991615,0.00039626093,0.00022867997,0.000066393404,0.00012481479,0.000022400778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086983223,0.0012035324,0.0006632228,0.00046288062,0.00036927318,0.0013717683,0.0009733276,0.00097347377,0.002952345],"category_scores_gemma":[0.0025712445,0.00044648768,0.00050025695,0.00039348105,0.0012406016,0.0013788635,0.0011574384,0.0013266122,0.00036719974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027980705,0.000008529596,0.00007782475,0.00005871003,0.000012074103,0.000042243075,0.00005807144,0.9414167,0.0015861826,0.04524254,0.0002673223,0.011201786],"study_design_scores_gemma":[0.000004097768,0.000018865323,0.00003114593,0.000006728628,0.0000025621935,0.0000061146357,0.0000063943353,0.98862714,0.00042043923,0.010379225,0.0004908881,0.0000063256884],"about_ca_topic_score_codex":0.00440368,"about_ca_topic_score_gemma":0.0029101034,"teacher_disagreement_score":0.00440368,"about_ca_system_score_codex":0.0010514291,"about_ca_system_score_gemma":0.0008310612,"threshold_uncertainty_score":0.009876549},"labels":[],"label_agreement":null},{"id":"W3179056670","doi":"10.1016/j.ijsolstr.2021.111157","title":"On the tri-dimensional constitutive theory identification of linearly viscoelastic solids based on Bayesian framework","year":2021,"lang":"en","type":"article","venue":"International Journal of Solids and Structures","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Viscoelasticity; Constitutive equation; Robustness (evolution); Mathematics; Bayesian probability; Inference; Applied mathematics; Identification (biology); Bayesian inference; Computer science; Algorithm; Physics; Finite element method; Statistics; Structural engineering; Artificial intelligence; Engineering; Thermodynamics","score_opus":0.02762728964409625,"score_gpt":0.3217894710819947,"score_spread":0.2941621814378984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3179056670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049301167,0.0004146089,0.99298966,0.00017697933,0.000017804101,0.0000134129405,0.000027329903,0.00003240292,0.0013976673],"genre_scores_gemma":[0.63452405,0.005678032,0.34784093,0.00049325736,0.00030886396,0.00028067725,0.00051396416,0.00012649776,0.010233731],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990503,0.00031213247,0.000050844377,0.00020602973,0.00031956402,0.00006116047],"domain_scores_gemma":[0.99807584,0.0012240638,0.00022918104,0.00010265723,0.00029035535,0.00007797254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018577548,0.0005847767,0.001367678,0.0014680933,0.00058859447,0.0014971267,0.0013968393,0.0013231037,0.0017585761],"category_scores_gemma":[0.004965922,0.0006131957,0.0011740325,0.0013171429,0.0015216961,0.0026052154,0.0016724875,0.0016232978,0.00041668492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000720489,0.00007616553,0.001136627,0.00020992407,0.00009971959,0.00016192632,0.00023381233,0.67486787,0.0037229166,0.25185952,0.0013053882,0.066254154],"study_design_scores_gemma":[0.0000031680609,0.000015167485,0.00021220325,0.0000156672,0.000011683986,0.000034588902,0.000013670493,0.95883054,0.00024871068,0.04004103,0.00055641466,0.000017090593],"about_ca_topic_score_codex":0.0043882327,"about_ca_topic_score_gemma":0.002821235,"teacher_disagreement_score":0.0043882327,"about_ca_system_score_codex":0.00070155435,"about_ca_system_score_gemma":0.0013231542,"threshold_uncertainty_score":0.009824872},"labels":[],"label_agreement":null},{"id":"W3180052233","doi":"10.1201/9781482265811-6","title":"Numerical recipes for reliability analysis – a primer","year":2008,"lang":"en","type":"book-chapter","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Primer (cosmetics); Reliability engineering; Reliability (semiconductor); Computer science; Engineering; Physics; Thermodynamics","score_opus":0.1203807303465108,"score_gpt":0.3364334801675647,"score_spread":0.21605274982105388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180052233","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022108507,0.002593784,0.9651155,0.00045009612,0.00049602875,0.0001237317,0.0010719587,0.0063944114,0.023533497],"genre_scores_gemma":[0.0053448556,0.0044720974,0.9403821,0.00055672793,0.00042204277,0.0009855395,0.002744828,0.006515383,0.038576353],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984831,0.00038956196,0.00017253077,0.00017506749,0.0007288416,0.000050948565],"domain_scores_gemma":[0.9970855,0.001175127,0.00009854679,0.00048101513,0.0011000544,0.000059771595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027649296,0.001531614,0.001189204,0.0016705252,0.0006058617,0.0019234995,0.0037433412,0.0015671939,0.08443288],"category_scores_gemma":[0.0063779475,0.0014924144,0.001964652,0.0018769795,0.0009383713,0.002641369,0.001742973,0.0040835817,0.07156148],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042470558,0.0001206951,0.0003405516,0.0014532078,0.000089323024,0.0002522916,0.00031042934,0.037416477,0.0044360403,0.3126615,0.255435,0.38744196],"study_design_scores_gemma":[0.000029076435,0.000024963161,0.00020057576,0.000357597,0.000015751308,0.00025183088,0.000044651286,0.069893144,0.0018705453,0.1934907,0.73376703,0.000054240507],"about_ca_topic_score_codex":0.0011016614,"about_ca_topic_score_gemma":0.0016156328,"teacher_disagreement_score":0.08443288,"about_ca_system_score_codex":0.00073901634,"about_ca_system_score_gemma":0.0012345485,"threshold_uncertainty_score":0.2824561},"labels":[],"label_agreement":null},{"id":"W3181853338","doi":"10.3390/risks10070141","title":"Reverse Sensitivity Analysis for Risk Modelling","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Connaught Fund","keywords":"Sensitivity (control systems); Monte Carlo method; Measure (data warehouse); Random variable; Distortion (music); Set (abstract data type); Probability distribution; Mathematics; Variance (accounting); Computer science; Baseline (sea); Mathematical optimization; Statistics; Engineering; Data mining","score_opus":0.283513783390662,"score_gpt":0.37956050230346655,"score_spread":0.09604671891280453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181853338","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004958383,0.00029912844,0.99166065,0.0004054876,0.000038750593,0.00004860061,0.000101568534,0.00012439038,0.0023630636],"genre_scores_gemma":[0.6752629,0.0012415443,0.3143889,0.0007926174,0.00021973593,0.00071207277,0.00048790316,0.00047653227,0.0064176586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99158394,0.0055204895,0.00029175216,0.0010525017,0.0012039428,0.0003474258],"domain_scores_gemma":[0.9758775,0.019700624,0.0015425029,0.0013841016,0.0011777362,0.00031754648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015118618,0.0026056787,0.0021941974,0.0024675005,0.0007208133,0.0031965887,0.0026655851,0.0027314683,0.0054257903],"category_scores_gemma":[0.045020014,0.0013494266,0.0037165724,0.0011903832,0.0031153208,0.0031915044,0.004386866,0.0048426026,0.00065521785],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034787892,0.000024296218,0.00062113785,0.00011053122,0.00012267313,0.00015309345,0.00008257244,0.85979766,0.00045620144,0.13118918,0.000650635,0.006757216],"study_design_scores_gemma":[0.0000062037448,0.000023364308,0.000106574465,0.00003504419,0.000023572788,0.00004794479,0.00002001614,0.834212,0.0003345085,0.16418456,0.0009834546,0.00002278856],"about_ca_topic_score_codex":0.00515353,"about_ca_topic_score_gemma":0.002294915,"teacher_disagreement_score":0.015118618,"about_ca_system_score_codex":0.0029578886,"about_ca_system_score_gemma":0.0020692034,"threshold_uncertainty_score":0.079955816},"labels":[],"label_agreement":null},{"id":"W3181911865","doi":"10.1007/s00158-021-02996-y","title":"Structural uncertainty analysis with the multiplicative dimensional reduction–based polynomial chaos expansion approach","year":2021,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Polynomial chaos; Univariate; Dimensionality reduction; Uncertainty quantification; Polynomial; Mathematics; Applied mathematics; Mathematical optimization; Multiplicative function; Curse of dimensionality; Monte Carlo method; Taylor series; Multivariate statistics; Computer science; Mathematical analysis; Statistics","score_opus":0.03497195667472834,"score_gpt":0.29316258415146934,"score_spread":0.258190627476741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181911865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005676895,0.000118888915,0.9913385,0.000108971944,0.00003183725,0.000014144761,0.000023025787,0.0000372785,0.0026505364],"genre_scores_gemma":[0.73327136,0.00077229564,0.25549024,0.00015783517,0.00023810974,0.00018350102,0.00015696572,0.00015260444,0.009577113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948114,0.00017156755,0.00001787492,0.00004773887,0.00024485498,0.000036874408],"domain_scores_gemma":[0.9995431,0.00021113818,0.000049071838,0.000065253604,0.00011303191,0.000018364983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064053887,0.00062347803,0.0007518575,0.0008032156,0.00037289035,0.00065130455,0.0008205563,0.0005700847,0.0015532766],"category_scores_gemma":[0.0018053623,0.00033590337,0.00093790697,0.0006299573,0.0008616183,0.0011468917,0.0012678812,0.0012122616,0.0003682035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041411735,0.00003084846,0.00027351564,0.00008964423,0.000052781306,0.00006156523,0.00005397555,0.7604113,0.00631759,0.19694884,0.0009772523,0.034741294],"study_design_scores_gemma":[0.0000018133908,0.00001336155,0.000071103605,0.0000028770057,0.0000049980067,0.000012990686,0.0000033035938,0.9826224,0.00053268974,0.016232237,0.00049645867,0.0000057958096],"about_ca_topic_score_codex":0.000882439,"about_ca_topic_score_gemma":0.0007679507,"teacher_disagreement_score":0.0015532766,"about_ca_system_score_codex":0.0003978452,"about_ca_system_score_gemma":0.00057411956,"threshold_uncertainty_score":0.0051962137},"labels":[],"label_agreement":null},{"id":"W3182247597","doi":"10.3390/w13131830","title":"Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam","year":2021,"lang":"en","type":"article","venue":"Water","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sobol sequence; Polynomial chaos; Surrogate model; Parametric statistics; Sensitivity (control systems); Artificial neural network; Finite element method; Uncertainty quantification; Structural engineering; Geotechnical engineering; Engineering; Computer science; Algorithm; Applied mathematics; Mathematical optimization; Mathematics; Monte Carlo method; Artificial intelligence; Statistics; Machine learning","score_opus":0.043400635239239566,"score_gpt":0.2859360813370207,"score_spread":0.24253544609778113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3182247597","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47704133,0.00037703372,0.51604813,0.00049370277,0.000038940758,0.00006098157,0.00019479888,0.0003948172,0.005350149],"genre_scores_gemma":[0.9814456,0.0000793018,0.017123913,0.000022648608,0.000005704662,0.000030783925,0.00006165895,0.000016296759,0.0012140976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998072,0.00008982594,0.000009521596,0.00002482972,0.000044747238,0.000023941056],"domain_scores_gemma":[0.9989448,0.0007427598,0.00009107837,0.000041351494,0.00014812125,0.000031855743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090554147,0.0005106343,0.000534869,0.00043749282,0.00027180766,0.0004903759,0.00047456715,0.000833525,0.0007924674],"category_scores_gemma":[0.0019358838,0.00027164936,0.00047616923,0.00035670513,0.00051888253,0.0005721625,0.00061865675,0.00073120365,0.000063151405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001550111,0.000011788608,0.00016030311,0.0000065904787,0.000004479822,0.000018666853,0.000007011822,0.9964181,0.00037519986,0.00083759497,0.00004148415,0.002103279],"study_design_scores_gemma":[4.0502528e-7,0.0000035605995,0.00002288332,2.9704745e-7,3.4783187e-7,9.543214e-7,6.903873e-7,0.999759,0.000070139846,0.00013050956,0.000010468179,7.2005594e-7],"about_ca_topic_score_codex":0.0068907244,"about_ca_topic_score_gemma":0.0040476373,"teacher_disagreement_score":0.0068907244,"about_ca_system_score_codex":0.00081259425,"about_ca_system_score_gemma":0.00064588286,"threshold_uncertainty_score":0.0137012005},"labels":[],"label_agreement":null},{"id":"W3186552400","doi":"10.1029/2020wr029149","title":"Uncertainty Analysis for Hydrological Models With Interdependent Parameters: An Improved Polynomial Chaos Expansion Approach","year":2021,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polynomial chaos; Soil and Water Assessment Tool; Monte Carlo method; Uncertainty analysis; Probabilistic logic; Propagation of uncertainty; Uncertainty quantification; Computer science; Principal component analysis; Mathematical optimization; Computation; Mathematics; Applied mathematics; Algorithm; Statistics; Streamflow","score_opus":0.25077175544469416,"score_gpt":0.3878037842030334,"score_spread":0.13703202875833925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186552400","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020268602,0.00014021128,0.9782784,0.00010379837,0.000011821616,0.000023410885,0.000036025653,0.00010656097,0.0010311696],"genre_scores_gemma":[0.84343034,0.0004990379,0.15381458,0.00006928485,0.00006363673,0.00015697743,0.00017783447,0.000117043186,0.0016712593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999428,0.00024338994,0.00002498524,0.000057299116,0.00020341888,0.000042892636],"domain_scores_gemma":[0.99869066,0.00092490437,0.00009003163,0.00006118021,0.00020625748,0.000027048258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011841824,0.0006090768,0.00079138635,0.0012464877,0.00044012396,0.0006508962,0.0006510181,0.000583934,0.0008551482],"category_scores_gemma":[0.0032577745,0.00040968382,0.00097148586,0.00085245253,0.00049522973,0.0010489238,0.00089935254,0.0010013665,0.00012173652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010941795,0.0000127673175,0.00049280835,0.00001935818,0.0000224589,0.000044097284,0.000020709178,0.9803325,0.00083909946,0.006954037,0.00015124546,0.011099915],"study_design_scores_gemma":[4.792971e-7,0.0000019653635,0.000044678007,7.976459e-7,0.0000013170563,0.0000023779744,0.0000012755173,0.9990337,0.000067377616,0.00079092145,0.000053492156,0.0000016324731],"about_ca_topic_score_codex":0.0076242685,"about_ca_topic_score_gemma":0.0038271807,"teacher_disagreement_score":0.0076242685,"about_ca_system_score_codex":0.00058357656,"about_ca_system_score_gemma":0.0010118951,"threshold_uncertainty_score":0.015159786},"labels":[],"label_agreement":null},{"id":"W3190090286","doi":"10.1017/9781108377447.022","title":"Total Variation Minimization","year":2021,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Variation (astronomy); Minification; Computer science; Content (measure theory); World Wide Web; Mathematics; Physics","score_opus":0.0576806881860146,"score_gpt":0.2366581711394153,"score_spread":0.1789774829534007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190090286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009056469,0.00223461,0.95102227,0.00058958307,0.00031911154,0.000036814003,0.00047032133,0.00065087137,0.043770675],"genre_scores_gemma":[0.0953574,0.007923328,0.6423466,0.0011733888,0.0009049458,0.00048643452,0.004510912,0.0041449857,0.24315204],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993755,0.00018503897,0.000026076852,0.00014614646,0.00021817167,0.00004904574],"domain_scores_gemma":[0.9995553,0.00020777715,0.000022616447,0.00006961495,0.000116431875,0.00002827784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009172575,0.0018078078,0.0016762832,0.0006967713,0.00040845544,0.0018018761,0.0013964117,0.001347866,0.035237584],"category_scores_gemma":[0.002309885,0.00048439225,0.0013690699,0.0010501202,0.0008293991,0.0013119514,0.0017906671,0.0020229146,0.017777676],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093430695,0.000060437924,0.00027000334,0.0009972681,0.00019560069,0.0001012278,0.00009785217,0.16816564,0.0052567655,0.21567921,0.16765659,0.44142595],"study_design_scores_gemma":[0.000023603161,0.000088265944,0.00041200998,0.00020081563,0.000060544935,0.000257567,0.00006019363,0.6589024,0.0037774243,0.18865436,0.14751609,0.0000467673],"about_ca_topic_score_codex":0.0018157165,"about_ca_topic_score_gemma":0.0026408313,"teacher_disagreement_score":0.035237584,"about_ca_system_score_codex":0.0006696143,"about_ca_system_score_gemma":0.00096177484,"threshold_uncertainty_score":0.11788148},"labels":[],"label_agreement":null},{"id":"W3194048789","doi":"","title":"CVaR-based Safety Analysis for the Infinite Time Setting.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"CVAR; State space; Outcome (game theory); Mathematical optimization; Computer science; Domain (mathematical analysis); Fraction (chemistry); Mathematics; Risk management; Expected shortfall; Mathematical economics; Economics; Statistics; Finance","score_opus":0.14632388486260442,"score_gpt":0.23761859728196214,"score_spread":0.09129471241935772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194048789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021497023,0.00018599883,0.9953751,0.00011375389,0.00002485086,0.000017571389,0.000029194198,0.000053416195,0.0020505062],"genre_scores_gemma":[0.7161344,0.0012498766,0.26904836,0.00037599102,0.00030818608,0.00040928015,0.00041504166,0.0002826303,0.01177623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972042,0.0009791944,0.00009571012,0.0003916383,0.0010980344,0.0002312088],"domain_scores_gemma":[0.99034435,0.0066004246,0.0010040291,0.0004528506,0.0012783639,0.00031991056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004639575,0.0011761189,0.0012842194,0.0015329504,0.00059269695,0.0016716144,0.001790696,0.0011890335,0.0046352586],"category_scores_gemma":[0.012836443,0.00059058686,0.0015973717,0.00086666126,0.0023827357,0.002333326,0.002564525,0.0037116024,0.00059810106],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044987646,0.00003236583,0.00039560124,0.000103775325,0.00006184239,0.0000737956,0.00007934762,0.7264487,0.0013856993,0.25391808,0.0010142252,0.016441502],"study_design_scores_gemma":[0.0000040388427,0.0000212264,0.00006043426,0.000015790421,0.000009076613,0.000017643348,0.0000085944575,0.90602314,0.0003839234,0.09252308,0.00092582084,0.000007292047],"about_ca_topic_score_codex":0.0028469055,"about_ca_topic_score_gemma":0.0017517629,"teacher_disagreement_score":0.004639575,"about_ca_system_score_codex":0.002032299,"about_ca_system_score_gemma":0.002550825,"threshold_uncertainty_score":0.024536729},"labels":[],"label_agreement":null},{"id":"W3196997600","doi":"10.22215/etd/2013-06186","title":"Parametric uncertainty quantification in coalescene flutter","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bibliothèque et Archives nationales du Québec; Environment and Climate Change Canada; Library and Archives Canada","funders":"","keywords":"Parametric statistics; Flutter; Mathematics; Statistics; Engineering; Aerospace engineering; Aerodynamics","score_opus":0.1037507907395604,"score_gpt":0.3635748638302658,"score_spread":0.25982407309070543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196997600","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032481346,0.00064833957,0.96106625,0.00016029511,0.000029028117,0.000027497374,0.00008616173,0.0002311157,0.0052700457],"genre_scores_gemma":[0.865205,0.00072908396,0.123154595,0.00010631975,0.00007871884,0.00009779365,0.00028787376,0.00032708776,0.010013592],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800843,0.00045777002,0.00010172448,0.0004010609,0.0008141161,0.00021686657],"domain_scores_gemma":[0.9944107,0.00394046,0.00041721086,0.0004287931,0.0006138717,0.00018907523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029437405,0.0007906461,0.0013529861,0.002259168,0.00081896223,0.002591873,0.0017893593,0.0014601724,0.0045420695],"category_scores_gemma":[0.012487564,0.0008446798,0.0012440461,0.0012086205,0.0025976803,0.0035233332,0.00346934,0.0014702394,0.00039501578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008841939,0.000014687513,0.000834591,0.000068803325,0.00003484781,0.00017954077,0.00021574952,0.8679526,0.002759768,0.093794316,0.0005851981,0.03347146],"study_design_scores_gemma":[0.000003410257,0.000022791066,0.00019186923,0.000019787705,0.000007543564,0.00005046201,0.000028816845,0.9592462,0.0011332849,0.038390476,0.0008883365,0.000017045464],"about_ca_topic_score_codex":0.005474153,"about_ca_topic_score_gemma":0.0039355042,"teacher_disagreement_score":0.005474153,"about_ca_system_score_codex":0.0020334588,"about_ca_system_score_gemma":0.0009248036,"threshold_uncertainty_score":0.015568197},"labels":[],"label_agreement":null},{"id":"W3197598799","doi":"10.15167/ottonello-andrea_phd2021-05-25","title":"Application of Uncertainty Quantification techniques to CFD simulation of twin entry radial turbines","year":2021,"lang":"it","type":"article","venue":"CINECA IRIS Institutial Research Information System (University of Genoa)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computational fluid dynamics; Computer science; Marine engineering; Engineering; Aerospace engineering","score_opus":0.0997605369986644,"score_gpt":0.34646292135695994,"score_spread":0.24670238435829556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197598799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28899166,0.0009077541,0.68636125,0.0006590496,0.00013351343,0.00016869763,0.0014208902,0.0025230704,0.018834017],"genre_scores_gemma":[0.90007615,0.00034794005,0.09665224,0.00006095692,0.000021091397,0.00012607547,0.0005194469,0.00025610474,0.0019399516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999556,0.00014268019,0.000028612529,0.000050426366,0.00017643653,0.000045800723],"domain_scores_gemma":[0.9979405,0.0014442973,0.00014547666,0.00010902237,0.00031405553,0.00004662524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011911568,0.0007172297,0.0005438542,0.0009410519,0.00048772577,0.001286406,0.000666486,0.0011021013,0.0020824207],"category_scores_gemma":[0.0038364085,0.0004204305,0.0007943511,0.000718411,0.00046539315,0.000796498,0.00081820367,0.0010299559,0.0002680938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045365377,0.000020656576,0.0013515652,0.00007377657,0.000020519488,0.00004891298,0.00009514075,0.97938603,0.0022629292,0.0028652668,0.00030297466,0.013526834],"study_design_scores_gemma":[0.0000031472591,0.000013228917,0.00028962904,0.0000117408,0.0000036452418,0.000009961152,0.000019764975,0.996648,0.001606673,0.00077480637,0.00061069196,0.000008721501],"about_ca_topic_score_codex":0.014243111,"about_ca_topic_score_gemma":0.007462808,"teacher_disagreement_score":0.014243111,"about_ca_system_score_codex":0.00068368216,"about_ca_system_score_gemma":0.0014575864,"threshold_uncertainty_score":0.028320432},"labels":[],"label_agreement":null},{"id":"W3199261976","doi":"10.1007/s10107-025-02259-4","title":"Learning and decision-making with data : optimal formulations and phase transitions","year":2025,"lang":"en","type":"preprint","venue":"Mathematical Programming","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Sample (material); Mathematical optimization; Computer science; Measure (data warehouse); Variance (accounting); Mathematics; Data mining; Economics","score_opus":0.09830864299050751,"score_gpt":0.4066926458346177,"score_spread":0.3083840028441102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199261976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018683713,0.001586378,0.97032285,0.0046651685,0.00008864545,0.000080466816,0.00028079303,0.000054809167,0.0042371647],"genre_scores_gemma":[0.66996235,0.0040515577,0.3143771,0.0011845627,0.00067597866,0.00068600394,0.0005000971,0.00021395508,0.008348449],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958097,0.002249829,0.00022578127,0.00082478154,0.0006078342,0.00028219027],"domain_scores_gemma":[0.9652152,0.030255256,0.002029013,0.00080671004,0.0012283849,0.0004654597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010408933,0.0012418092,0.0025325888,0.0015558426,0.0008218584,0.0054099243,0.0025517715,0.0040759617,0.003850532],"category_scores_gemma":[0.047441494,0.0017963919,0.0016049593,0.0023484745,0.0047340565,0.009842051,0.0031801928,0.005435709,0.00037553112],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007812386,0.000100492376,0.0005084015,0.00030603466,0.00007671012,0.000053570177,0.0001533722,0.24740641,0.00029904302,0.7337079,0.0017085177,0.015601445],"study_design_scores_gemma":[0.000016821981,0.000019503312,0.000109790446,0.00005346413,0.000014560378,0.00001906817,0.000026592304,0.48961678,0.00019749977,0.5093669,0.0005460564,0.000012904641],"about_ca_topic_score_codex":0.0026481082,"about_ca_topic_score_gemma":0.0019880107,"teacher_disagreement_score":0.010408933,"about_ca_system_score_codex":0.0038560668,"about_ca_system_score_gemma":0.0024179157,"threshold_uncertainty_score":0.055048406},"labels":[],"label_agreement":null},{"id":"W3203941567","doi":"10.1007/s11009-021-09891-5","title":"Revisiting Best Linear Unbiased Estimation of Location-Scale Parameters Based on Optimally Selected Order Statistics Using Compound Design","year":2021,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Best linear unbiased prediction; Statistics; Scale (ratio); Order statistic; Unbiased Estimation; Order (exchange); Location parameter; Estimator; Computer science; Geography; Artificial intelligence; Selection (genetic algorithm)","score_opus":0.25895612568556287,"score_gpt":0.39217537139787517,"score_spread":0.1332192457123123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203941567","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026556123,0.0000841038,0.9967955,0.0000391478,0.00001164187,0.000011398484,0.000017297503,0.00008196599,0.00030333974],"genre_scores_gemma":[0.23845904,0.00052832044,0.7575211,0.00020985493,0.000098627825,0.00017162018,0.00025398962,0.00025662017,0.0025007694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956157,0.0023425142,0.00023755908,0.0006019885,0.0009940127,0.0002081254],"domain_scores_gemma":[0.98721826,0.009567401,0.0006210351,0.0011022612,0.0013745744,0.000116455245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067467154,0.0015435603,0.002566432,0.0013106712,0.00055139465,0.0022008878,0.0013454292,0.0018150831,0.0021251654],"category_scores_gemma":[0.026852489,0.0016131111,0.0012092959,0.0011696968,0.0016557131,0.0033617476,0.0017477842,0.0017952567,0.00089364784],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033391337,0.000113071845,0.0018018145,0.00033735455,0.00027858216,0.00011201284,0.00017122616,0.7901077,0.013010053,0.07316698,0.0014862078,0.11908101],"study_design_scores_gemma":[0.000016672615,0.00008183867,0.00035259788,0.000020753558,0.00003168677,0.0000269132,0.000009707878,0.97700953,0.0033014345,0.018414691,0.00071460486,0.00001950124],"about_ca_topic_score_codex":0.002530554,"about_ca_topic_score_gemma":0.0035258285,"teacher_disagreement_score":0.0067467154,"about_ca_system_score_codex":0.00096032495,"about_ca_system_score_gemma":0.0031830692,"threshold_uncertainty_score":0.035680413},"labels":[],"label_agreement":null},{"id":"W3204410243","doi":"10.1108/ec-06-2021-0318","title":"Topological derivatives via one-sided derivative of parametrized minima and minimax","year":2021,"lang":"en","type":"article","venue":"Engineering Computations","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Bounded function; Maxima and minima; Mathematics; Generalizations of the derivative; Euclidean space; Fréchet derivative; Derivative (finance); Minimax; Applied mathematics; Topology (electrical circuits); Mathematical optimization; Second derivative; Mathematical analysis; Banach space; Combinatorics","score_opus":0.10207347327412594,"score_gpt":0.3324775140468712,"score_spread":0.23040404077274526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204410243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025647854,0.0004025574,0.96045697,0.0004838089,0.00009561997,0.000032966178,0.00006254247,0.00007317432,0.012744473],"genre_scores_gemma":[0.7178039,0.0007992265,0.2681302,0.00034703853,0.000145015,0.00017629945,0.000109402325,0.00022578565,0.012263089],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99913293,0.000295346,0.00006862977,0.00018251443,0.00026755958,0.000053123793],"domain_scores_gemma":[0.9982389,0.0008448298,0.0002790013,0.00024532972,0.00027931467,0.000112607886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026836682,0.0006432572,0.0006766423,0.00183572,0.0006096767,0.0022768886,0.0010391234,0.0009745005,0.0038438584],"category_scores_gemma":[0.0071925516,0.00033345804,0.001089758,0.0007675198,0.0033122068,0.0037833685,0.0021927424,0.0023173874,0.0004887953],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000107695905,0.000008945682,0.00024068574,0.00003737295,0.000007832257,0.00008698923,0.00009326905,0.01426718,0.0015431907,0.97708434,0.0003202193,0.0062991874],"study_design_scores_gemma":[0.0000076138676,0.00006431297,0.000301161,0.00003669723,0.000009101568,0.00020651914,0.000068248286,0.19344485,0.0019825646,0.8000812,0.0037752897,0.000022481827],"about_ca_topic_score_codex":0.00053673587,"about_ca_topic_score_gemma":0.000500421,"teacher_disagreement_score":0.0038438584,"about_ca_system_score_codex":0.0012527884,"about_ca_system_score_gemma":0.00072911166,"threshold_uncertainty_score":0.01419276},"labels":[],"label_agreement":null},{"id":"W3204586775","doi":"10.1007/978-3-030-81362-8_10","title":"Efficiently Transforming from Values of a Function on a Sparse Grid to Basis Coefficients","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computational science and engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Sparse grid; Mathematics; Basis (linear algebra); Function (biology); Transformation (genetics); Piecewise; Basis function; Combinatorics; Discrete mathematics; Applied mathematics; Mathematical analysis; Geometry","score_opus":0.039387742497858015,"score_gpt":0.27524849155213443,"score_spread":0.23586074905427643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204586775","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011496304,0.000058435096,0.9826355,0.000061046965,0.00004236342,0.000028818484,0.0001472864,0.001411677,0.0041185906],"genre_scores_gemma":[0.10064684,0.00024259186,0.8918248,0.00006378423,0.000027733451,0.00007151858,0.0007373267,0.0007105755,0.005674962],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997696,0.000035758127,0.000010866681,0.00002810373,0.00013114039,0.000024559355],"domain_scores_gemma":[0.9995796,0.00016877662,0.000018858827,0.00013739918,0.00007820919,0.000017269775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030258053,0.0005343785,0.00053893815,0.0004858449,0.00020175589,0.0010075172,0.0006479168,0.0004910551,0.0057607],"category_scores_gemma":[0.0018731955,0.00028666327,0.0004298003,0.0009879825,0.00042034395,0.0010896132,0.0013985358,0.0009788685,0.0028745336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018451863,0.000066403205,0.0004878901,0.00019575744,0.00003174608,0.00015636747,0.00014703254,0.08588625,0.061704297,0.056629904,0.012691351,0.7818185],"study_design_scores_gemma":[0.000033315653,0.000073938405,0.0004234045,0.000030806776,0.000016298003,0.0002764388,0.00012833427,0.8603419,0.045237932,0.073454365,0.019962586,0.000020717593],"about_ca_topic_score_codex":0.0012477618,"about_ca_topic_score_gemma":0.001981503,"teacher_disagreement_score":0.0057607,"about_ca_system_score_codex":0.00020695201,"about_ca_system_score_gemma":0.0003780808,"threshold_uncertainty_score":0.019271433},"labels":[],"label_agreement":null},{"id":"W3205032112","doi":"10.33774/chemrxiv-2021-cn0px","title":"Efficiently transforming from values of a function on a sparse grid to basis coefficients","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Sparse grid; Basis (linear algebra); Function (biology); Transformation (genetics); Piecewise; Combinatorics; Basis function; Discrete mathematics; Applied mathematics; Mathematical analysis; Geometry","score_opus":0.09258603349882324,"score_gpt":0.3224028351409567,"score_spread":0.22981680164213344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205032112","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022196308,0.0001034622,0.97223425,0.00008866148,0.000037172827,0.000045400175,0.00018083231,0.0012705809,0.0038434002],"genre_scores_gemma":[0.13995601,0.0002906654,0.85283136,0.000101571415,0.000028295375,0.00014110284,0.000870819,0.0010623848,0.0047177626],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99938834,0.000089912726,0.000028271634,0.00004722923,0.00036664595,0.000079624006],"domain_scores_gemma":[0.9991823,0.00039843598,0.000044733002,0.0001533606,0.00018378647,0.000037313603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081088906,0.0007318025,0.0007094507,0.00072905474,0.0003995649,0.0014352616,0.0009992822,0.0005617472,0.006423122],"category_scores_gemma":[0.0037920987,0.0004336416,0.0006375026,0.0010415904,0.0006454606,0.0014320961,0.0014042411,0.0016032921,0.003686301],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028176783,0.00010908521,0.0021043946,0.00044235354,0.00006124514,0.0002950617,0.00050981366,0.3360526,0.064907245,0.090849906,0.012885186,0.49150142],"study_design_scores_gemma":[0.000023386518,0.00004879666,0.0004462842,0.000039566366,0.000015970714,0.00008302255,0.0001448798,0.9386127,0.017573921,0.03138647,0.011604198,0.000020755733],"about_ca_topic_score_codex":0.0046086404,"about_ca_topic_score_gemma":0.007537832,"teacher_disagreement_score":0.006423122,"about_ca_system_score_codex":0.00058318453,"about_ca_system_score_gemma":0.0014259449,"threshold_uncertainty_score":0.021487474},"labels":[],"label_agreement":null},{"id":"W3206976204","doi":"10.22215/etd/2020-13941","title":"Model Comparison and Sparse Learning of Nonlinear Physics-Based Models Using Bayesian Inference","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Markov chain Monte Carlo; Hyperparameter; Bayesian inference; Prior probability; Computer science; Overfitting; Posterior probability; Artificial intelligence; Bayesian probability; Machine learning; Parameterized complexity; Algorithm; Mathematical optimization; Mathematics; Artificial neural network","score_opus":0.23120882117676017,"score_gpt":0.3912129297562675,"score_spread":0.1600041085795073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206976204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041237045,0.00008909869,0.99473417,0.00013796744,0.000009123279,0.000020661028,0.000024859315,0.00011079062,0.00074966473],"genre_scores_gemma":[0.351815,0.0007336702,0.6430595,0.00022818272,0.000121761404,0.00041599988,0.00033624662,0.00024263948,0.0030470777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987244,0.0006187452,0.0000463594,0.00021679647,0.0003275637,0.000066227476],"domain_scores_gemma":[0.9950854,0.0038149972,0.000440837,0.00030100913,0.0002641177,0.000093626186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039637308,0.0009334517,0.0014657974,0.0014587089,0.00072451116,0.0017343821,0.0025591904,0.0012673174,0.0020226373],"category_scores_gemma":[0.012477185,0.0009959333,0.001587241,0.0010849729,0.0018958217,0.002648414,0.0028802084,0.0025459356,0.00034349333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022705091,0.000037809194,0.0004956414,0.00009411206,0.00004824767,0.000039661085,0.00009414365,0.8949834,0.0007625386,0.07494021,0.00038915026,0.028092282],"study_design_scores_gemma":[0.0000037252341,0.000009687518,0.000063136824,0.0000088625175,0.0000041435505,0.00000854007,0.0000058633163,0.9738425,0.00016452854,0.025561444,0.00032133659,0.0000063991088],"about_ca_topic_score_codex":0.004179655,"about_ca_topic_score_gemma":0.004097356,"teacher_disagreement_score":0.004179655,"about_ca_system_score_codex":0.0012745946,"about_ca_system_score_gemma":0.002275494,"threshold_uncertainty_score":0.020962417},"labels":[],"label_agreement":null},{"id":"W3207260507","doi":"10.22215/etd/2019-13428","title":"Scalable Domain Decomposition Algorithms for Uncertainty Quantification in High Performance Computing","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Solver; Sparse grid; Uncertainty quantification; Computer science; Domain decomposition methods; Scalability; Finite element method; Grid; Polynomial chaos; Curse of dimensionality; Partial differential equation; Algorithm; Mathematical optimization; Computational science; Mathematics; Monte Carlo method","score_opus":0.05826894164298011,"score_gpt":0.35617399515555015,"score_spread":0.29790505351257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207260507","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022639732,0.00020188061,0.9958406,0.000092701426,0.000029122348,0.000026909918,0.00003038683,0.00024885603,0.0012655415],"genre_scores_gemma":[0.13163333,0.0008332482,0.86364,0.00013012173,0.00007880647,0.0003069333,0.00037088443,0.0003010897,0.0027056048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990978,0.0002368291,0.00004685428,0.000090265254,0.00047481217,0.000053388154],"domain_scores_gemma":[0.99840564,0.0007761972,0.000109061526,0.00028255867,0.00037191273,0.000054682176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013288383,0.00077757786,0.0007807796,0.0006210977,0.00050060375,0.0013212545,0.0010848462,0.00093138166,0.002244008],"category_scores_gemma":[0.0046093664,0.00044794867,0.0008935061,0.0008499277,0.0009144438,0.0014570319,0.0021838185,0.002041082,0.0009420422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048488448,0.000041961015,0.00055615004,0.00021314195,0.0000550682,0.000078538964,0.00014580444,0.7344554,0.0067610624,0.14548379,0.0041416786,0.10801901],"study_design_scores_gemma":[0.0000056861363,0.000007744526,0.000050456987,0.000010708074,0.000002640519,0.000015798936,0.0000121088915,0.97424763,0.0011794681,0.021946669,0.0025164927,0.000004651124],"about_ca_topic_score_codex":0.0017911941,"about_ca_topic_score_gemma":0.0018464468,"teacher_disagreement_score":0.002244008,"about_ca_system_score_codex":0.0007898987,"about_ca_system_score_gemma":0.0012098806,"threshold_uncertainty_score":0.007506907},"labels":[],"label_agreement":null},{"id":"W3208582465","doi":"10.48550/arxiv.2110.11074","title":"A Unified Framework for Regularized Estimating Equations via Fixed-Point and Variational Inequality Problems","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematics; Applied mathematics; Operator (biology); Point (geometry); Variational inequality; Fixed point; Estimating equations; Regularization (linguistics); Generalized estimating equation; Mathematical optimization; Calculus (dental); Mathematical analysis; Computer science; Maximum likelihood; Statistics","score_opus":0.22683752950858907,"score_gpt":0.274964696346097,"score_spread":0.04812716683750792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208582465","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007949848,0.00026414954,0.99787414,0.00026028825,0.000021385249,0.000015575915,0.000033196975,0.000018682596,0.0007175934],"genre_scores_gemma":[0.16448249,0.0025347618,0.8238571,0.0005850451,0.0007255506,0.00047058784,0.000458834,0.00021248065,0.0066730957],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99440503,0.0034341337,0.0002349738,0.0008635902,0.0008650401,0.00019733768],"domain_scores_gemma":[0.9918641,0.005707165,0.00066814537,0.00056345586,0.0009867804,0.0002102916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010183553,0.001860474,0.00219142,0.0020986183,0.0005693538,0.0027140987,0.003220529,0.00248785,0.0029270907],"category_scores_gemma":[0.017291155,0.0011931714,0.002362319,0.001915087,0.0033881792,0.0045275763,0.0039627836,0.0052964003,0.000548917],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013965732,0.000021219379,0.00021175804,0.0001019432,0.00006193189,0.000068907866,0.00008315943,0.11307034,0.0006433136,0.87422055,0.0008766557,0.010626242],"study_design_scores_gemma":[0.000015659989,0.00003948231,0.00012818047,0.0000417865,0.000025972617,0.000046821697,0.000020913414,0.6533142,0.00030479624,0.342675,0.0033650259,0.000022140935],"about_ca_topic_score_codex":0.002961864,"about_ca_topic_score_gemma":0.0021072368,"teacher_disagreement_score":0.010183553,"about_ca_system_score_codex":0.0018295759,"about_ca_system_score_gemma":0.0025203282,"threshold_uncertainty_score":0.053856432},"labels":[],"label_agreement":null},{"id":"W3209005210","doi":"10.5194/wes-2021-110","title":"Surrogate models for the blade element momentum aerodynamic model using non-intrusive Polynomial Chaos Expansions","year":2021,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Polynomial chaos; Aeroelasticity; Aerodynamics; Turbine; Wind power; Computational fluid dynamics; Uncertainty quantification; Control theory (sociology); Computer science; Engineering; Mathematics; Aerospace engineering; Statistics; Monte Carlo method; Artificial intelligence","score_opus":0.1374701302752156,"score_gpt":0.3500751599622708,"score_spread":0.2126050296870552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209005210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028069124,0.00008442145,0.9691004,0.00009967419,0.000021094696,0.000031351105,0.00009409023,0.000107543,0.002392304],"genre_scores_gemma":[0.9215425,0.00023433333,0.07269549,0.000050454375,0.000028192993,0.0001693835,0.00030621147,0.00006242071,0.0049110088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995788,0.00015476343,0.000018503502,0.00004395724,0.0001694474,0.00003462872],"domain_scores_gemma":[0.9990408,0.0005298717,0.00014663185,0.00007927927,0.0001695401,0.00003383221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000789052,0.00048060156,0.0005393231,0.00050837523,0.0002381506,0.00074854214,0.0006354822,0.00071234483,0.001103065],"category_scores_gemma":[0.00264207,0.00030857802,0.0005679433,0.00043433873,0.00051794614,0.00075831043,0.00056963635,0.0009057489,0.0003415333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000117962445,0.000008532522,0.00026180636,0.000012552811,0.000004911843,0.000018543768,0.000012051227,0.9894148,0.0007782965,0.0071121026,0.00011314587,0.0022516],"study_design_scores_gemma":[4.5768354e-7,0.0000033248907,0.000028565026,8.594393e-7,3.8413745e-7,0.0000025760723,7.185857e-7,0.99929655,0.000085467655,0.0005144383,0.00006574704,9.3205693e-7],"about_ca_topic_score_codex":0.0017703039,"about_ca_topic_score_gemma":0.0015225811,"teacher_disagreement_score":0.0017703039,"about_ca_system_score_codex":0.00044677488,"about_ca_system_score_gemma":0.000611818,"threshold_uncertainty_score":0.004172921},"labels":[],"label_agreement":null},{"id":"W3210125627","doi":"","title":"Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data.","year":2021,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Simon Fraser University","funders":"","keywords":"Computer science; Artificial neural network; Deep learning; Artificial intelligence; Deep neural networks","score_opus":0.24666348722630996,"score_gpt":0.35980335415809317,"score_spread":0.11313986693178321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210125627","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028068598,0.0013306002,0.96826607,0.00061597815,0.00007894229,0.000028736127,0.00028324453,0.00048857776,0.00083923386],"genre_scores_gemma":[0.76320964,0.0011191488,0.22948448,0.0003602476,0.0001864652,0.00019114598,0.0017510789,0.00015362217,0.0035442258],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994609,0.00024696582,0.000043336717,0.00008499844,0.00010364419,0.00006015686],"domain_scores_gemma":[0.99597627,0.0029233722,0.00022484387,0.00034975604,0.00037978461,0.0001459536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023276117,0.000958594,0.0012694667,0.00065083575,0.0003041136,0.0012777732,0.0017689385,0.0015719389,0.0019004679],"category_scores_gemma":[0.01023529,0.0008342814,0.00072539964,0.00092482293,0.0011114675,0.0040647294,0.002160523,0.0028942146,0.00049946934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031502073,0.0001863624,0.0017859638,0.00024423838,0.00013917345,0.000112400616,0.000098533674,0.8092857,0.0025608158,0.022403382,0.005934976,0.15693346],"study_design_scores_gemma":[0.0000034359236,0.000010033195,0.00005699195,0.0000059057875,0.0000023724347,0.0000046341615,0.0000046601103,0.99270415,0.00020467023,0.0069010267,0.00009923925,0.0000030081676],"about_ca_topic_score_codex":0.0037191636,"about_ca_topic_score_gemma":0.004487017,"teacher_disagreement_score":0.0037191636,"about_ca_system_score_codex":0.00083419913,"about_ca_system_score_gemma":0.001078749,"threshold_uncertainty_score":0.01230973},"labels":[],"label_agreement":null},{"id":"W3211177747","doi":"10.3390/sym13112041","title":"Advanced Approach for Estimating Failure Rate Using Saddlepoint Approximation","year":2021,"lang":"en","type":"article","venue":"Symmetry","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Regina","keywords":"Cumulative distribution function; Random variable; Applied mathematics; Failure rate; Probability density function; Mathematics; Probability mass function; Hazard; Linear approximation; Function (biology); Probability distribution; Distribution (mathematics); Mathematical optimization; Statistics; Mathematical analysis; Nonlinear system; Physics","score_opus":0.09258108343115388,"score_gpt":0.3416710482703476,"score_spread":0.2490899648391937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211177747","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014132484,0.00006262254,0.9981816,0.000018375436,0.000007932882,0.0000080071195,0.000009512607,0.000041654857,0.0002571008],"genre_scores_gemma":[0.4540406,0.0013403317,0.53805816,0.00011106321,0.00012172573,0.0003892004,0.00029184268,0.00020467342,0.0054424093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939513,0.00026935662,0.00002928232,0.00009692677,0.0001820668,0.000027234839],"domain_scores_gemma":[0.9988374,0.0007595395,0.00009723032,0.00009358488,0.00019141668,0.00002072381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020864576,0.0008860534,0.0011138115,0.0010663109,0.00032662504,0.0007919763,0.0014927265,0.0009742949,0.001984638],"category_scores_gemma":[0.004528468,0.0005137197,0.0011428337,0.00069862243,0.0005322762,0.0011289266,0.0008491311,0.0013816614,0.00063692534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028869132,0.000017593171,0.0006871409,0.00009520079,0.000052472886,0.000113412694,0.000064179505,0.9280819,0.0030276312,0.041545533,0.0005017279,0.025784194],"study_design_scores_gemma":[0.000002168281,0.000009588845,0.000058905523,0.0000054674374,0.0000040784903,0.000023704686,0.0000028023621,0.9932828,0.000322244,0.005918036,0.00036559263,0.0000046228397],"about_ca_topic_score_codex":0.0019480786,"about_ca_topic_score_gemma":0.00075506844,"teacher_disagreement_score":0.0020864576,"about_ca_system_score_codex":0.00041141018,"about_ca_system_score_gemma":0.00068554166,"threshold_uncertainty_score":0.0110343695},"labels":[],"label_agreement":null},{"id":"W3213372560","doi":"10.12989/sem.2021.80.2.143","title":"Practical method for determining load and resistance factorsusing third-moment transformation","year":2021,"lang":"en","type":"article","venue":"STRUCTURAL ENGINEERING AND MECHANICS","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"First-order reliability method; Reliability (semiconductor); Benchmark (surveying); Moment (physics); Transformation (genetics); Computation; Range (aeronautics); Resistance Factors; Reliability engineering; Computer science; Limit state design; Limit (mathematics); Mathematical optimization; Structural engineering; Algorithm; Mathematics; Random variable; Engineering; Statistics","score_opus":0.07189593186894123,"score_gpt":0.3639891985846468,"score_spread":0.29209326671570557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213372560","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00083861966,0.0000325719,0.9979292,0.000014663116,0.0000097061675,0.000027048494,0.000017819832,0.00029427343,0.0008360623],"genre_scores_gemma":[0.066380076,0.00017886222,0.9295531,0.000039795763,0.00002378769,0.0002426046,0.00015271055,0.00023298904,0.0031961233],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986254,0.00032640365,0.000077575816,0.00020746115,0.00071346306,0.000049666374],"domain_scores_gemma":[0.99880207,0.00040400567,0.00013187228,0.00017003159,0.00046866303,0.000023263947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011736372,0.0011189337,0.0007943537,0.001564225,0.00060971535,0.0007544556,0.0008745786,0.0008956536,0.007763221],"category_scores_gemma":[0.0031852094,0.00047041482,0.00075114524,0.0009662354,0.00059240474,0.0010693708,0.0008072158,0.0011193979,0.0024797122],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013149242,0.00010424877,0.0015838182,0.00051850517,0.00004198183,0.00014240028,0.00035707743,0.23060594,0.06267235,0.055179823,0.0054792245,0.6431831],"study_design_scores_gemma":[0.000044260203,0.00011683095,0.0007600975,0.000046880148,0.000027048882,0.0003001819,0.000053373587,0.9381922,0.023610283,0.014691124,0.02209891,0.000058757825],"about_ca_topic_score_codex":0.0018894972,"about_ca_topic_score_gemma":0.0021803987,"teacher_disagreement_score":0.007763221,"about_ca_system_score_codex":0.00056064053,"about_ca_system_score_gemma":0.0015880209,"threshold_uncertainty_score":0.025970519},"labels":[],"label_agreement":null},{"id":"W3214041973","doi":"10.1115/1.4053052","title":"Deterministic and Probabilistic Evaluations of Structures and Components Credited for Seismic Design Extension Conditions","year":2021,"lang":"en","type":"article","venue":"Journal of Pressure Vessel Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Kinectrics (Canada)","funders":"","keywords":"Fragility; Probabilistic logic; Reliability engineering; Percentile; Seismic analysis; Component (thermodynamics); Engineering; Confidence interval; Margin (machine learning); Computer science; Structural engineering; Statistics; Mathematics; Artificial intelligence","score_opus":0.1023985437707082,"score_gpt":0.36659415181941213,"score_spread":0.2641956080487039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214041973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39425614,0.00035439077,0.5939278,0.00022214367,0.00003293141,0.00021614307,0.00048859796,0.0004968508,0.010005021],"genre_scores_gemma":[0.9714903,0.00004608643,0.027328173,0.000013693342,0.000007662743,0.00009939158,0.00017224501,0.000021686737,0.0008208033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99692047,0.0010570108,0.00012263954,0.0002744538,0.0013661552,0.00025929016],"domain_scores_gemma":[0.98683935,0.00903637,0.0015636415,0.00054483966,0.0018348924,0.00018096011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045107626,0.00093983195,0.00059987337,0.0020397748,0.00039069046,0.00090799166,0.0008842086,0.0008256371,0.0029777975],"category_scores_gemma":[0.015439086,0.0006788323,0.0010657182,0.0007080019,0.0008724249,0.00074194657,0.00083649077,0.0007050232,0.00020942312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005064003,0.000009890221,0.0016600592,0.000022500413,0.000012467786,0.000023224991,0.000013499172,0.99066156,0.00063024665,0.0018860074,0.00007563274,0.004954169],"study_design_scores_gemma":[0.000005392456,0.00007692355,0.0014803327,0.0000089959885,0.000014270862,0.000020093019,0.00001333928,0.9958305,0.0010744659,0.0012876675,0.00017584751,0.000012207725],"about_ca_topic_score_codex":0.0060718963,"about_ca_topic_score_gemma":0.005991358,"teacher_disagreement_score":0.0060718963,"about_ca_system_score_codex":0.002263722,"about_ca_system_score_gemma":0.0012979785,"threshold_uncertainty_score":0.023855448},"labels":[],"label_agreement":null},{"id":"W3214787019","doi":"10.1016/j.anucene.2021.108816","title":"Implementation and testing of unscented transformation and low rank approximation to enhance SCALE code uncertainty calculations","year":2021,"lang":"en","type":"article","venue":"Annals of Nuclear Energy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"International Atomic Energy Agency","keywords":"Sampling (signal processing); Monte Carlo method; Curse of dimensionality; Transformation (genetics); Algorithm; Covariance; Computer science; Rank (graph theory); Covariance matrix; Uncertainty quantification; Multivariate normal distribution; Unscented transform; Multivariate statistics; Applied mathematics; Mathematical optimization; Mathematics; Statistics; Kalman filter; Detector","score_opus":0.09097972581681156,"score_gpt":0.37742104788371206,"score_spread":0.2864413220669005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214787019","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26308194,0.000059453876,0.72343063,0.000251181,0.00008309879,0.00010465155,0.00017403718,0.0077438243,0.005071136],"genre_scores_gemma":[0.69879556,0.00002127745,0.29899877,0.0000833188,0.000011433879,0.00006270351,0.00033463835,0.0005318673,0.0011605344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736625,0.0008787689,0.00012512517,0.00024864185,0.0011483845,0.0002328159],"domain_scores_gemma":[0.9882288,0.005447175,0.00048166208,0.0025014183,0.0031048034,0.00023614039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029242507,0.00065145106,0.0004038023,0.0006141119,0.0004822906,0.0007870837,0.001513947,0.00097248284,0.004277713],"category_scores_gemma":[0.018543405,0.00027546316,0.00057230185,0.0005282001,0.0007018051,0.0013916913,0.0011538211,0.00092290965,0.0009188696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013370365,0.0010125827,0.0073555172,0.00014549625,0.000117454765,0.00019570297,0.00025866885,0.6765386,0.031792432,0.019112354,0.0031609356,0.25897324],"study_design_scores_gemma":[0.000039922877,0.00013295042,0.00035356169,0.00000413348,0.0000068358763,0.000016740083,0.000021656419,0.980634,0.017280046,0.0010344457,0.0004660387,0.000009640067],"about_ca_topic_score_codex":0.004585435,"about_ca_topic_score_gemma":0.0033400152,"teacher_disagreement_score":0.004585435,"about_ca_system_score_codex":0.0005806429,"about_ca_system_score_gemma":0.0017359878,"threshold_uncertainty_score":0.015465081},"labels":[],"label_agreement":null},{"id":"W3215844863","doi":"10.22075/jrce.2021.19660.1376","title":"Considering the Yielding Displacement Uncertainty in Reliability of Mid-Rise R.C. Structures","year":2022,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Iron Ore Company (Canada)","funders":"","keywords":"Reliability (semiconductor); Displacement (psychology); Environmental science; Reliability engineering; Engineering; Physics; Psychology; Thermodynamics","score_opus":0.357739719255955,"score_gpt":0.5525290234122251,"score_spread":0.1947893041562701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215844863","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32591417,0.0023861765,0.6662523,0.0003545997,0.000054564254,0.000026611851,0.000108949804,0.00019053441,0.004712102],"genre_scores_gemma":[0.98183423,0.0002834614,0.01751337,0.000017891478,0.000016417609,0.000010567766,0.000041751988,0.000011157753,0.00027124822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992143,0.00017828944,0.00003684818,0.00015706387,0.0003595528,0.00005392772],"domain_scores_gemma":[0.99876034,0.00079476903,0.00015538932,0.000060804752,0.00021096943,0.00001768932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090133725,0.00048717353,0.00036818103,0.00088306266,0.00034029922,0.0005035489,0.00047422753,0.00045327083,0.00043743322],"category_scores_gemma":[0.0033853727,0.00020739844,0.00040044327,0.0005237323,0.00043939997,0.0007490113,0.0004634625,0.0004539276,0.000087155735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007272665,0.00003063377,0.010921355,0.00012967896,0.0000497524,0.0003387074,0.00014652906,0.8769332,0.017605366,0.012670123,0.0003008474,0.080801085],"study_design_scores_gemma":[0.0000025722445,0.00009687181,0.008300592,0.000018719502,0.00003495756,0.00017442547,0.000045189932,0.9776829,0.0074376194,0.005255412,0.00092686556,0.000023823213],"about_ca_topic_score_codex":0.0048681214,"about_ca_topic_score_gemma":0.0038984332,"teacher_disagreement_score":0.0048681214,"about_ca_system_score_codex":0.000586055,"about_ca_system_score_gemma":0.00060334924,"threshold_uncertainty_score":0.009679556},"labels":[],"label_agreement":null},{"id":"W41273840","doi":"","title":"Utilizing general information theories for uncertainty quantification","year":2002,"lang":"en","type":"article","venue":"University of North Texas Digital Library (University of North Texas)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Janeway Children's Health and Rehabilitation Centre","funders":"Los Alamos National Laboratory; George Washington University; U.S. Department of Energy","keywords":"Vagueness; Computer science; Reliability (semiconductor); Mathematical theory; Uncertainty quantification; Information theory; Probability theory; Noise (video); Complex system; Decision theory; Theoretical computer science; Artificial intelligence; Data mining; Algorithm; Mathematics; Machine learning; Fuzzy logic; Statistics","score_opus":0.039671987933898555,"score_gpt":0.2122861598818965,"score_spread":0.17261417194799794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W41273840","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015980857,0.005957191,0.9616786,0.0031702055,0.0003001374,0.0000730757,0.00019897064,0.000107236134,0.026916498],"genre_scores_gemma":[0.27162918,0.028773217,0.6801137,0.0024656735,0.0033966932,0.0011436355,0.00077690335,0.00017772414,0.01152327],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.992722,0.0033464045,0.00053807936,0.0005728491,0.0025144718,0.00030612998],"domain_scores_gemma":[0.98910224,0.00800934,0.00065921934,0.0010735418,0.0010230462,0.00013260562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009449416,0.002267907,0.001664543,0.006064816,0.0013445972,0.0075175334,0.0028887514,0.0034194055,0.0043842928],"category_scores_gemma":[0.020832306,0.00076031836,0.0026209934,0.004871114,0.008213457,0.011666028,0.004126329,0.005131021,0.001227919],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002392641,0.0000045175393,0.0000451203,0.000053281856,0.000014709073,0.000041856118,0.00005576206,0.007691925,0.000047025154,0.98398364,0.00082173076,0.0072379955],"study_design_scores_gemma":[0.0000019494466,0.0000035234027,0.000023317973,0.000043603734,0.0000057367947,0.000023333456,0.000020602094,0.013021409,0.000052722702,0.9833174,0.0034777643,0.000008570917],"about_ca_topic_score_codex":0.0023398842,"about_ca_topic_score_gemma":0.0017708265,"teacher_disagreement_score":0.009449416,"about_ca_system_score_codex":0.0037176441,"about_ca_system_score_gemma":0.0020037729,"threshold_uncertainty_score":0.049973905},"labels":[],"label_agreement":null},{"id":"W4200068785","doi":"10.1142/s0218539321500509","title":"Parametric Plots of Limit-State Surfaces as a Design Tool in Time-Variant System Reliability","year":2021,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Parametric statistics; Reliability (semiconductor); Limit (mathematics); Limit state design; Magnitude (astronomy); Computation; Event (particle physics); Computer science; Series (stratigraphy); Failure mode and effects analysis; State space; Surface (topology); Component (thermodynamics); Mathematics; Control theory (sociology); Reliability engineering; Algorithm; Structural engineering; Statistics; Engineering; Physics; Mathematical analysis; Geometry; Geology; Artificial intelligence","score_opus":0.05803391798627165,"score_gpt":0.32731073273131334,"score_spread":0.26927681474504167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200068785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051390033,0.00014029685,0.94086546,0.00013018967,0.000030184778,0.000037195816,0.00011166468,0.0011127767,0.0061820606],"genre_scores_gemma":[0.8549221,0.0002336508,0.14196748,0.00003427519,0.000022178234,0.00012601342,0.00018343011,0.00025730502,0.002253499],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995136,0.00018831478,0.00002043885,0.000046297293,0.0001982605,0.000033052747],"domain_scores_gemma":[0.9978461,0.0014480547,0.00017484956,0.00025958684,0.0002486194,0.000022780692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012022869,0.0004973197,0.0002572543,0.00090887287,0.00017032103,0.0010290635,0.0004802881,0.0004284422,0.003665967],"category_scores_gemma":[0.005015236,0.00022398158,0.0003249367,0.00072140264,0.0006702809,0.001034513,0.00060594577,0.000807679,0.00032771082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028275378,0.000062380845,0.0024480652,0.00017522107,0.000024734616,0.0001821858,0.0007598723,0.7125293,0.027570233,0.085851416,0.0020929489,0.16802089],"study_design_scores_gemma":[0.000009553598,0.00012812902,0.0013222251,0.00002374017,0.0000065875047,0.00010087998,0.00012816972,0.96488035,0.010632775,0.017718527,0.005013771,0.000035305915],"about_ca_topic_score_codex":0.0005669989,"about_ca_topic_score_gemma":0.00028411136,"teacher_disagreement_score":0.003665967,"about_ca_system_score_codex":0.000324902,"about_ca_system_score_gemma":0.00025892066,"threshold_uncertainty_score":0.0122638345},"labels":[],"label_agreement":null},{"id":"W4200253628","doi":"10.3390/app112411814","title":"Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Universidade de São Paulo; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Simulated annealing; Mathematical optimization; Computer science; Algorithm; Heuristic; Mathematics","score_opus":0.11168470928646797,"score_gpt":0.3487635209220039,"score_spread":0.23707881163553596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200253628","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038715303,0.00055806316,0.9543672,0.00016215978,0.000041863255,0.00009931989,0.000019692334,0.00039026895,0.0056461124],"genre_scores_gemma":[0.76891714,0.0003601943,0.22840214,0.00010779501,0.000018444453,0.00020717997,0.000038419148,0.00007953871,0.0018690458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942315,0.00027368276,0.000026738026,0.00007330213,0.00014784465,0.00005536672],"domain_scores_gemma":[0.9990422,0.00067780766,0.000059452086,0.000077265235,0.00011224083,0.00003103968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010129658,0.0007522897,0.00068713963,0.0006946569,0.00046409844,0.0006163477,0.0008638111,0.0009138471,0.0015156694],"category_scores_gemma":[0.0029503005,0.00032026318,0.0005667134,0.0005492459,0.00091619673,0.00062416174,0.0007324109,0.00060102256,0.00019109428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005988161,0.0000466738,0.0003098124,0.00006729834,0.000024268515,0.0000471867,0.000090578884,0.940206,0.0050651683,0.017082257,0.00031319936,0.036687743],"study_design_scores_gemma":[0.000011985667,0.0000418392,0.00005343701,0.000009588635,0.000008852355,0.00001552502,0.000014723374,0.9930355,0.0023764046,0.0035397941,0.00088651245,0.0000057438638],"about_ca_topic_score_codex":0.004379148,"about_ca_topic_score_gemma":0.0045452593,"teacher_disagreement_score":0.004379148,"about_ca_system_score_codex":0.001052384,"about_ca_system_score_gemma":0.0011800623,"threshold_uncertainty_score":0.0087073445},"labels":[],"label_agreement":null},{"id":"W4200392092","doi":"10.1016/j.envsoft.2021.105282","title":"The pie sharing problem: Unbiased sampling of N+1 summative weights","year":2021,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Global Water Futures; Canada First Research Excellence Fund","keywords":"Sampling (signal processing); Independent and identically distributed random variables; Simple random sample; Mathematics; Slice sampling; Set (abstract data type); Algorithm; Probability sampling; Statistics; Cumulative distribution function; Probability distribution; Simple (philosophy); Probability density function; Sensitivity (control systems); Calibration; Random variable; Importance sampling; Computer science; Monte Carlo method","score_opus":0.10021791195030288,"score_gpt":0.29413041540825147,"score_spread":0.19391250345794858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200392092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005155516,0.000059046382,0.9932359,0.00006254147,0.000013641429,0.000105294086,0.000057016983,0.00008838369,0.0012225359],"genre_scores_gemma":[0.16217685,0.000183766,0.8326465,0.0001439801,0.000052475545,0.00089247053,0.00033978058,0.00010957315,0.0034546228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99526864,0.0024052628,0.00019137522,0.0007380892,0.0010931714,0.00030338418],"domain_scores_gemma":[0.9931155,0.004889954,0.00037507134,0.0008689463,0.0005892568,0.00016139819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008909317,0.0012680226,0.001870401,0.0011784856,0.0008716435,0.0014548516,0.002696084,0.0012006018,0.0044901767],"category_scores_gemma":[0.022922354,0.0006842972,0.000867225,0.001969054,0.0017330898,0.0019228415,0.002924053,0.0010921016,0.00062421977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003597627,0.00012417698,0.0020215956,0.00022969117,0.00014800497,0.00017088818,0.00018170734,0.61343,0.004153945,0.13963692,0.0028576478,0.23668566],"study_design_scores_gemma":[0.000053606986,0.00006808883,0.00032028637,0.00003396644,0.000025626061,0.00007194091,0.000036272508,0.90948176,0.002739236,0.08478092,0.0023609537,0.000027338701],"about_ca_topic_score_codex":0.002383789,"about_ca_topic_score_gemma":0.0021195111,"teacher_disagreement_score":0.008909317,"about_ca_system_score_codex":0.0010841262,"about_ca_system_score_gemma":0.0021069972,"threshold_uncertainty_score":0.04711753},"labels":[],"label_agreement":null},{"id":"W4205220474","doi":"10.1061/(asce)st.1943-541x.0003261","title":"Calibration of Resistance Factor for Self-Tapping Screws in Canada","year":2022,"lang":"en","type":"article","venue":"Journal of Structural Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","funders":"","keywords":"Tapping; Reliability (semiconductor); Reliability engineering; Resistance Factors; Calibration; Consistency (knowledge bases); Resistance (ecology); Computer science; Structural engineering; Engineering; Statistics; Mathematics; Mechanical engineering","score_opus":0.0353358429119923,"score_gpt":0.26260665740632844,"score_spread":0.22727081449433614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205220474","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87449944,0.00063108583,0.08773947,0.00022523207,0.00007065316,0.0004007824,0.0031260985,0.0022249501,0.031082323],"genre_scores_gemma":[0.95209587,0.00027402202,0.040966325,0.000048193433,0.0000043954897,0.00008557007,0.0015183608,0.00020076174,0.0048064278],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99332035,0.00044634193,0.0002884298,0.00079395087,0.004661196,0.00048968196],"domain_scores_gemma":[0.986779,0.00089224963,0.0007467171,0.0006521118,0.010697555,0.00023232434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004379714,0.00081574905,0.00045377036,0.003180625,0.0011204552,0.0012260817,0.00229312,0.0006254987,0.0019341119],"category_scores_gemma":[0.014277947,0.00046097927,0.0005034356,0.0030094134,0.00083727064,0.00073165394,0.00081885926,0.000530329,0.0007490252],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093578844,0.00041554694,0.3066812,0.0004982297,0.00019309181,0.0005862899,0.0026038622,0.21115753,0.07863837,0.008636294,0.013089091,0.37656468],"study_design_scores_gemma":[0.00010725433,0.000998947,0.5471151,0.00025910206,0.00018534793,0.0006119715,0.0021600549,0.25260606,0.14837763,0.0021121746,0.045093127,0.0003732559],"about_ca_topic_score_codex":0.84042853,"about_ca_topic_score_gemma":0.9067878,"teacher_disagreement_score":0.15957147,"about_ca_system_score_codex":0.019595498,"about_ca_system_score_gemma":0.015430638,"threshold_uncertainty_score":0.3210224},"labels":[],"label_agreement":null},{"id":"W4210253203","doi":"10.1080/03610918.2022.2034865","title":"Accurate approximation of the expected value, standard deviation, and probability density function of extreme order statistics from Gaussian samples","year":2022,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact; McMaster University","funders":"","keywords":"Order statistic; Standard deviation; Statistic; Statistics; Mathematics; Gaussian; Probability density function; Gaussian function; Function (biology); Applied mathematics; Physics; Quantum mechanics","score_opus":0.24203581035540955,"score_gpt":0.40233554476031935,"score_spread":0.1602997344049098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210253203","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013833033,0.0005973901,0.9834705,0.00015923144,0.000049456234,0.000025463205,0.00010061773,0.00025106178,0.0015133363],"genre_scores_gemma":[0.59770197,0.002455495,0.39360195,0.00038986767,0.00024048939,0.0003097814,0.00096110103,0.0003318418,0.004007503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99592423,0.0012480143,0.00018311471,0.0004507178,0.0017909119,0.0004030724],"domain_scores_gemma":[0.96882373,0.024100935,0.001585542,0.0029201263,0.0022318459,0.0003377509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007582568,0.0009629522,0.0015619976,0.0021509626,0.0004674824,0.0015551131,0.0018184842,0.0013290902,0.0018176602],"category_scores_gemma":[0.05244152,0.0005967939,0.0011314389,0.001811316,0.0019139149,0.0030036345,0.0016798658,0.0027135953,0.0009186622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013963637,0.000057423887,0.0032113255,0.00016449403,0.00006437461,0.00025001125,0.00014502379,0.84643614,0.0023988714,0.100968756,0.0025260043,0.043637972],"study_design_scores_gemma":[0.000005463263,0.000018329463,0.00064690894,0.000024732377,0.000009082373,0.00010742876,0.000017293303,0.97141874,0.0008264234,0.026209587,0.000698273,0.000017640135],"about_ca_topic_score_codex":0.0057427166,"about_ca_topic_score_gemma":0.004745195,"teacher_disagreement_score":0.007582568,"about_ca_system_score_codex":0.0020908625,"about_ca_system_score_gemma":0.0021144082,"threshold_uncertainty_score":0.04010099},"labels":[],"label_agreement":null},{"id":"W4210510713","doi":"10.3390/sym14020280","title":"j-Dimensional Integral Involving the Logarithmic and Exponential Functions: Derivation and Evaluation","year":2022,"lang":"en","type":"article","venue":"Symmetry","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Logarithm; Exponential function; Exponential integral; Class (philosophy); Applied mathematics; Mathematics; Volume integral; Exponential formula; Calculus (dental); Mathematical analysis; Computer science; Double exponential function; Integral equation; Artificial intelligence","score_opus":0.08477434330639483,"score_gpt":0.3206266336608059,"score_spread":0.23585229035441108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210510713","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021501929,0.0023129464,0.92933047,0.0008312896,0.0003712627,0.000029364966,0.000054395077,0.00014725786,0.04542102],"genre_scores_gemma":[0.66521376,0.0056269323,0.3008636,0.000872533,0.0007407461,0.000117902426,0.00011418279,0.0005760956,0.0258743],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99942964,0.00017482649,0.00004436832,0.000054714514,0.00025888096,0.00003753691],"domain_scores_gemma":[0.9984787,0.00078392145,0.00012902457,0.00023030922,0.00029958488,0.00007842171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025749907,0.00080495485,0.00062239094,0.0012871076,0.0006048771,0.0017542562,0.0010395666,0.0011470016,0.0034857013],"category_scores_gemma":[0.0066513913,0.000267728,0.000674579,0.0011051876,0.0022397575,0.0033377558,0.0013869077,0.0022103502,0.00096797827],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013606845,0.00001812455,0.00017600397,0.00008644284,0.000010360153,0.0001753454,0.00013372832,0.0058384957,0.0019371614,0.9783532,0.0010270344,0.012230492],"study_design_scores_gemma":[0.0000063690413,0.000027101114,0.0003559296,0.00007998047,0.000014306187,0.00064473576,0.000090713285,0.16231608,0.0023780894,0.8270181,0.007032539,0.000036093716],"about_ca_topic_score_codex":0.0006640922,"about_ca_topic_score_gemma":0.0006259629,"teacher_disagreement_score":0.0034857013,"about_ca_system_score_codex":0.0009983316,"about_ca_system_score_gemma":0.000752877,"threshold_uncertainty_score":0.013618052},"labels":[],"label_agreement":null},{"id":"W4210711410","doi":"10.1016/j.forsciint.2022.111213","title":"Uncertain inverse traffic accident reconstruction by combining the modified arbitrary orthogonal polynomial expansion and novel optimization technique","year":2022,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"Latin hypercube sampling; Inverse; Computer science; Mathematical optimization; Monte Carlo method; Inverse problem; Algorithm; Uncertainty quantification; Mathematics; Statistics; Machine learning","score_opus":0.050145809906869114,"score_gpt":0.2974355525518417,"score_spread":0.24728974264497255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210711410","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047509847,0.000096488235,0.9943309,0.00004286518,0.0000149311345,0.0000065627196,0.000018342538,0.000052141328,0.0006867087],"genre_scores_gemma":[0.46179694,0.00086401304,0.5330821,0.00008065965,0.000118486394,0.000077092474,0.0002595876,0.000105507475,0.0036155528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996093,0.00008696366,0.000018314915,0.00008266149,0.00016164451,0.000041085186],"domain_scores_gemma":[0.9997185,0.000114533905,0.00004564211,0.00003242346,0.00007671146,0.00001217531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005265845,0.00066192704,0.0007378398,0.00059273,0.00026685366,0.0005299652,0.00073458545,0.0004989469,0.0010565439],"category_scores_gemma":[0.0013585046,0.00034256637,0.0007791132,0.0008688602,0.00046217802,0.001306529,0.00081724057,0.00089512934,0.00023798128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001523929,0.000047503574,0.0010151367,0.00020109954,0.0000802483,0.00014509486,0.00007830507,0.77833056,0.020591017,0.04116883,0.001661516,0.1565283],"study_design_scores_gemma":[0.0000028196405,0.000011954883,0.00011398214,0.000003425357,0.000008196975,0.000050767543,0.00000487642,0.9950636,0.0013372897,0.0028915168,0.00050416513,0.0000073173023],"about_ca_topic_score_codex":0.0018434024,"about_ca_topic_score_gemma":0.001715017,"teacher_disagreement_score":0.0018434024,"about_ca_system_score_codex":0.00026952464,"about_ca_system_score_gemma":0.0008615888,"threshold_uncertainty_score":0.003665328},"labels":[],"label_agreement":null},{"id":"W4211154451","doi":"10.1017/9781139629010.012","title":"Basics of Normal and Lognormal Distributions","year":2019,"lang":"en","type":"other","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Log-normal distribution; Content (measure theory); Computer science; Statistics; Mathematics; Econometrics; Mathematical analysis","score_opus":0.04863780128246459,"score_gpt":0.30127838282513575,"score_spread":0.25264058154267116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211154451","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022817266,0.0059741056,0.91938466,0.0024401364,0.0006910149,0.00010795291,0.0015875763,0.00048195367,0.06705092],"genre_scores_gemma":[0.22578745,0.035629332,0.59218657,0.0032494834,0.0049016136,0.0014459126,0.0048617,0.0014722655,0.13046582],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9952744,0.001225437,0.0004186612,0.00075696077,0.0020754705,0.0002489442],"domain_scores_gemma":[0.9930641,0.0037866079,0.00051248854,0.000764483,0.0016739762,0.00019841523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005437086,0.0011206863,0.0010327883,0.0034661535,0.0011213105,0.004327167,0.002199281,0.0018686499,0.019237082],"category_scores_gemma":[0.018723669,0.00064885634,0.0015433134,0.0040322784,0.0031153548,0.0054398687,0.0022329395,0.0039842203,0.010401077],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023711535,0.000015291633,0.00060869026,0.00013939738,0.000015907912,0.0001743877,0.00017391934,0.0042621274,0.00036014692,0.9422606,0.009016465,0.042949308],"study_design_scores_gemma":[0.000009112197,0.00001817858,0.00037534555,0.00013146811,0.0000129619675,0.00068440114,0.00006509069,0.017607855,0.00032015605,0.9116099,0.06913536,0.000030124462],"about_ca_topic_score_codex":0.004629432,"about_ca_topic_score_gemma":0.003488838,"teacher_disagreement_score":0.019237082,"about_ca_system_score_codex":0.0018194086,"about_ca_system_score_gemma":0.0022572393,"threshold_uncertainty_score":0.06435442},"labels":[],"label_agreement":null},{"id":"W4211211665","doi":"10.1017/9781108762366.009","title":"Diagrammatic Perturbation Methods","year":2020,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Diagrammatic reasoning; Perturbation (astronomy); Mathematics; Physics; Computer science; Quantum mechanics; Programming language","score_opus":0.11679963024468452,"score_gpt":0.29967719397502113,"score_spread":0.1828775637303366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211211665","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056815904,0.005083948,0.8660047,0.00066770124,0.0013039828,0.000050098897,0.00063554774,0.0023933086,0.123292536],"genre_scores_gemma":[0.056730345,0.013560555,0.655009,0.0014957319,0.0013305245,0.00057120674,0.0026730655,0.0050288537,0.26360077],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99938774,0.00017359474,0.000023528472,0.000097392316,0.000285834,0.000031995423],"domain_scores_gemma":[0.999548,0.00019345904,0.000018073004,0.000121716614,0.000095376796,0.000023367917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073435315,0.00083348044,0.00068746484,0.0012184231,0.00048078643,0.0013673832,0.0013249846,0.00093082414,0.07462045],"category_scores_gemma":[0.0017175081,0.00042432742,0.0007447657,0.0011664212,0.0008487073,0.0016113259,0.0013817239,0.0021234732,0.03545342],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034013618,0.000033967714,0.000084182015,0.00048067747,0.000033592572,0.000059057755,0.00008988878,0.013072607,0.0036978307,0.6259196,0.13460149,0.22189318],"study_design_scores_gemma":[0.000016008355,0.000023503619,0.00011441773,0.00012283427,0.000013776249,0.00015597597,0.000024867799,0.050227743,0.002872047,0.5555691,0.39083502,0.00002464025],"about_ca_topic_score_codex":0.0005149976,"about_ca_topic_score_gemma":0.00084971596,"teacher_disagreement_score":0.07462045,"about_ca_system_score_codex":0.00064161967,"about_ca_system_score_gemma":0.00051016285,"threshold_uncertainty_score":0.24963027},"labels":[],"label_agreement":null},{"id":"W4213088885","doi":"10.1016/j.apor.2022.103065","title":"Plunger-type wavemakers with flow: Sensitivity analysis and experimental validation","year":2022,"lang":"en","type":"article","venue":"Applied Ocean Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Plunger; Sensitivity (control systems); Flow (mathematics); Range (aeronautics); Control theory (sociology); Observational error; Volumetric flow rate; Variance (accounting); Data flow model; Mathematics; Mechanics; Statistics; Computer science; Engineering; Thermodynamics; Physics; Electronic engineering","score_opus":0.12340184331573516,"score_gpt":0.38643444488164364,"score_spread":0.26303260156590846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213088885","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85409224,0.00037067017,0.14091644,0.00019642852,0.000079803285,0.00042807072,0.00036277215,0.0003184919,0.003235063],"genre_scores_gemma":[0.9776613,0.00016697083,0.020501582,0.00003585279,0.000008121432,0.00014598816,0.00010158575,0.000030017667,0.0013486134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99897873,0.00027855064,0.000067648245,0.00018036143,0.00037817186,0.000116505296],"domain_scores_gemma":[0.9933836,0.0048147305,0.0003244666,0.00072074874,0.00063895126,0.000117398406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023386055,0.000692612,0.00078723487,0.0007962514,0.0006221158,0.0011533079,0.0011946879,0.0016846018,0.0023594596],"category_scores_gemma":[0.009218999,0.0005461828,0.0006187983,0.0007767625,0.001456328,0.0021955485,0.0013548532,0.0011847031,0.00029409086],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023121058,0.0013765431,0.004251492,0.0006083874,0.00008756898,0.00029808498,0.00047165444,0.71893185,0.2250861,0.0045147813,0.00062537595,0.041436058],"study_design_scores_gemma":[0.00016199976,0.0012743307,0.001588183,0.000028655806,0.00004602494,0.0000565415,0.00008587921,0.80955374,0.18499473,0.0016713622,0.0004961823,0.00004233511],"about_ca_topic_score_codex":0.0023244848,"about_ca_topic_score_gemma":0.0010831305,"teacher_disagreement_score":0.0023594596,"about_ca_system_score_codex":0.0007090545,"about_ca_system_score_gemma":0.0005983012,"threshold_uncertainty_score":0.012367845},"labels":[],"label_agreement":null},{"id":"W4225153321","doi":"10.11159/icsect22.148","title":"Experimental Investigation on the Impact of Longitudinal Rebar Ratio on the Cracking Characteristics of R/C Components","year":2022,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Civil, Structural, and Environmental Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cracking; Rebar; Materials science; Structural engineering; Composite material; Forensic engineering; Engineering","score_opus":0.04304980945165185,"score_gpt":0.257221581173242,"score_spread":0.21417177172159016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225153321","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9982324,0.000094794435,0.001075098,0.000008319613,0.000006681912,0.000010012846,0.00007693493,0.000021790247,0.00047401807],"genre_scores_gemma":[0.9981292,0.00008953281,0.001015213,0.0000052236956,0.0000038130977,0.000011345517,0.000087070395,0.000009796335,0.00064877304],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995302,0.00006717397,0.000045884964,0.00009443111,0.00017650939,0.00008583058],"domain_scores_gemma":[0.9972524,0.0011221732,0.0004913073,0.00035925515,0.0006359274,0.00013891289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000525215,0.0004324882,0.00022419801,0.00037793143,0.00021822228,0.00015513436,0.00035416143,0.0005875506,0.0024834198],"category_scores_gemma":[0.0012521639,0.00023118772,0.0001955997,0.0002206863,0.0004641737,0.00039469523,0.00024778533,0.00038027842,0.00029361207],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042942178,0.00022844238,0.002515481,0.00007816887,0.00000799164,0.00012854433,0.00013240524,0.0012200448,0.9915004,0.000051761257,0.000045619163,0.0036617178],"study_design_scores_gemma":[0.000013604648,0.0042233695,0.016454054,0.000012831457,0.00002733321,0.00014487232,0.00016435311,0.0034987612,0.97500914,0.000025549229,0.00041118346,0.0000149995485],"about_ca_topic_score_codex":0.0003272634,"about_ca_topic_score_gemma":0.0008228273,"teacher_disagreement_score":0.0024834198,"about_ca_system_score_codex":0.00010647038,"about_ca_system_score_gemma":0.00008292813,"threshold_uncertainty_score":0.008307874},"labels":[],"label_agreement":null},{"id":"W4225467506","doi":"10.48550/arxiv.2111.15067","title":"Caffarelli-Kohn-Nirenberg inequalities for curl-free vector fields and second order derivatives","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Curl (programming language); Mathematics; Vector field; Scalar (mathematics); Order (exchange); Factorization; Pure mathematics; Applied mathematics; Mathematical analysis; Geometry; Computer science; Algorithm","score_opus":0.19828490008519395,"score_gpt":0.24881756039605818,"score_spread":0.05053266031086423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225467506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016728414,0.0012876996,0.9610863,0.0009936091,0.00018608464,0.000045384164,0.00023945521,0.000058140693,0.019375026],"genre_scores_gemma":[0.6246415,0.003742371,0.3348346,0.0013567742,0.00078791275,0.00047413624,0.0007191372,0.00026733623,0.033176254],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983536,0.0004959103,0.00010475653,0.0003424992,0.00053519127,0.00016802519],"domain_scores_gemma":[0.99588925,0.0023574224,0.00047740032,0.00032126668,0.0007818392,0.0001728314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003946867,0.0013418365,0.0007179706,0.002063096,0.00070553465,0.0018352091,0.0016152429,0.00127435,0.0043512397],"category_scores_gemma":[0.009334608,0.00039246975,0.0014357837,0.0010466833,0.0020143834,0.004061383,0.0020047028,0.0036965916,0.00064018427],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040275194,0.000023347626,0.00033373074,0.00016351434,0.00003200242,0.000060073704,0.00010145568,0.033540048,0.0040612975,0.9423077,0.0016201034,0.017716449],"study_design_scores_gemma":[0.000009059354,0.00007636883,0.0006243355,0.00006494022,0.000025861851,0.00008830202,0.000052126616,0.3485542,0.0032378782,0.6400346,0.0071892045,0.0000431279],"about_ca_topic_score_codex":0.0020777478,"about_ca_topic_score_gemma":0.002259844,"teacher_disagreement_score":0.0043512397,"about_ca_system_score_codex":0.0022246682,"about_ca_system_score_gemma":0.0012647604,"threshold_uncertainty_score":0.020873308},"labels":[],"label_agreement":null},{"id":"W4225835000","doi":"10.1109/tsg.2022.3159579","title":"Kernel Structure Design for Data-Driven Probabilistic Load Flow Studies","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Overfitting; Probabilistic logic; Kernel (algebra); Mathematical optimization; Gaussian process; Iterative and incremental development; Machine learning; Artificial intelligence; Gaussian; Artificial neural network; Mathematics","score_opus":0.20634354919768272,"score_gpt":0.3584791856207649,"score_spread":0.1521356364230822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225835000","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002686493,0.00002776376,0.99693525,0.00003462896,0.0000034078155,0.000012121976,0.000007840842,0.00007150647,0.00022097203],"genre_scores_gemma":[0.68074036,0.0002740742,0.31657138,0.000096401505,0.000029160206,0.0002717303,0.00016981566,0.00016802526,0.0016791082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990088,0.00053454185,0.000039607887,0.00013371068,0.00020974345,0.000073618095],"domain_scores_gemma":[0.99649185,0.002444323,0.00023120256,0.00024739045,0.0005068073,0.00007843902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031350416,0.0007647881,0.00076020684,0.00054003653,0.00034210586,0.00086039794,0.0013789803,0.0009891918,0.0017396765],"category_scores_gemma":[0.012832789,0.000544679,0.0006487843,0.0005376455,0.0009960083,0.0017943864,0.0016844077,0.0015618764,0.00043682443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041596322,0.00002477818,0.0004044788,0.000042297976,0.00002132815,0.000031459367,0.000058193003,0.95655274,0.0008911409,0.023313738,0.00030094167,0.018317314],"study_design_scores_gemma":[0.0000018888659,0.0000066355374,0.000019315275,0.0000020525474,0.0000012366074,0.0000039662677,0.0000025936836,0.99636275,0.00028728685,0.0031486144,0.00016189103,0.0000017784812],"about_ca_topic_score_codex":0.0018075558,"about_ca_topic_score_gemma":0.0012211202,"teacher_disagreement_score":0.0031350416,"about_ca_system_score_codex":0.000877863,"about_ca_system_score_gemma":0.001051111,"threshold_uncertainty_score":0.016579866},"labels":[],"label_agreement":null},{"id":"W4225900855","doi":"10.1109/tmag.2022.3159760","title":"Non-Parametric Belief Propagation Solver for Stochastic Systems of Linear Equations","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Probabilistic logic; Solver; Belief propagation; Parametric statistics; Mathematical optimization; Partial differential equation; Stochastic partial differential equation; Probabilistic analysis of algorithms; Finite element method; Applied mathematics; Monte Carlo method; Algorithm; Mathematics; Artificial intelligence","score_opus":0.08036451097807835,"score_gpt":0.31391952439023457,"score_spread":0.23355501341215623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225900855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014015508,0.00013065244,0.99647325,0.00016737319,0.000028633303,0.000025700381,0.00004309672,0.00013229718,0.0015974328],"genre_scores_gemma":[0.28105313,0.00080459064,0.70276743,0.00036080147,0.000174499,0.0007585148,0.0005148275,0.00027728584,0.013288908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993511,0.00024779688,0.0000345543,0.00008153198,0.0002276833,0.000057344074],"domain_scores_gemma":[0.996409,0.0029102608,0.00016642764,0.00007534641,0.00036632575,0.00007263651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018155306,0.00082378794,0.0014172993,0.0005230114,0.00056221266,0.0014538075,0.0013445395,0.0016688387,0.004650477],"category_scores_gemma":[0.0060449913,0.0006969234,0.00091699534,0.0009293597,0.0008789523,0.00090607995,0.0016272306,0.0030108157,0.0008299743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036209458,0.000022350183,0.00026597866,0.00010422781,0.000037036407,0.00007252649,0.000057208556,0.9465313,0.0005039274,0.02909985,0.0012089489,0.022060435],"study_design_scores_gemma":[0.000004310169,0.0000030400881,0.000013250516,0.0000040824834,0.0000019961014,0.000004343365,0.0000029428643,0.9959008,0.00008437658,0.0036113877,0.0003677299,0.0000017903043],"about_ca_topic_score_codex":0.00848007,"about_ca_topic_score_gemma":0.008607922,"teacher_disagreement_score":0.00848007,"about_ca_system_score_codex":0.0009472106,"about_ca_system_score_gemma":0.0024294136,"threshold_uncertainty_score":0.016861439},"labels":[],"label_agreement":null},{"id":"W4225904178","doi":"10.3934/math.2022648","title":"A new very simply explicitly invertible approximation for the standard normal cumulative distribution function","year":2022,"lang":"en","type":"article","venue":"AIMS Mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Saskatchewan","funders":"","keywords":"Invertible matrix; Mathematics; Cumulative distribution function; Range (aeronautics); Function (biology); Normal distribution; Applied mathematics; Standard error; MATLAB; Algorithm; Statistics; Pure mathematics; Computer science; Probability density function","score_opus":0.09096044302158655,"score_gpt":0.31712428643501395,"score_spread":0.22616384341342738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225904178","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024351531,0.00006703591,0.99522865,0.000065308894,0.00003499536,0.000015614993,0.000028233548,0.0003172711,0.0018077744],"genre_scores_gemma":[0.18012778,0.00039717896,0.8105222,0.00027015983,0.000089425754,0.00018556856,0.0003091739,0.00073761336,0.0073609124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989165,0.00032543196,0.00004260933,0.000115581635,0.00051852927,0.000081357604],"domain_scores_gemma":[0.9978672,0.0012523406,0.00012508003,0.00027380057,0.0004372747,0.00004435844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002176316,0.0007162914,0.0006301873,0.0009462599,0.00036866503,0.0013592105,0.0014934118,0.0011886791,0.004862334],"category_scores_gemma":[0.00999552,0.0003553539,0.00091161515,0.000698084,0.00078953296,0.0014956746,0.00092442834,0.0015109702,0.0021284989],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008352537,0.000067201305,0.0012724014,0.00014583542,0.000052690444,0.00017016423,0.00014295062,0.716813,0.0098109525,0.12112868,0.0037519664,0.14656061],"study_design_scores_gemma":[0.000008269233,0.000020622741,0.00021548968,0.00002525619,0.000007077753,0.00010201334,0.000011133234,0.98289216,0.0016517314,0.009667369,0.005377623,0.000021292948],"about_ca_topic_score_codex":0.0039965278,"about_ca_topic_score_gemma":0.0038061158,"teacher_disagreement_score":0.004862334,"about_ca_system_score_codex":0.00083489454,"about_ca_system_score_gemma":0.0017952629,"threshold_uncertainty_score":0.016266167},"labels":[],"label_agreement":null},{"id":"W4226031889","doi":"10.2139/ssrn.4046894","title":"Sensitivity Measures Based on Scoring Functions","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sensitivity (control systems); Mathematics; Statistics; Psychology; Econometrics; Computer science; Engineering","score_opus":0.052003002215432206,"score_gpt":0.2858839139848692,"score_spread":0.23388091176943698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226031889","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014115895,0.0006168765,0.9798851,0.0002678786,0.0000672662,0.00021414045,0.00016420535,0.00033384355,0.0043348833],"genre_scores_gemma":[0.7034659,0.0009496543,0.2874319,0.00043778165,0.0003565547,0.0009525742,0.0009695137,0.00034362858,0.005092469],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95830107,0.026770387,0.0021245785,0.0028084728,0.008818924,0.0011765637],"domain_scores_gemma":[0.8819231,0.09812413,0.004498909,0.0061870213,0.008075047,0.0011917865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035249967,0.003262254,0.0037059481,0.008621659,0.0009708188,0.0053089433,0.002936131,0.003274817,0.004325103],"category_scores_gemma":[0.118859105,0.001038738,0.0026155943,0.004135568,0.002696243,0.0059936726,0.0042293617,0.0034006936,0.001155985],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000351087,0.00028716217,0.0040716417,0.00061400875,0.00067252916,0.00020788622,0.00019081333,0.63758624,0.003486155,0.17275147,0.0045472113,0.17523383],"study_design_scores_gemma":[0.000019891453,0.00020766347,0.0013173516,0.00009428187,0.0000960342,0.00017037451,0.000032360535,0.8613568,0.001403359,0.13429376,0.00093654706,0.00007150042],"about_ca_topic_score_codex":0.000981128,"about_ca_topic_score_gemma":0.00071050884,"teacher_disagreement_score":0.035249967,"about_ca_system_score_codex":0.0021837084,"about_ca_system_score_gemma":0.0015397964,"threshold_uncertainty_score":0.18642187},"labels":[],"label_agreement":null},{"id":"W4226041970","doi":"10.1007/978-981-19-0656-5_24","title":"Considering Non-stationary Loading Due to Climate Change in the Reliability Analysis of Structures","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Climate change; Reliability (semiconductor); Environmental science; Reliability engineering; Econometrics; Engineering; Mathematics; Geology; Physics; Thermodynamics; Oceanography","score_opus":0.051893119917752015,"score_gpt":0.29939804189415087,"score_spread":0.24750492197639884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226041970","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24880071,0.008986326,0.6942881,0.002229378,0.0010558838,0.00004242203,0.00019368922,0.00019152291,0.04421208],"genre_scores_gemma":[0.95932955,0.004838213,0.018894449,0.00017706159,0.00050840416,0.000036572295,0.00011787267,0.00020759153,0.015890267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997824,0.00007217648,0.0000064989563,0.00004046194,0.00006268416,0.00003579782],"domain_scores_gemma":[0.99953854,0.00029488068,0.000053155545,0.000032590593,0.000060227954,0.000020749792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056428974,0.0008945304,0.00087427814,0.00049139926,0.0004453398,0.0010622294,0.0012557888,0.0014246792,0.0016401433],"category_scores_gemma":[0.0018157267,0.00047605074,0.0010916891,0.0007076902,0.0011787399,0.0012325939,0.0007873864,0.0010755479,0.00031866663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017115735,0.000026341833,0.0008458171,0.00008748189,0.00003928689,0.00034533546,0.00008707878,0.95362204,0.0030855802,0.029191183,0.0010280835,0.011624603],"study_design_scores_gemma":[0.0000021435744,0.000031949847,0.0011589475,0.000015502123,0.000025692016,0.00006900824,0.00003649594,0.972203,0.00041433127,0.025213663,0.0008192095,0.000009957303],"about_ca_topic_score_codex":0.0052145724,"about_ca_topic_score_gemma":0.005843594,"teacher_disagreement_score":0.0052145724,"about_ca_system_score_codex":0.000637563,"about_ca_system_score_gemma":0.0005945383,"threshold_uncertainty_score":0.010368466},"labels":[],"label_agreement":null},{"id":"W4226201508","doi":"10.22215/etd/2022-14921","title":"Uncertainty Quantification in Large Scale Systems Design","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Model order reduction; Sparse grid; Algorithm; Parametric statistics; Computer science; Uncertainty quantification; Monte Carlo method; Dimensionality reduction; Reduction (mathematics); Projection (relational algebra); Mathematical optimization; Mathematics","score_opus":0.10235242678540665,"score_gpt":0.3619767064339983,"score_spread":0.2596242796485917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226201508","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004485846,0.0024448403,0.987926,0.0005954252,0.000062102794,0.00003482648,0.00007148888,0.000105912375,0.0042736414],"genre_scores_gemma":[0.71186674,0.006330507,0.27344444,0.0004822945,0.0006252519,0.0005427873,0.00042040666,0.00021523178,0.006072298],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99671304,0.001318809,0.00012170056,0.00037638712,0.001347666,0.00012242474],"domain_scores_gemma":[0.9897966,0.008430706,0.00049091247,0.00045164957,0.00069747714,0.00013266732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040055043,0.0010547146,0.0013535087,0.00106427,0.0006442121,0.00241612,0.0009340107,0.0010775632,0.0017529349],"category_scores_gemma":[0.014211099,0.0007643706,0.000928579,0.0011298992,0.0024251346,0.0022835874,0.002082066,0.0021638307,0.00024709172],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002299386,0.000011036615,0.0003440552,0.00019752247,0.00008561362,0.000065301385,0.00006803707,0.8181504,0.0006514995,0.15097766,0.0010072105,0.028418671],"study_design_scores_gemma":[0.0000054733364,0.000020517899,0.00015860848,0.000037003327,0.000014227439,0.0000188232,0.000014506862,0.7308858,0.00037459243,0.26640075,0.0020579551,0.000011811588],"about_ca_topic_score_codex":0.0028558034,"about_ca_topic_score_gemma":0.0016415078,"teacher_disagreement_score":0.0040055043,"about_ca_system_score_codex":0.0019295905,"about_ca_system_score_gemma":0.0013659865,"threshold_uncertainty_score":0.021183372},"labels":[],"label_agreement":null},{"id":"W4229013505","doi":"10.3390/math10091554","title":"A Dynamic Analysis for Probabilistic/Possibilistic Problems Model Reduction Analysis Using Special Functions","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Probabilistic logic; Probabilistic analysis of algorithms; Constraint (computer-aided design); Computer science; Reliability (semiconductor); Uncertain data; Reduction (mathematics); Probability distribution; Extension (predicate logic); Mathematical optimization; Mathematics; Algorithm; Data mining; Statistics; Artificial intelligence","score_opus":0.13123849681985544,"score_gpt":0.3438121935286529,"score_spread":0.21257369670879744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229013505","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002940989,0.0001995699,0.99248415,0.0002476424,0.000022796738,0.000018101178,0.000025287409,0.00003790902,0.0040235873],"genre_scores_gemma":[0.50219715,0.0015966891,0.48012492,0.0003604787,0.00035675228,0.0005521631,0.00026895144,0.0001984308,0.014344506],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867225,0.0005334997,0.000061106075,0.00018430225,0.00046026794,0.0000886571],"domain_scores_gemma":[0.9985267,0.00087913795,0.00011178616,0.00015477107,0.000275615,0.000052072966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021543968,0.0012226275,0.0010179131,0.0016962426,0.00086306327,0.0017893839,0.0010964004,0.0010134948,0.0037194532],"category_scores_gemma":[0.0037339884,0.00049553195,0.002212862,0.00085851725,0.0018767535,0.002121309,0.0019045633,0.0020842894,0.00067397754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023753868,0.000027762133,0.00028899108,0.00011698465,0.000041213778,0.00009468016,0.00011840867,0.2865448,0.002491101,0.68728995,0.0013087592,0.021653537],"study_design_scores_gemma":[0.0000029694943,0.000019294503,0.00008999459,0.000020300437,0.000013913846,0.000053995238,0.000021647822,0.8079599,0.00043890465,0.18774381,0.0036246057,0.00001060405],"about_ca_topic_score_codex":0.0024239733,"about_ca_topic_score_gemma":0.0012025394,"teacher_disagreement_score":0.0037194532,"about_ca_system_score_codex":0.0012377836,"about_ca_system_score_gemma":0.001426402,"threshold_uncertainty_score":0.012442768},"labels":[],"label_agreement":null},{"id":"W4229936406","doi":"10.1002/0471667196.ess5081.pub2","title":"Monte Carlo Studies, Empirical Response Surfaces in","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Statistical Sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Monte Carlo method; Statistical physics; Monte Carlo method in statistical physics; Computer science; Simple (philosophy); Monte Carlo molecular modeling; Bridge (graph theory); Hybrid Monte Carlo; Dynamic Monte Carlo method; Mathematics; Markov chain Monte Carlo; Physics; Statistics; Medicine; Epistemology","score_opus":0.12373919994245026,"score_gpt":0.4286557338885917,"score_spread":0.30491653394614143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229936406","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016405262,0.0050671618,0.9574031,0.0022087044,0.0001870395,0.00025071722,0.00015935037,0.00030199994,0.018016648],"genre_scores_gemma":[0.66575205,0.006862101,0.31758794,0.0013018086,0.00038742155,0.0015328309,0.00029069587,0.00029160522,0.005993459],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.968765,0.027149653,0.0005066853,0.0009723301,0.0023330785,0.0002733383],"domain_scores_gemma":[0.8215369,0.16343692,0.0042493106,0.0067415843,0.0036950968,0.0003402062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031985503,0.0012700012,0.001514992,0.0022335695,0.00047412314,0.002802527,0.0016792446,0.002209302,0.0087709045],"category_scores_gemma":[0.16572462,0.0005757778,0.0007872454,0.0017568767,0.003986537,0.0039135893,0.0017569336,0.002722084,0.00073483295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007858799,0.00009837853,0.0011411756,0.00036815068,0.00008531997,0.00006780455,0.00021134343,0.20912747,0.00035223181,0.75565964,0.0018216327,0.030988287],"study_design_scores_gemma":[0.000041866882,0.00012048449,0.0007366737,0.0002929931,0.000027329537,0.000051075534,0.00013210339,0.2779788,0.00079721824,0.71113867,0.008643927,0.000038897968],"about_ca_topic_score_codex":0.0012815088,"about_ca_topic_score_gemma":0.0008228637,"teacher_disagreement_score":0.031985503,"about_ca_system_score_codex":0.0015759217,"about_ca_system_score_gemma":0.0012575501,"threshold_uncertainty_score":0.16915756},"labels":[],"label_agreement":null},{"id":"W4230880328","doi":"10.1007/978-0-8176-8361-0","title":"Quantile-Based Reliability Analysis","year":2013,"lang":"en","type":"book","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":166,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Quantile; Reliability (semiconductor); Reliability engineering; Computer science; Environmental science; Econometrics; Mathematics; Engineering; Physics","score_opus":0.08700946297613259,"score_gpt":0.32835494356686246,"score_spread":0.24134548059072986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230880328","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005353449,0.012956255,0.9456495,0.0005483923,0.0007489321,0.000019717172,0.00025911,0.001184929,0.03809786],"genre_scores_gemma":[0.10890152,0.039245814,0.5349713,0.0014546193,0.0034946038,0.00021930011,0.0024584946,0.0029914898,0.30626297],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993057,0.00012129271,0.000027928336,0.000112831476,0.00040930166,0.000022913422],"domain_scores_gemma":[0.9992818,0.00031911817,0.000035527,0.00015042855,0.00019792307,0.000015239643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001080308,0.0012115475,0.0013571604,0.001163424,0.00021258411,0.0012657468,0.0014227078,0.00073641026,0.020698195],"category_scores_gemma":[0.0027763336,0.0006623461,0.00079684536,0.0016966704,0.0008588192,0.0015313457,0.00090925436,0.0023440232,0.0114973765],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043231226,0.000037880673,0.00021716383,0.0003278168,0.00007761388,0.00007022162,0.00005050956,0.072488256,0.0028199994,0.20117001,0.15132336,0.5713739],"study_design_scores_gemma":[0.000013450182,0.000044552165,0.00081683724,0.00017934399,0.000049343584,0.0002975873,0.00002640691,0.21564892,0.0020967396,0.5536555,0.2271159,0.000055504155],"about_ca_topic_score_codex":0.0009246273,"about_ca_topic_score_gemma":0.0012127236,"teacher_disagreement_score":0.020698195,"about_ca_system_score_codex":0.0006987816,"about_ca_system_score_gemma":0.00046668053,"threshold_uncertainty_score":0.06924236},"labels":[],"label_agreement":null},{"id":"W4231666922","doi":"10.1017/9781316585146.003","title":"Introduction","year":2017,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Event (particle physics); Limit (mathematics); Structural failure; Engineering; Aeronautics; Forensic engineering; Mathematics; Risk analysis (engineering); Business; Structural engineering; Physics","score_opus":0.07276688302016399,"score_gpt":0.2548629898334889,"score_spread":0.1820961068133249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231666922","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00050435076,0.009856904,0.009941903,0.0038663503,0.0029014363,0.000053528864,0.0005881085,0.00026284182,0.97202456],"genre_scores_gemma":[0.007432437,0.0073668654,0.0037278703,0.0016753529,0.0010277986,0.00005653213,0.0006340356,0.00020730741,0.97787184],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99938786,0.000071364666,0.000025398576,0.00017757117,0.000288384,0.000049398033],"domain_scores_gemma":[0.9995443,0.00012277321,0.000022584818,0.000070742426,0.00018548899,0.00005399695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053679955,0.0007942879,0.0004899732,0.00081082067,0.0012565395,0.0038043894,0.0012609228,0.0018663664,0.19065677],"category_scores_gemma":[0.0017048377,0.00025077735,0.00036670087,0.00096631044,0.0016585088,0.0037773347,0.0016512942,0.0021691043,0.09960283],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002431007,0.00002944505,0.00013567903,0.00023263902,0.0000040023137,0.00007313279,0.00037874503,0.0005002318,0.00033108043,0.4309859,0.37790155,0.1894032],"study_design_scores_gemma":[9.815423e-7,0.000005056212,0.000060502218,0.00009842315,8.3819754e-7,0.00006645369,0.00004003955,0.00006800559,0.000066769346,0.030888801,0.968701,0.0000030484416],"about_ca_topic_score_codex":0.0019153603,"about_ca_topic_score_gemma":0.0024487192,"teacher_disagreement_score":0.19065677,"about_ca_system_score_codex":0.0019717314,"about_ca_system_score_gemma":0.0012009478,"threshold_uncertainty_score":0.6378104},"labels":[],"label_agreement":null},{"id":"W4232329261","doi":"10.1017/s002190020001857x","title":"Boundary crossing probability for Brownian motion","year":2001,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mathematics; Boundary (topology); Piecewise; Brownian motion; Mathematical analysis; Piecewise linear function; Monte Carlo method; Applied mathematics; Statistics","score_opus":0.10990585948246177,"score_gpt":0.3361828883833318,"score_spread":0.22627702890087004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232329261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07489239,0.0015433396,0.9112746,0.00038660766,0.000111492605,0.000050476378,0.00006990636,0.0002762544,0.01139488],"genre_scores_gemma":[0.88982546,0.0017693648,0.0999609,0.00018550716,0.00015682034,0.0001794487,0.00020487883,0.00015001639,0.007567689],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985845,0.00030606784,0.00005730473,0.00034811196,0.0004911849,0.0002129391],"domain_scores_gemma":[0.99465203,0.0033883064,0.00057437463,0.00043064935,0.0005734486,0.00038120555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036173416,0.00046331252,0.0011174275,0.0018832809,0.0009434323,0.0020952625,0.0017922294,0.0021947892,0.0060596787],"category_scores_gemma":[0.021936951,0.00043445383,0.0009658267,0.0009015932,0.0026929753,0.004449482,0.0022855417,0.0023108567,0.0008702916],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007783878,0.000020784153,0.0011630564,0.000106811334,0.000020985955,0.00017342929,0.00021791465,0.18453154,0.002476767,0.7942271,0.00093085377,0.016052792],"study_design_scores_gemma":[0.000014912282,0.000030916148,0.0006826295,0.00004056796,0.000013109511,0.00020130929,0.000030422532,0.68838197,0.0013322282,0.30763775,0.0015975712,0.00003662627],"about_ca_topic_score_codex":0.0014366173,"about_ca_topic_score_gemma":0.0003713663,"teacher_disagreement_score":0.0060596787,"about_ca_system_score_codex":0.0014670423,"about_ca_system_score_gemma":0.00051865017,"threshold_uncertainty_score":0.020271659},"labels":[],"label_agreement":null},{"id":"W4232332523","doi":"10.1017/cbo9780511804861","title":"Decisions under Uncertainty","year":2005,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Probability theory; Applied probability; Probability distribution; Ask price; Bayesian probability; Probability and statistics; Computer science; Decision theory; Frequentist probability; Core (optical fiber); Mathematical economics; Imprecise probability; Principle of maximum entropy; Management science; Operations research; Mathematics; Artificial intelligence; Engineering; Statistics; Economics","score_opus":0.09214188346745263,"score_gpt":0.2749800248101285,"score_spread":0.1828381413426759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232332523","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0127543,0.013069143,0.2240417,0.042471655,0.0016263785,0.00014506893,0.0006449412,0.00015744261,0.70508933],"genre_scores_gemma":[0.7004077,0.022393275,0.08208303,0.008022878,0.002109561,0.00041762562,0.00073017133,0.00017962506,0.18365605],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99705696,0.0012539615,0.000113849645,0.00045540812,0.0008492377,0.0002706146],"domain_scores_gemma":[0.997147,0.0018796021,0.00018323884,0.00031054884,0.00030829734,0.00017120034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035779763,0.00074929214,0.00079678005,0.00054984196,0.0014833842,0.0061338698,0.0012849849,0.0031593451,0.018432068],"category_scores_gemma":[0.010928532,0.0003558197,0.00058243715,0.0005838316,0.0034597868,0.005144773,0.002325094,0.003299029,0.0029749915],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016794565,0.000013448985,0.00012504126,0.00006155612,0.000020007534,0.00010293307,0.00021599876,0.007211738,0.00012437535,0.9581672,0.012438675,0.021502197],"study_design_scores_gemma":[0.0000057062325,0.000014543872,0.00010041274,0.00009047913,0.000007944851,0.000060546896,0.00016938601,0.005382039,0.000102636746,0.94008005,0.053974904,0.000011320375],"about_ca_topic_score_codex":0.0014679282,"about_ca_topic_score_gemma":0.0015737825,"teacher_disagreement_score":0.018432068,"about_ca_system_score_codex":0.0024927135,"about_ca_system_score_gemma":0.002091792,"threshold_uncertainty_score":0.06166148},"labels":[],"label_agreement":null},{"id":"W4235223134","doi":"10.22215/etd/2021-14507","title":"Sensitivity Analysis and Experimental Validation of Plunger-type Wavemakers Modelled with a Steady Flow","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Plunger; Sensitivity (control systems); Flow (mathematics); Mechanics; Flow conditions; Range (aeronautics); Control theory (sociology); Variance (accounting); Open-channel flow; Steady state (chemistry); Simulation; Mathematics; Engineering; Computer science; Physics; Thermodynamics; Electronic engineering; Chemistry","score_opus":0.04360336636607756,"score_gpt":0.3194489611715654,"score_spread":0.27584559480548787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235223134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9643813,0.00010720839,0.03309266,0.000053822132,0.000028037855,0.00010320588,0.00020939301,0.00011733948,0.001906956],"genre_scores_gemma":[0.9965257,0.000039534654,0.0029797335,0.00000968807,0.0000015049267,0.000044146727,0.00006341035,0.000008357575,0.00032785683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907494,0.00029920737,0.00006171614,0.00019173701,0.0002704681,0.000101893325],"domain_scores_gemma":[0.9972699,0.0019194493,0.00013976927,0.00026640846,0.0003691931,0.000035272336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018215976,0.0007418564,0.0004943075,0.00054837705,0.00037760768,0.0007283113,0.00058813463,0.0009388985,0.0013238903],"category_scores_gemma":[0.004726888,0.00039784974,0.0008471874,0.00028182924,0.0005571837,0.0005927283,0.00068480667,0.0007212328,0.00018618439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005269763,0.00030245414,0.0040669004,0.0002259,0.000061733306,0.00014004437,0.00011622471,0.8979593,0.08652367,0.00092490396,0.00015441107,0.008997585],"study_design_scores_gemma":[0.000030139287,0.0010632948,0.00450236,0.000025173096,0.000057789093,0.00003892254,0.00010082403,0.8764216,0.11676936,0.0005640969,0.0003915771,0.000034862067],"about_ca_topic_score_codex":0.0022578097,"about_ca_topic_score_gemma":0.0015088035,"teacher_disagreement_score":0.0022578097,"about_ca_system_score_codex":0.0007791311,"about_ca_system_score_gemma":0.00034872792,"threshold_uncertainty_score":0.009633601},"labels":[],"label_agreement":null},{"id":"W4235257475","doi":"10.26434/chemrxiv-2021-cn0px","title":"Efficiently transforming from values of a function on a sparse grid to basis coefficients","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sparse grid; Mathematics; Basis (linear algebra); Function (biology); Transformation (genetics); Piecewise; Combinatorics; Basis function; Series (stratigraphy); Discrete mathematics; Mathematical analysis; Applied mathematics; Geometry","score_opus":0.08549221265203982,"score_gpt":0.31072260108767374,"score_spread":0.22523038843563392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235257475","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022196308,0.0001034622,0.97223425,0.00008866148,0.000037172827,0.000045400175,0.00018083231,0.0012705809,0.0038434002],"genre_scores_gemma":[0.13995601,0.0002906654,0.85283136,0.000101571415,0.000028295375,0.00014110284,0.000870819,0.0010623848,0.0047177626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938834,0.000089912726,0.000028271634,0.00004722923,0.00036664595,0.000079624006],"domain_scores_gemma":[0.9991823,0.00039843598,0.000044733002,0.0001533606,0.00018378647,0.000037313603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081088906,0.0007318025,0.0007094507,0.00072905474,0.0003995649,0.0014352616,0.0009992822,0.0005617472,0.006423122],"category_scores_gemma":[0.0037920987,0.0004336416,0.0006375026,0.0010415904,0.0006454606,0.0014320961,0.0014042411,0.0016032921,0.003686301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028176783,0.00010908521,0.0021043946,0.00044235354,0.00006124514,0.0002950617,0.00050981366,0.3360526,0.064907245,0.090849906,0.012885186,0.49150142],"study_design_scores_gemma":[0.000023386518,0.00004879666,0.0004462842,0.000039566366,0.000015970714,0.00008302255,0.0001448798,0.9386127,0.017573921,0.03138647,0.011604198,0.000020755733],"about_ca_topic_score_codex":0.0046086404,"about_ca_topic_score_gemma":0.007537832,"teacher_disagreement_score":0.006423122,"about_ca_system_score_codex":0.00058318453,"about_ca_system_score_gemma":0.0014259449,"threshold_uncertainty_score":0.021487474},"labels":[],"label_agreement":null},{"id":"W4236385688","doi":"10.32920/ryerson.14648304","title":"Multidisciplinary Aircraft Conceptual Design Optimization Considering Fidelity Uncertainties","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Conceptual design; Multidisciplinary design optimization; Computer science; Conceptual framework; Engineering design process; Engineering optimization; Systems engineering; Optimization problem; Process (computing); Multidisciplinary approach; Engineering; Industrial engineering; Reliability engineering; Operations research","score_opus":0.17919157819360412,"score_gpt":0.3519479987644634,"score_spread":0.1727564205708593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236385688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04463129,0.00042316903,0.9505333,0.00017598704,0.00002376924,0.00004989667,0.000051337105,0.00007080576,0.0040404857],"genre_scores_gemma":[0.84879845,0.00044561265,0.14846368,0.0000686089,0.000028393462,0.00017072169,0.0001006102,0.000059072518,0.001864848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999198,0.00034528936,0.000028973955,0.00012583983,0.00022403295,0.00007784803],"domain_scores_gemma":[0.9978982,0.0015149392,0.00021377191,0.00013101469,0.00018509151,0.000056995163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022119756,0.0008037852,0.0010420027,0.00069499924,0.00037867398,0.001334554,0.0007522646,0.0012195348,0.0010529858],"category_scores_gemma":[0.0041470313,0.000595763,0.00094562623,0.00050305476,0.00084207684,0.00087494025,0.0015009943,0.0010407344,0.00012846784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008036787,0.0000044949807,0.00010638077,0.000017439841,0.000007118617,0.000012219546,0.000008440928,0.9938407,0.0003345039,0.0025673083,0.000035378325,0.0030579222],"study_design_scores_gemma":[0.0000030241101,0.000015667758,0.00008806464,0.0000051549987,0.000003845654,0.000005759423,0.0000065410354,0.9973629,0.00023778406,0.0020544522,0.00021419095,0.0000025241854],"about_ca_topic_score_codex":0.0028047573,"about_ca_topic_score_gemma":0.0018220424,"teacher_disagreement_score":0.0028047573,"about_ca_system_score_codex":0.00097033975,"about_ca_system_score_gemma":0.0011055461,"threshold_uncertainty_score":0.011698186},"labels":[],"label_agreement":null},{"id":"W4236544653","doi":"10.1002/0471667196.ess5023.pub2","title":"Principal Differential Analysis","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Statistical Sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Basis (linear algebra); Differential equation; Mathematics; Fourier series; Principal (computer security); Set (abstract data type); Applied mathematics; Differential (mechanical device); Linear differential equation; Principal component analysis; Basis function; Mathematical analysis; Computer science; Statistics","score_opus":0.04081291168442181,"score_gpt":0.3454178622534515,"score_spread":0.3046049505690297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236544653","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032814334,0.0024617803,0.9612756,0.0010171589,0.00029218252,0.000081026745,0.0005786785,0.00058660714,0.030425532],"genre_scores_gemma":[0.36737245,0.0119443415,0.49194756,0.00082560384,0.0019938806,0.00064687664,0.002211495,0.00088745414,0.12217043],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99789447,0.00055485516,0.00010107921,0.00046690414,0.0008872075,0.000095541014],"domain_scores_gemma":[0.9972487,0.0010317113,0.00026725142,0.00040189843,0.0009350239,0.000115381175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001849093,0.0013521004,0.0015212485,0.0023073335,0.0006693253,0.0031857498,0.0009719715,0.0010526633,0.01736248],"category_scores_gemma":[0.0067906333,0.0004366456,0.0009760118,0.0023880953,0.0018112081,0.0015516113,0.001890941,0.0017870876,0.007047593],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060919414,0.000047201014,0.0014938615,0.00035157058,0.00011211411,0.00027969986,0.00017407251,0.06726827,0.0029296658,0.6965894,0.03638927,0.19430403],"study_design_scores_gemma":[0.000025944131,0.000051881412,0.0014403734,0.000114449154,0.000054317556,0.00039286015,0.00010289295,0.3673856,0.0025183728,0.5159042,0.11194692,0.00006212413],"about_ca_topic_score_codex":0.0014108911,"about_ca_topic_score_gemma":0.0008765582,"teacher_disagreement_score":0.01736248,"about_ca_system_score_codex":0.0010063952,"about_ca_system_score_gemma":0.0013922773,"threshold_uncertainty_score":0.058083296},"labels":[],"label_agreement":null},{"id":"W4236823615","doi":"10.22215/etd/2013-09627","title":"Domain decomposition methods for uncertainty quantification","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Domain (mathematical analysis); Computer science; Mathematics; Mathematical analysis","score_opus":0.1192814992430518,"score_gpt":0.480398611901475,"score_spread":0.36111711265842317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236823615","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036976038,0.0007681704,0.9972133,0.000099772195,0.000034174423,0.000011975901,0.000049511218,0.0000676413,0.001385737],"genre_scores_gemma":[0.080175005,0.003482099,0.90552336,0.00020283424,0.00035350467,0.00028485223,0.00073376443,0.00037446193,0.008870177],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973966,0.0012030294,0.0001309009,0.00033961964,0.0008074933,0.00012238995],"domain_scores_gemma":[0.9953432,0.0031521556,0.00021631712,0.00045384045,0.0007108845,0.00012360456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036130801,0.001283784,0.0011273732,0.0023553765,0.0005552346,0.0023720474,0.0011257157,0.0009851251,0.005188231],"category_scores_gemma":[0.008736522,0.0007115802,0.0017596888,0.0020724938,0.001290549,0.0023925502,0.0028766983,0.0035280504,0.0015410901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062427025,0.000045312063,0.00042143892,0.0005394472,0.00019346674,0.00007824214,0.00018349051,0.22194956,0.0029622552,0.43364435,0.011959173,0.32796085],"study_design_scores_gemma":[0.000007226969,0.000015842188,0.00014561538,0.00011504589,0.000023457465,0.00004699018,0.000032719294,0.7010438,0.0010562809,0.28378978,0.013706958,0.000016355365],"about_ca_topic_score_codex":0.0027267805,"about_ca_topic_score_gemma":0.002190143,"teacher_disagreement_score":0.005188231,"about_ca_system_score_codex":0.0012858369,"about_ca_system_score_gemma":0.0013831242,"threshold_uncertainty_score":0.019108057},"labels":[],"label_agreement":null},{"id":"W4237367077","doi":"10.1007/978-1-4939-7131-2_100948","title":"Random Structures","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.10250098362858347,"score_gpt":0.3187830999016776,"score_spread":0.21628211627309413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237367077","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019752034,0.00831869,0.28333068,0.0033801787,0.001311646,0.000061530016,0.0006376153,0.0006938842,0.70029056],"genre_scores_gemma":[0.06774055,0.00993412,0.060027197,0.0015311657,0.0019503548,0.00024914212,0.0012752579,0.0008724515,0.85641974],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993692,0.00012688799,0.000020588599,0.00014578765,0.00030341034,0.000034124416],"domain_scores_gemma":[0.999516,0.00020868803,0.0000295378,0.000119993725,0.000099575576,0.00002613851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005223881,0.0009994064,0.0007518926,0.0010795894,0.0006362197,0.002387376,0.0008267971,0.0011838711,0.059448086],"category_scores_gemma":[0.002438465,0.00044558843,0.00044876523,0.0010345101,0.0016972731,0.0028264406,0.0012127426,0.0021482685,0.024646787],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064902765,0.000012856975,0.000038966176,0.00007741003,0.000007448998,0.00001791939,0.00004336222,0.002536609,0.00037016204,0.8823979,0.045712493,0.068778425],"study_design_scores_gemma":[0.0000047490735,0.000009026109,0.000083367006,0.000066907465,0.000005823194,0.000069655674,0.000018204122,0.0050007636,0.00046114312,0.7307383,0.26353115,0.000010911734],"about_ca_topic_score_codex":0.0007007873,"about_ca_topic_score_gemma":0.0009906018,"teacher_disagreement_score":0.059448086,"about_ca_system_score_codex":0.001113663,"about_ca_system_score_gemma":0.0007766178,"threshold_uncertainty_score":0.1988737},"labels":[],"label_agreement":null},{"id":"W4237709553","doi":"10.1155/2010/845609","title":"Study of the Fatigue Life and Weight Optimization of an Automobile Aluminium Alloy Part under Random Road Excitation","year":2010,"lang":"en","type":"article","venue":"Shock and Vibration","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Centre québécois de recherche et de développement de l’aluminium","keywords":"Aluminium; Structural engineering; Aluminium alloy; Natural frequency; Stiffness; Alloy; Power (physics); Constraint (computer-aided design); Materials science; Spectral density; Acceleration; Bending; Process (computing); Weight function; Engineering; Computer science; Acoustics; Vibration; Mathematics; Composite material; Mechanical engineering; Mathematical analysis; Physics","score_opus":0.054153855056106946,"score_gpt":0.3103697661580754,"score_spread":0.25621591110196845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237709553","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8815197,0.000083944484,0.11735082,0.00004056639,0.0000028826032,0.000014391837,0.000018113455,0.00004799023,0.0009216813],"genre_scores_gemma":[0.98996264,0.000020107243,0.009648561,0.0000033139718,0.0000012760523,0.00000885913,0.000011507023,0.0000073572214,0.00033638792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999243,0.000022308388,0.0000030810843,0.000013732086,0.000023132587,0.000013474498],"domain_scores_gemma":[0.99942774,0.00038184924,0.00007717712,0.000023084349,0.00007253218,0.000017580682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037572664,0.0003012642,0.00025730237,0.00035232338,0.00014699073,0.0001670521,0.00019272907,0.0003147784,0.00040239736],"category_scores_gemma":[0.0011465477,0.00014844474,0.0002087487,0.00016165829,0.00030211444,0.00020364024,0.00013611004,0.00014053531,0.000039926996],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057259997,0.000028287883,0.0014550063,0.000025189174,0.000016354199,0.00004231835,0.0000322699,0.968312,0.017628865,0.00064242195,0.00004232295,0.011717636],"study_design_scores_gemma":[0.0000020419639,0.000059403457,0.0009965262,0.0000011189827,0.00000586446,0.000011962904,0.000008131489,0.994672,0.0040445444,0.00014569353,0.000050079576,0.000002524559],"about_ca_topic_score_codex":0.001857135,"about_ca_topic_score_gemma":0.0016933308,"teacher_disagreement_score":0.001857135,"about_ca_system_score_codex":0.0003299073,"about_ca_system_score_gemma":0.00020308976,"threshold_uncertainty_score":0.0036926866},"labels":[],"label_agreement":null},{"id":"W4238757703","doi":"10.1017/cbo9780511804861.001","title":"Preface","year":2005,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Philosophy","score_opus":0.07754973420517637,"score_gpt":0.25292958176430147,"score_spread":0.1753798475591251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238757703","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007241706,0.009656451,0.0047186064,0.009501742,0.027869837,0.00017337763,0.003639109,0.0010867087,0.94262993],"genre_scores_gemma":[0.002920808,0.0035738435,0.0018143217,0.0018241511,0.0035473641,0.00007279975,0.0025776706,0.00050148973,0.9831676],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993345,0.000074897944,0.000037774178,0.00013858527,0.00035985385,0.000054279415],"domain_scores_gemma":[0.99836487,0.00031705733,0.00006950591,0.00017993423,0.000821286,0.0002473018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067182694,0.00094576686,0.0007776994,0.0020808221,0.0018726294,0.0042918376,0.0013672978,0.001482632,0.5013315],"category_scores_gemma":[0.004340274,0.00038180166,0.00059543055,0.001942831,0.00073700846,0.003679138,0.0019480082,0.0029114417,0.3263718],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019703477,0.000023643675,0.00007527572,0.00012345835,0.00000205632,0.00004869937,0.00011727781,0.00009983089,0.00014497299,0.015926877,0.92004514,0.06337319],"study_design_scores_gemma":[0.0000014875743,0.000006436683,0.00010674564,0.000084405474,7.74625e-7,0.00005394552,0.0000384421,0.000027330076,0.00003972727,0.0026589674,0.99697936,0.0000024844371],"about_ca_topic_score_codex":0.0026294633,"about_ca_topic_score_gemma":0.003932496,"teacher_disagreement_score":0.5013315,"about_ca_system_score_codex":0.002212463,"about_ca_system_score_gemma":0.0014969904,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4239364725","doi":"10.1115/1.1626128","title":"Probabilistic Analysis of LIST Data for the Estimation of Extreme Design Loads for Wind Turbine Components*†","year":2003,"lang":"en","type":"article","venue":"Journal of Solar Energy Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gumbel distribution; Extreme value theory; Weibull distribution; Turbine; Extrapolation; Inflow; Generalized extreme value distribution; Sampling (signal processing); Computer science; Probabilistic logic; Wind engineering; Statistics; Mathematics; Engineering; Meteorology; Structural engineering; Geography","score_opus":0.1882710307936077,"score_gpt":0.32093103428089387,"score_spread":0.13266000348728615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239364725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10313031,0.00011385991,0.89507186,0.00019383895,0.00001238507,0.0000902695,0.00042617755,0.0003508989,0.0006105078],"genre_scores_gemma":[0.8711649,0.0001858187,0.12577194,0.00007868959,0.0000427352,0.0003253078,0.0020137322,0.0000667651,0.000350127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9926763,0.005001827,0.00035044752,0.00043180253,0.0013640871,0.00017557679],"domain_scores_gemma":[0.8945222,0.088704176,0.0075966166,0.0051743793,0.0036150164,0.000387634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015597683,0.0007253782,0.00082997937,0.0019166925,0.00051009574,0.0011342722,0.0015261977,0.0011395084,0.001455478],"category_scores_gemma":[0.07308641,0.00060368807,0.00082134944,0.0013853565,0.0011057041,0.002435204,0.0016251868,0.0013632099,0.00042423906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078264973,0.00021055593,0.039915785,0.00038198198,0.00021070239,0.00023118914,0.00024340716,0.8087181,0.0040174006,0.03643725,0.0015281341,0.10732283],"study_design_scores_gemma":[0.000023339742,0.00016233453,0.005633293,0.00003504776,0.000016490345,0.00006607691,0.000043164197,0.97728217,0.0021112906,0.0140244,0.0005670267,0.00003535655],"about_ca_topic_score_codex":0.00085862284,"about_ca_topic_score_gemma":0.0011031185,"teacher_disagreement_score":0.015597683,"about_ca_system_score_codex":0.000762708,"about_ca_system_score_gemma":0.0007122481,"threshold_uncertainty_score":0.08248943},"labels":[],"label_agreement":null},{"id":"W4240815986","doi":"10.1520/stp11327s","title":"Fuzzy Probabilistic Assessment of Aging Aircraft Structures Subjected to Multiple Site Fatigue Damage","year":2005,"lang":"en","type":"book-chapter","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Martec (Canada)","funders":"","keywords":"Probabilistic logic; Structural engineering; Fuzzy logic; Forensic engineering; Computer science; Reliability engineering; Engineering; Artificial intelligence","score_opus":0.08223787837131451,"score_gpt":0.34365873494143584,"score_spread":0.2614208565701213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240815986","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010346191,0.00066454586,0.98216486,0.00009086155,0.000020779173,0.000020754062,0.000045150387,0.00013896509,0.006507838],"genre_scores_gemma":[0.51832634,0.00236595,0.4612096,0.000082308754,0.00008442715,0.00010585155,0.00021904249,0.00007344699,0.017533066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998299,0.000029955554,0.000006291283,0.000026963895,0.00009934284,0.0000075206012],"domain_scores_gemma":[0.9998062,0.000107112544,0.000016791724,0.000012883922,0.00005001016,0.0000069863736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045314964,0.0005115765,0.00041099056,0.0006994978,0.0001790954,0.000650993,0.0008802325,0.00052057434,0.0015692478],"category_scores_gemma":[0.0008688747,0.00022713777,0.0004797063,0.00039312037,0.00033855202,0.0006829193,0.0003617728,0.0003544409,0.00031304528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039204147,0.000022464768,0.00043207675,0.00013750647,0.000031102423,0.00017229504,0.00010517881,0.75001794,0.016639631,0.059637137,0.0021995225,0.17056589],"study_design_scores_gemma":[0.0000022930844,0.000034883615,0.00040809318,0.00002411759,0.00001110233,0.000097100594,0.000018679564,0.9672076,0.0025279757,0.026373548,0.0032806443,0.000013885181],"about_ca_topic_score_codex":0.001042245,"about_ca_topic_score_gemma":0.0010518814,"teacher_disagreement_score":0.0015692478,"about_ca_system_score_codex":0.0004423865,"about_ca_system_score_gemma":0.00029184658,"threshold_uncertainty_score":0.0052496195},"labels":[],"label_agreement":null},{"id":"W4241528521","doi":"10.2139/ssrn.2557067","title":"Joint Mixability","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Joint (building); Engineering; Structural engineering","score_opus":0.13204514064922757,"score_gpt":0.33187876221917795,"score_spread":0.19983362156995038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241528521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052547164,0.00037027686,0.7923312,0.00090793,0.00017836395,0.00017921356,0.00072596106,0.0007017853,0.15205805],"genre_scores_gemma":[0.79887784,0.00047961698,0.055120617,0.00045369612,0.0001816297,0.00059324736,0.001170802,0.0007949269,0.14232765],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99674135,0.0005375802,0.00016510204,0.0009884309,0.001040238,0.0005272444],"domain_scores_gemma":[0.99546057,0.0012755977,0.0005166686,0.0012816588,0.0009949455,0.00047060417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018839199,0.0013222395,0.0011590574,0.002744218,0.0019793392,0.0053117843,0.0013985729,0.0019290817,0.05072699],"category_scores_gemma":[0.0094987415,0.0010542389,0.001791188,0.0017372576,0.0026622494,0.008744384,0.006741227,0.0034913984,0.008677684],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010151853,0.00005228792,0.00088191533,0.00009788243,0.000053572832,0.00012365347,0.00017363658,0.009697124,0.0065084794,0.9447232,0.0030062445,0.03458055],"study_design_scores_gemma":[0.000016947246,0.000085308115,0.00093010376,0.000040391773,0.000043984357,0.00025950436,0.00018560363,0.058789477,0.008159188,0.9166255,0.014821862,0.000042062326],"about_ca_topic_score_codex":0.0006785367,"about_ca_topic_score_gemma":0.00063875417,"teacher_disagreement_score":0.05072699,"about_ca_system_score_codex":0.0012341376,"about_ca_system_score_gemma":0.0010672921,"threshold_uncertainty_score":0.16969872},"labels":[],"label_agreement":null},{"id":"W4241869693","doi":"10.22215/etd/2013-06237","title":"Bayesian Model selection and parameter estimation for a nonlinear fluid-structure interaction problem","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Selection (genetic algorithm); Art; Mathematics; Computer science; Artificial intelligence","score_opus":0.04064819363926395,"score_gpt":0.3388042965447824,"score_spread":0.2981561029055184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241869693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010202727,0.00049298274,0.98591006,0.0010830202,0.000041709674,0.000066493914,0.00013611357,0.00027102503,0.0017958957],"genre_scores_gemma":[0.59572667,0.0016778547,0.38130674,0.0007773464,0.00040004935,0.001092007,0.0015299713,0.0005051713,0.016984181],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786407,0.0011851481,0.00009097168,0.00036104527,0.00031444145,0.00018432106],"domain_scores_gemma":[0.9810193,0.016910812,0.0006845535,0.0002667475,0.00074912567,0.00036929332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059676785,0.0016149115,0.0037385179,0.0022353916,0.0011758148,0.0026900545,0.0032909517,0.0038741634,0.0048874808],"category_scores_gemma":[0.022500835,0.0022956172,0.0019295802,0.0017613466,0.0019942238,0.0029159926,0.0031304965,0.0037507603,0.0009858819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015525674,0.000054322983,0.0007632285,0.00013826253,0.00009682429,0.00008713334,0.00008405583,0.95856935,0.00026618515,0.017740078,0.0014118025,0.020633556],"study_design_scores_gemma":[0.000018917906,0.000011799479,0.00009023129,0.000011574921,0.000009278456,0.000009776125,0.000008009368,0.9904018,0.00006997166,0.0090551535,0.00030307847,0.00001033057],"about_ca_topic_score_codex":0.01886323,"about_ca_topic_score_gemma":0.012793259,"teacher_disagreement_score":0.01886323,"about_ca_system_score_codex":0.0020184366,"about_ca_system_score_gemma":0.0036744934,"threshold_uncertainty_score":0.03750688},"labels":[],"label_agreement":null},{"id":"W4244015182","doi":"10.1007/978-3-540-70529-1_549","title":"A Posteriori Error Estimates of Quantities of Interest","year":2015,"lang":"en","type":"book-chapter","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"A priori and a posteriori; Mathematics; Statistics; Calculus (dental); Applied mathematics; Computer science; Medicine; Philosophy; Epistemology; Orthodontics","score_opus":0.46262745718653714,"score_gpt":0.390329034199289,"score_spread":0.07229842298724815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244015182","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039561247,0.0028152098,0.9841665,0.00023259752,0.00022736676,0.000009557046,0.00011043352,0.00022358511,0.0118192015],"genre_scores_gemma":[0.04543474,0.018683642,0.84035075,0.0006299766,0.0023078248,0.00019584273,0.0012142868,0.0018119637,0.089371026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981664,0.00051136117,0.00009566525,0.0003398657,0.0008532416,0.00003343371],"domain_scores_gemma":[0.9966106,0.0024566466,0.00014797908,0.00036745102,0.0003900533,0.000027264683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026578773,0.0017165657,0.0012887089,0.0015752714,0.00025028244,0.0022641895,0.0013420448,0.0017635545,0.009160846],"category_scores_gemma":[0.008344616,0.0009337759,0.00090308144,0.0012320546,0.001583645,0.0033013301,0.0018359605,0.0036454587,0.007653091],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006440636,0.000031144464,0.00020069497,0.0007997355,0.000088122266,0.00006216232,0.00013511132,0.03929238,0.007502861,0.5310641,0.022705654,0.39805362],"study_design_scores_gemma":[0.000014809799,0.00005903504,0.0005564814,0.00046335874,0.00006655629,0.0003677814,0.00003289507,0.18863499,0.01321681,0.6630871,0.13341214,0.00008804185],"about_ca_topic_score_codex":0.0005478808,"about_ca_topic_score_gemma":0.00052781537,"teacher_disagreement_score":0.009160846,"about_ca_system_score_codex":0.00052989676,"about_ca_system_score_gemma":0.00054333033,"threshold_uncertainty_score":0.030646086},"labels":[],"label_agreement":null},{"id":"W4244110092","doi":"10.22215/etd/2016-11708","title":"Efficient Stochastic Collocation Based Variability Analysis Using Model-Order Reduction Techniques","year":2016,"lang":"en","type":"dissertation","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Sparse grid; Collocation (remote sensing); Model order reduction; Computer science; Reduction (mathematics); Monte Carlo method; Algorithm; Mathematical optimization; Convergence (economics); Rate of convergence; Applied mathematics; Mathematics; Statistics; Machine learning; Projection (relational algebra)","score_opus":0.05762753026929306,"score_gpt":0.3541917896330007,"score_spread":0.2965642593637076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244110092","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021410412,0.00004341109,0.9971968,0.000030366638,0.0000077696595,0.000009980676,0.000020253481,0.00017313308,0.00037730997],"genre_scores_gemma":[0.3959704,0.00040923143,0.59797204,0.00009281545,0.00006946996,0.0002386779,0.00046930765,0.00047207458,0.004305954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950016,0.00015705428,0.00002075054,0.000055744516,0.00023351058,0.000032811917],"domain_scores_gemma":[0.99858946,0.000921344,0.000097530625,0.0001552367,0.00021098947,0.00002544942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007985222,0.00083759456,0.0009081128,0.00061819964,0.00039705486,0.0007653721,0.0007969775,0.0006017998,0.0020038367],"category_scores_gemma":[0.0030194677,0.0005673606,0.0009321177,0.0006026967,0.0004343936,0.0008207052,0.00084401044,0.0015799325,0.00080961274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000321903,0.000026126889,0.00021881577,0.00004671492,0.000033638575,0.000038309332,0.000042009742,0.94526356,0.0040892856,0.010472166,0.0010952387,0.038641892],"study_design_scores_gemma":[0.0000012641012,0.000003856057,0.000023530047,0.0000011695575,0.000001406355,0.000003637344,0.0000011973783,0.9978327,0.00027674937,0.0016684269,0.00018397758,0.0000019938852],"about_ca_topic_score_codex":0.004012507,"about_ca_topic_score_gemma":0.0044352873,"teacher_disagreement_score":0.004012507,"about_ca_system_score_codex":0.0005102467,"about_ca_system_score_gemma":0.0010290717,"threshold_uncertainty_score":0.0079782605},"labels":[],"label_agreement":null},{"id":"W4244204137","doi":"10.1007/978-1-4939-7131-2_100923","title":"Propagation of Uncertainty","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Propagation of uncertainty; Environmental science; Computer science; Algorithm","score_opus":0.10870917084647376,"score_gpt":0.317952191853365,"score_spread":0.20924302100689124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244204137","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022374,0.02975403,0.5725282,0.0073170634,0.0025114056,0.00006710411,0.000582613,0.00071531447,0.38428688],"genre_scores_gemma":[0.3056255,0.06834676,0.22649844,0.00415013,0.0068579265,0.0005822778,0.0018187984,0.0012036603,0.3849165],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99812466,0.00055407983,0.00007158215,0.00031782797,0.00084004074,0.00009170527],"domain_scores_gemma":[0.99774545,0.0011934565,0.00012686753,0.00044285215,0.00042411676,0.000067188004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017470348,0.0013883461,0.0009988714,0.0017847652,0.00088198297,0.005382908,0.0014559543,0.002336944,0.017712114],"category_scores_gemma":[0.008047993,0.00083843304,0.000801252,0.0018268748,0.003987757,0.005785255,0.0025395562,0.004058491,0.0064002546],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008758085,0.000007866164,0.00006154996,0.00009945932,0.000015492506,0.000035969802,0.00012109526,0.0037695982,0.00028107737,0.93841857,0.018752329,0.038428176],"study_design_scores_gemma":[0.0000028740192,0.0000052326227,0.00006927172,0.00009566757,0.00000801857,0.00007011609,0.00003736523,0.007371705,0.00028856227,0.9027038,0.089334145,0.0000131316365],"about_ca_topic_score_codex":0.0016623987,"about_ca_topic_score_gemma":0.0009518847,"teacher_disagreement_score":0.017712114,"about_ca_system_score_codex":0.0020004848,"about_ca_system_score_gemma":0.0012324227,"threshold_uncertainty_score":0.059252918},"labels":[],"label_agreement":null},{"id":"W4244235592","doi":"10.32920/ryerson.14645310.v1","title":"Integrated uncertainty techniques in aircraft derivative design optimization.","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Multidisciplinary design optimization; Sensitivity (control systems); Conceptual design; Computer science; Material derivative; Process (computing); Engineering design process; Mathematical optimization; Optimization problem; Engineering optimization; Uncertainty analysis; Reliability engineering; Fidelity; Engineering; Simulation; Mathematics","score_opus":0.11401135817254757,"score_gpt":0.338233022895713,"score_spread":0.22422166472316543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244235592","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024004083,0.0008715332,0.9930577,0.000087992354,0.00004244005,0.000040672912,0.000025745512,0.00008944855,0.0033840337],"genre_scores_gemma":[0.4005124,0.0027428896,0.58974236,0.00022822618,0.00014470633,0.00057735085,0.0002160529,0.0002881487,0.0055477875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988463,0.00038795453,0.00005723148,0.00012987315,0.0005191287,0.00005946231],"domain_scores_gemma":[0.99868613,0.000912808,0.00011885481,0.0000658533,0.00018853626,0.00002776013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021659671,0.0016519735,0.0011964787,0.0014089731,0.00063176046,0.0011429247,0.0007179829,0.0010714015,0.0024052237],"category_scores_gemma":[0.0035033703,0.00089850294,0.0019008233,0.0010481642,0.0010402437,0.0010512958,0.0018524308,0.0021975331,0.00034525912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003747547,0.000032236996,0.00041858075,0.0002244629,0.00006919257,0.000052128376,0.00007771455,0.9132533,0.00191923,0.029590674,0.00062195864,0.053703096],"study_design_scores_gemma":[0.000006544914,0.000049195543,0.00011677396,0.000028099184,0.000020552372,0.000019543324,0.000012932621,0.9841217,0.000958726,0.011909836,0.0027461308,0.000009944593],"about_ca_topic_score_codex":0.0039206217,"about_ca_topic_score_gemma":0.0028812084,"teacher_disagreement_score":0.0039206217,"about_ca_system_score_codex":0.0009275768,"about_ca_system_score_gemma":0.0013247555,"threshold_uncertainty_score":0.01145488},"labels":[],"label_agreement":null},{"id":"W4245541007","doi":"10.1007/978-1-4614-6170-8_100310","title":"Propagation of Uncertainty","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Environmental science; Computer science","score_opus":0.08829934758349388,"score_gpt":0.3030913250354763,"score_spread":0.21479197745198242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245541007","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020744533,0.032972816,0.5167408,0.007045184,0.0022804365,0.000065756416,0.00051932083,0.00062700105,0.43767416],"genre_scores_gemma":[0.2844696,0.07178976,0.21561565,0.0038589376,0.0058739646,0.00054266903,0.001531053,0.0009974597,0.41532087],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983254,0.0005107753,0.00006304915,0.00026857018,0.00075273326,0.00007947502],"domain_scores_gemma":[0.9981834,0.0009483029,0.000100895886,0.00036436072,0.00034973843,0.00005321075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015909434,0.00128791,0.0009295705,0.0017533612,0.0008628733,0.0049016853,0.0013985017,0.0022182718,0.016619638],"category_scores_gemma":[0.0066998163,0.00079243677,0.0007228946,0.0017985043,0.0040614274,0.005533283,0.0022915145,0.0037487706,0.0058282507],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007521072,0.0000071204813,0.00005841849,0.00008819692,0.000013894637,0.0000358406,0.00013234107,0.0032089232,0.00025927884,0.9381347,0.019276464,0.03877732],"study_design_scores_gemma":[0.0000026008822,0.000004885732,0.000068417066,0.00009183198,0.0000069568846,0.000076420896,0.00004141466,0.006214747,0.00027062264,0.89440924,0.09880036,0.000012511911],"about_ca_topic_score_codex":0.0017835796,"about_ca_topic_score_gemma":0.0011077847,"teacher_disagreement_score":0.016619638,"about_ca_system_score_codex":0.0019202789,"about_ca_system_score_gemma":0.0011506337,"threshold_uncertainty_score":0.05559826},"labels":[],"label_agreement":null},{"id":"W4245945006","doi":"10.1017/s0269964813000211","title":"AUTHORS' REJOINDER","year":2013,"lang":"en","type":"article","venue":"Probability in the Engineering and Informational Sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Order (exchange); Mathematical economics; Epistemology; Management science; Mathematics; Philosophy; Economics","score_opus":0.09174195619849758,"score_gpt":0.30631487513425193,"score_spread":0.21457291893575436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245945006","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003496806,0.0019064693,0.0008286891,0.8239331,0.16942534,0.000021860216,0.00012487006,0.00016932904,0.0032406359],"genre_scores_gemma":[0.006757563,0.00083175744,0.0012371269,0.90692115,0.06553857,0.000108215754,0.000054284355,0.00022989573,0.018321505],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9829692,0.0044436427,0.0014162025,0.0033122175,0.0069633713,0.00089540175],"domain_scores_gemma":[0.93415594,0.024060417,0.0036986854,0.0037965034,0.029584868,0.004703564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011274815,0.0012528874,0.0018138489,0.0012826968,0.004224669,0.0076041957,0.0049406076,0.021518698,0.010461176],"category_scores_gemma":[0.119316466,0.0006508243,0.0018430005,0.001111142,0.0055977153,0.0077499654,0.00520255,0.042459175,0.009949411],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027268488,0.000007584096,0.00016737731,0.00005537539,0.000016588396,0.000231722,0.00047081584,0.000061206185,0.00009022322,0.0065338137,0.98948157,0.0028564923],"study_design_scores_gemma":[0.000018545958,0.000012946472,0.00030163565,0.0001620959,0.000023215101,0.00037716687,0.0010086118,0.00021300663,0.00029360564,0.011855097,0.98567563,0.000058475183],"about_ca_topic_score_codex":0.0033123156,"about_ca_topic_score_gemma":0.003558065,"teacher_disagreement_score":0.021518698,"about_ca_system_score_codex":0.0034113082,"about_ca_system_score_gemma":0.005185768,"threshold_uncertainty_score":0.059627652},"labels":[],"label_agreement":null},{"id":"W4246476594","doi":"10.1002/9781119549345.ch1","title":"The Concept of Probability","year":2019,"lang":"en","type":"other","venue":"Wiley series in probability and statistics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Limit (mathematics); Context (archaeology); Range (aeronautics); Computer science; Probability theory; Reliability (semiconductor); Outcome (game theory); Coin flipping; Process (computing); Applied probability; Mathematics; Mathematical economics; Statistics; Engineering; Geography; Power (physics)","score_opus":0.04721272677147289,"score_gpt":0.30039152875663666,"score_spread":0.2531788019851638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246476594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005678435,0.11071529,0.5177162,0.032495312,0.00728887,0.00025428334,0.0014519736,0.0006095497,0.32379007],"genre_scores_gemma":[0.41030404,0.14143579,0.30222863,0.023661666,0.027623309,0.001925023,0.0017869774,0.00091661443,0.09011795],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9903182,0.00349186,0.0006996923,0.002083755,0.0029604891,0.00044607912],"domain_scores_gemma":[0.9886734,0.0079895565,0.0006524042,0.0012313803,0.0010780852,0.00037527108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066738543,0.0018154667,0.0021650346,0.0060128164,0.0031437937,0.010497048,0.0025894505,0.0050476408,0.014161385],"category_scores_gemma":[0.012939594,0.0007898773,0.0020125993,0.0044427724,0.023521552,0.017847417,0.00475518,0.008641314,0.004983043],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003535492,0.0000036066335,0.00004095827,0.000069178976,0.00000690989,0.000030217503,0.0001299021,0.00027836353,0.000048058417,0.99174505,0.0026501417,0.0049939686],"study_design_scores_gemma":[0.0000038121557,0.000013719015,0.00007612665,0.00017837573,0.0000075169273,0.00016673018,0.00009494846,0.00094565935,0.000075638025,0.8874631,0.11095895,0.00001541697],"about_ca_topic_score_codex":0.0024199479,"about_ca_topic_score_gemma":0.0008399752,"teacher_disagreement_score":0.014161385,"about_ca_system_score_codex":0.005451645,"about_ca_system_score_gemma":0.003080926,"threshold_uncertainty_score":0.047374547},"labels":[],"label_agreement":null},{"id":"W4246986565","doi":"10.1109/wsc.2016.7822328","title":"A Bayesian inference based simulation approach for estimating fraction nonconforming of pipe spool welding processes","year":2016,"lang":"en","type":"article","venue":"2016 Winter Simulation Conference (WSC)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo method; Fraction (chemistry); Process (computing); Schedule; Reliability (semiconductor); Computer science; Welding; Bayesian inference; Inference; Bayesian probability; Engineering; Reliability engineering; Mechanical engineering; Artificial intelligence; Mathematics; Statistics","score_opus":0.12215942026626554,"score_gpt":0.37691133022490614,"score_spread":0.2547519099586406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246986565","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054125108,0.00018084492,0.942524,0.00014793602,0.000015985166,0.000069221715,0.000110734836,0.00024350203,0.0025826138],"genre_scores_gemma":[0.8665857,0.0003531074,0.13002925,0.00007755823,0.000028706127,0.00026333574,0.0004157423,0.000052667594,0.0021940195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866617,0.0006099924,0.00008461595,0.00022256946,0.0002949511,0.00012163289],"domain_scores_gemma":[0.994041,0.004414781,0.00062408944,0.00022479724,0.0005571187,0.00013820792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043069967,0.0007406252,0.0012762406,0.0017513726,0.00066546,0.0012450104,0.0019002706,0.0014755854,0.0019229788],"category_scores_gemma":[0.011975098,0.0011325625,0.0011984486,0.0012928821,0.0009484988,0.0015796405,0.0009747977,0.0014925267,0.00023832255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021028533,0.00000962023,0.00054731366,0.000006671554,0.000010482592,0.000013594843,0.000016888556,0.99308336,0.00011780823,0.003926078,0.000041787578,0.0022053136],"study_design_scores_gemma":[0.0000030957467,0.000005566528,0.00010262162,0.0000025826569,0.0000029642542,0.0000035480184,0.0000020623988,0.9987588,0.00006664324,0.0009949147,0.00005323736,0.000003959286],"about_ca_topic_score_codex":0.026614385,"about_ca_topic_score_gemma":0.014573365,"teacher_disagreement_score":0.026614385,"about_ca_system_score_codex":0.0022858647,"about_ca_system_score_gemma":0.0019558596,"threshold_uncertainty_score":0.05291897},"labels":[],"label_agreement":null},{"id":"W4247442685","doi":"10.32920/ryerson.14664006.v1","title":"Aircraft repair damage tolerance analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Airplane; Aviation; Aeronautics; Damage tolerance; Structural integrity; Aircraft maintenance; Structural failure; Engineering; Service (business); Point (geometry); Computer science; Forensic engineering; Structural engineering; Aerospace engineering; Business","score_opus":0.0836851712836384,"score_gpt":0.345221342222341,"score_spread":0.26153617093870263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247442685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3131899,0.0017245094,0.56004924,0.00030452624,0.00014427563,0.0003502152,0.002668588,0.0011904512,0.12037838],"genre_scores_gemma":[0.9543797,0.0005832272,0.023424933,0.00007657875,0.000026976872,0.00011620686,0.0014979807,0.00016254061,0.019731805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995433,0.00004036159,0.000021061847,0.000080565864,0.00024271727,0.000071936876],"domain_scores_gemma":[0.99950254,0.0001277598,0.000082472274,0.000060323335,0.0002114732,0.000015384832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042682365,0.0005165696,0.0004347086,0.0020982986,0.0003387428,0.00060647615,0.0007697582,0.00053983764,0.0064973957],"category_scores_gemma":[0.0011724059,0.00016812018,0.00096599606,0.0006738247,0.00022363887,0.00044125188,0.0004614074,0.00040670604,0.001394279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049394625,0.000053821976,0.0047249063,0.00013059577,0.000037453443,0.00015803069,0.00009306036,0.89816004,0.009782432,0.009050424,0.0022458194,0.075513944],"study_design_scores_gemma":[0.0000049497385,0.00018249961,0.010098742,0.00004584866,0.000029055793,0.00039527466,0.00013002209,0.9659402,0.008875333,0.0041485056,0.0101207,0.000028898468],"about_ca_topic_score_codex":0.0054262844,"about_ca_topic_score_gemma":0.002819277,"teacher_disagreement_score":0.0064973957,"about_ca_system_score_codex":0.00049141684,"about_ca_system_score_gemma":0.00042254775,"threshold_uncertainty_score":0.021735907},"labels":[],"label_agreement":null},{"id":"W4248520451","doi":"10.32920/ryerson.14664006","title":"Aircraft repair damage tolerance analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Airplane; Aviation; Aeronautics; Damage tolerance; Structural integrity; Aircraft maintenance; Engineering; Structural failure; Service (business); Air transport; Point (geometry); Computer science; Forensic engineering; Aerospace engineering; Structural engineering; Business","score_opus":0.0836851712836384,"score_gpt":0.345221342222341,"score_spread":0.26153617093870263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248520451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3131899,0.0017245094,0.56004924,0.00030452624,0.00014427563,0.0003502152,0.002668588,0.0011904512,0.12037838],"genre_scores_gemma":[0.9543797,0.0005832272,0.023424933,0.00007657875,0.000026976872,0.00011620686,0.0014979807,0.00016254061,0.019731805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995433,0.00004036159,0.000021061847,0.000080565864,0.00024271727,0.000071936876],"domain_scores_gemma":[0.99950254,0.0001277598,0.000082472274,0.000060323335,0.0002114732,0.000015384832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042682365,0.0005165696,0.0004347086,0.0020982986,0.0003387428,0.00060647615,0.0007697582,0.00053983764,0.0064973957],"category_scores_gemma":[0.0011724059,0.00016812018,0.00096599606,0.0006738247,0.00022363887,0.00044125188,0.0004614074,0.00040670604,0.001394279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049394625,0.000053821976,0.0047249063,0.00013059577,0.000037453443,0.00015803069,0.00009306036,0.89816004,0.009782432,0.009050424,0.0022458194,0.075513944],"study_design_scores_gemma":[0.0000049497385,0.00018249961,0.010098742,0.00004584866,0.000029055793,0.00039527466,0.00013002209,0.9659402,0.008875333,0.0041485056,0.0101207,0.000028898468],"about_ca_topic_score_codex":0.0054262844,"about_ca_topic_score_gemma":0.002819277,"teacher_disagreement_score":0.0064973957,"about_ca_system_score_codex":0.00049141684,"about_ca_system_score_gemma":0.00042254775,"threshold_uncertainty_score":0.021735907},"labels":[],"label_agreement":null},{"id":"W4248998235","doi":"10.3166/rfgc.7.831-880","title":"Numerical response analysis in dynamic engineering problems","year":2003,"lang":"en","type":"article","venue":"Revue française de génie civil","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science","score_opus":0.026736684444831012,"score_gpt":0.27686159166215796,"score_spread":0.25012490721732694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248998235","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047617615,0.00070634816,0.98528767,0.00027843824,0.00007837199,0.000048439928,0.000027415661,0.00014640152,0.008665239],"genre_scores_gemma":[0.39417624,0.0022859038,0.5824944,0.00023057865,0.0002789185,0.00075925945,0.0001811942,0.00031516442,0.01927841],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988656,0.00048688636,0.000045046356,0.00009645191,0.00045716934,0.000048905145],"domain_scores_gemma":[0.9979678,0.0014063147,0.000115992705,0.00013950432,0.0003376387,0.000032775486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015317533,0.00066345226,0.0006665419,0.0010056491,0.00048062784,0.0009839048,0.0007522814,0.00094995584,0.0045888294],"category_scores_gemma":[0.004936493,0.0003132665,0.00074414705,0.0007208301,0.0015739767,0.00075990224,0.0015295468,0.001253824,0.00097126054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041575397,0.00004335296,0.0005236065,0.00040222562,0.000048032038,0.00009349693,0.00019586988,0.74887353,0.008123151,0.17882122,0.0016604456,0.061173487],"study_design_scores_gemma":[0.000009744627,0.000018126155,0.00012476036,0.0000350474,0.000004488241,0.000031271495,0.00003153284,0.9631914,0.0010248761,0.02941389,0.006107874,0.000006979783],"about_ca_topic_score_codex":0.0015300754,"about_ca_topic_score_gemma":0.00081612816,"teacher_disagreement_score":0.0045888294,"about_ca_system_score_codex":0.00067111314,"about_ca_system_score_gemma":0.0007260799,"threshold_uncertainty_score":0.015351236},"labels":[],"label_agreement":null},{"id":"W4251971715","doi":"10.1007/978-1-4614-6170-8_100775","title":"Random Structures","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.08249749849221974,"score_gpt":0.30324955629878947,"score_spread":0.22075205780656973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251971715","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017990947,0.008094707,0.25119382,0.002984428,0.0010344691,0.000054289212,0.0004929139,0.0005602424,0.7337861],"genre_scores_gemma":[0.060376234,0.008971547,0.050925884,0.0012644845,0.0014523803,0.00020308534,0.0008920659,0.00064542325,0.8752688],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99946016,0.00011140439,0.000017329658,0.00011947559,0.00026294368,0.00002877001],"domain_scores_gemma":[0.9996264,0.00016196113,0.000022722372,0.00009191353,0.0000770478,0.00001984326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004556211,0.00091574644,0.00070389966,0.0010397002,0.00061322225,0.0020890296,0.00077058206,0.0010628348,0.054166064],"category_scores_gemma":[0.0019609893,0.0004222987,0.00039005314,0.000990689,0.0016828384,0.0025789875,0.001089399,0.001972056,0.020475218],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054061816,0.000011490896,0.00003706199,0.00006448752,0.0000066491584,0.000017638498,0.000045316458,0.0023549667,0.00034455882,0.8806325,0.045011222,0.071468726],"study_design_scores_gemma":[0.0000041820604,0.0000085005495,0.000086729175,0.00006111751,0.0000052957685,0.0000749016,0.000020156416,0.0048788744,0.00046660536,0.71536815,0.27901486,0.000010556892],"about_ca_topic_score_codex":0.00080686214,"about_ca_topic_score_gemma":0.0012346902,"teacher_disagreement_score":0.054166064,"about_ca_system_score_codex":0.0010910555,"about_ca_system_score_gemma":0.0007341988,"threshold_uncertainty_score":0.18120354},"labels":[],"label_agreement":null},{"id":"W4252421686","doi":"10.2514/6.2012-1587","title":"Robust and Reliability Based Design Optimization Framework for Wing Design","year":2012,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Wing; Reliability (semiconductor); Reliability engineering; Engineering; Aerospace engineering","score_opus":0.21194126258762566,"score_gpt":0.3368856767100388,"score_spread":0.12494441412241311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252421686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005923738,0.00014516052,0.998005,0.000049781098,0.000013918998,0.000012498751,0.000031879845,0.000056643115,0.001092682],"genre_scores_gemma":[0.26987204,0.0013028319,0.7168147,0.00022493074,0.000252434,0.00053064775,0.00042952184,0.000309854,0.010262912],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987668,0.0004253521,0.000044732402,0.00014388782,0.0005404237,0.00007882282],"domain_scores_gemma":[0.99910754,0.00040623845,0.00011974022,0.00008535083,0.00024769283,0.00003343767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029130923,0.0017649048,0.0020639827,0.0015217939,0.00037016688,0.0013735539,0.002237954,0.0012987909,0.004467642],"category_scores_gemma":[0.003641126,0.0009092197,0.00157342,0.001076173,0.0009334552,0.0012000187,0.0013522685,0.0016244272,0.0010764443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017943696,0.000020236454,0.000051663377,0.00007415224,0.00003153394,0.000023836983,0.000011893131,0.93416405,0.0010539017,0.05091245,0.0006958844,0.012942436],"study_design_scores_gemma":[0.0000048758066,0.000019768726,0.000026297073,0.000007638535,0.000010232793,0.000008174337,0.0000020934137,0.98167175,0.00019810462,0.017285042,0.00076117966,0.000004847987],"about_ca_topic_score_codex":0.0020888387,"about_ca_topic_score_gemma":0.0020160414,"teacher_disagreement_score":0.004467642,"about_ca_system_score_codex":0.0009841865,"about_ca_system_score_gemma":0.0014196383,"threshold_uncertainty_score":0.015406072},"labels":[],"label_agreement":null},{"id":"W4252708749","doi":"10.32920/ryerson.14645310","title":"Integrated uncertainty techniques in aircraft derivative design optimization.","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Multidisciplinary design optimization; Sensitivity (control systems); Conceptual design; Computer science; Engineering design process; Material derivative; Mathematical optimization; Process (computing); Uncertainty analysis; Engineering optimization; Optimization problem; Reliability engineering; Engineering; Simulation; Mathematics","score_opus":0.11401135817254757,"score_gpt":0.338233022895713,"score_spread":0.22422166472316543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252708749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024004083,0.0008715332,0.9930577,0.000087992354,0.00004244005,0.000040672912,0.000025745512,0.00008944855,0.0033840337],"genre_scores_gemma":[0.4005124,0.0027428896,0.58974236,0.00022822618,0.00014470633,0.00057735085,0.0002160529,0.0002881487,0.0055477875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988463,0.00038795453,0.00005723148,0.00012987315,0.0005191287,0.00005946231],"domain_scores_gemma":[0.99868613,0.000912808,0.00011885481,0.0000658533,0.00018853626,0.00002776013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021659671,0.0016519735,0.0011964787,0.0014089731,0.00063176046,0.0011429247,0.0007179829,0.0010714015,0.0024052237],"category_scores_gemma":[0.0035033703,0.00089850294,0.0019008233,0.0010481642,0.0010402437,0.0010512958,0.0018524308,0.0021975331,0.00034525912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003747547,0.000032236996,0.00041858075,0.0002244629,0.00006919257,0.000052128376,0.00007771455,0.9132533,0.00191923,0.029590674,0.00062195864,0.053703096],"study_design_scores_gemma":[0.000006544914,0.000049195543,0.00011677396,0.000028099184,0.000020552372,0.000019543324,0.000012932621,0.9841217,0.000958726,0.011909836,0.0027461308,0.000009944593],"about_ca_topic_score_codex":0.0039206217,"about_ca_topic_score_gemma":0.0028812084,"teacher_disagreement_score":0.0039206217,"about_ca_system_score_codex":0.0009275768,"about_ca_system_score_gemma":0.0013247555,"threshold_uncertainty_score":0.01145488},"labels":[],"label_agreement":null},{"id":"W4253319988","doi":"10.13052/remn.17.31-61","title":"The sensitivity equation method in fluid mechanics","year":2008,"lang":"en","type":"article","venue":"European Journal of Computational Mechanics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Sensitivity (control systems); Computational fluid dynamics; Cascade; Adjoint equation; Finite element method; Fluid mechanics; Flow (mathematics); Applied mathematics; Mathematical optimization; Fluid dynamics; Computer science; Mathematics; Mechanics; Mathematical analysis; Physics; Geometry; Engineering; Partial differential equation; Structural engineering","score_opus":0.12338952374985267,"score_gpt":0.32536442179585756,"score_spread":0.20197489804600488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253319988","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047939792,0.0012150237,0.99449545,0.00029156703,0.00017001618,0.000025700005,0.000053168886,0.00012496018,0.0031447646],"genre_scores_gemma":[0.11358943,0.006990448,0.8562435,0.0009084446,0.0010180274,0.0005259688,0.000342383,0.00062963093,0.019752186],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988594,0.00041407804,0.000049161048,0.00013455817,0.000497537,0.00004533249],"domain_scores_gemma":[0.99904376,0.00062960957,0.00005382512,0.000092945156,0.00014519732,0.00003463789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001599797,0.00095858035,0.00095429516,0.0011658802,0.00049772585,0.0012112401,0.0010004959,0.0015646614,0.0044368585],"category_scores_gemma":[0.0033640186,0.00056507636,0.001538821,0.0011884391,0.0016238063,0.0018581323,0.002435496,0.0026912533,0.0017997444],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023536622,0.000031944146,0.00041814783,0.0004978858,0.00011034987,0.00016370064,0.00013803497,0.20678496,0.007771458,0.6576505,0.007966367,0.11844319],"study_design_scores_gemma":[0.000013131298,0.000035449633,0.00025277864,0.00008502247,0.000028457103,0.00019373209,0.000015688252,0.6770243,0.0028731436,0.2735338,0.045893148,0.000051405128],"about_ca_topic_score_codex":0.0013823082,"about_ca_topic_score_gemma":0.00061613944,"teacher_disagreement_score":0.0044368585,"about_ca_system_score_codex":0.0005282905,"about_ca_system_score_gemma":0.0010365023,"threshold_uncertainty_score":0.014842749},"labels":[],"label_agreement":null},{"id":"W4254934540","doi":"10.1080/00401706.2000.10485973","title":"Introduction to Two Classics in Reliability Theory","year":2000,"lang":"en","type":"article","venue":"Technometrics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reliability (semiconductor); Reliability theory; Mathematics; Mathematical economics; Econometrics; Philosophy; Computer science; Statistics; Physics; Generalizability theory; Thermodynamics","score_opus":0.0468481963317129,"score_gpt":0.3281044199743199,"score_spread":0.281256223642607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254934540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034507245,0.06358207,0.6599609,0.10504255,0.03583936,0.00014591018,0.0014849366,0.0009612099,0.12953234],"genre_scores_gemma":[0.15132885,0.07425545,0.34827206,0.07157007,0.12827386,0.0011632113,0.0012066242,0.0018742194,0.2220557],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99772626,0.0007038448,0.00017195877,0.0003584942,0.00089409365,0.00014541687],"domain_scores_gemma":[0.98772615,0.009359703,0.00044620503,0.0008770781,0.001264731,0.00032607408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003174452,0.0017232151,0.0015254827,0.0051281764,0.001757581,0.0037566272,0.002006562,0.0041902885,0.01762382],"category_scores_gemma":[0.015652426,0.00070013775,0.0016617159,0.0055398494,0.00599467,0.008250389,0.002557858,0.0073025185,0.0066447817],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022281583,0.00003338612,0.00014138171,0.000251774,0.000016430773,0.000086977576,0.0001343116,0.0005756989,0.00022878764,0.87624663,0.08087929,0.04138293],"study_design_scores_gemma":[0.000013665459,0.000029927287,0.00023220989,0.00012324462,0.000020539399,0.00023340862,0.00004060643,0.0015594802,0.00024363602,0.8006225,0.19684522,0.000035551566],"about_ca_topic_score_codex":0.0009953097,"about_ca_topic_score_gemma":0.0007826396,"teacher_disagreement_score":0.01762382,"about_ca_system_score_codex":0.0020755029,"about_ca_system_score_gemma":0.0016461672,"threshold_uncertainty_score":0.058957517},"labels":[],"label_agreement":null},{"id":"W4255352387","doi":"10.1007/978-3-662-53120-4_300562","title":"Quasi-static Errors","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Geology","score_opus":0.1259643590093711,"score_gpt":0.32677752424762774,"score_spread":0.20081316523825662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255352387","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024219379,0.0058528744,0.61226445,0.0012291479,0.0027939668,0.00004314478,0.00036563148,0.00058283046,0.374446],"genre_scores_gemma":[0.1113504,0.007362671,0.070492946,0.00086480496,0.001478423,0.00011459858,0.0005780509,0.0008485356,0.8069096],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990908,0.00012054102,0.00003991394,0.0001874857,0.000521293,0.000039964787],"domain_scores_gemma":[0.9991941,0.00024053386,0.00006187473,0.00024060774,0.00023506678,0.000027965085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007151514,0.0011113278,0.0008966082,0.0009241478,0.00053455733,0.0018447383,0.0010620367,0.0012317295,0.036082044],"category_scores_gemma":[0.0023064238,0.00046678956,0.0004922253,0.0010857313,0.0019420238,0.0022980473,0.0016015245,0.001959013,0.012575525],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037240272,0.000015084302,0.00006697843,0.00018335739,0.000013914902,0.00004646372,0.00006643748,0.012157908,0.0029096254,0.8219529,0.022723258,0.13982683],"study_design_scores_gemma":[0.000013012403,0.00009396276,0.00040792485,0.00021633568,0.00002592897,0.00036045842,0.00006109348,0.050882332,0.0061405576,0.65675145,0.2850037,0.000043317465],"about_ca_topic_score_codex":0.00074092706,"about_ca_topic_score_gemma":0.0007872911,"teacher_disagreement_score":0.036082044,"about_ca_system_score_codex":0.0008682842,"about_ca_system_score_gemma":0.0006835779,"threshold_uncertainty_score":0.1207065},"labels":[],"label_agreement":null},{"id":"W4255574251","doi":"10.32920/ryerson.14663364","title":"Analysis of passing sight distance using first-order reliability method","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sight; Moment (physics); Reliability (semiconductor); Computer science; Variable (mathematics); Measure (data warehouse); Mathematics; Simulation; Statistics; Algorithm; Mathematical analysis; Data mining","score_opus":0.13175269685108018,"score_gpt":0.40070741969816154,"score_spread":0.26895472284708133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255574251","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01805499,0.00019631129,0.9802419,0.000030313637,0.000012037641,0.000027216358,0.000048459347,0.00017202809,0.0012167544],"genre_scores_gemma":[0.8175269,0.0005895985,0.177852,0.000024156849,0.00004102988,0.00012624795,0.00024462416,0.00008401269,0.0035114656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985461,0.0003617289,0.000066522574,0.00018850155,0.000738438,0.00009870866],"domain_scores_gemma":[0.9961125,0.0021886851,0.00052622927,0.0002617942,0.00086162076,0.000049142785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015187416,0.00079665857,0.00073874876,0.0018812509,0.00027278328,0.0008564305,0.0008051862,0.0006162026,0.0016498311],"category_scores_gemma":[0.00541209,0.00027635437,0.0011816927,0.00073649595,0.00044081424,0.000793953,0.0004038296,0.00090205594,0.00049616006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078616606,0.000053575426,0.0054777064,0.00023546763,0.0000939036,0.00015631724,0.00023123172,0.8676271,0.014758761,0.030277556,0.0007917969,0.080218084],"study_design_scores_gemma":[0.0000023458251,0.00006858403,0.001566347,0.000012389005,0.0000166753,0.00010038794,0.000026513564,0.9898487,0.0029488173,0.004555201,0.0008368794,0.000017068642],"about_ca_topic_score_codex":0.004444631,"about_ca_topic_score_gemma":0.002125492,"teacher_disagreement_score":0.004444631,"about_ca_system_score_codex":0.00075207924,"about_ca_system_score_gemma":0.0009201575,"threshold_uncertainty_score":0.008837521},"labels":[],"label_agreement":null},{"id":"W4256558515","doi":"10.1002/9781119516651.ch10","title":"Elements of Reliability Theory","year":2020,"lang":"en","type":"other","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Weibull distribution; Exponential distribution; Reliability (semiconductor); Failure rate; Statistics; Log-normal distribution; Hazard; Exponential function; Mathematics; Reliability theory; Constant (computer programming); Hazard ratio; Distribution (mathematics); Exponentiated Weibull distribution; Reliability engineering; Computer science; Engineering; Mathematical analysis; Physics; Confidence interval; Thermodynamics","score_opus":0.0691952218490958,"score_gpt":0.3328622238428894,"score_spread":0.26366700199379356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256558515","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030269488,0.025381885,0.6817928,0.009004248,0.0016788094,0.0002656737,0.0012315507,0.00069337885,0.27692473],"genre_scores_gemma":[0.33500314,0.09731576,0.43131104,0.0057581444,0.0068217837,0.0016194629,0.002049797,0.00059194426,0.11952882],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995921,0.00094754825,0.0002701012,0.00060048024,0.0020463062,0.00021447791],"domain_scores_gemma":[0.9957633,0.0022781042,0.0002654254,0.00054041855,0.0010509088,0.000101872094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036633133,0.0017209066,0.001485421,0.0031025552,0.0014993951,0.004189618,0.0022445407,0.002333004,0.01733671],"category_scores_gemma":[0.008386918,0.00077233394,0.0013960493,0.002502787,0.0044930335,0.004551821,0.0021023694,0.0039840993,0.0085111465],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046878736,0.000012985251,0.00019465797,0.00018781482,0.000015685655,0.00006775121,0.00013375675,0.0054247375,0.00021898364,0.96202016,0.0077749765,0.023943827],"study_design_scores_gemma":[0.0000049656414,0.00003156127,0.00022276556,0.0002612351,0.000014903255,0.00032493332,0.00007756097,0.007245971,0.00023404347,0.8583745,0.13318445,0.000023195396],"about_ca_topic_score_codex":0.0026277744,"about_ca_topic_score_gemma":0.0010811677,"teacher_disagreement_score":0.01733671,"about_ca_system_score_codex":0.0023884315,"about_ca_system_score_gemma":0.0020192128,"threshold_uncertainty_score":0.057997048},"labels":[],"label_agreement":null},{"id":"W4256708831","doi":"10.1002/9781118445112.stat02406","title":"Principal Differential Analysis","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Differential equation; Basis (linear algebra); Mathematics; Fourier series; Principal (computer security); Applied mathematics; Set (abstract data type); Differential (mechanical device); Linear differential equation; Basis function; Mathematical analysis; Computer science","score_opus":0.09261492496268822,"score_gpt":0.3674687307731049,"score_spread":0.27485380581041663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256708831","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003046165,0.0015442742,0.973827,0.00092742295,0.00022190367,0.00008019783,0.0006547505,0.0005292144,0.019169038],"genre_scores_gemma":[0.37507102,0.008192613,0.5292516,0.0009015772,0.0018903068,0.0006956425,0.0024547854,0.0010555574,0.08048693],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99710435,0.00091370236,0.00013826611,0.0006332352,0.0010775583,0.00013299877],"domain_scores_gemma":[0.99579775,0.0018060227,0.00038803992,0.0006198626,0.0012426802,0.00014565047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027997112,0.0012906953,0.0016283155,0.0027342753,0.0007051794,0.0031668835,0.0010596415,0.0010529336,0.014977502],"category_scores_gemma":[0.0098595945,0.00047924084,0.0011332553,0.002553872,0.00200126,0.0014721951,0.0019900524,0.0019217556,0.005866782],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007658948,0.000053283045,0.002170677,0.0004139575,0.00016343499,0.0002671984,0.00017606605,0.089874975,0.0025842458,0.68585277,0.03608112,0.18228568],"study_design_scores_gemma":[0.00002642992,0.000052062438,0.0016647091,0.00012097907,0.00005778939,0.000307018,0.00008891154,0.43284243,0.0021456247,0.48190594,0.080725215,0.00006293688],"about_ca_topic_score_codex":0.0016716581,"about_ca_topic_score_gemma":0.0009934712,"teacher_disagreement_score":0.014977502,"about_ca_system_score_codex":0.0011955451,"about_ca_system_score_gemma":0.0017615041,"threshold_uncertainty_score":0.050104797},"labels":[],"label_agreement":null},{"id":"W4280591244","doi":"10.1017/aer.2022.49","title":"On the multi-fidelity approach in surrogate-based multidisciplinary design optimisation of high-aspect-ratio wing aircraft","year":2022,"lang":"en","type":"article","venue":"The Aeronautical Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada); University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reduction (mathematics); Flutter; Context (archaeology); Multidisciplinary approach; Aerodynamics; Multidisciplinary design optimization; Constraint (computer-aided design); Surrogate model; Computer science; Fidelity; Range (aeronautics); Drag; Wing; Weight estimation; Mathematical optimization; Engineering; Structural engineering; Aerospace engineering; Mathematics; Mechanical engineering; Statistics","score_opus":0.13939038239235618,"score_gpt":0.3264574794927136,"score_spread":0.1870670971003574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280591244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13521077,0.0002746208,0.85896724,0.00023741281,0.000029865536,0.0000792436,0.00006876174,0.000106955034,0.0050250324],"genre_scores_gemma":[0.8963796,0.00010522981,0.10254257,0.000050160197,0.000014888679,0.00008792833,0.00006761344,0.000028733104,0.0007232963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895144,0.0005987874,0.00003309416,0.000053615327,0.00030227803,0.000060793423],"domain_scores_gemma":[0.997535,0.0016788095,0.00027624075,0.00018997342,0.00024936788,0.00007061597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026602575,0.0005618104,0.000967709,0.00070917484,0.00032065148,0.0010242922,0.0005865512,0.0011498678,0.0010942844],"category_scores_gemma":[0.005293954,0.0004053067,0.00079980685,0.00039484937,0.0005899056,0.00068263983,0.0010303883,0.0008434653,0.00016920686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026348673,0.000028042532,0.00026585264,0.000021945378,0.000009246615,0.000021385944,0.000010651563,0.99336785,0.001034166,0.0013722946,0.00003578938,0.0038063645],"study_design_scores_gemma":[0.0000018139468,0.000023373257,0.00009257174,0.0000044683493,0.0000015959398,0.0000072569014,0.0000026833668,0.9992331,0.0002827416,0.0002890147,0.00005971622,0.0000016997345],"about_ca_topic_score_codex":0.001291042,"about_ca_topic_score_gemma":0.0012278664,"teacher_disagreement_score":0.0026602575,"about_ca_system_score_codex":0.00042550414,"about_ca_system_score_gemma":0.0005401104,"threshold_uncertainty_score":0.014068961},"labels":[],"label_agreement":null},{"id":"W4281256076","doi":"10.3390/en15103793","title":"Data-Driven Calibration of Rough Heat Transfer Prediction Using Bayesian Inversion and Genetic Algorithm","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Reynolds-averaged Navier–Stokes equations; Heat transfer; Surface finish; Algorithm; A priori and a posteriori; Computer science; Surface roughness; Turbulence; Mechanics; Mechanical engineering; Engineering; Physics; Thermodynamics","score_opus":0.0800264899519412,"score_gpt":0.2905586307128256,"score_spread":0.21053214076088442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281256076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1519465,0.000089842244,0.8456787,0.000086160835,0.00001800416,0.000069525086,0.000075751064,0.00067377574,0.0013618344],"genre_scores_gemma":[0.8362155,0.000049408132,0.16290233,0.000036672773,0.00000834117,0.00014902004,0.00021858014,0.00007131387,0.0003488242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953103,0.00014969688,0.0000196891,0.000109045126,0.00013336034,0.000057246416],"domain_scores_gemma":[0.9986945,0.00064265373,0.00018951579,0.00014727208,0.0002826043,0.0000434981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015453722,0.00074047776,0.0006811974,0.00089046767,0.00035827214,0.00070978305,0.0008649626,0.0009076499,0.0005950627],"category_scores_gemma":[0.0042406064,0.0004506078,0.00058172754,0.0004756058,0.0006172962,0.00067173317,0.0007715719,0.0008595392,0.00021421695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003133793,0.00005315497,0.0011653475,0.000020717693,0.000021064068,0.000015722308,0.000029779416,0.97324264,0.0046484144,0.0012534356,0.00009973765,0.019418715],"study_design_scores_gemma":[0.0000038917674,0.000009524293,0.00020605608,0.0000022385227,0.0000021139506,0.000002375498,0.0000028184247,0.9980781,0.0011992736,0.00042914783,0.000059859634,0.0000045107163],"about_ca_topic_score_codex":0.005603587,"about_ca_topic_score_gemma":0.0038156158,"teacher_disagreement_score":0.005603587,"about_ca_system_score_codex":0.0007910271,"about_ca_system_score_gemma":0.0012751537,"threshold_uncertainty_score":0.011141956},"labels":[],"label_agreement":null},{"id":"W4281285560","doi":"10.1007/978-981-19-1004-3_12","title":"Reliability Analysis of Slope Stability Using Censored Samples and Genetic Algorithm","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Randomness; Mathematics; Random variable; Reliability (semiconductor); Stability (learning theory); Probability distribution; Principle of maximum entropy; Probabilistic logic; Statistics; Order statistic; Akaike information criterion; Algorithm; First-order reliability method; Mathematical optimization; Applied mathematics; Computer science; Machine learning","score_opus":0.05876801063872297,"score_gpt":0.27581553967771777,"score_spread":0.2170475290389948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281285560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04333791,0.00031061133,0.9548002,0.00005784663,0.00002120658,0.00002872774,0.00005211127,0.0002910383,0.0011002406],"genre_scores_gemma":[0.8396397,0.00026097673,0.15813147,0.000026719787,0.000032456115,0.000076874596,0.000233738,0.00010951424,0.001488577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998691,0.0006469138,0.000051594245,0.00019276392,0.0003465128,0.000071255825],"domain_scores_gemma":[0.99395967,0.004400037,0.00040999637,0.00046144723,0.00072181283,0.000047056034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029931418,0.00066988525,0.0009994279,0.0016446015,0.00032818667,0.0009636222,0.0013747462,0.0008825786,0.0011496383],"category_scores_gemma":[0.0132491905,0.0004609261,0.0010731197,0.0013829876,0.0008930409,0.0009357766,0.0006850649,0.0008358904,0.0002149441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111825386,0.000027186035,0.001373207,0.000053308348,0.00007773425,0.000042977466,0.000046129775,0.9529925,0.0013790057,0.008290562,0.00026637732,0.035339247],"study_design_scores_gemma":[0.0000028064019,0.000011772012,0.0003341956,0.000004074487,0.0000063308953,0.000007775513,0.000003280778,0.9967182,0.0002823653,0.0025765102,0.00004850963,0.0000040761474],"about_ca_topic_score_codex":0.004719397,"about_ca_topic_score_gemma":0.0025275778,"teacher_disagreement_score":0.004719397,"about_ca_system_score_codex":0.0010305203,"about_ca_system_score_gemma":0.0007390699,"threshold_uncertainty_score":0.015829384},"labels":[],"label_agreement":null},{"id":"W4281706659","doi":"10.1007/978-981-19-0503-2_13","title":"Quantile Based Probabilistic Characterization of Geotechnical Variables Using Maximum Entropy Principle","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Quantile function; Quantile; Principle of maximum entropy; Mathematics; Probability density function; Cumulative distribution function; Random variable; Probability distribution; Maximum entropy probability distribution; Probabilistic logic; Applied mathematics; Moment-generating function; Statistics","score_opus":0.04484827996218819,"score_gpt":0.2766793914744616,"score_spread":0.2318311115122734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281706659","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012963755,0.0003378801,0.9846138,0.000102585334,0.00001846632,0.00001201065,0.00014927262,0.000089905916,0.0017122751],"genre_scores_gemma":[0.88250214,0.0014565476,0.10596396,0.00015544385,0.00035982236,0.00016048926,0.00091626734,0.00021563425,0.008269801],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993286,0.0002344943,0.000030147605,0.00014260678,0.00020061836,0.00006351863],"domain_scores_gemma":[0.9983943,0.0010424432,0.00018566576,0.00013393974,0.00019563198,0.000047970127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015113098,0.0005653363,0.0011545566,0.0013801556,0.00035568047,0.0013505856,0.0011170917,0.0006470809,0.0026981528],"category_scores_gemma":[0.0035166126,0.00041309473,0.0009715262,0.0014526512,0.001090526,0.0023562775,0.0013039396,0.0014428521,0.00032628083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009855352,0.00004703239,0.0024847183,0.00020536185,0.00008055741,0.00019438949,0.0001221175,0.6002584,0.008136756,0.3243587,0.0018230778,0.062190406],"study_design_scores_gemma":[0.0000020801924,0.000017384415,0.001191018,0.0000114249,0.000009085543,0.00004277224,0.000010694487,0.91966456,0.00069559575,0.077636704,0.00070488005,0.000013863486],"about_ca_topic_score_codex":0.0006228316,"about_ca_topic_score_gemma":0.00047185554,"teacher_disagreement_score":0.0026981528,"about_ca_system_score_codex":0.00060361903,"about_ca_system_score_gemma":0.0004029411,"threshold_uncertainty_score":0.009026229},"labels":[],"label_agreement":null},{"id":"W4281926383","doi":"10.1016/j.sandf.2022.101175","title":"Using a genetic algorithm to develop a pile design method","year":2022,"lang":"en","type":"article","venue":"SOILS AND FOUNDATIONS","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère des Transports","keywords":"Pile; Genetic algorithm; Nonlinear system; Engineering; Structural engineering; Standard penetration test; Design methods; Geotechnical engineering; Algorithm; Mathematics; Mathematical optimization; Mechanical engineering","score_opus":0.2406007779574856,"score_gpt":0.4102431362990491,"score_spread":0.16964235834156352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281926383","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005410101,0.00004074181,0.9929958,0.000025613039,0.000015024941,0.000073551826,0.000017928696,0.00021305955,0.0012082739],"genre_scores_gemma":[0.14176951,0.00008754974,0.8561617,0.000067673136,0.000017725813,0.00047723402,0.00009534281,0.000075202166,0.0012480771],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999385,0.00023191892,0.00003421098,0.00010737254,0.00019490162,0.000046552053],"domain_scores_gemma":[0.9991221,0.0005071173,0.000057491405,0.000045867804,0.00024898932,0.000018396502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011512685,0.0009364715,0.0007517583,0.0012687282,0.0004069056,0.00059684255,0.000731972,0.0009787118,0.0019819755],"category_scores_gemma":[0.0026220905,0.0004792867,0.0008512901,0.0006535268,0.00045384478,0.00042744822,0.00049773033,0.0007441158,0.00045573126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018851513,0.000028888775,0.00047945106,0.000048936665,0.000035954818,0.000043906723,0.000033489167,0.93199444,0.0024446747,0.0034796817,0.00031480857,0.061076973],"study_design_scores_gemma":[0.000009220346,0.00002754838,0.000084373074,0.000007228366,0.000007684208,0.000017405675,0.000005615393,0.997617,0.0006381974,0.0009522127,0.00062901113,0.0000045994866],"about_ca_topic_score_codex":0.0043011564,"about_ca_topic_score_gemma":0.0035009857,"teacher_disagreement_score":0.0043011564,"about_ca_system_score_codex":0.0006141271,"about_ca_system_score_gemma":0.0012870792,"threshold_uncertainty_score":0.008552253},"labels":[],"label_agreement":null},{"id":"W4283027172","doi":"10.1007/978-981-19-0511-7_45","title":"Application of Adaptive Kriging Method in Bridge Girder Reliability Analysis","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Kriging; Girder; Monte Carlo method; Reliability (semiconductor); Bridge (graph theory); Engineering; Reliability engineering; Sensitivity (control systems); Computer science; Structural engineering; Mathematics; Statistics; Machine learning","score_opus":0.04074097131086944,"score_gpt":0.30949688356692445,"score_spread":0.268755912256055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283027172","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01334115,0.00085391087,0.9832253,0.000037169197,0.000038272126,0.000012757747,0.000031853804,0.0002505522,0.0022090226],"genre_scores_gemma":[0.42407247,0.002034178,0.5675254,0.000030599578,0.000047725385,0.000052042058,0.00010581898,0.00017784833,0.005953899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997931,0.00008616223,0.00000921551,0.000034541044,0.000066236986,0.0000107175465],"domain_scores_gemma":[0.99966896,0.0002235918,0.000015924381,0.000032155283,0.000054839475,0.0000044663866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032249524,0.000361046,0.00045189392,0.0003192127,0.00019555794,0.00024661748,0.000619093,0.00041661758,0.0012525247],"category_scores_gemma":[0.0008060583,0.00026462914,0.00043900326,0.00069288333,0.00024152163,0.0003396387,0.00028805446,0.00056093396,0.0003757209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038110786,0.00004339854,0.0009956874,0.00018076038,0.000042742988,0.000071064234,0.00007262332,0.69131327,0.013677888,0.0066601005,0.0008646868,0.2860397],"study_design_scores_gemma":[0.0000016908157,0.000020413725,0.0005870905,0.0000074832515,0.000009452151,0.00002987651,0.000008142282,0.9936924,0.0017008706,0.002327878,0.0016061118,0.000008619095],"about_ca_topic_score_codex":0.00420846,"about_ca_topic_score_gemma":0.0067478684,"teacher_disagreement_score":0.00420846,"about_ca_system_score_codex":0.00018685736,"about_ca_system_score_gemma":0.00035566877,"threshold_uncertainty_score":0.008367896},"labels":[],"label_agreement":null},{"id":"W4283068822","doi":"10.31234/osf.io/ygkjn","title":"d'o: Sensitivity at the optimal criterion location","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Observer (physics); Detection theory; Base (topology); Noise (video); Variance (accounting); Measure (data warehouse); Response bias; Set (abstract data type); Range (aeronautics); Mathematics; SIGNAL (programming language); Interpretation (philosophy); Statistics; Sensitivity (control systems); Computer science; Algorithm; Pattern recognition (psychology); Artificial intelligence; Data mining; Physics; Detector","score_opus":0.10713062266650741,"score_gpt":0.3541852125127003,"score_spread":0.24705458984619288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283068822","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24195924,0.0013215814,0.73511827,0.0009085348,0.0002604431,0.000263987,0.0005602736,0.0009128496,0.018694783],"genre_scores_gemma":[0.91460043,0.00021272719,0.083154164,0.00045290915,0.000039585822,0.00018971952,0.00022267058,0.00015651004,0.0009712412],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99568826,0.0013259038,0.000353644,0.0014798947,0.00088561926,0.00026669228],"domain_scores_gemma":[0.97832733,0.015583118,0.0023304794,0.0020204901,0.0011420249,0.000596533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057043927,0.00090966286,0.0011206361,0.0014443528,0.0005838728,0.002843264,0.0015345132,0.0016712658,0.0028208552],"category_scores_gemma":[0.046090662,0.00061814464,0.0011073306,0.0007572904,0.0025957415,0.0026080604,0.0051359944,0.0018421672,0.0004158827],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025425397,0.00031203838,0.06254685,0.0013630745,0.0010406955,0.0006214685,0.0015797381,0.2659318,0.09607102,0.19616444,0.0057215067,0.36610484],"study_design_scores_gemma":[0.00031603704,0.00155002,0.058541495,0.00033212564,0.00031086503,0.0013706005,0.00071766035,0.52632475,0.058518697,0.34271088,0.008735708,0.00057113287],"about_ca_topic_score_codex":0.002336098,"about_ca_topic_score_gemma":0.0011118922,"teacher_disagreement_score":0.0057043927,"about_ca_system_score_codex":0.0017914677,"about_ca_system_score_gemma":0.0011825355,"threshold_uncertainty_score":0.030168056},"labels":[],"label_agreement":null},{"id":"W4283593245","doi":"10.5194/wes-7-1289-2022","title":"Surrogate models for the blade element momentum aerodynamic model using non-intrusive polynomial chaos expansions","year":2022,"lang":"en","type":"article","venue":"Wind energy science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Polynomial chaos; Aeroelasticity; Turbine; Aerodynamics; Uncertainty quantification; Wind power; Computer science; Control theory (sociology); Computational fluid dynamics; Engineering; Mathematics; Aerospace engineering; Statistics; Monte Carlo method; Artificial intelligence","score_opus":0.08636747331947528,"score_gpt":0.3170119388018217,"score_spread":0.23064446548234643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283593245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021139301,0.000078190285,0.976435,0.00009336846,0.000020050156,0.000027376003,0.00006771189,0.00009103282,0.0020479388],"genre_scores_gemma":[0.9087406,0.00023960095,0.085520364,0.000056649158,0.0000314798,0.0001786012,0.0002751585,0.000065695385,0.004891767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995491,0.00017483655,0.000019275161,0.000047369005,0.00017719158,0.000032184435],"domain_scores_gemma":[0.9990508,0.0005334638,0.00013737379,0.00008074079,0.00016466595,0.000032928794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008631792,0.0004837181,0.0005234712,0.0004912372,0.00023901799,0.0006973203,0.00059691333,0.00070927525,0.0010344156],"category_scores_gemma":[0.0027703398,0.0003094772,0.0005436847,0.0004235269,0.0005574803,0.0007743462,0.00058286637,0.0009454533,0.0003189949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011789714,0.000009191871,0.00025477746,0.000012932709,0.0000052732703,0.000017712624,0.000012518922,0.98704207,0.0008126145,0.009050818,0.00012550787,0.002644714],"study_design_scores_gemma":[4.871566e-7,0.0000031168495,0.000024484234,7.952518e-7,3.5809035e-7,0.0000023758707,6.2467615e-7,0.9992481,0.00008172277,0.00056647597,0.00007047336,8.857473e-7],"about_ca_topic_score_codex":0.0016420832,"about_ca_topic_score_gemma":0.0013946374,"teacher_disagreement_score":0.0016420832,"about_ca_system_score_codex":0.0004434534,"about_ca_system_score_gemma":0.0006060168,"threshold_uncertainty_score":0.0045650005},"labels":[],"label_agreement":null},{"id":"W4284688082","doi":"10.1002/eqe.3711","title":"Uncertainty quantification in the calibration of numerical elements in nonlinear seismic analysis","year":2022,"lang":"en","type":"article","venue":"Earthquake Engineering & Structural Dynamics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Calibration; Nonlinear system; Propagation of uncertainty; Uncertainty quantification; Component (thermodynamics); Probabilistic logic; Relevance (law); Computer science; Sensitivity (control systems); Uncertainty analysis; Reliability (semiconductor); Errors-in-variables models; Algorithm; Engineering; Mathematics; Statistics; Simulation; Machine learning; Artificial intelligence","score_opus":0.026577149318557612,"score_gpt":0.28688347648659684,"score_spread":0.26030632716803925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284688082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06826091,0.0002136434,0.9300879,0.00011044633,0.00001598417,0.000034433833,0.000022668955,0.00011643273,0.0011376166],"genre_scores_gemma":[0.94285774,0.00016346022,0.056570187,0.00003034118,0.000013061531,0.00004829212,0.00004298444,0.000042986365,0.00023092159],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974292,0.0013218211,0.00013491546,0.0002946681,0.0007154562,0.00010394375],"domain_scores_gemma":[0.99228704,0.0052600293,0.0008744904,0.00079224765,0.00070992514,0.0000761564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005661983,0.0008323872,0.00053761376,0.0010634784,0.0004278537,0.0009475053,0.00084856054,0.00089377427,0.00053090736],"category_scores_gemma":[0.022282569,0.00050137733,0.00053352193,0.0006875258,0.0012364644,0.0013297569,0.0017629847,0.0010049934,0.00009467761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047469246,0.000024094952,0.0015001358,0.000053481413,0.00003223243,0.000029866726,0.00008904465,0.96350056,0.0031968306,0.009285872,0.00007929454,0.022161096],"study_design_scores_gemma":[0.0000025051206,0.000018507017,0.00046319774,0.0000138453015,0.0000066251055,0.000009478674,0.0000130574,0.99350786,0.0022585082,0.0035381475,0.00015796992,0.000010328998],"about_ca_topic_score_codex":0.002387743,"about_ca_topic_score_gemma":0.0013802075,"teacher_disagreement_score":0.005661983,"about_ca_system_score_codex":0.000833977,"about_ca_system_score_gemma":0.00075348635,"threshold_uncertainty_score":0.029943764},"labels":[],"label_agreement":null},{"id":"W4285260804","doi":"10.20517/dpr.2022.01","title":"Unconditional and conditional simulation of nonstationary and non-Gaussian vector and field with prescribed marginal and correlation by using iteratively matched correlation","year":2022,"lang":"en","type":"article","venue":"Disaster Prevention and Resilience","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Gaussian random field; Gaussian; Mathematics; Gaussian process; Random field; Discretization; Applied mathematics; Field (mathematics); Kriging; Algorithm; Translation (biology); Mathematical optimization; Mathematical analysis; Statistics; Physics","score_opus":0.024691291592171597,"score_gpt":0.30211495237894287,"score_spread":0.2774236607867713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285260804","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03637195,0.000043507658,0.96201485,0.000058691752,0.000011074975,0.000028944785,0.000032332362,0.00019550079,0.0012431515],"genre_scores_gemma":[0.8004861,0.000075362885,0.19728185,0.00004655818,0.000011880269,0.00011679781,0.00011085828,0.000070648966,0.0018000529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996141,0.00014642498,0.00002016684,0.00005336656,0.00011719774,0.000048710226],"domain_scores_gemma":[0.99831057,0.0010599034,0.00021312872,0.00015814381,0.00019730856,0.000060952894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012333844,0.00041474486,0.0005793585,0.00045032307,0.0003100451,0.0006398363,0.00087016966,0.00062013976,0.0012021466],"category_scores_gemma":[0.003934794,0.00033398904,0.0006062912,0.00044751054,0.0009925705,0.0007962123,0.001292626,0.00067514635,0.0001625159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003845865,0.000016237613,0.00071262557,0.000016977257,0.00001300411,0.000038055423,0.000043218082,0.9774007,0.000931292,0.014949908,0.0001075158,0.0057319566],"study_design_scores_gemma":[0.0000014536172,0.0000034515062,0.00003994182,9.4096526e-7,8.2842865e-7,0.0000033820538,0.0000021978644,0.99864715,0.0002443858,0.0010068668,0.00004732513,0.0000020282387],"about_ca_topic_score_codex":0.007824983,"about_ca_topic_score_gemma":0.006232797,"teacher_disagreement_score":0.007824983,"about_ca_system_score_codex":0.0009145457,"about_ca_system_score_gemma":0.0013180533,"threshold_uncertainty_score":0.015558898},"labels":[],"label_agreement":null},{"id":"W4286960338","doi":"","title":"Probabilistic evaluation of changes in dynamic properties of structures","year":2021,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Probabilistic logic; Computer science; Artificial intelligence","score_opus":0.07249225508007412,"score_gpt":0.2968410440162476,"score_spread":0.2243487889361735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286960338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26565272,0.00051694736,0.7269789,0.00045645403,0.000059065766,0.00006976081,0.00031083482,0.00047675092,0.005478608],"genre_scores_gemma":[0.9746953,0.00019905978,0.023475954,0.000026242973,0.00004182595,0.00003600526,0.00018336375,0.0000948683,0.0012474504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99583143,0.0011381869,0.00014994988,0.0007492984,0.0018415984,0.00028963335],"domain_scores_gemma":[0.97491723,0.017859828,0.0022206262,0.0019913968,0.00227774,0.0007331261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048499713,0.0007102222,0.0010032863,0.0025033774,0.00049233955,0.0026843906,0.0014459478,0.0017336385,0.004500567],"category_scores_gemma":[0.032165967,0.00068108836,0.0009205232,0.0015399664,0.0021279044,0.003962598,0.0022641788,0.0011181266,0.0002644972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049705076,0.00007085861,0.0073976484,0.00024060666,0.00007190024,0.00017259737,0.00013865532,0.8751308,0.012421069,0.053560954,0.00052280736,0.049775153],"study_design_scores_gemma":[0.000006043461,0.000075337826,0.002028456,0.000011941827,0.000017323287,0.0000483378,0.000025840858,0.97470427,0.0031155997,0.019640177,0.00031365952,0.000013016296],"about_ca_topic_score_codex":0.0016786404,"about_ca_topic_score_gemma":0.0015913282,"teacher_disagreement_score":0.0048499713,"about_ca_system_score_codex":0.0019183882,"about_ca_system_score_gemma":0.0006359721,"threshold_uncertainty_score":0.025649428},"labels":[],"label_agreement":null},{"id":"W4287576222","doi":"10.1016/j.anucene.2022.109311","title":"Emulating loss of coolant simulations in a pressurized heavy water reactor","year":2022,"lang":"en","type":"article","venue":"Annals of Nuclear Energy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Canadian Nuclear Laboratories","funders":"","keywords":"Coolant; Nuclear engineering; Environmental science; Pressurized water reactor; Water cooled; Heavy water; Materials science; Water cooling; Thermodynamics; Nuclear physics; Physics; Engineering","score_opus":0.13724993976773872,"score_gpt":0.3451608885888132,"score_spread":0.2079109488210745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287576222","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9630987,0.00009455483,0.0280576,0.0002810165,0.000040913692,0.00004448167,0.00016537558,0.0002493748,0.007967956],"genre_scores_gemma":[0.9965578,0.000018520419,0.002378418,0.000025862384,0.0000048745014,0.000015935753,0.000053894742,0.000039535968,0.0009051242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971956,0.00009874317,0.000010791379,0.000038089365,0.000056836398,0.000075902986],"domain_scores_gemma":[0.9962094,0.0029940922,0.00020856732,0.00015680793,0.00027775805,0.00015338509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008800635,0.00062858657,0.0008005755,0.00046898032,0.00053174276,0.0008623271,0.0013058162,0.0015599813,0.0023472914],"category_scores_gemma":[0.004049458,0.00046387664,0.00056322874,0.00032802994,0.0010613282,0.0007487968,0.00074339,0.0011265299,0.00015707429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007351046,0.000035885194,0.00046244304,0.000009190785,0.0000053592353,0.000025188143,0.000020534118,0.9977975,0.00053588545,0.0005141406,0.000055927176,0.0004644866],"study_design_scores_gemma":[0.000005019066,0.000011881935,0.00006392045,7.494447e-7,0.0000012850431,0.0000012383598,0.0000046663954,0.9995977,0.00021449038,0.000076030505,0.000021737305,0.0000012282788],"about_ca_topic_score_codex":0.020041905,"about_ca_topic_score_gemma":0.007896418,"teacher_disagreement_score":0.020041905,"about_ca_system_score_codex":0.0012426185,"about_ca_system_score_gemma":0.0008814684,"threshold_uncertainty_score":0.039850533},"labels":[],"label_agreement":null},{"id":"W4289731380","doi":"10.1115/1.4055149","title":"Iterative Uncertainty Calibration for Modeling Metal Additive Manufacturing Processes Using Statistical Moment-Based Metric","year":2022,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Calibration; Metric (unit); Metamodeling; Moment (physics); Computer science; Iterative and incremental development; Process (computing); Mathematical optimization; Algorithm; Mathematics; Engineering; Statistics","score_opus":0.16131076374946765,"score_gpt":0.35288857626645764,"score_spread":0.19157781251699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289731380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011749777,0.000109700064,0.98725235,0.000036571237,0.00000729667,0.0000252067,0.000018040771,0.00014905541,0.00065204303],"genre_scores_gemma":[0.85381573,0.00018536893,0.14481823,0.000036633443,0.000017717632,0.00017125605,0.000101802274,0.00006768449,0.0007855837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986499,0.0005407022,0.00007612746,0.00020357517,0.00043641735,0.00009336497],"domain_scores_gemma":[0.9976419,0.0013589461,0.00034592554,0.0001514644,0.0004536063,0.000048093738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027468696,0.0009512777,0.00097256526,0.0013289972,0.00037167783,0.0009638503,0.0011206175,0.0010196315,0.0009271844],"category_scores_gemma":[0.005469148,0.00051408267,0.0011171439,0.0008535592,0.00087406574,0.0008424573,0.0012259592,0.0009911215,0.00017112846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000142749195,0.000009524667,0.00025484213,0.000022587119,0.000012642367,0.000013831611,0.000017895902,0.9878473,0.0008794486,0.0029689898,0.00006190863,0.0078967875],"study_design_scores_gemma":[7.7951313e-7,0.000008631481,0.000071693874,0.0000020869886,0.0000018557123,0.000003891478,0.0000016186845,0.9988543,0.00033635765,0.0006378139,0.00007809083,0.0000029357755],"about_ca_topic_score_codex":0.0047290716,"about_ca_topic_score_gemma":0.0020376649,"teacher_disagreement_score":0.0047290716,"about_ca_system_score_codex":0.0011491074,"about_ca_system_score_gemma":0.0012186285,"threshold_uncertainty_score":0.014526963},"labels":[],"label_agreement":null},{"id":"W4293795497","doi":"10.48550/arxiv.2104.03107","title":"Two-Stage Robust Quadratic Optimization with Equalities and its Application to Optimal Power Flow","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Mathematical optimization; Quadratic equation; Optimization problem; Set (abstract data type); Affine transformation; Sequence (biology); Ellipsoid; Computer science; Mathematics; Power flow; Electric power system; Power (physics)","score_opus":0.1280434409188444,"score_gpt":0.23841159509839194,"score_spread":0.11036815417954754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293795497","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046657394,0.00020428291,0.99305904,0.00013889006,0.000021487753,0.00003155433,0.000020602703,0.0000840057,0.0017744339],"genre_scores_gemma":[0.62579626,0.00044980794,0.36623573,0.00017398733,0.0001015778,0.00032793125,0.00016173662,0.00021957091,0.00653332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981602,0.00086806086,0.00008049329,0.0003621143,0.00037957254,0.00014965562],"domain_scores_gemma":[0.9966395,0.0024347815,0.00025517808,0.00018855081,0.00037733346,0.00010460759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004891852,0.0016650843,0.0019955244,0.0007907908,0.00050486333,0.0015096298,0.0014875188,0.00183143,0.0034054727],"category_scores_gemma":[0.0093311295,0.00092756,0.0013114791,0.001137313,0.0015900802,0.0019974026,0.0024466864,0.002478268,0.0003531565],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035913123,0.000022553557,0.0000890873,0.000049129096,0.000020260228,0.000032263928,0.000027315557,0.9739455,0.000505986,0.01837487,0.0002238085,0.006673374],"study_design_scores_gemma":[0.000003530834,0.000014399765,0.000013546137,0.000002268976,0.0000020694813,0.0000032811554,0.0000016446483,0.99569833,0.0001724453,0.0039647217,0.00012054384,0.0000032138246],"about_ca_topic_score_codex":0.0050122435,"about_ca_topic_score_gemma":0.0027359074,"teacher_disagreement_score":0.0050122435,"about_ca_system_score_codex":0.001347678,"about_ca_system_score_gemma":0.0013160921,"threshold_uncertainty_score":0.02587092},"labels":[],"label_agreement":null},{"id":"W4296052704","doi":"10.1049/pbpo217e_ch2","title":"Reliable solutions of uncertain optimal power flow problems by affine arithmetic","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Affine transformation; Affine arithmetic; Mathematical optimization; Minification; Power flow; Variety (cybernetics); Conservatism; Mathematics; Reduction (mathematics); Computer science; Power (physics); Electric power system","score_opus":0.07769872209410414,"score_gpt":0.27916148243247585,"score_spread":0.2014627603383717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296052704","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029282845,0.00029709804,0.9900338,0.00006803124,0.00002574662,0.0000124373055,0.000021394773,0.00007047067,0.0065427995],"genre_scores_gemma":[0.28994504,0.0024522617,0.69450486,0.00009634209,0.00020464712,0.0002155181,0.0001932732,0.00017197551,0.012216123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967456,0.00007922455,0.000018667142,0.00004677865,0.00016170286,0.000019048503],"domain_scores_gemma":[0.9997404,0.0001370108,0.000031159045,0.00004039512,0.000044228273,0.0000068770037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005953449,0.0006984716,0.00056120456,0.00045258517,0.00029987327,0.0013939111,0.00075785926,0.00043137072,0.00291213],"category_scores_gemma":[0.0012924869,0.00029029505,0.0005402876,0.0007529158,0.0008687948,0.0013936118,0.0009247392,0.0012210767,0.00071664376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024736932,0.000012903092,0.0000849868,0.00016244357,0.000014008179,0.00004478045,0.000094561845,0.4886138,0.006837369,0.41970754,0.0013855824,0.08301726],"study_design_scores_gemma":[0.000004284709,0.00002741935,0.000052356332,0.000027493954,0.000005500101,0.00003289008,0.000018248911,0.87984353,0.002634508,0.10958849,0.0077568046,0.000008520649],"about_ca_topic_score_codex":0.0004215327,"about_ca_topic_score_gemma":0.0003920655,"teacher_disagreement_score":0.00291213,"about_ca_system_score_codex":0.00041221987,"about_ca_system_score_gemma":0.0005130769,"threshold_uncertainty_score":0.009742081},"labels":[],"label_agreement":null},{"id":"W4297347715","doi":"10.3390/app12199668","title":"Projection Pursuit Multivariate Sampling of Parameter Uncertainty","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Latin hypercube sampling; Projection pursuit; Monte Carlo method; Sampling (signal processing); Multivariate statistics; Rejection sampling; Mathematics; Projection (relational algebra); Statistics; Slice sampling; Algorithm; Computer science; Importance sampling; Markov chain Monte Carlo; Hybrid Monte Carlo","score_opus":0.20537949278323764,"score_gpt":0.37585876511663296,"score_spread":0.17047927233339533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297347715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071422323,0.000062088475,0.9922082,0.000037684902,0.000005156024,0.000024331477,0.000014793227,0.00007366645,0.00043184962],"genre_scores_gemma":[0.5730708,0.00041764288,0.4248446,0.00007457434,0.00003715872,0.00034208293,0.00017669337,0.0000724689,0.00096389785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99700505,0.001933184,0.00009901012,0.00026938392,0.0005791565,0.00011418601],"domain_scores_gemma":[0.992831,0.0055920174,0.0003960489,0.0004887541,0.0006242729,0.00006782732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056957337,0.00086191396,0.0012796317,0.00082180585,0.00042450544,0.0009738675,0.00079907366,0.00066643354,0.0011871957],"category_scores_gemma":[0.018354459,0.00047102055,0.00092426845,0.0010225981,0.0012234767,0.0013336823,0.001457331,0.0013788602,0.00019115058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001639806,0.00003304707,0.0010022151,0.00011651088,0.00006329658,0.000066141336,0.000061537954,0.9227395,0.0031068602,0.024308927,0.00029183482,0.04804614],"study_design_scores_gemma":[0.0000078201765,0.000040498777,0.00019311086,0.0000074707664,0.000006150954,0.000023458217,0.000008146951,0.9913119,0.0015354381,0.006600922,0.0002571387,0.000007987317],"about_ca_topic_score_codex":0.002237456,"about_ca_topic_score_gemma":0.0011550776,"teacher_disagreement_score":0.0056957337,"about_ca_system_score_codex":0.000688551,"about_ca_system_score_gemma":0.001505704,"threshold_uncertainty_score":0.03012228},"labels":[],"label_agreement":null},{"id":"W4297830357","doi":"10.1063/5.0107547","title":"Quantification of Reynolds-averaged-Navier–Stokes model-form uncertainty in transitional boundary layer and airfoil flows","year":2022,"lang":"en","type":"article","venue":"Physics of Fluids","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reynolds-averaged Navier–Stokes equations; Laminar flow; Turbulence; Airfoil; Physics; Boundary layer; Mechanics; Flow separation; Reynolds number; Context (archaeology); Flow (mathematics); Laminar sublayer; Laminar-turbulent transition; Classical mechanics; Statistical physics; Geology","score_opus":0.08166811381457863,"score_gpt":0.31147983668814666,"score_spread":0.22981172287356805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297830357","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5844519,0.0002363103,0.4134449,0.00008173428,0.000011708381,0.000039107035,0.00009004535,0.0002446459,0.0013995736],"genre_scores_gemma":[0.99005616,0.000036496986,0.009750321,0.0000073252127,0.0000026723355,0.00001317515,0.000042293297,0.000007134516,0.00008447667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943656,0.00015263262,0.00003367147,0.00008435431,0.00022360291,0.00006905874],"domain_scores_gemma":[0.99842346,0.00085192843,0.00035292853,0.00013968589,0.00016487768,0.00006720417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014715035,0.0006080858,0.00042284696,0.00075236673,0.00029341507,0.0008405345,0.0006877259,0.000597742,0.00027051088],"category_scores_gemma":[0.003904764,0.00026653658,0.00045902276,0.00032581488,0.00094391464,0.0010310531,0.0010194542,0.0005507578,0.000027715048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006765239,0.000039493927,0.0026528325,0.000028982222,0.000022908267,0.000061349165,0.00004320517,0.9802532,0.0062038745,0.0036534732,0.000060035552,0.0069129304],"study_design_scores_gemma":[0.0000019405745,0.00002413163,0.0009186221,0.0000028420657,0.0000023878288,0.000009573101,0.0000064476544,0.9959984,0.0020106484,0.0009749819,0.000042826767,0.000007291101],"about_ca_topic_score_codex":0.00386048,"about_ca_topic_score_gemma":0.0016350984,"teacher_disagreement_score":0.00386048,"about_ca_system_score_codex":0.00069090025,"about_ca_system_score_gemma":0.0007675593,"threshold_uncertainty_score":0.0077821612},"labels":[],"label_agreement":null},{"id":"W4298110919","doi":"10.1063/5.0116282","title":"Model-form uncertainty quantification of Reynolds-averaged Navier–Stokes modeling of flows over a SD7003 airfoil","year":2022,"lang":"en","type":"article","venue":"Physics of Fluids","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reynolds-averaged Navier–Stokes equations; Turbulence; Physics; Airfoil; Laminar flow; Turbulence kinetic energy; Statistical physics; Reynolds number; K-omega turbulence model; Eigenvalues and eigenvectors; K-epsilon turbulence model; Reynolds stress equation model; Mechanics; Classical mechanics","score_opus":0.11575777728613122,"score_gpt":0.3248031757977717,"score_spread":0.20904539851164045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298110919","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56181955,0.0001371542,0.43528938,0.00017909463,0.00001911549,0.000031635394,0.00013634915,0.00030003692,0.002087729],"genre_scores_gemma":[0.9895684,0.000023691928,0.010186945,0.000008527541,0.0000026635935,0.000015370315,0.00006468472,0.00001087009,0.00011877989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996001,0.00012844142,0.000023599232,0.00007252982,0.00013397649,0.000041384577],"domain_scores_gemma":[0.9989396,0.00056201994,0.00016842393,0.00009548122,0.00019529059,0.000039216295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011263168,0.00069960684,0.00042973066,0.0004761985,0.00031587118,0.0006370607,0.00059723033,0.00054953917,0.0003385954],"category_scores_gemma":[0.0029845338,0.00025776596,0.00044714278,0.00026904425,0.000624044,0.00069420494,0.00055388274,0.00066362525,0.00004216233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013782013,0.000010033285,0.0006254299,0.000006228846,0.0000061093833,0.000008530631,0.000011617567,0.99503726,0.0010303314,0.00074183545,0.000036241683,0.00247251],"study_design_scores_gemma":[5.437942e-7,0.0000043819264,0.00014389196,6.9713843e-7,5.914083e-7,0.0000012163425,0.0000013580842,0.99918324,0.0004962937,0.00014698067,0.000018839397,0.000001882028],"about_ca_topic_score_codex":0.011413044,"about_ca_topic_score_gemma":0.0057699136,"teacher_disagreement_score":0.011413044,"about_ca_system_score_codex":0.0007900884,"about_ca_system_score_gemma":0.0010487095,"threshold_uncertainty_score":0.022693217},"labels":[],"label_agreement":null},{"id":"W4299796245","doi":"10.1007/978-3-031-79855-9_6","title":"Noise-tolerance via error correcting","year":2013,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on digital circuits and systems","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Noise (video); Computer science; Artificial intelligence","score_opus":0.07416403201413696,"score_gpt":0.26857714789726056,"score_spread":0.1944131158831236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299796245","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00682514,0.008725172,0.895487,0.0010338251,0.0011990545,0.00003173446,0.00008428886,0.0013508109,0.08526293],"genre_scores_gemma":[0.48221117,0.018484237,0.3158359,0.0015365306,0.0019035042,0.00019937691,0.00029888118,0.0011809622,0.17834948],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931216,0.00008351037,0.000028524259,0.00012230534,0.00039955063,0.000054055454],"domain_scores_gemma":[0.99953663,0.0001939225,0.000046050354,0.00011556034,0.0000953537,0.000012636643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044120557,0.0014208264,0.0008185386,0.0009135459,0.000405949,0.0013561131,0.0010545147,0.00091087277,0.005906834],"category_scores_gemma":[0.0017485514,0.00053229905,0.0004302705,0.00108669,0.0014406733,0.0014743156,0.0015154601,0.0019557434,0.0022485328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008747681,0.00003483216,0.000127851,0.00031410184,0.000054671,0.000096923526,0.00014489159,0.06440015,0.019487966,0.513886,0.016471745,0.38489345],"study_design_scores_gemma":[0.000026581647,0.00010160646,0.0002522521,0.00026311493,0.00007048297,0.0003689928,0.00003546737,0.16086675,0.057431053,0.64654773,0.1339539,0.00008219056],"about_ca_topic_score_codex":0.00045751105,"about_ca_topic_score_gemma":0.00037651617,"teacher_disagreement_score":0.005906834,"about_ca_system_score_codex":0.00077293697,"about_ca_system_score_gemma":0.00038559665,"threshold_uncertainty_score":0.01976031},"labels":[],"label_agreement":null},{"id":"W4302014076","doi":"10.1109/emceurope51680.2022.9900966","title":"Efficient Frequency-Domain Uncertainty Quantification Using Parameterized Model Order Reduction","year":2022,"lang":"en","type":"article","venue":"2022 International Symposium on Electromagnetic Compatibility – EMC Europe","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Parameterized complexity; Reduction (mathematics); Model order reduction; Uncertainty quantification; Frequency domain; Computer science; Algorithm; Process (computing); Domain (mathematical analysis); Measurement uncertainty; Mathematical optimization; Mathematics; Statistics; Machine learning","score_opus":0.06061945739001827,"score_gpt":0.31383297915253144,"score_spread":0.25321352176251316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302014076","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016309483,0.000036794634,0.99772793,0.000016672748,0.000004590081,0.000012037563,0.000019094412,0.00012461455,0.00042736492],"genre_scores_gemma":[0.34661722,0.00042314336,0.64933187,0.0000744464,0.000032507403,0.000207782,0.00043627448,0.00030349282,0.0025732797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909437,0.0002636946,0.000040158247,0.00010165208,0.00045300365,0.000047090103],"domain_scores_gemma":[0.99874985,0.0006230712,0.0001135169,0.0002756261,0.00022187199,0.00001607088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009094835,0.0011117395,0.0008647755,0.00083981996,0.00032556092,0.0010522853,0.0008077691,0.0005154663,0.0018176343],"category_scores_gemma":[0.0027484153,0.0003593776,0.0010200877,0.0006973609,0.0005738176,0.001320772,0.0009119583,0.0012455811,0.00054978224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006791993,0.000041864412,0.00031158974,0.00014580044,0.000055229677,0.00007720271,0.00006734537,0.7871266,0.03209242,0.05509645,0.0008704278,0.124047115],"study_design_scores_gemma":[0.0000027689014,0.00002380124,0.0000611286,0.0000048468773,0.000007066296,0.000026014064,0.0000046018467,0.9850911,0.0050373534,0.008894336,0.00083820807,0.000008815349],"about_ca_topic_score_codex":0.00167109,"about_ca_topic_score_gemma":0.0017292282,"teacher_disagreement_score":0.0018176343,"about_ca_system_score_codex":0.0006185389,"about_ca_system_score_gemma":0.0011106727,"threshold_uncertainty_score":0.0060806274},"labels":[],"label_agreement":null},{"id":"W4304609415","doi":"10.1002/essoar.10512586.1","title":"pyVISCOUS: An open-source tool for computationally frugal global sensitivity analysis","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Saskatchewan","funders":"Global Water Futures","keywords":"Sobol sequence; Python (programming language); Sensitivity (control systems); Computer science; Variance (accounting); Variance-based sensitivity analysis; Copula (linguistics); Applied mathematics; Mathematical optimization; Algorithm; Mathematics; Econometrics; Machine learning; Analysis of variance; Programming language; Engineering","score_opus":0.11672538914445713,"score_gpt":0.4041932366893085,"score_spread":0.2874678475448514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304609415","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023888657,0.00014111093,0.9180619,0.00021379169,0.00009335619,0.0001239696,0.0035040625,0.07130631,0.0041665714],"genre_scores_gemma":[0.11302647,0.0005463721,0.8110201,0.00070332893,0.00013713392,0.0012997622,0.009081615,0.05499982,0.009185394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871135,0.00029609722,0.000114441005,0.00021185301,0.00054075714,0.00012559851],"domain_scores_gemma":[0.9944389,0.0037919313,0.00040301087,0.0006410367,0.00058063073,0.00014441165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029188863,0.0021410445,0.0013457251,0.0023086898,0.0007425252,0.0023855143,0.0024756216,0.0015191827,0.05035587],"category_scores_gemma":[0.016361557,0.0012302323,0.002449502,0.0011297836,0.0011758445,0.002545415,0.003868235,0.0030546582,0.009339918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034486243,0.00023925988,0.0055717593,0.0023186656,0.0004623536,0.0008508518,0.0004902473,0.5234047,0.00987637,0.105214246,0.1558517,0.19537498],"study_design_scores_gemma":[0.000080493184,0.0000356452,0.0005630627,0.00016884934,0.000033041015,0.00015975772,0.00004180793,0.8591794,0.005391579,0.08126961,0.053005178,0.0000715842],"about_ca_topic_score_codex":0.0039416924,"about_ca_topic_score_gemma":0.004876165,"teacher_disagreement_score":0.05035587,"about_ca_system_score_codex":0.0009580416,"about_ca_system_score_gemma":0.0024459905,"threshold_uncertainty_score":0.16845715},"labels":[],"label_agreement":null},{"id":"W4306411814","doi":"10.1007/s43670-022-00040-8","title":"CAS4DL: Christoffel adaptive sampling for function approximation via deep learning","year":2022,"lang":"en","type":"article","venue":"Sampling Theory Signal Processing and Data Analysis","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Sampling (signal processing); Deep learning; Function approximation; Adaptive sampling; Function (biology); Parameterized complexity; Artificial intelligence; Artificial neural network; Algorithm; Multivariate statistics; Sample (material); Monte Carlo method; Mathematical optimization; Mathematics; Machine learning; Statistics","score_opus":0.14608657932618052,"score_gpt":0.3546864623493639,"score_spread":0.20859988302318339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306411814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001982931,0.00016169717,0.9942362,0.00009219256,0.000061216306,0.00003098371,0.000102666934,0.0022105842,0.001121565],"genre_scores_gemma":[0.15236808,0.00033137994,0.8387521,0.00039306996,0.00011804047,0.00034189498,0.00090794865,0.0014242284,0.005363246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990746,0.00023885067,0.000038854807,0.000108070104,0.00046218676,0.00007747272],"domain_scores_gemma":[0.99870765,0.0006109208,0.00006075222,0.0002602844,0.00026545857,0.0000949535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00149095,0.001025188,0.0011143204,0.00091438973,0.00046465575,0.0013107118,0.002512031,0.0016960503,0.007320353],"category_scores_gemma":[0.005924946,0.00049565005,0.0008072047,0.00094894355,0.0009601937,0.0010324921,0.0022866498,0.0031005882,0.002142398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042897018,0.00014591774,0.00093303807,0.00031977604,0.00017270393,0.00014271589,0.00008596275,0.37141836,0.0083287535,0.14826292,0.029421661,0.44033924],"study_design_scores_gemma":[0.0000138443365,0.000011579296,0.000031086784,0.000006707727,0.0000042804068,0.0000115112625,0.000002582121,0.98437274,0.0014264242,0.012436659,0.001678456,0.0000042228276],"about_ca_topic_score_codex":0.006228613,"about_ca_topic_score_gemma":0.01056008,"teacher_disagreement_score":0.007320353,"about_ca_system_score_codex":0.001266311,"about_ca_system_score_gemma":0.0017758557,"threshold_uncertainty_score":0.024488986},"labels":[],"label_agreement":null},{"id":"W4307515697","doi":"10.30574/wjaets.2022.7.1.0107","title":"Time series difference approach for evaluating sensitivity of nonlinear dynamic systems","year":2022,"lang":"en","type":"article","venue":"World Journal of Advanced Engineering Technology and Sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; University of Regina","keywords":"Nonlinear system; Sensitivity (control systems); Series (stratigraphy); Control theory (sociology); Duffing equation; Computer science; Applied mathematics; Mathematics; Engineering; Artificial intelligence; Physics","score_opus":0.034945882861199394,"score_gpt":0.306835869126933,"score_spread":0.27188998626573363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307515697","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02174406,0.00022109544,0.97597355,0.000039595452,0.000037936923,0.00006934065,0.00005060295,0.00014615803,0.0017176582],"genre_scores_gemma":[0.70307946,0.0004937465,0.29347643,0.00007805867,0.00006883518,0.00027627553,0.00017268037,0.00008153576,0.0022730532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835074,0.0006832391,0.00011962989,0.00022436382,0.0005582347,0.00006378448],"domain_scores_gemma":[0.99727863,0.0019374128,0.00025333773,0.00013717772,0.0003449121,0.000048577054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027236405,0.00084811874,0.0006356903,0.0021125376,0.0002440217,0.00079433294,0.00071944314,0.000830447,0.0018116238],"category_scores_gemma":[0.006434931,0.00021703145,0.00074382796,0.0008327021,0.00056270533,0.0011355577,0.0007459294,0.0010203535,0.00022651166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029765416,0.00017531842,0.0041763834,0.0004947888,0.00024687365,0.0003014539,0.00032207364,0.7199064,0.05872756,0.04826062,0.00058524194,0.16650555],"study_design_scores_gemma":[0.0000044843323,0.00017608878,0.0009781698,0.000013723378,0.000020572938,0.00007824579,0.000034173143,0.9853254,0.006390433,0.0060098353,0.00093848386,0.00003046753],"about_ca_topic_score_codex":0.0013597162,"about_ca_topic_score_gemma":0.0007560959,"teacher_disagreement_score":0.0027236405,"about_ca_system_score_codex":0.00061144284,"about_ca_system_score_gemma":0.0004200952,"threshold_uncertainty_score":0.014404118},"labels":[],"label_agreement":null},{"id":"W4308520947","doi":"10.1080/19401493.2022.2137236","title":"Improved calibration of building models using approximate Bayesian calibration and neural networks","year":2022,"lang":"en","type":"article","venue":"Journal of Building Performance Simulation","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Resources Canada","keywords":"Calibration; Bayesian inference; Inference; Sensitivity (control systems); Computer science; Artificial neural network; Bayesian probability; Approximate Bayesian computation; Uncertainty quantification; Frequentist inference; Computation; Monte Carlo method; Variable-order Bayesian network; Bayesian network; Machine learning; Algorithm; Artificial intelligence; Statistics; Mathematics; Engineering","score_opus":0.06709170752371582,"score_gpt":0.31595778861340595,"score_spread":0.24886608108969013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308520947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018745031,0.00007206091,0.97874796,0.0001407868,0.0000142000035,0.00002302825,0.00005732685,0.00029085556,0.0019088378],"genre_scores_gemma":[0.79448307,0.00018015999,0.20266832,0.000115472845,0.00002746922,0.00012746669,0.00029676504,0.0001595403,0.0019417129],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999161,0.00040214794,0.000040780687,0.00012269581,0.00022685592,0.00004646631],"domain_scores_gemma":[0.9975069,0.0014695906,0.00028309965,0.00029832922,0.00040121694,0.00004086835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019737573,0.0007150917,0.000652002,0.00070707675,0.0004387144,0.0010759275,0.0011388459,0.00094199507,0.0021227803],"category_scores_gemma":[0.0094841095,0.0006934997,0.00062512053,0.0007685434,0.00065365824,0.0015991102,0.0011877465,0.0016814877,0.00041253553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008047482,0.000007565644,0.00028744774,0.000007823616,0.000007806325,0.0000051099328,0.000009516994,0.9898665,0.00020412664,0.0023976364,0.00008742164,0.0071110046],"study_design_scores_gemma":[0.0000011680931,0.0000019893064,0.00007584859,0.0000026462724,9.677193e-7,0.0000021158855,0.0000015534174,0.9977968,0.0001009232,0.0019352783,0.00007837055,0.000002303568],"about_ca_topic_score_codex":0.014934482,"about_ca_topic_score_gemma":0.015804524,"teacher_disagreement_score":0.014934482,"about_ca_system_score_codex":0.0013945743,"about_ca_system_score_gemma":0.0013135618,"threshold_uncertainty_score":0.029695094},"labels":[],"label_agreement":null},{"id":"W4309787599","doi":"10.3390/stats5040071","title":"Model Validation of a Single Degree-of-Freedom Oscillator: A Case Study","year":2022,"lang":"en","type":"article","venue":"Stats","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"King Abdullah University of Science and Technology; Qatar Foundation","keywords":"Representation (politics); Computer science; Degrees of freedom (physics and chemistry); Bayesian probability; Oracle; Standard deviation; Term (time); Linear model; Process (computing); Algorithm; Mathematics; Statistics; Artificial intelligence; Machine learning","score_opus":0.2811533763356427,"score_gpt":0.36526540181752426,"score_spread":0.08411202548188157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309787599","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7522691,0.0005790175,0.23977037,0.0006370235,0.00006920709,0.00026070306,0.00028317608,0.0003766629,0.005754781],"genre_scores_gemma":[0.9838378,0.000068891866,0.015094943,0.00002500837,0.000006133827,0.00006968168,0.000082488565,0.000021556732,0.0007935363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804085,0.00096369203,0.000098758224,0.0003056752,0.00042467384,0.00016646634],"domain_scores_gemma":[0.9902768,0.007324495,0.00050407776,0.0009996019,0.00066615595,0.00022893358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050680465,0.00064734917,0.00077711424,0.00052919844,0.0007786066,0.0012676228,0.0015248532,0.0025174087,0.0013663046],"category_scores_gemma":[0.011045522,0.00027340764,0.0009752268,0.0004956403,0.0013484746,0.0010389715,0.0011657154,0.0012763349,0.00026307674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034834456,0.00026330134,0.008900624,0.0002301249,0.00007160232,0.0013487604,0.0003148703,0.9575057,0.004092821,0.009558683,0.0005006999,0.01686442],"study_design_scores_gemma":[0.000068458496,0.000573541,0.0021638132,0.000044151675,0.000038230464,0.00031865304,0.00015227761,0.9816296,0.009031395,0.004103688,0.0018378472,0.00003830276],"about_ca_topic_score_codex":0.0053781997,"about_ca_topic_score_gemma":0.0026646154,"teacher_disagreement_score":0.0053781997,"about_ca_system_score_codex":0.0008166113,"about_ca_system_score_gemma":0.0009591038,"threshold_uncertainty_score":0.026802719},"labels":[],"label_agreement":null},{"id":"W4311356850","doi":"10.18280/mmep.090523","title":"Dynamic Response and Reliability Analysis of Stochastic Multi-Story Frame Structures under Random Excitation","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Baghdad","keywords":"Randomness; Structural engineering; Finite element method; Probability density function; Random vibration; Moment (physics); Monte Carlo method; Stiffness; Mathematics; Vibration; Engineering; Physics; Classical mechanics; Statistics","score_opus":0.05786095225659297,"score_gpt":0.29303518026933584,"score_spread":0.23517422801274288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311356850","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91988754,0.00016722712,0.07860765,0.000070336624,0.000009712403,0.000012188579,0.000043367287,0.000057238645,0.0011445868],"genre_scores_gemma":[0.99912804,0.000026710024,0.00071053224,0.0000022587074,0.0000015846798,0.000003622605,0.000011522928,0.0000019909046,0.00011388915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970645,0.00008980549,0.000011595218,0.000048321384,0.00009645194,0.00004742818],"domain_scores_gemma":[0.9992035,0.00044648032,0.00017053296,0.000045999197,0.00011439797,0.000019138122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000661069,0.00031566163,0.00034186218,0.0004127285,0.00014095202,0.0002285725,0.00030395013,0.00043115017,0.00042193936],"category_scores_gemma":[0.0016297292,0.0001986852,0.00036630136,0.00019371862,0.00036215392,0.00023415817,0.00020176782,0.0001834061,0.000070540445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040716935,0.000012501749,0.002221868,0.000016270917,0.000016914479,0.0000933713,0.000027691383,0.98685247,0.007293402,0.0006873153,0.000035670422,0.0027018706],"study_design_scores_gemma":[0.000001231486,0.000042907708,0.0023119485,0.0000015439771,0.0000050765843,0.000020115733,0.000010656155,0.996402,0.00096149184,0.00020714934,0.000031959367,0.000003823655],"about_ca_topic_score_codex":0.002169855,"about_ca_topic_score_gemma":0.0011290374,"teacher_disagreement_score":0.002169855,"about_ca_system_score_codex":0.0003257158,"about_ca_system_score_gemma":0.00018592717,"threshold_uncertainty_score":0.0043144226},"labels":[],"label_agreement":null},{"id":"W4312707143","doi":"10.1115/detc2022-89511","title":"Iterative Uncertainty Calibration for Modeling Metal Additive Manufacturing Processes Using Statistical Moment-Based Metric","year":2022,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Metric (unit); Calibration; Metamodeling; Moment (physics); Pooling; Computer science; Iterative and incremental development; Process (computing); Mathematical optimization; Algorithm; Mathematics; Artificial intelligence; Engineering; Statistics","score_opus":0.12502534767208234,"score_gpt":0.350761914216273,"score_spread":0.22573656654419066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312707143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012002838,0.0001188269,0.9869774,0.000040043524,0.0000074364757,0.000026942625,0.000021980368,0.00015610151,0.00064840424],"genre_scores_gemma":[0.8495966,0.00020940481,0.14897107,0.000043239674,0.000020498934,0.00018270762,0.00012292746,0.00007181951,0.00078171893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855655,0.0005985236,0.00008537758,0.00023174305,0.0004245364,0.00010331782],"domain_scores_gemma":[0.9974954,0.0014740801,0.0003657343,0.00015573918,0.0004569081,0.00005222971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030225914,0.0010477324,0.0010972887,0.0014731671,0.00036063645,0.0010807393,0.0012543161,0.0010708302,0.0009548668],"category_scores_gemma":[0.0057879766,0.00055751624,0.0011678032,0.0009711439,0.0009087477,0.0009988301,0.001326662,0.0009907958,0.0001680269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014471111,0.000009592938,0.00023470583,0.000020431766,0.000013795255,0.000011922516,0.00001433592,0.98874015,0.0006942229,0.002848614,0.000059627255,0.0073380983],"study_design_scores_gemma":[7.881667e-7,0.000008119006,0.00006629262,0.0000020574475,0.0000020024938,0.0000033629744,0.0000015218051,0.9988385,0.00032229704,0.0006803153,0.000071932234,0.000002874426],"about_ca_topic_score_codex":0.0049919733,"about_ca_topic_score_gemma":0.002038489,"teacher_disagreement_score":0.0049919733,"about_ca_system_score_codex":0.0012402713,"about_ca_system_score_gemma":0.0012271327,"threshold_uncertainty_score":0.01598519},"labels":[],"label_agreement":null},{"id":"W4313420413","doi":"10.1016/j.compgeo.2022.105135","title":"Optimized active learning Kriging reliability based assessment of laterally loaded pile groups modeled using random finite element analysis","year":2022,"lang":"en","type":"article","venue":"Computers and Geotechnics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Kriging; Monte Carlo method; Reliability (semiconductor); Parametric statistics; Finite element method; Sampling (signal processing); Discretization; Pile; Random field; Mathematical optimization; Ranking (information retrieval); Mathematics; Consistency (knowledge bases); Applied mathematics; Computer science; Algorithm; Engineering; Structural engineering; Statistics; Machine learning; Mathematical analysis; Geometry; Physics","score_opus":0.0418603366567027,"score_gpt":0.3144193769673295,"score_spread":0.27255904031062683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313420413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45711198,0.00009323406,0.54021484,0.000059495975,0.000008418567,0.000036364167,0.00009235979,0.0005568277,0.0018264349],"genre_scores_gemma":[0.98099124,0.000017626015,0.018448144,0.000005071211,0.0000015873643,0.000014809168,0.00005015183,0.000019210196,0.00045208677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986637,0.000044377422,0.0000064310875,0.000022148957,0.000044281882,0.000016310822],"domain_scores_gemma":[0.999281,0.00040417956,0.000085840366,0.00005535066,0.00015274648,0.00002090597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046414885,0.0004935897,0.00047504168,0.0004854292,0.00019534305,0.0004258018,0.00056302,0.00059675664,0.00056572683],"category_scores_gemma":[0.001201013,0.00033945093,0.00034203345,0.00024996788,0.00035819958,0.00052586297,0.000329488,0.00032196715,0.00017636029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034312263,0.000013903341,0.0004914506,0.000012903718,0.0000058414766,0.000013369156,0.000013876529,0.9917796,0.0016938148,0.0002171406,0.000036160498,0.005687679],"study_design_scores_gemma":[9.669677e-7,0.0000082447805,0.00015008877,8.3449305e-7,0.0000016552614,0.0000021359456,0.000002660586,0.99923885,0.00048315947,0.00009802462,0.000011976914,0.0000014198915],"about_ca_topic_score_codex":0.004015729,"about_ca_topic_score_gemma":0.0058406196,"teacher_disagreement_score":0.004015729,"about_ca_system_score_codex":0.0003499113,"about_ca_system_score_gemma":0.00051278283,"threshold_uncertainty_score":0.007984757},"labels":[],"label_agreement":null},{"id":"W4313549867","doi":"10.1145/3551349.3556936","title":"SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design","year":2022,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada); Polytechnique Montréal","funders":"","keywords":"Surrogate model; Computer science; Smoothness; Artificial neural network; Gaussian process; Kriging; Overhead (engineering); Artificial intelligence; Machine learning; Mathematical optimization; Reliability engineering; Gaussian; Engineering; Mathematics","score_opus":0.10956343849701576,"score_gpt":0.31572429246276906,"score_spread":0.2061608539657533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313549867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017794654,0.00013828487,0.9803231,0.00012942434,0.000023412365,0.00003797713,0.00005259304,0.0005976171,0.00090290006],"genre_scores_gemma":[0.6980429,0.00025762286,0.2980533,0.00026322738,0.000037921247,0.0002934249,0.0005044095,0.00034120772,0.002206004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993674,0.00019523455,0.000041822874,0.000098654935,0.00023858529,0.000058308473],"domain_scores_gemma":[0.99818295,0.0010600438,0.00022339167,0.0001393917,0.00031930913,0.00007502341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020120812,0.0011981894,0.00083321723,0.00079830503,0.000451458,0.0008527491,0.001360233,0.0011207542,0.0015964982],"category_scores_gemma":[0.0056086476,0.00065700524,0.0009927219,0.000368694,0.00085259264,0.0009799168,0.0019049267,0.0020969394,0.0002603075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010347884,0.0000557175,0.0021552532,0.00010156171,0.000050220577,0.000079959886,0.000064967266,0.93584734,0.003539098,0.0052346215,0.0007120297,0.052055713],"study_design_scores_gemma":[0.0000034127133,0.000016073012,0.00010017966,0.0000053047825,0.0000028525546,0.0000074188756,0.000004644084,0.9977028,0.00064457604,0.0013213556,0.00018806953,0.000003428241],"about_ca_topic_score_codex":0.0048792106,"about_ca_topic_score_gemma":0.006015816,"teacher_disagreement_score":0.0048792106,"about_ca_system_score_codex":0.00079465314,"about_ca_system_score_gemma":0.0016407892,"threshold_uncertainty_score":0.010640979},"labels":[],"label_agreement":null},{"id":"W4315642155","doi":"10.1016/j.ast.2023.108106","title":"Uncertainty quantification of separation control with synthetic jet actuator over a NACA0025 airfoil","year":2023,"lang":"en","type":"article","venue":"Aerospace Science and Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council; CMC Microsystems","keywords":"Airfoil; Polynomial chaos; Synthetic jet; Actuator; Aerodynamics; Uncertainty quantification; Monte Carlo method; Control theory (sociology); Drag; Flow control (data); Lift (data mining); Mathematics; Mechanics; Computer science; Physics; Statistics; Control (management)","score_opus":0.031609906640025894,"score_gpt":0.32524376675827904,"score_spread":0.29363386011825315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315642155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47539315,0.00032073937,0.5192526,0.0003043485,0.00006109211,0.000026636557,0.000070392474,0.0001790773,0.004391982],"genre_scores_gemma":[0.9956676,0.000018174485,0.0040030857,0.000008398985,0.0000041991143,0.000006501718,0.0000138353225,0.0000053012263,0.000272947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967647,0.000070757436,0.00001498483,0.000053228276,0.00014139677,0.000043070268],"domain_scores_gemma":[0.99905294,0.00047209623,0.0001698349,0.00004551118,0.00021447717,0.00004517984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083678256,0.0004766969,0.00052422145,0.000396541,0.0004701398,0.0007220591,0.00039538593,0.0006044086,0.00048272018],"category_scores_gemma":[0.0016685273,0.00020487967,0.0003164722,0.00020053898,0.0008227704,0.00053358503,0.0006977664,0.0004769469,0.000027221104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016964313,0.000022726112,0.00043219677,0.000046273937,0.000022285445,0.000040796902,0.00003095155,0.9742391,0.012599356,0.004583176,0.00011874609,0.0076946956],"study_design_scores_gemma":[0.0000021709113,0.000032264208,0.0002590257,0.0000016886898,0.000002370063,0.000003151493,0.0000037582383,0.99796486,0.0013044812,0.00037735724,0.000045753335,0.0000030302958],"about_ca_topic_score_codex":0.005975009,"about_ca_topic_score_gemma":0.0025213533,"teacher_disagreement_score":0.005975009,"about_ca_system_score_codex":0.00088914763,"about_ca_system_score_gemma":0.0008232026,"threshold_uncertainty_score":0.011880457},"labels":[],"label_agreement":null},{"id":"W4317583846","doi":"10.2514/6.2023-0833","title":"Unsupervised Residual Vector Analysis for Mesh Optimization","year":2023,"lang":"en","type":"article","venue":"AIAA SCITECH 2023 Forum","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Residual; Jacobian matrix and determinant; Computer science; Eigenvalues and eigenvectors; Outlier; Unsupervised learning; Algorithm; Stability (learning theory); Pattern recognition (psychology); Artificial intelligence; Mathematics; Applied mathematics; Machine learning; Physics","score_opus":0.0951096287036868,"score_gpt":0.34455367701109535,"score_spread":0.24944404830740854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317583846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019058918,0.00006800249,0.9959288,0.00006261238,0.00001999714,0.000027146383,0.00009336587,0.0008005515,0.0010935717],"genre_scores_gemma":[0.14536236,0.00025366497,0.84349996,0.000095861484,0.000073367955,0.00035179395,0.0011735157,0.0011235933,0.008065759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995023,0.00014887056,0.00002479461,0.00006834028,0.00022233218,0.000033313],"domain_scores_gemma":[0.9989248,0.00047905897,0.000096210315,0.00016908947,0.00029667836,0.000034212353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084775675,0.0006960635,0.0007146653,0.0010954427,0.00033721374,0.0008075347,0.0009908524,0.00072886195,0.0072890087],"category_scores_gemma":[0.0031585025,0.0004162091,0.0006815451,0.0006998612,0.0005375307,0.00073853304,0.0011489404,0.0011491544,0.0024225565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010849303,0.00007349748,0.00094569626,0.00019957815,0.00006638045,0.00004188367,0.00006901566,0.6420171,0.011276725,0.03725549,0.010550935,0.29739514],"study_design_scores_gemma":[0.0000027122032,0.000009944091,0.000086975815,0.000004840312,0.0000015702389,0.0000070847223,0.000005457837,0.9930641,0.0009757076,0.0041633947,0.0016742004,0.0000039873953],"about_ca_topic_score_codex":0.0026281257,"about_ca_topic_score_gemma":0.0041384446,"teacher_disagreement_score":0.0072890087,"about_ca_system_score_codex":0.0005511167,"about_ca_system_score_gemma":0.0009399076,"threshold_uncertainty_score":0.0243842},"labels":[],"label_agreement":null},{"id":"W4317637372","doi":"10.2514/6.2023-2371","title":"Control Allocation with Physics-Based Reliability Models for Multirotor UAVs","year":2023,"lang":"en","type":"article","venue":"AIAA SCITECH 2023 Forum","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Multirotor; Reliability (semiconductor); Computer science; Underactuation; Reliability engineering; Component (thermodynamics); Process (computing); Control (management); Work (physics); Control engineering; Engineering; Aerospace engineering; Artificial intelligence","score_opus":0.07683134770152542,"score_gpt":0.3248406082415793,"score_spread":0.24800926054005387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317637372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023265969,0.00018332085,0.97069776,0.0001246405,0.000028390861,0.0000441748,0.000039923452,0.00016262855,0.00545317],"genre_scores_gemma":[0.95103145,0.00024342151,0.043123264,0.00004538966,0.000026675267,0.00012374388,0.000056680783,0.00006669565,0.005282827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978334,0.000054572825,0.0000073456945,0.00004601211,0.00007809939,0.000030685915],"domain_scores_gemma":[0.9996195,0.00017248794,0.00009465288,0.000025053861,0.00007024298,0.000018093026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005305366,0.0006917897,0.00047619778,0.00039766682,0.00023268373,0.0006512319,0.0007024756,0.00057957805,0.002244688],"category_scores_gemma":[0.0012637688,0.0003785244,0.00062431704,0.00019594262,0.0005545399,0.0005722329,0.0006515091,0.0006882026,0.00025028124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009024627,0.000007288389,0.00009614973,0.000011863008,0.0000045976412,0.000011936388,0.000013169932,0.9927834,0.0007096205,0.0030413938,0.00009250563,0.003219019],"study_design_scores_gemma":[0.0000013366018,0.00000839739,0.00003708464,0.0000014133913,0.0000016637372,0.000002519575,0.0000018414452,0.9989802,0.000110138244,0.00072979054,0.00012438308,0.0000011531616],"about_ca_topic_score_codex":0.0046151197,"about_ca_topic_score_gemma":0.0033702222,"teacher_disagreement_score":0.0046151197,"about_ca_system_score_codex":0.00071928656,"about_ca_system_score_gemma":0.0005849085,"threshold_uncertainty_score":0.009176552},"labels":[],"label_agreement":null},{"id":"W4318771371","doi":"10.2139/ssrn.4345300","title":"A Simple Specification Test for Models with Many Conditional Moment Inequalities","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Carleton University; Université de Montréal","funders":"","keywords":"Simple (philosophy); Test (biology); Econometrics; Mathematics; Inequality; Moment (physics); Applied mathematics; Computer science; Calculus (dental); Statistics; Mathematical analysis; Physics; Epistemology; Philosophy","score_opus":0.09960793520177531,"score_gpt":0.3272631066371995,"score_spread":0.2276551714354242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318771371","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07074724,0.00016702805,0.9211016,0.0010357975,0.00011193272,0.0003149411,0.0014285189,0.0011331162,0.003959808],"genre_scores_gemma":[0.82561827,0.00016438006,0.16428019,0.0006449025,0.00036269493,0.00075472746,0.003566274,0.00019984104,0.0044087046],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9873052,0.0076164603,0.00064860255,0.0017868323,0.0019309481,0.0007119927],"domain_scores_gemma":[0.8418247,0.14572012,0.003772319,0.0051163067,0.002449328,0.0011172494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015730228,0.001588386,0.0022152343,0.0024877293,0.0007163525,0.0018593946,0.0033113917,0.002810999,0.023083005],"category_scores_gemma":[0.08530148,0.00090840267,0.002792525,0.002002848,0.0017898304,0.005107702,0.0031649573,0.0024711392,0.0021614013],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0040156688,0.001306293,0.0487372,0.0010851745,0.0019258974,0.002408883,0.00050042383,0.18473819,0.012076063,0.45756367,0.016302852,0.26933965],"study_design_scores_gemma":[0.00095832295,0.0013211758,0.007606588,0.00006771703,0.0003194981,0.00047049116,0.00019320098,0.71698356,0.00333393,0.2654389,0.003168233,0.00013838681],"about_ca_topic_score_codex":0.0018872513,"about_ca_topic_score_gemma":0.0018303947,"teacher_disagreement_score":0.023083005,"about_ca_system_score_codex":0.00088053197,"about_ca_system_score_gemma":0.0026840498,"threshold_uncertainty_score":0.08319044},"labels":[],"label_agreement":null},{"id":"W4319297880","doi":"10.1002/eqe.3835","title":"Reliability analysis of structures using probability density evolution method and stochastic spectral embedding surrogate model","year":2023,"lang":"en","type":"article","venue":"Earthquake Engineering & Structural Dynamics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probability density function; Mathematics; Monte Carlo method; Applied mathematics; Mathematical optimization; Algorithm; Statistics","score_opus":0.04360042414357388,"score_gpt":0.3290413386996324,"score_spread":0.2854409145560585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319297880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020368641,0.0001309301,0.9782526,0.000074949145,0.000010890627,0.000013881226,0.00002068494,0.00006946407,0.0010580275],"genre_scores_gemma":[0.9133207,0.00026532455,0.08428392,0.000039151946,0.00001819971,0.00010994309,0.000120064935,0.0000451184,0.0017975365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952817,0.00018941045,0.000019394298,0.00005487439,0.00017293928,0.000035279965],"domain_scores_gemma":[0.9990447,0.0005292574,0.00010628507,0.0000699261,0.00021770794,0.00003215237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010886266,0.0005835326,0.0006925304,0.000652208,0.00024389655,0.000555395,0.00065890024,0.00074155466,0.00084554876],"category_scores_gemma":[0.0024668034,0.00038113512,0.0007381929,0.0003781356,0.0005620077,0.0008709515,0.0006524303,0.00063473434,0.00014719563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010680317,0.000009056896,0.00040916994,0.000021498263,0.00001018169,0.000031972802,0.000015489979,0.98740876,0.0011513098,0.0054931366,0.00010316067,0.0053356714],"study_design_scores_gemma":[4.132789e-7,0.0000029102594,0.000032840708,8.9808515e-7,6.392705e-7,0.000003112884,9.389976e-7,0.99944836,0.000077302844,0.0003963286,0.00003536793,9.372485e-7],"about_ca_topic_score_codex":0.0023795925,"about_ca_topic_score_gemma":0.0011244274,"teacher_disagreement_score":0.0023795925,"about_ca_system_score_codex":0.0004581301,"about_ca_system_score_gemma":0.0006336228,"threshold_uncertainty_score":0.0057572126},"labels":[],"label_agreement":null},{"id":"W4319592428","doi":"10.1016/b978-0-12-398387-9.00020-9","title":"Prediction methods","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Censoring (clinical trials); Prediction interval; Bayesian probability; Computer science; Interval (graph theory); Statistics; Mathematics; Artificial intelligence; Algorithm; Machine learning","score_opus":0.16301314485710888,"score_gpt":0.3683697772734298,"score_spread":0.2053566324163209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319592428","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019167503,0.0037084902,0.9155532,0.00073499884,0.0010686667,0.000102574435,0.00163204,0.0056184907,0.069664836],"genre_scores_gemma":[0.0912655,0.0058918297,0.51045835,0.0010354718,0.0014840103,0.0005987338,0.010917238,0.0023992169,0.37594974],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999151,0.00015063005,0.000041575797,0.00026451098,0.0003473233,0.000044925266],"domain_scores_gemma":[0.9989619,0.00036403665,0.000038471775,0.00035436708,0.000250729,0.000030561536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011921932,0.0013618739,0.001233808,0.0012018656,0.000525046,0.0020669694,0.0014810778,0.0013906864,0.05414371],"category_scores_gemma":[0.003523457,0.000666352,0.00094186456,0.0015356578,0.00052990863,0.0018141493,0.0013888256,0.0015936018,0.0412334],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057746514,0.00006320886,0.00053098047,0.00019887305,0.00006085361,0.00005585307,0.000037011945,0.035915613,0.0016871869,0.045027513,0.10432911,0.81203616],"study_design_scores_gemma":[0.000028404243,0.000063716274,0.0012020682,0.00017321265,0.0000610863,0.00022980697,0.0000483944,0.6190926,0.0049212887,0.13856713,0.23556085,0.00005152387],"about_ca_topic_score_codex":0.0033253534,"about_ca_topic_score_gemma":0.0028131842,"teacher_disagreement_score":0.05414371,"about_ca_system_score_codex":0.00042362176,"about_ca_system_score_gemma":0.0007680792,"threshold_uncertainty_score":0.18112874},"labels":[],"label_agreement":null},{"id":"W4319870398","doi":"10.1016/j.ast.2023.108179","title":"Quantifying and mitigating uncertainties in design optimization including off-the-shelf components: Application to an electric multirotor UAV","year":2023,"lang":"en","type":"article","venue":"Aerospace Science and Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Agence Nationale de la Recherche; Indian National Science Academy","keywords":"Multirotor; Conceptual design; Component (thermodynamics); Multidisciplinary design optimization; Computer science; Engineering design process; Scope (computer science); Propeller; Design process; Systems engineering; Control engineering; Engineering; Automotive engineering; Marine engineering; Multidisciplinary approach; Aerospace engineering; Process engineering; Mechanical engineering; Process integration","score_opus":0.12312568444804822,"score_gpt":0.3598731142705405,"score_spread":0.23674742982249225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319870398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44062158,0.00040255248,0.55367845,0.00023512934,0.000027500759,0.000056298402,0.00003212225,0.0002039097,0.004742361],"genre_scores_gemma":[0.9715916,0.00006125092,0.027715996,0.000018888459,0.000004490001,0.000011615275,0.0000088618535,0.000015966649,0.00057136774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997416,0.000080183905,0.000010204025,0.000031665764,0.000109904664,0.000026424605],"domain_scores_gemma":[0.99876356,0.00091105024,0.0001277863,0.00006088346,0.00011491245,0.0000219091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000775877,0.0008973291,0.0004571573,0.00032067712,0.0003173124,0.0009818111,0.0003524932,0.0008208147,0.00046604726],"category_scores_gemma":[0.0021230853,0.00034882317,0.00034867058,0.00026936329,0.000469804,0.0006415164,0.000694895,0.0005430236,0.00004979097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006796157,0.000032846398,0.0007975311,0.00003613352,0.000016197973,0.00006165257,0.000042027452,0.97528154,0.00819344,0.00092011085,0.00005675695,0.01449381],"study_design_scores_gemma":[0.000004564926,0.00009047009,0.00050548115,0.0000048733486,0.000008943421,0.000016464252,0.000016843844,0.99378467,0.0047560087,0.0006629651,0.0001436682,0.0000049199252],"about_ca_topic_score_codex":0.0031422847,"about_ca_topic_score_gemma":0.0038287316,"teacher_disagreement_score":0.0031422847,"about_ca_system_score_codex":0.00047412855,"about_ca_system_score_gemma":0.00067638187,"threshold_uncertainty_score":0.0062479973},"labels":[],"label_agreement":null},{"id":"W4320485914","doi":"10.1080/23249935.2023.2174356","title":"Incorporating design consistency into risk-based geometric design of horizontal curves: a reliability-based optimization framework","year":2023,"lang":"en","type":"article","venue":"Transportmetrica A Transport Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University Canada West; University of British Columbia","funders":"","keywords":"Consistency (knowledge bases); Reliability (semiconductor); Geometric design; Computer science; Crash; Mathematical optimization; Reliability engineering; Engineering; Transport engineering; Mathematics","score_opus":0.08262073535516266,"score_gpt":0.31888629491133064,"score_spread":0.23626555955616796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320485914","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014178555,0.00010279746,0.9843151,0.000059127226,0.000008060938,0.000038856946,0.000019990437,0.000053402477,0.0012241151],"genre_scores_gemma":[0.6856766,0.00028954272,0.31195986,0.00005021532,0.00003035813,0.0002552988,0.000115721305,0.00010482953,0.0015177365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987882,0.0004340896,0.000059585494,0.00019586919,0.0004081334,0.00011418565],"domain_scores_gemma":[0.99838686,0.00077534694,0.00031920138,0.0001341067,0.000339493,0.000045109777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030354343,0.0012611432,0.0012141983,0.0014826709,0.0004175442,0.00094327313,0.0013363741,0.0009933354,0.0013996182],"category_scores_gemma":[0.0043975213,0.0008472222,0.0011862682,0.0008275869,0.0009729114,0.0010824224,0.0012509617,0.0010468235,0.00018669086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005550252,0.000007198105,0.00017757276,0.000015756164,0.000008531885,0.000010114709,0.000011535452,0.99212646,0.0005743394,0.0029909417,0.00004434432,0.0040276875],"study_design_scores_gemma":[0.0000032543865,0.000044196957,0.00013698492,0.0000057170264,0.000008572114,0.000010870946,0.000007151322,0.9966467,0.00035205646,0.002522527,0.00025696217,0.000004978895],"about_ca_topic_score_codex":0.003421632,"about_ca_topic_score_gemma":0.002416344,"teacher_disagreement_score":0.003421632,"about_ca_system_score_codex":0.00092828844,"about_ca_system_score_gemma":0.002384888,"threshold_uncertainty_score":0.01605314},"labels":[],"label_agreement":null},{"id":"W4320524434","doi":"10.1016/j.jwpe.2023.103489","title":"Arbitrary polynomial chaos expansion for uncertainty analysis of the one-dimensional hindered-compression continuous settling model","year":2023,"lang":"en","type":"article","venue":"Journal of Water Process Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polynomial chaos; Arithmetic underflow; Benchmark (surveying); Uncertainty quantification; Computer science; Monte Carlo method; Probabilistic logic; Mathematical optimization; Polynomial; Mathematics; Statistics; Machine learning; Artificial intelligence","score_opus":0.06378478475078403,"score_gpt":0.3103040960577065,"score_spread":0.24651931130692245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320524434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.104868986,0.0006559508,0.88534,0.00054731447,0.00006174735,0.000028749131,0.00016457247,0.0001368221,0.008195852],"genre_scores_gemma":[0.9753091,0.00044780405,0.017373601,0.00007164287,0.000059413283,0.00004321234,0.00014447076,0.00007334505,0.006477492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999775,0.0000680283,0.000009473656,0.000033370845,0.000081161445,0.000032981086],"domain_scores_gemma":[0.99914527,0.00047336123,0.00011677424,0.00006715009,0.00014710859,0.000050371622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074316404,0.00064351474,0.0010583284,0.0007349531,0.00045563412,0.000877009,0.0009246481,0.0008838498,0.0010105955],"category_scores_gemma":[0.0022673479,0.00025593565,0.0008653017,0.00055892597,0.0012069719,0.0012613378,0.0010814571,0.0013994863,0.00013619754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054712596,0.000021771657,0.00038867127,0.00008354155,0.00003101375,0.00010042296,0.00006001426,0.8370729,0.0031414262,0.15400209,0.00047559317,0.0045677936],"study_design_scores_gemma":[0.0000010122516,0.0000033208883,0.000041141677,0.000001391923,0.0000018918548,0.0000050498606,0.0000021593714,0.99393785,0.000087981694,0.0058460124,0.000069056965,0.0000031694242],"about_ca_topic_score_codex":0.006311442,"about_ca_topic_score_gemma":0.003390797,"teacher_disagreement_score":0.006311442,"about_ca_system_score_codex":0.0008933818,"about_ca_system_score_gemma":0.00090679765,"threshold_uncertainty_score":0.0125494},"labels":[],"label_agreement":null},{"id":"W4320719009","doi":"10.1016/j.jcp.2023.112011","title":"Stochastic Galerkin particle methods for kinetic equations of plasmas with uncertainties","year":2023,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Gruppo Nazionale per la Fisica Matematica; Gruppo Nazionale per il Calcolo Scientifico; Banff International Research Station for Mathematical Innovation and Discovery; Ministero dell’Istruzione, dell’Università e della Ricerca; Istituto Nazionale di Alta Matematica \"Francesco Severi\"","keywords":"Relaxation (psychology); Landau damping; Statistical physics; Solver; Physics; Mathematics; Applied mathematics; Plasma; Mathematical analysis; Mathematical optimization","score_opus":0.16283275858334448,"score_gpt":0.42058887698385017,"score_spread":0.2577561184005057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320719009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047111553,0.0001439018,0.9944289,0.000079581005,0.000016644899,0.00001984246,0.000018425895,0.000039968054,0.00054164336],"genre_scores_gemma":[0.6043596,0.0009036559,0.38774666,0.00015039544,0.00015976049,0.00042821578,0.00022313032,0.00017060201,0.005857896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996087,0.00017833141,0.000018041776,0.00004058018,0.00012686975,0.000027459339],"domain_scores_gemma":[0.99879223,0.00086715707,0.00013689286,0.000055878492,0.00010671989,0.000041125033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012222187,0.0008412404,0.00090267195,0.0005537215,0.00045407153,0.00070530106,0.0009967928,0.0010592852,0.0008727384],"category_scores_gemma":[0.002363289,0.00039423254,0.00077635254,0.0004946438,0.0011602588,0.00074343506,0.0012161244,0.0013914051,0.00016732776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019954903,0.000012253943,0.00028021977,0.000049491693,0.000019579526,0.00003480844,0.000041175288,0.96450317,0.0010660067,0.026589323,0.00012281361,0.007261105],"study_design_scores_gemma":[0.0000022885397,0.000003876381,0.000016213766,0.000001833846,0.0000010446568,0.0000043219334,0.0000020627137,0.99641293,0.00014228295,0.0031977918,0.00021357062,0.0000017255505],"about_ca_topic_score_codex":0.0035960246,"about_ca_topic_score_gemma":0.001837012,"teacher_disagreement_score":0.0035960246,"about_ca_system_score_codex":0.0006954306,"about_ca_system_score_gemma":0.0012574053,"threshold_uncertainty_score":0.007150233},"labels":[],"label_agreement":null},{"id":"W4321489559","doi":"10.1137/22m1472693","title":"An Adaptive Sampling and Domain Learning Strategy for Multivariate Function Approximation on Unknown Domains","year":2023,"lang":"en","type":"article","venue":"SIAM Journal on Scientific Computing","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Pacific Institute for the Mathematical Sciences","keywords":"Sampling (signal processing); Adaptive sampling; Curse of dimensionality; Mathematics; Mathematical optimization; Domain (mathematical analysis); Sample (material); Function (biology); Function approximation; Computer science; Algorithm; Artificial intelligence; Statistics; Monte Carlo method; Filter (signal processing)","score_opus":0.18516486448505085,"score_gpt":0.3870509912395628,"score_spread":0.20188612675451198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321489559","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00187572,0.00015463162,0.99729973,0.00007553426,0.00001773414,0.000022688864,0.000014444425,0.000039482857,0.0005000906],"genre_scores_gemma":[0.19636899,0.0008187299,0.7987265,0.00029279583,0.00016492313,0.00033654197,0.0002586218,0.00008729996,0.0029456227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876344,0.00067297264,0.00005154369,0.00014542941,0.00030296683,0.00006360828],"domain_scores_gemma":[0.9977246,0.0015062538,0.00013521695,0.00020929812,0.0003107133,0.00011398685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031118353,0.0009907028,0.0010916663,0.0011939427,0.0004130343,0.00089913065,0.0018168229,0.0013127133,0.001977444],"category_scores_gemma":[0.0077661565,0.00045311335,0.0012498933,0.0011946623,0.0015735084,0.0014669906,0.0027910727,0.0022750814,0.00056664395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021368466,0.00012776941,0.0015565086,0.0003142384,0.00014719107,0.0001701208,0.00021914364,0.6222533,0.0074756104,0.20789182,0.0037212023,0.15590933],"study_design_scores_gemma":[0.000009658118,0.000028362136,0.00006756263,0.000010448813,0.000006928991,0.000024059778,0.000007791172,0.9823569,0.00059029163,0.015930798,0.0009608656,0.0000062974427],"about_ca_topic_score_codex":0.0025615713,"about_ca_topic_score_gemma":0.0023266089,"teacher_disagreement_score":0.0031118353,"about_ca_system_score_codex":0.00086274795,"about_ca_system_score_gemma":0.0008946301,"threshold_uncertainty_score":0.0164572},"labels":[],"label_agreement":null},{"id":"W4321849621","doi":"10.1016/j.istruc.2023.02.050","title":"Vibration and resonance reliability analysis of non-uniform beam with randomly varying boundary conditions based on Kriging model","year":2023,"lang":"en","type":"article","venue":"Structures","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; University of Manitoba","keywords":"Vibration; Resonance (particle physics); Beam (structure); Natural frequency; Stiffness; Boundary value problem; Structural engineering; Reliability (semiconductor); Boundary (topology); Kriging; Amplitude; Mechanics; Acoustics; Mathematics; Mathematical analysis; Engineering; Physics; Statistics; Optics","score_opus":0.030399468497122054,"score_gpt":0.3112647037759694,"score_spread":0.2808652352788473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321849621","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34254706,0.00050653296,0.6547381,0.000099484336,0.000015921607,0.00002365269,0.00005592489,0.00016289894,0.0018502622],"genre_scores_gemma":[0.9927797,0.0000880692,0.006660939,0.0000038651165,0.0000028624306,0.000009904941,0.000027263033,0.000010429427,0.0004169673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995987,0.00013498409,0.000015639975,0.00008317286,0.000117588446,0.000049904407],"domain_scores_gemma":[0.9985784,0.00093777693,0.0001877704,0.000074207754,0.0002012969,0.000020594449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010144579,0.00055990304,0.0007499351,0.00061943114,0.0002506024,0.0003867469,0.00086623104,0.0006886444,0.0005314983],"category_scores_gemma":[0.0021370132,0.00046687588,0.00068230456,0.00045871915,0.00061756105,0.0006458727,0.00026980077,0.00042949925,0.00008715956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022772452,0.000008292236,0.0006450235,0.000020941985,0.000011845319,0.000026799875,0.000020992436,0.99454814,0.0015125716,0.00094998674,0.000034310822,0.0021982016],"study_design_scores_gemma":[5.946976e-7,0.0000058371234,0.0003117988,9.830279e-7,0.0000037048455,0.0000034994077,0.0000026951525,0.9993975,0.00013134236,0.00013106408,0.000008972746,0.0000020071332],"about_ca_topic_score_codex":0.011666929,"about_ca_topic_score_gemma":0.0072700973,"teacher_disagreement_score":0.011666929,"about_ca_system_score_codex":0.0006416423,"about_ca_system_score_gemma":0.00048561365,"threshold_uncertainty_score":0.023198009},"labels":[],"label_agreement":null},{"id":"W4321995706","doi":"10.5194/egusphere-egu23-10510","title":"Uncertainty Quantification in Hydrological and Environmental Modeling based on Polynomial Chaos Expansion","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Polynomial chaos; Monte Carlo method; Uncertainty quantification; Propagation of uncertainty; Computer science; Polynomial; Sensitivity analysis; Uncertainty analysis; Mathematical optimization; Mathematics; Algorithm; Statistics; Simulation; Machine learning","score_opus":0.18854679439005248,"score_gpt":0.3345355135048453,"score_spread":0.1459887191147928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321995706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016180184,0.00020451377,0.98223233,0.000094476374,0.000009423553,0.000015942356,0.000017230253,0.000045745084,0.0012002061],"genre_scores_gemma":[0.84857625,0.00086607115,0.14916083,0.0000452387,0.000055235123,0.00008654316,0.00006636424,0.00006308764,0.0010803405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991779,0.00041195354,0.000031377018,0.00006174846,0.00026941852,0.00004761955],"domain_scores_gemma":[0.99827504,0.0012903282,0.00015982964,0.000092665825,0.00015126435,0.000030849613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016256263,0.00047847148,0.00044931585,0.0010024088,0.00032958697,0.0006971655,0.00044316333,0.0004867358,0.00046860383],"category_scores_gemma":[0.0040656365,0.00026716347,0.00055382465,0.00066901057,0.0009180777,0.0012130025,0.0009885516,0.0008563803,0.00007335368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001797331,0.000009228269,0.0003156381,0.000037391706,0.000017741162,0.00003078276,0.000039067738,0.94865894,0.0023069354,0.032951888,0.00012578095,0.015488556],"study_design_scores_gemma":[6.5444584e-7,0.0000050846265,0.00005507861,0.000002530643,0.0000016043065,0.0000056182644,0.0000028058828,0.9938433,0.00039011094,0.0055669695,0.00012300034,0.0000031333902],"about_ca_topic_score_codex":0.0019858605,"about_ca_topic_score_gemma":0.0010169961,"teacher_disagreement_score":0.0019858605,"about_ca_system_score_codex":0.0005852035,"about_ca_system_score_gemma":0.00059273705,"threshold_uncertainty_score":0.008597255},"labels":[],"label_agreement":null},{"id":"W4328030925","doi":"10.2139/ssrn.4394439","title":"Evaluating the Efficiency of Scms to Avoid or Mitigate Asr-Induced Expansion and Deterioration Through a Multi-Level Assessment","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Risk analysis (engineering); Reliability engineering; Computer science; Business; Engineering","score_opus":0.31655064558768636,"score_gpt":0.46962219213763023,"score_spread":0.15307154654994387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4328030925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66837764,0.0005394961,0.32023364,0.0002957266,0.000054670338,0.0002782062,0.00022231769,0.0007147397,0.009283557],"genre_scores_gemma":[0.9862076,0.000047131616,0.012977163,0.000017802822,0.0000056890763,0.00003085839,0.000037716658,0.000010485171,0.00066550594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989134,0.0003062685,0.000049046957,0.00012707492,0.00042489637,0.00017930334],"domain_scores_gemma":[0.996342,0.0017643931,0.0004967292,0.0002765463,0.0009271613,0.00019317996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029775598,0.0011809287,0.0010234729,0.0012227643,0.000432839,0.0013763302,0.0010339337,0.0017882371,0.0017092167],"category_scores_gemma":[0.005579264,0.00025123014,0.00064819807,0.0005403707,0.000685123,0.0012913008,0.0010375753,0.0007146949,0.00025145157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027758622,0.00009859571,0.0034850119,0.00010681815,0.0000779931,0.0000673063,0.000030511388,0.9642104,0.009098435,0.0014402206,0.00020178677,0.020905465],"study_design_scores_gemma":[0.000024434203,0.00061637163,0.0029222504,0.000017745688,0.000076760094,0.000027806764,0.00004956354,0.98858845,0.006128104,0.0012007551,0.0003320088,0.000015696054],"about_ca_topic_score_codex":0.003490055,"about_ca_topic_score_gemma":0.0032976859,"teacher_disagreement_score":0.003490055,"about_ca_system_score_codex":0.0007523413,"about_ca_system_score_gemma":0.0014362931,"threshold_uncertainty_score":0.01574707},"labels":[],"label_agreement":null},{"id":"W4360858829","doi":"10.3390/axioms12040320","title":"Renyi Entropy of the Residual Lifetime of a Reliability System at the System Level","year":2023,"lang":"en","type":"article","venue":"Axioms","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; King Saud University","keywords":"Predictability; Rényi entropy; Residual; Residual entropy; Entropy (arrow of time); Statistical physics; Mathematics; Computer science; Statistics; Algorithm; Physics; Principle of maximum entropy; Quantum mechanics; Configuration entropy","score_opus":0.08997669490152801,"score_gpt":0.3029101391444567,"score_spread":0.21293344424292868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360858829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.120344855,0.0013288741,0.8633954,0.00080428825,0.00013322412,0.000043768177,0.00039479838,0.00017842172,0.013376398],"genre_scores_gemma":[0.94038844,0.0010961959,0.053830814,0.00014411348,0.00020170562,0.00012643932,0.00029001903,0.00007964366,0.0038427142],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99773175,0.00058785407,0.00017419095,0.0004869913,0.00083175226,0.00018750335],"domain_scores_gemma":[0.99123865,0.005009875,0.0013513227,0.0011464361,0.001047872,0.0002058347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031914571,0.0004747117,0.0006496809,0.0016567404,0.00042128767,0.0015837929,0.00087865884,0.00074408745,0.001575362],"category_scores_gemma":[0.013600951,0.00022572368,0.0005719605,0.00084869633,0.0033518553,0.00375271,0.0011846483,0.0012387135,0.00030257678],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054455886,0.000018845509,0.002241468,0.00013934379,0.000047454436,0.00013684334,0.00027548004,0.11662237,0.0072829663,0.85541016,0.0006262218,0.017144328],"study_design_scores_gemma":[0.00000985039,0.00012020975,0.0033576102,0.0000644714,0.000033635057,0.00028441314,0.00009252689,0.3546831,0.0053470517,0.6321526,0.0037768832,0.000077679],"about_ca_topic_score_codex":0.0007746119,"about_ca_topic_score_gemma":0.00036972214,"teacher_disagreement_score":0.0031914571,"about_ca_system_score_codex":0.0011671564,"about_ca_system_score_gemma":0.00087513577,"threshold_uncertainty_score":0.016878247},"labels":[],"label_agreement":null},{"id":"W4366085553","doi":"10.1016/j.strusafe.2023.102348","title":"Stochastic modelling of non-stationary environmental loads for reliability analysis under the changing climate","year":2023,"lang":"en","type":"article","venue":"Structural Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Climate change; Quantile; Environmental science; Poisson distribution; Percentile; Return period; Econometrics; Probabilistic logic; Stochastic process; Interval (graph theory); Extreme value theory; Poisson process; Computer science; Statistics; Reliability engineering; Mathematics; Engineering; Geography; Geology","score_opus":0.05669161941875018,"score_gpt":0.3146062651208289,"score_spread":0.2579146457020787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366085553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08599689,0.00034312697,0.90840745,0.0005605319,0.000115464434,0.00002974906,0.00026167431,0.00014636942,0.004138793],"genre_scores_gemma":[0.98076147,0.0004982156,0.013646936,0.00005859527,0.00008391632,0.00005910222,0.00019676743,0.00007502172,0.0046199188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993674,0.00026061514,0.000025418412,0.00010740137,0.00014841276,0.00009073104],"domain_scores_gemma":[0.9986456,0.0007675575,0.00027763812,0.00007787343,0.00017203747,0.00005936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013594126,0.0007775075,0.00095974363,0.0006602068,0.0003842199,0.0010222571,0.0015913502,0.0012835517,0.0021300875],"category_scores_gemma":[0.0044812695,0.0007294407,0.0008454077,0.00072107953,0.0012728664,0.0015559508,0.0008849099,0.0011194588,0.0002760826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004479819,0.000004876516,0.00018343511,0.000005775376,0.0000071473655,0.000009907957,0.0000073950227,0.9916803,0.0001968974,0.007191971,0.000075686046,0.0006321596],"study_design_scores_gemma":[9.316346e-7,0.000003831153,0.00010979904,0.0000012661857,0.000002574819,0.0000027567403,0.0000028123807,0.9963966,0.000037100297,0.003351121,0.000088510766,0.0000027467524],"about_ca_topic_score_codex":0.011537663,"about_ca_topic_score_gemma":0.014013157,"teacher_disagreement_score":0.011537663,"about_ca_system_score_codex":0.0011693767,"about_ca_system_score_gemma":0.001059852,"threshold_uncertainty_score":0.022941053},"labels":[],"label_agreement":null},{"id":"W4366493678","doi":"10.4050/f-0077-2021-16853","title":"Integration of System Reliability Theory into Quantitative Risk Assessment","year":2021,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Risk assessment; Reliability engineering; Reliability (semiconductor); Component (thermodynamics); Risk analysis (engineering); Risk management; Computer science; Failure mode and effects analysis; Interdependence; Reliability theory; Hazard analysis; Hazard; Engineering; Failure rate","score_opus":0.0722316470367265,"score_gpt":0.38520583798539426,"score_spread":0.31297419094866774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366493678","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013985056,0.001006255,0.9927254,0.00037685435,0.000074801304,0.000028030596,0.000039268525,0.00013088236,0.0042200335],"genre_scores_gemma":[0.50595033,0.008810593,0.47633716,0.0004499742,0.0009704605,0.00045136284,0.00032142998,0.00031352145,0.0063952305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99722123,0.0010012878,0.00013093305,0.00017554713,0.0013783013,0.000092754366],"domain_scores_gemma":[0.9938194,0.0039029394,0.00048485326,0.00055002514,0.001149142,0.00009357077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004237249,0.0014603246,0.0013308218,0.0022656021,0.00033569784,0.0018991387,0.0014074965,0.00094186456,0.002697514],"category_scores_gemma":[0.009703435,0.00068358774,0.0010529296,0.00097265514,0.001855338,0.0027986611,0.001572049,0.0020760165,0.000690862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008620601,0.00003054366,0.0004671032,0.00025371654,0.00007391118,0.00006094583,0.0000564009,0.7203213,0.0011351403,0.23318075,0.0013228129,0.04308882],"study_design_scores_gemma":[0.0000039659258,0.000039879404,0.00022478262,0.000058061338,0.000015755195,0.000042807864,0.000022034044,0.80818725,0.0003953144,0.18671124,0.004271986,0.00002689312],"about_ca_topic_score_codex":0.0021327164,"about_ca_topic_score_gemma":0.001208434,"teacher_disagreement_score":0.004237249,"about_ca_system_score_codex":0.0013289042,"about_ca_system_score_gemma":0.0014638542,"threshold_uncertainty_score":0.022408962},"labels":[],"label_agreement":null},{"id":"W4366687528","doi":"10.3390/jrfm16040251","title":"ν-Generalized Hyperbolic Distributions","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Class (philosophy); Random variable; Probability distribution; Statistical physics; Inverse distribution; Statistical parameter; Applied mathematics; Distribution (mathematics); Pure mathematics; Statistics; Heavy-tailed distribution; Mathematical analysis; Computer science; Physics; Artificial intelligence","score_opus":0.03965925210181299,"score_gpt":0.29147655763758257,"score_spread":0.2518173055357696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366687528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059119236,0.0013534891,0.9159657,0.0007791248,0.00030569592,0.00008200043,0.0004181591,0.00033063447,0.02164588],"genre_scores_gemma":[0.8588964,0.0027990919,0.092644624,0.0008461365,0.00081021676,0.00023740908,0.00068501214,0.00028879388,0.042792372],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989629,0.0002619396,0.000048173166,0.00021956975,0.00034859212,0.00015885576],"domain_scores_gemma":[0.99704367,0.0010788001,0.00046148218,0.00039014244,0.0007118862,0.00031398854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001696622,0.00068955566,0.0007102797,0.0017622296,0.00061061705,0.0023752102,0.0014703163,0.00089798303,0.005971009],"category_scores_gemma":[0.0055469335,0.00043029062,0.0011271642,0.0011632788,0.0019546184,0.0032115392,0.0016213731,0.001969552,0.0010691747],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043199892,0.000016478283,0.0009784703,0.00006191346,0.000031376152,0.00018915746,0.000110220884,0.018677332,0.0021964991,0.95849574,0.0018757522,0.017324032],"study_design_scores_gemma":[0.000024671239,0.00005105018,0.001005393,0.000036635094,0.000017215712,0.0003959395,0.00009229406,0.22003308,0.0010911534,0.7652236,0.011980833,0.000048172875],"about_ca_topic_score_codex":0.0015018815,"about_ca_topic_score_gemma":0.0006750903,"teacher_disagreement_score":0.005971009,"about_ca_system_score_codex":0.0012532042,"about_ca_system_score_gemma":0.0008836102,"threshold_uncertainty_score":0.019975007},"labels":[],"label_agreement":null},{"id":"W4376639929","doi":"10.46254/an12.20220777","title":"A Structured Method for Requirements Analysis with Application to CFR-14 Part-21 Subpart-G","year":2023,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science","score_opus":0.12144669955871983,"score_gpt":0.41242048110611357,"score_spread":0.29097378154739373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376639929","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003723905,0.000019497264,0.99757534,0.00006624225,0.000009044813,0.0002565365,0.0000899159,0.00049217255,0.0011188893],"genre_scores_gemma":[0.006568571,0.00003260861,0.9919572,0.000032760436,0.0000059498743,0.0005658003,0.0002080444,0.00007753209,0.00055166014],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99152523,0.0045123403,0.0008066156,0.00092754996,0.0020217418,0.00020651327],"domain_scores_gemma":[0.98956746,0.0070652966,0.00060512155,0.0010906901,0.0015367083,0.000134624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007838574,0.0019420341,0.00073382247,0.0036396992,0.0012525145,0.0031118146,0.0015156564,0.0014116492,0.009075423],"category_scores_gemma":[0.014749276,0.0011455778,0.0028150333,0.0020831234,0.0019095625,0.0022291746,0.001910176,0.0025721865,0.0032610293],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000145128,0.00030924077,0.001372921,0.0013399399,0.00018450471,0.00082150736,0.0032212888,0.05647492,0.02233738,0.41516674,0.014023002,0.48460346],"study_design_scores_gemma":[0.00022128379,0.00035671456,0.0011863393,0.00077540777,0.00011947958,0.0012273389,0.0013236888,0.6018352,0.013387465,0.23106407,0.14834827,0.00015469697],"about_ca_topic_score_codex":0.0038632862,"about_ca_topic_score_gemma":0.004678455,"teacher_disagreement_score":0.009075423,"about_ca_system_score_codex":0.0018934043,"about_ca_system_score_gemma":0.004538104,"threshold_uncertainty_score":0.04145485},"labels":[],"label_agreement":null},{"id":"W4378603287","doi":"10.1016/j.jsv.2023.117816","title":"Encoding nonlinear and unsteady aerodynamics of limit cycle oscillations using nonlinear sparse Bayesian learning","year":2023,"lang":"en","type":"article","venue":"Journal of Sound and Vibration","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Carleton University","funders":"National Nuclear Security Administration; Sandia National Laboratories; U.S. Department of Energy","keywords":"Overfitting; Nonlinear system; Aerodynamics; Computer science; Aeroelasticity; Limit cycle; Limit (mathematics); Flutter; Surrogate model; Bayesian probability; Algorithm; Mathematics; Machine learning; Artificial intelligence; Engineering; Physics; Artificial neural network","score_opus":0.08458936151880472,"score_gpt":0.33291684018008894,"score_spread":0.2483274786612842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378603287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069522314,0.0001693108,0.92845404,0.0002996491,0.000047401387,0.00001677349,0.00012392418,0.00020196478,0.001164617],"genre_scores_gemma":[0.90101206,0.0002645331,0.09501468,0.00012973284,0.00010024881,0.000059812795,0.00044075292,0.00007359808,0.0029045092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998134,0.000046427394,0.000009285461,0.000034875102,0.000067001296,0.000029014036],"domain_scores_gemma":[0.9983517,0.0011494041,0.00015268025,0.00010291883,0.0001853951,0.000057927464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005501167,0.00040703668,0.00051247794,0.0004911213,0.00024563423,0.0006057246,0.0007718777,0.0010202462,0.001262253],"category_scores_gemma":[0.0040539145,0.00046728685,0.0004639927,0.00042527684,0.00056379323,0.0013333998,0.0008639667,0.0010901672,0.00022871768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007584089,0.00005159195,0.00055883615,0.000029741415,0.000015842963,0.000026983967,0.000030319683,0.9541629,0.0024810873,0.0073684184,0.00049055467,0.034707885],"study_design_scores_gemma":[0.0000013427587,0.000003597012,0.000050226117,0.0000010657366,8.222817e-7,0.000002444006,0.000001231932,0.9984763,0.00012229188,0.0013050388,0.000033894292,0.0000016906288],"about_ca_topic_score_codex":0.0055727703,"about_ca_topic_score_gemma":0.0080460105,"teacher_disagreement_score":0.0055727703,"about_ca_system_score_codex":0.0004186099,"about_ca_system_score_gemma":0.0007134301,"threshold_uncertainty_score":0.011080682},"labels":[],"label_agreement":null},{"id":"W4378674120","doi":"10.1016/j.jsv.2023.117819","title":"Probabilistic prediction of coalescence flutter using measurements: Application to the flutter margin method","year":2023,"lang":"en","type":"article","venue":"Journal of Sound and Vibration","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Carleton University","funders":"","keywords":"Flutter; Aeroelasticity; Modal; Control theory (sociology); Engineering; Structural engineering; Airspeed; Mathematics; Computer science; Aerodynamics; Aerospace engineering; Artificial intelligence","score_opus":0.17053049607345122,"score_gpt":0.36937194067703516,"score_spread":0.19884144460358394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378674120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06843462,0.00019455681,0.9303514,0.00009947856,0.000020895912,0.000020962898,0.000059650003,0.00038036544,0.00043803724],"genre_scores_gemma":[0.95094204,0.000110238514,0.048192516,0.00003639738,0.00003336522,0.000041286214,0.0000823936,0.000049363865,0.00051237346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994666,0.00015856509,0.000031813284,0.00014748028,0.00014905377,0.000046563087],"domain_scores_gemma":[0.9962373,0.002862419,0.00033836107,0.00019987211,0.00027999,0.00008197441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001703136,0.00072980614,0.001067586,0.00056368957,0.0003394721,0.0007359077,0.0009701331,0.0012386575,0.00074211176],"category_scores_gemma":[0.008193155,0.0005413009,0.0005586679,0.00044557697,0.0005289851,0.0010810405,0.0010070921,0.0012367936,0.00020853365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019581974,0.000041275685,0.002735566,0.00004806872,0.000032609463,0.000053940945,0.000045566358,0.94777644,0.005961246,0.0015156916,0.00019434238,0.041399416],"study_design_scores_gemma":[0.0000024503072,0.0000090243475,0.00035678205,0.0000016551709,0.0000017973666,0.0000056516405,0.0000012747596,0.99877924,0.00044722873,0.0003652826,0.000025360292,0.0000043195605],"about_ca_topic_score_codex":0.0032233957,"about_ca_topic_score_gemma":0.002251135,"teacher_disagreement_score":0.0032233957,"about_ca_system_score_codex":0.00037035553,"about_ca_system_score_gemma":0.0005881635,"threshold_uncertainty_score":0.0090072155},"labels":[],"label_agreement":null},{"id":"W4378718490","doi":"10.48550/arxiv.2305.17076","title":"Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Discovery Air (Canada)","funders":"Agence Nationale de la Recherche","keywords":"Curse of dimensionality; Generalization; Spurious relationship; Estimator; Mathematical optimization; Computer science; Cover (algebra); Mathematics; Applied mathematics; Artificial intelligence; Statistics; Machine learning","score_opus":0.32948233761862167,"score_gpt":0.2577434931103554,"score_spread":0.07173884450826629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378718490","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013237683,0.00045457366,0.9839242,0.00065871666,0.00002661343,0.00002968436,0.00015897813,0.0002047139,0.001304926],"genre_scores_gemma":[0.7262875,0.0018838451,0.26366025,0.0010932513,0.0004161962,0.00045385,0.0011956447,0.0005921035,0.004417442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9917109,0.0037677675,0.0004322919,0.0020494345,0.0016183082,0.00042119154],"domain_scores_gemma":[0.9620667,0.026129749,0.0041084043,0.0049681747,0.002060506,0.000666451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01488124,0.0019936515,0.002392467,0.0012486184,0.00075821223,0.0021699478,0.003359783,0.0033224074,0.0025021883],"category_scores_gemma":[0.062432222,0.0010997693,0.0018600549,0.00129846,0.0034250405,0.007994701,0.005444256,0.0065779923,0.0006428341],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010502784,0.00006685493,0.0013291312,0.00018320637,0.00012370771,0.00011971973,0.00018500461,0.7154945,0.0014538661,0.24669285,0.0021866832,0.03205949],"study_design_scores_gemma":[0.0000078368175,0.000029779023,0.00020019508,0.000020601632,0.000009804462,0.000033873606,0.000011596832,0.8245049,0.00035755266,0.17438562,0.00042412308,0.00001409187],"about_ca_topic_score_codex":0.0021040477,"about_ca_topic_score_gemma":0.0018350737,"teacher_disagreement_score":0.01488124,"about_ca_system_score_codex":0.0026587055,"about_ca_system_score_gemma":0.0014678094,"threshold_uncertainty_score":0.07870042},"labels":[],"label_agreement":null},{"id":"W4379790553","doi":"10.1016/j.jobe.2023.107022","title":"Two-stage multi-objective optimization of reinforced concrete buildings based on non-dominated sorting genetic algorithm (NSGA-III)","year":2023,"lang":"en","type":"article","venue":"Journal of Building Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Sorting; Multi-objective optimization; Genetic algorithm; Mathematical optimization; Computer science; Frame (networking); Sensitivity (control systems); Structural engineering; Algorithm; Engineering; Mathematics","score_opus":0.02981883036383924,"score_gpt":0.30181473354205723,"score_spread":0.271995903178218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379790553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20334122,0.00044537714,0.783605,0.00017719182,0.000114061244,0.00025847557,0.00012730932,0.00034032366,0.011591083],"genre_scores_gemma":[0.8762167,0.00014323341,0.11794633,0.000076713404,0.00002284444,0.00042208692,0.00013911685,0.00004443393,0.0049884263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995003,0.0001684945,0.000020651752,0.00006244085,0.00013674243,0.000111326015],"domain_scores_gemma":[0.99956125,0.00022302468,0.00005240795,0.000019513473,0.00009809666,0.00004569608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010276864,0.00090747874,0.0016852259,0.00070313906,0.0006727698,0.0010880968,0.0013375832,0.0018098906,0.0022039413],"category_scores_gemma":[0.0010189202,0.00067473494,0.0012176138,0.00080103526,0.0005415703,0.0005724374,0.0009760797,0.0007958084,0.00019056391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034239976,0.00003764027,0.00020390567,0.000028030654,0.000016988904,0.00002226521,0.000014714357,0.9927539,0.0006949675,0.0006778976,0.00011767002,0.0053977687],"study_design_scores_gemma":[0.0000109519515,0.00005054009,0.000115360104,0.0000035355745,0.0000066601833,0.0000046612513,0.0000059493586,0.9993808,0.0001815307,0.00016203968,0.00007490421,0.000002958196],"about_ca_topic_score_codex":0.013500244,"about_ca_topic_score_gemma":0.013766779,"teacher_disagreement_score":0.013500244,"about_ca_system_score_codex":0.0009498033,"about_ca_system_score_gemma":0.0020710751,"threshold_uncertainty_score":0.02684331},"labels":[],"label_agreement":null},{"id":"W4381573116","doi":"10.48550/arxiv.2306.11188","title":"Invariant correlation under marginal transforms","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Invariant (physics); Correlation; Mathematics; Pure mathematics; Geometry; Mathematical physics","score_opus":0.30272162743718006,"score_gpt":0.24833505822417373,"score_spread":0.05438656921300633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381573116","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06876847,0.00020621846,0.9231638,0.00034718003,0.000034990917,0.00004456683,0.00033006945,0.00022467229,0.006880108],"genre_scores_gemma":[0.9337511,0.0005479189,0.05762367,0.0003162187,0.00020626059,0.00024403562,0.00072596874,0.00024968223,0.006335041],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9942321,0.0018849546,0.00019238667,0.0016287445,0.0013418976,0.000719902],"domain_scores_gemma":[0.98616517,0.0070820544,0.002086486,0.0027827553,0.0014120619,0.0004714854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005101113,0.0009345427,0.0013495376,0.0015435213,0.00082523824,0.0025744997,0.0014308337,0.001076791,0.003836527],"category_scores_gemma":[0.025249911,0.0006627221,0.0017225787,0.0017538087,0.004795071,0.0038163862,0.0025616274,0.0024930846,0.000807374],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007333468,0.00004446446,0.0023401962,0.00005720534,0.000070879265,0.0002634122,0.00017722207,0.08738231,0.0027908415,0.8902114,0.0012366392,0.015352125],"study_design_scores_gemma":[0.000022333117,0.00007727034,0.0018970039,0.00002138831,0.000040736286,0.00029701964,0.000068362024,0.44092894,0.002243366,0.5522401,0.002118269,0.000045290886],"about_ca_topic_score_codex":0.0037155715,"about_ca_topic_score_gemma":0.0016676711,"teacher_disagreement_score":0.005101113,"about_ca_system_score_codex":0.0018868868,"about_ca_system_score_gemma":0.0017984256,"threshold_uncertainty_score":0.026977599},"labels":[],"label_agreement":null},{"id":"W4381855979","doi":"10.1002/cjce.25015","title":"Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Linearization; Uncertainty quantification; Range (aeronautics); Propagation of uncertainty; Monte Carlo method; Sensitivity analysis; Uncertainty analysis; Nonlinear system; Computer science; Estimation theory; Mathematics; Algorithm; Statistics; Engineering; Machine learning; Simulation","score_opus":0.05221063776025226,"score_gpt":0.27699292436157424,"score_spread":0.224782286601322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381855979","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000632636,0.9652112,0.029126525,0.00056834926,0.0004211384,0.000028813913,0.000108525834,0.000073508694,0.0038294545],"genre_scores_gemma":[0.00871968,0.9808023,0.009026706,0.00023308059,0.00046373642,0.000035849687,0.00011113646,0.000026616315,0.00058089796],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984565,0.00034042372,0.00019755319,0.0003054143,0.00064007664,0.000060009243],"domain_scores_gemma":[0.9948607,0.0037087474,0.00033794285,0.00017531493,0.00087121164,0.000045941797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028703683,0.0020094851,0.0019995968,0.004152062,0.0004499217,0.0024495882,0.0024006127,0.0017126626,0.002539532],"category_scores_gemma":[0.005434495,0.00094931613,0.0015002069,0.0047279294,0.0013666345,0.0032064999,0.0013625174,0.0016138093,0.0010978082],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005517347,0.000086477485,0.00080143334,0.02884576,0.0004103937,0.0002538222,0.00020065432,0.03197523,0.002074769,0.059342533,0.014763758,0.86119],"study_design_scores_gemma":[0.0000179588,0.0001938783,0.0020113266,0.023290426,0.00078467035,0.0012989903,0.00024250161,0.023269301,0.006770264,0.07340712,0.86847115,0.00024236312],"about_ca_topic_score_codex":0.0032369536,"about_ca_topic_score_gemma":0.0017825,"teacher_disagreement_score":0.004152062,"about_ca_system_score_codex":0.001285794,"about_ca_system_score_gemma":0.002280603,"threshold_uncertainty_score":0.015180111},"labels":[],"label_agreement":null},{"id":"W4381995994","doi":"10.1016/j.strusafe.2023.102363","title":"Importance ranking of correlated variables in one analysis","year":2023,"lang":"en","type":"article","venue":"Structural Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Sobol sequence; Ranking (information retrieval); Multiplicative function; Mathematics; Sensitivity (control systems); Measure (data warehouse); Contrast (vision); Correlation; Random variable; Statistics; Applied mathematics; Computer science; Monte Carlo method; Data mining; Machine learning; Mathematical analysis; Artificial intelligence; Engineering","score_opus":0.06820766897925265,"score_gpt":0.3202305357329595,"score_spread":0.2520228667537069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381995994","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07594928,0.0005756122,0.9205889,0.00036584632,0.000066965884,0.000090539535,0.00013224126,0.0001636602,0.0020668888],"genre_scores_gemma":[0.83177394,0.0005596103,0.16498563,0.00012352424,0.00025138576,0.00013625323,0.0005201951,0.00014035976,0.0015091628],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98754346,0.0071440795,0.00047786065,0.0010977827,0.0029342978,0.00080259255],"domain_scores_gemma":[0.9653648,0.027549656,0.0014956273,0.0019915765,0.0028034912,0.00079494575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012975804,0.001623688,0.0029541096,0.0059979786,0.0011511582,0.0034695296,0.0017954167,0.0011794958,0.0020887533],"category_scores_gemma":[0.035492435,0.0006868852,0.0023312988,0.0031757683,0.0017567228,0.002276371,0.0022334154,0.0020979005,0.0002474996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013978824,0.00063831144,0.037906222,0.0008950709,0.0016393038,0.0005587355,0.0004192719,0.56207055,0.007366498,0.11626092,0.0034726195,0.26737463],"study_design_scores_gemma":[0.000042301606,0.00018847326,0.005046224,0.00006386913,0.00031947574,0.000069074806,0.000091361886,0.90102583,0.0017759887,0.09052189,0.0008063608,0.000049188464],"about_ca_topic_score_codex":0.0022156653,"about_ca_topic_score_gemma":0.0027974637,"teacher_disagreement_score":0.012975804,"about_ca_system_score_codex":0.0012748374,"about_ca_system_score_gemma":0.0021839703,"threshold_uncertainty_score":0.06862342},"labels":[],"label_agreement":null},{"id":"W4382862920","doi":"10.1016/j.cma.2023.116182","title":"Optimal design of validation experiments for the prediction of quantities of interest","year":2023,"lang":"en","type":"article","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computation; Computer science; Model selection; Experimental data; Cross-validation; Design of experiments; Model validation; Algorithm; Mathematical optimization; Mathematics; Machine learning; Statistics","score_opus":0.33234813841529426,"score_gpt":0.40244372036444054,"score_spread":0.07009558194914628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382862920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03387529,0.00026481997,0.9639967,0.0001814315,0.00003923904,0.00034356595,0.000074602925,0.00025065,0.00097367866],"genre_scores_gemma":[0.6921678,0.00021463643,0.30520022,0.0001372238,0.000031802003,0.0011999594,0.00022192879,0.00007981859,0.0007466636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99604,0.0024005233,0.00014636271,0.00064197485,0.00049862487,0.00027251788],"domain_scores_gemma":[0.9876881,0.008842225,0.0014923495,0.0007048846,0.0010352969,0.00023708002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009245732,0.0011956453,0.0015540998,0.000962838,0.00046480904,0.0010781058,0.0011871606,0.0016235241,0.0017639439],"category_scores_gemma":[0.019397395,0.0009585575,0.0008123561,0.00030064874,0.0017885588,0.001523763,0.0013538613,0.001275656,0.00030589206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033507922,0.0008782297,0.0029013436,0.0006970107,0.0001713242,0.00008794305,0.00014890684,0.7703506,0.064232446,0.031680677,0.0010251488,0.12447547],"study_design_scores_gemma":[0.00022093834,0.0005967244,0.0012164501,0.00004689518,0.00004247193,0.000021141124,0.000022956441,0.96112627,0.02429772,0.011478396,0.0009015448,0.000028451612],"about_ca_topic_score_codex":0.0009875163,"about_ca_topic_score_gemma":0.0008969465,"teacher_disagreement_score":0.009245732,"about_ca_system_score_codex":0.0010700831,"about_ca_system_score_gemma":0.0027221423,"threshold_uncertainty_score":0.04889673},"labels":[],"label_agreement":null},{"id":"W4383681675","doi":"10.1016/j.cam.2023.115452","title":"Robust inference for destructive one-shot device test data under Weibull lifetimes and competing risks","year":2023,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Weibull distribution; Divergence (linguistics); Mathematics; Shot (pellet); Computer science; Algorithm; Data mining; Statistics; Artificial intelligence","score_opus":0.43692623470868225,"score_gpt":0.40952146480562873,"score_spread":0.027404769903053516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383681675","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025802035,0.0003161945,0.9729753,0.00013785221,0.00001447338,0.000039782008,0.00017151714,0.00015293171,0.0003898304],"genre_scores_gemma":[0.72456425,0.00053360243,0.27068815,0.00022753516,0.00009856674,0.00024006021,0.0016590818,0.00011455051,0.0018741777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9954377,0.0024875272,0.00023875434,0.0009967147,0.00066900306,0.00017035507],"domain_scores_gemma":[0.94043577,0.052517142,0.0024550306,0.0027699666,0.001439738,0.00038233996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017565787,0.0010487325,0.0017863213,0.0015134483,0.00041114574,0.0012980612,0.0031136612,0.0018211624,0.0013032345],"category_scores_gemma":[0.053816125,0.0005601782,0.0012530115,0.00094304804,0.0020583998,0.0020400418,0.0020149634,0.002663261,0.00025873655],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029466968,0.00015257351,0.010577484,0.0003388806,0.00029345002,0.00047665086,0.00026871168,0.8010366,0.005217482,0.09997513,0.0012894422,0.080079],"study_design_scores_gemma":[0.000017622857,0.000060914495,0.0013595213,0.000022864187,0.0000254106,0.00012794683,0.000030300702,0.9446986,0.0017976403,0.05137688,0.00045477046,0.00002748453],"about_ca_topic_score_codex":0.0012124735,"about_ca_topic_score_gemma":0.0015779269,"teacher_disagreement_score":0.017565787,"about_ca_system_score_codex":0.00070920156,"about_ca_system_score_gemma":0.00087502785,"threshold_uncertainty_score":0.09289789},"labels":[],"label_agreement":null},{"id":"W4383887988","doi":"10.1016/j.jsv.2023.117920","title":"Multi-element polynomial chaos expansion based on automatic discontinuity detection for nonlinear systems","year":2023,"lang":"en","type":"article","venue":"Journal of Sound and Vibration","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Canada Research Chairs","keywords":"Polynomial chaos; Polynomial expansion; Classification of discontinuities; Robustness (evolution); Nonlinear system; Polynomial; Discontinuity (linguistics); Algorithm; Applied mathematics; Parametric statistics; Computer science; Mathematics; Mathematical analysis","score_opus":0.0803972330711092,"score_gpt":0.3363912603868019,"score_spread":0.2559940273156927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383887988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026166404,0.00010351737,0.97268504,0.000045300054,0.000029754628,0.000015600699,0.0000200342,0.0001693302,0.0007650686],"genre_scores_gemma":[0.72474223,0.00016544941,0.27245295,0.000033149925,0.000032699474,0.000062881714,0.000089227135,0.0000669523,0.0023545488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982435,0.000041915166,0.0000088040715,0.000029578541,0.00007888965,0.000016449065],"domain_scores_gemma":[0.99963367,0.00020390906,0.00003283289,0.00003243401,0.000080609956,0.000016512895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026877763,0.00031043997,0.0005049136,0.00047330942,0.00024813804,0.0003482145,0.00047206468,0.00046198443,0.0014022011],"category_scores_gemma":[0.0010761403,0.0002149445,0.00039294895,0.000319861,0.00025613044,0.0005323833,0.0005010678,0.0006561333,0.00030003968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073245703,0.00014397893,0.0016647679,0.00032843463,0.00009990141,0.00019747023,0.00020239173,0.38312778,0.18568127,0.028942153,0.0021733746,0.39670604],"study_design_scores_gemma":[0.0000028927739,0.000021122982,0.00017466226,0.0000021513758,0.0000037092657,0.000019438536,0.0000025018426,0.9958604,0.003035297,0.00065344654,0.00022029532,0.0000041349003],"about_ca_topic_score_codex":0.0008598784,"about_ca_topic_score_gemma":0.0010786615,"teacher_disagreement_score":0.0014022011,"about_ca_system_score_codex":0.00021943492,"about_ca_system_score_gemma":0.00032911505,"threshold_uncertainty_score":0.004690826},"labels":[],"label_agreement":null},{"id":"W4384135076","doi":"10.1007/978-3-030-85040-1_58","title":"Circular Error Probability","year":2023,"lang":"en","type":"book-chapter","venue":"Encyclopedia of earth sciences series/Encyclopedia of earth sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Mathematics; Statistics","score_opus":0.08529861824941809,"score_gpt":0.3007527246478037,"score_spread":0.21545410639838564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384135076","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010758859,0.0039032074,0.69180113,0.0020801965,0.001638227,0.00007959521,0.0007820028,0.001227968,0.28772888],"genre_scores_gemma":[0.51345444,0.008197206,0.111251,0.0021479162,0.0021179367,0.00035680993,0.0019427756,0.0013923575,0.35913947],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99768865,0.00037686244,0.00009708006,0.0006340481,0.0010009262,0.00020249514],"domain_scores_gemma":[0.9916073,0.0036321622,0.0005695578,0.0021141812,0.0018665912,0.00021021793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015570979,0.0010783051,0.0010180401,0.0019467024,0.0011241833,0.0031390078,0.0011097345,0.0020375096,0.03847187],"category_scores_gemma":[0.01521872,0.00046605576,0.0005831145,0.0023516002,0.0023102993,0.002949023,0.00242654,0.002104326,0.013123958],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008339523,0.000017643904,0.0004495861,0.00013862,0.000019989178,0.00010528551,0.00008898465,0.012242866,0.0013637905,0.89056146,0.022451507,0.07247691],"study_design_scores_gemma":[0.000020096817,0.00006286635,0.0008451387,0.00013659934,0.000031990214,0.0007665119,0.00006893168,0.086157076,0.006013785,0.82610583,0.07972187,0.00006933611],"about_ca_topic_score_codex":0.0010770172,"about_ca_topic_score_gemma":0.00046841134,"teacher_disagreement_score":0.03847187,"about_ca_system_score_codex":0.0012975686,"about_ca_system_score_gemma":0.0013466803,"threshold_uncertainty_score":0.12870127},"labels":[],"label_agreement":null},{"id":"W4384296658","doi":"10.32920/23688708","title":"An Advanced Statistical Method for Point Process Modelling With Missing Event Histories","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Missing data; Markov chain Monte Carlo; Computer science; Point estimation; Event (particle physics); Monte Carlo method; Econometrics; Point process; Rare events; Process (computing); Statistics; Mathematics; Machine learning","score_opus":0.1817028435562654,"score_gpt":0.44517612910826376,"score_spread":0.26347328555199834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384296658","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021847954,0.00003639947,0.9995448,0.000017141156,0.000011092491,0.00001033508,0.000017991331,0.000052930416,0.00009084534],"genre_scores_gemma":[0.056111395,0.0005900908,0.9399643,0.00010053777,0.00020152928,0.0005056271,0.0003704143,0.00017467588,0.0019814186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99667513,0.0016999352,0.00020909592,0.0004595438,0.00086894527,0.00008726961],"domain_scores_gemma":[0.9902553,0.0071676914,0.0006839984,0.00089993275,0.0008776743,0.000115397765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005987957,0.0012136804,0.0013051298,0.0021784855,0.00059605495,0.0014076714,0.0024051655,0.0015188999,0.0033118404],"category_scores_gemma":[0.016833277,0.0009236614,0.0024769302,0.0021329476,0.0015619635,0.0021408612,0.0019279894,0.0036597804,0.0010503243],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007699269,0.000064598156,0.0018557071,0.00040152867,0.0002568367,0.00029993613,0.00026794837,0.50155663,0.005120343,0.346715,0.0018618823,0.14152256],"study_design_scores_gemma":[0.000009903579,0.000040973315,0.00021419156,0.000035254307,0.00002422085,0.00009859612,0.000011494829,0.92620385,0.0010327732,0.06697368,0.0053271367,0.000027917828],"about_ca_topic_score_codex":0.0021651417,"about_ca_topic_score_gemma":0.0014991481,"teacher_disagreement_score":0.005987957,"about_ca_system_score_codex":0.000704167,"about_ca_system_score_gemma":0.0018592037,"threshold_uncertainty_score":0.03166771},"labels":[],"label_agreement":null},{"id":"W4384296835","doi":"10.32920/23688708.v1","title":"An Advanced Statistical Method for Point Process Modelling With Missing Event Histories","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Missing data; Markov chain Monte Carlo; Computer science; Point estimation; Event (particle physics); Econometrics; Point process; Monte Carlo method; Rare events; Process (computing); Estimation; Novelty; Statistics; Mathematics; Machine learning; Engineering","score_opus":0.1817028435562654,"score_gpt":0.44517612910826376,"score_spread":0.26347328555199834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384296835","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021847954,0.00003639947,0.9995448,0.000017141156,0.000011092491,0.00001033508,0.000017991331,0.000052930416,0.00009084534],"genre_scores_gemma":[0.056111395,0.0005900908,0.9399643,0.00010053777,0.00020152928,0.0005056271,0.0003704143,0.00017467588,0.0019814186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99667513,0.0016999352,0.00020909592,0.0004595438,0.00086894527,0.00008726961],"domain_scores_gemma":[0.9902553,0.0071676914,0.0006839984,0.00089993275,0.0008776743,0.000115397765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005987957,0.0012136804,0.0013051298,0.0021784855,0.00059605495,0.0014076714,0.0024051655,0.0015188999,0.0033118404],"category_scores_gemma":[0.016833277,0.0009236614,0.0024769302,0.0021329476,0.0015619635,0.0021408612,0.0019279894,0.0036597804,0.0010503243],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007699269,0.000064598156,0.0018557071,0.00040152867,0.0002568367,0.00029993613,0.00026794837,0.50155663,0.005120343,0.346715,0.0018618823,0.14152256],"study_design_scores_gemma":[0.000009903579,0.000040973315,0.00021419156,0.000035254307,0.00002422085,0.00009859612,0.000011494829,0.92620385,0.0010327732,0.06697368,0.0053271367,0.000027917828],"about_ca_topic_score_codex":0.0021651417,"about_ca_topic_score_gemma":0.0014991481,"teacher_disagreement_score":0.005987957,"about_ca_system_score_codex":0.000704167,"about_ca_system_score_gemma":0.0018592037,"threshold_uncertainty_score":0.03166771},"labels":[],"label_agreement":null},{"id":"W4384340293","doi":"10.1007/s00158-023-03627-4","title":"New learning functions for active learning Kriging reliability analysis using a probabilistic approach: KO and WKO functions","year":2023,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Dalhousie University","keywords":"Kriging; Reliability (semiconductor); Computer science; Probabilistic logic; Machine learning; Process (computing); Design of experiments; Artificial intelligence; Selection (genetic algorithm); Limit (mathematics); Function (biology); Mathematical optimization; Mathematics; Statistics","score_opus":0.06516174326543149,"score_gpt":0.33457227386353383,"score_spread":0.2694105305981023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384340293","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028539554,0.00020718265,0.99618405,0.00007618336,0.000017715094,0.000016475398,0.00001413315,0.00005684719,0.00057344494],"genre_scores_gemma":[0.32129171,0.0015147438,0.6674282,0.00024814947,0.00018375351,0.00066477054,0.00031265992,0.0005750285,0.0077809845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988985,0.00048357702,0.00008557183,0.000116400326,0.00034572152,0.00007017632],"domain_scores_gemma":[0.99624664,0.0020672523,0.00027240213,0.00025362597,0.0010599999,0.00010013692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00464934,0.0014630446,0.0014018399,0.0017822116,0.0004897558,0.0012934444,0.00195889,0.0016550622,0.0014647961],"category_scores_gemma":[0.010243322,0.0008133879,0.0012788561,0.0014286552,0.0013852754,0.0036522837,0.0016632543,0.0020895973,0.0006561436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008006071,0.00010147402,0.00073245337,0.00022568779,0.00006963773,0.00004650629,0.00010297003,0.7893188,0.0038093065,0.0813377,0.0015291881,0.12264616],"study_design_scores_gemma":[0.0000027040473,0.000009929545,0.00009502352,0.000013307819,0.000008001519,0.000009429442,0.0000047693393,0.9944601,0.00045045975,0.0045075626,0.00043039751,0.000008341197],"about_ca_topic_score_codex":0.0020452237,"about_ca_topic_score_gemma":0.0023897726,"teacher_disagreement_score":0.00464934,"about_ca_system_score_codex":0.0010581923,"about_ca_system_score_gemma":0.0010710898,"threshold_uncertainty_score":0.024588346},"labels":[],"label_agreement":null},{"id":"W4384519197","doi":"10.1109/tcsii.2023.3295805","title":"A Comparative Study of Polynomial Chaos Expansion-Based Methods for Global Sensitivity Analysis in Power System Uncertainty Control","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec","keywords":"Polynomial chaos; Sensitivity (control systems); Decorrelation; Covariance; Analysis of covariance; Polynomial; Computer science; Mathematics; Contrast (vision); Statistics; Monte Carlo method; Engineering; Artificial intelligence; Electronic engineering","score_opus":0.08377796114882108,"score_gpt":0.3814924838225168,"score_spread":0.29771452267369575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384519197","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011383794,0.00068130525,0.98596084,0.00007730308,0.000028748418,0.00002404445,0.000022126465,0.00012432324,0.0016974806],"genre_scores_gemma":[0.75042176,0.0014507713,0.24533316,0.00008321951,0.00014301419,0.00013656272,0.00014301582,0.00028138276,0.002007054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998315,0.00092699414,0.00006287682,0.00017259535,0.00045981287,0.000062731815],"domain_scores_gemma":[0.99512583,0.003824241,0.00018019517,0.00028653347,0.00052269205,0.00006053093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003022998,0.001083868,0.0011064453,0.00107961,0.00031014506,0.0008860869,0.00075023173,0.00076873304,0.0010009778],"category_scores_gemma":[0.0068911975,0.00031201376,0.0011435671,0.00077768834,0.0007170564,0.0013064464,0.0010879801,0.001342702,0.00015649931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007816543,0.000035722227,0.0006200599,0.00014770178,0.000113675706,0.000045310484,0.00009630875,0.894839,0.002480695,0.025805846,0.00033231953,0.07540522],"study_design_scores_gemma":[0.000001564134,0.000020351485,0.00011452248,0.0000047887106,0.000007599009,0.000007212741,0.0000053971135,0.9973078,0.00036561652,0.0019407028,0.00021815383,0.0000062827307],"about_ca_topic_score_codex":0.0026522193,"about_ca_topic_score_gemma":0.0015141078,"teacher_disagreement_score":0.003022998,"about_ca_system_score_codex":0.00053669495,"about_ca_system_score_gemma":0.00056647067,"threshold_uncertainty_score":0.015987337},"labels":[],"label_agreement":null},{"id":"W4384569004","doi":"10.23952/asvao.5.2023.1.02","title":"Parameterized Douglas-Rachford dynamical system for monotone inclusion problems","year":2023,"lang":"en","type":"article","venue":"Applied Set-Valued Analysis and Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Indian Institute of Technology Madras","keywords":"Parameterized complexity; Monotone polygon; Inclusion (mineral); Dynamical systems theory; Mathematics; Applied mathematics; Computer science; Mathematical optimization; Algorithm; Physics; Geometry; Thermodynamics","score_opus":0.04734309323369852,"score_gpt":0.3053740057426913,"score_spread":0.2580309125089928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384569004","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03650487,0.00025371098,0.959592,0.00015862964,0.00003690746,0.000042351672,0.000028975408,0.000040282423,0.003342409],"genre_scores_gemma":[0.72646093,0.00034425434,0.26622924,0.000120294324,0.000048375558,0.00026662412,0.00009494155,0.000034326054,0.006401015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999798,0.00009053964,0.00000973145,0.000032517797,0.000050007315,0.000019120165],"domain_scores_gemma":[0.9997327,0.00010911092,0.000045356286,0.000024453746,0.000058461268,0.000029819568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058993144,0.000478917,0.00050792866,0.00033016657,0.00045718128,0.0005513697,0.00059962645,0.0008572752,0.001013015],"category_scores_gemma":[0.0009135607,0.00021144057,0.0004608112,0.00020965708,0.0009369637,0.0006584536,0.0011046076,0.00088685844,0.0001278824],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010413589,0.00007380792,0.0014584194,0.00020221065,0.00005148526,0.00024225208,0.00029701093,0.61321884,0.022991456,0.3250355,0.0010682299,0.035256647],"study_design_scores_gemma":[0.000006049109,0.000029564271,0.00006782164,0.000005745771,0.0000024953767,0.000023439088,0.0000126939285,0.9873122,0.0008911858,0.01092872,0.00071441673,0.0000057267116],"about_ca_topic_score_codex":0.0014677158,"about_ca_topic_score_gemma":0.0014916295,"teacher_disagreement_score":0.0014677158,"about_ca_system_score_codex":0.00047670034,"about_ca_system_score_gemma":0.0008125621,"threshold_uncertainty_score":0.0034587383},"labels":[],"label_agreement":null},{"id":"W4385201802","doi":"10.30699/ijrrs.5.2.7","title":"Direct Quantile Function Estimation Using Information Principles and Its Applications in Reliability Analysis","year":2023,"lang":"en","type":"article","venue":"International Journal of Reliability Risk and Safety Theory and Application","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Principle of maximum entropy; Random variable; Mathematics; Quantile; Maximum entropy probability distribution; Entropy (arrow of time); Statistics; Akaike information criterion; Randomness; Maximum entropy spectral estimation; Order statistic; Kullback–Leibler divergence; Applied mathematics; Mathematical optimization","score_opus":0.025475081389098812,"score_gpt":0.32248847056610563,"score_spread":0.2970133891770068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385201802","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020681717,0.0006411941,0.996572,0.00010840549,0.0000108017,0.000009809525,0.000024492016,0.000042701227,0.00052243],"genre_scores_gemma":[0.47094408,0.006647769,0.51836073,0.00027169276,0.00051330874,0.00035336704,0.00029182725,0.00015406105,0.002463203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99829584,0.00082580873,0.00008849167,0.0002532293,0.0004588102,0.000077803714],"domain_scores_gemma":[0.9930736,0.0058038514,0.00038197494,0.00026607089,0.0004194334,0.000055019947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004437703,0.000926399,0.0010283573,0.0020387706,0.0003553165,0.00095581304,0.0009517618,0.0008769091,0.0011165678],"category_scores_gemma":[0.013694945,0.0005145273,0.0010722255,0.0019479635,0.001772465,0.0018047728,0.0015299699,0.0015754716,0.0002456424],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061436614,0.000056086483,0.0017800957,0.000348085,0.00013684752,0.00017499944,0.00018745393,0.5552031,0.0044803293,0.28949285,0.0013413393,0.14673734],"study_design_scores_gemma":[0.000006913058,0.00003691262,0.0007709894,0.000037659793,0.000019056519,0.000055420864,0.000013356886,0.89443177,0.0011394805,0.10212373,0.0013360779,0.000028680104],"about_ca_topic_score_codex":0.0014485499,"about_ca_topic_score_gemma":0.00082139723,"teacher_disagreement_score":0.004437703,"about_ca_system_score_codex":0.0008087203,"about_ca_system_score_gemma":0.00079763547,"threshold_uncertainty_score":0.02346909},"labels":[],"label_agreement":null},{"id":"W4385580723","doi":"10.1002/cjs.11791","title":"Improved inference for a boundary parameter","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Università degli Studi di Padova; Ministero dell’Istruzione, dell’Università e della Ricerca; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Estimator; Inference; Boundary (topology); Limiting; Mathematics; Applied mathematics; Range (aeronautics); Statistical inference; Statistical hypothesis testing; Boundary value problem; Computer science; Statistics; Mathematical analysis; Artificial intelligence; Engineering","score_opus":0.1120026833599349,"score_gpt":0.33551048574938636,"score_spread":0.22350780238945145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385580723","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039518908,0.00029359254,0.9568287,0.0004864606,0.00005700139,0.000036299454,0.000085969696,0.0005023217,0.0021906975],"genre_scores_gemma":[0.7823102,0.00024849008,0.21367851,0.0005435116,0.00017054401,0.00015157639,0.0004585068,0.0003083827,0.0021302314],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98329216,0.010506367,0.00055431284,0.0026376834,0.0023719403,0.000637613],"domain_scores_gemma":[0.8717958,0.10413934,0.004990171,0.012350279,0.0049311826,0.0017931936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032314766,0.0009438346,0.0030386455,0.0031113902,0.0013846193,0.003744723,0.004379699,0.0031969107,0.0049626874],"category_scores_gemma":[0.17516777,0.0011522439,0.0018509607,0.0017154417,0.005989007,0.0070278463,0.0052959356,0.007040046,0.0010137314],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011093705,0.00025808127,0.028525377,0.00046345242,0.0004290461,0.00069335755,0.0010320717,0.28468505,0.006119323,0.54467154,0.0039394414,0.12807383],"study_design_scores_gemma":[0.000051059385,0.00007742261,0.0025029427,0.00012226126,0.000042667172,0.00011947579,0.00007152388,0.7561651,0.0021323883,0.23754916,0.0011201103,0.000045944624],"about_ca_topic_score_codex":0.004581034,"about_ca_topic_score_gemma":0.0022838144,"teacher_disagreement_score":0.032314766,"about_ca_system_score_codex":0.0017827216,"about_ca_system_score_gemma":0.0017292465,"threshold_uncertainty_score":0.17089885},"labels":[],"label_agreement":null},{"id":"W4385665207","doi":"10.3390/e25081180","title":"Capacity-Achieving Input Distributions of Additive Vector Gaussian Noise Channels: Even-Moment Constraints and Unbounded or Compact Support","year":2023,"lang":"en","type":"article","venue":"Entropy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Countable set; Mathematics; Constraint (computer-aided design); Moment (physics); Gaussian; Lebesgue measure; Dimension (graph theory); Noise (video); Gaussian noise; Measure (data warehouse); Distribution (mathematics); Topology (electrical circuits); Mathematical analysis; Lebesgue integration; Discrete mathematics; Pure mathematics; Statistical physics; Applied mathematics; Combinatorics; Computer science; Algorithm; Physics; Geometry; Data mining; Quantum mechanics","score_opus":0.08850546179520614,"score_gpt":0.32679336439172535,"score_spread":0.23828790259651922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385665207","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40308028,0.00055419846,0.58473605,0.00061365037,0.00002004725,0.00003789432,0.0003316744,0.0001734471,0.010452814],"genre_scores_gemma":[0.9897516,0.00028051418,0.008661636,0.00003806868,0.000035903962,0.000050529477,0.0000926187,0.000028858673,0.0010603156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999015,0.00034747197,0.000032790547,0.000082706785,0.00030318045,0.00021881421],"domain_scores_gemma":[0.9923921,0.005695171,0.00083302736,0.00027365462,0.00051679095,0.00028924807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017907881,0.0007563128,0.0008598914,0.0005382462,0.00034840158,0.0014949434,0.0011239713,0.00075394387,0.0015322763],"category_scores_gemma":[0.008806933,0.00035333348,0.00052627764,0.00047709057,0.0021470492,0.0017362366,0.0015399929,0.001081945,0.00023205981],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004255946,0.00007730155,0.0016028476,0.00022991975,0.000057042304,0.0006067268,0.00029027901,0.574908,0.018847076,0.39429522,0.0005875306,0.008072374],"study_design_scores_gemma":[0.000015269175,0.00005741244,0.0004916217,0.000030531028,0.000008545943,0.000090557645,0.00006485085,0.9170416,0.003938341,0.078005195,0.00023611901,0.000019952726],"about_ca_topic_score_codex":0.00080264895,"about_ca_topic_score_gemma":0.0004171183,"teacher_disagreement_score":0.0017907881,"about_ca_system_score_codex":0.0009631431,"about_ca_system_score_gemma":0.0006422486,"threshold_uncertainty_score":0.009470701},"labels":[],"label_agreement":null},{"id":"W4385774847","doi":"10.48550/arxiv.2308.05414","title":"Unifying Distributionally Robust Optimization via Optimal Transport Theory","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Mathematical optimization; Computer science; Divergence (linguistics); Duality (order theory); Measure (data warehouse); Robust optimization; Moment (physics); Baseline (sea); Adversarial system; Mathematics; Artificial intelligence; Data mining","score_opus":0.2109900018308093,"score_gpt":0.23565557903198936,"score_spread":0.024665577201180067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385774847","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016818666,0.00018928832,0.9951792,0.00040031067,0.000029621817,0.000017818513,0.000037505317,0.000048485934,0.0024159763],"genre_scores_gemma":[0.6218532,0.0025198443,0.36218017,0.0007521607,0.00047088586,0.00045457858,0.0003949534,0.00050409266,0.010870104],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962043,0.001718063,0.00020109102,0.00070244056,0.0009295527,0.00024462544],"domain_scores_gemma":[0.9963018,0.0023718395,0.00045643144,0.0003774179,0.00036371982,0.000128916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060455645,0.0020541237,0.0019441943,0.0013948652,0.00069007574,0.0028603682,0.002343265,0.0025880444,0.0034633975],"category_scores_gemma":[0.012164188,0.000896305,0.0016361292,0.0013270823,0.0042475485,0.0058425884,0.005782206,0.0036064563,0.0005935302],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030313073,0.000024665984,0.00016762697,0.00008237794,0.000036753332,0.000060308204,0.000060039292,0.42257127,0.0005999219,0.56246686,0.00083057425,0.013069322],"study_design_scores_gemma":[0.000008568409,0.000034178458,0.000047426616,0.000023672084,0.000008595524,0.000024148114,0.000015712145,0.6883497,0.00037195755,0.3095756,0.0015244333,0.000015970476],"about_ca_topic_score_codex":0.0022308105,"about_ca_topic_score_gemma":0.0013140717,"teacher_disagreement_score":0.0060455645,"about_ca_system_score_codex":0.0033662652,"about_ca_system_score_gemma":0.0025327695,"threshold_uncertainty_score":0.03197241},"labels":[],"label_agreement":null},{"id":"W4385804190","doi":"10.1016/j.probengmech.2023.103509","title":"MDOF stochastic stability analysis and applications to a coupled rotating shaft system","year":2023,"lang":"en","type":"article","venue":"Probabilistic Engineering Mechanics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Generalization; White noise; Mathematics; Perturbation (astronomy); Eigenvalues and eigenvectors; Moment (physics); Lyapunov function; Control theory (sociology); Applied mathematics; Stability (learning theory); Lyapunov exponent; Transformation (genetics); Nonlinear system; Computer science; Mathematical analysis; Classical mechanics; Physics","score_opus":0.05832794750719184,"score_gpt":0.3007387363597272,"score_spread":0.24241078885253536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385804190","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03080641,0.00028670783,0.96453196,0.00023114624,0.000032711618,0.00001634384,0.000048262358,0.0000887464,0.0039576776],"genre_scores_gemma":[0.9562761,0.00034471185,0.039066397,0.000051542367,0.00007538507,0.000053144937,0.00006883161,0.000048112182,0.0040157246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997148,0.00007424347,0.000014234815,0.00006080432,0.00010570344,0.000030254494],"domain_scores_gemma":[0.99857354,0.00087102305,0.00020490488,0.00006807698,0.00023909863,0.0000433134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097225537,0.00042769505,0.00069705787,0.00076670805,0.00047892184,0.00071518676,0.0005577658,0.0006563675,0.0025020132],"category_scores_gemma":[0.0037145126,0.0002647359,0.0006624474,0.00051281194,0.0006637536,0.00054717803,0.0010656939,0.0005505644,0.00017496657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025078021,0.000014439713,0.0005097831,0.00003753608,0.000022276772,0.00003841908,0.000027117101,0.93046355,0.0013002878,0.05363656,0.0003024651,0.013622481],"study_design_scores_gemma":[6.526452e-7,0.0000035650605,0.000080097336,0.0000021119183,0.0000016194679,0.000005465803,0.000001588455,0.9946502,0.00009224828,0.0050292565,0.00013126551,0.0000019737238],"about_ca_topic_score_codex":0.0042418228,"about_ca_topic_score_gemma":0.0032829845,"teacher_disagreement_score":0.0042418228,"about_ca_system_score_codex":0.00063044863,"about_ca_system_score_gemma":0.0008903111,"threshold_uncertainty_score":0.008434296},"labels":[],"label_agreement":null},{"id":"W4386020699","doi":"","title":"A sensitivity equation method for fast evaluation of nearby flows and uncertainty analysis for shape parameters","year":2006,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Sensitivity (control systems); Uncertainty analysis; Mathematics; Applied mathematics; Computer science; Statistics; Engineering","score_opus":0.07433351501082809,"score_gpt":0.3214262434620217,"score_spread":0.24709272845119362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386020699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006005443,0.000026217755,0.9988206,0.000016576698,0.000011461118,0.000017478786,0.000015689084,0.000111808076,0.00037965525],"genre_scores_gemma":[0.0977405,0.00027000575,0.8949161,0.00008617409,0.00006270034,0.00032220062,0.00013862447,0.00039967505,0.0060640825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993445,0.00018715685,0.00003435505,0.000080253936,0.00031931445,0.00003444392],"domain_scores_gemma":[0.9974093,0.0018892053,0.00008436315,0.00014028348,0.0004158131,0.00006098726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016433852,0.0010762762,0.0011945843,0.0013235606,0.00059457,0.00095517474,0.0012388168,0.0015589299,0.005510928],"category_scores_gemma":[0.00506839,0.0008858349,0.0015195878,0.00089066365,0.00074347865,0.0015399891,0.0016684359,0.0022627497,0.0010545318],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000090217596,0.000093139395,0.00043514086,0.00026349674,0.00009060422,0.0002292358,0.00017979082,0.68087494,0.03727467,0.086052015,0.003644057,0.19077274],"study_design_scores_gemma":[0.0000027750316,0.000007414326,0.000033215714,0.0000048050324,0.000005464177,0.000022365124,0.000002939152,0.99389344,0.0013742719,0.0038071692,0.00083803467,0.000008126628],"about_ca_topic_score_codex":0.004377313,"about_ca_topic_score_gemma":0.0027625193,"teacher_disagreement_score":0.005510928,"about_ca_system_score_codex":0.00064330414,"about_ca_system_score_gemma":0.0010992226,"threshold_uncertainty_score":0.018435895},"labels":[],"label_agreement":null},{"id":"W4386613339","doi":"10.1061/jccee5.cpeng-5601","title":"Erratum for “MPI Parallel Monte Carlo Framework for the Reliability Analysis of Highway Bridges”","year":2023,"lang":"en","type":"erratum","venue":"Journal of Computing in Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Monte Carlo method; Reliability (semiconductor); Computer science; Physics; Mathematics; Statistics","score_opus":0.06528307720517175,"score_gpt":0.3367546961255487,"score_spread":0.27147161892037697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386613339","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00068730424,0.0016727421,0.029327946,0.07022564,0.842824,0.00016731671,0.0061820582,0.004276583,0.04463637],"genre_scores_gemma":[0.014033982,0.004795515,0.05757744,0.06906542,0.12776709,0.000596892,0.014229815,0.010226719,0.7017072],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99682593,0.00047999597,0.00028843945,0.00027049438,0.0019398864,0.00019532279],"domain_scores_gemma":[0.9857319,0.0036367255,0.00040082974,0.0010575135,0.008592119,0.00058100186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028244522,0.001975476,0.0016183648,0.00294268,0.00332487,0.0032366756,0.0035463215,0.004511422,0.10251616],"category_scores_gemma":[0.029520245,0.0011419653,0.0016002838,0.0023196496,0.001603922,0.0031029712,0.0018136341,0.0072285333,0.056590084],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026286829,0.0000070162846,0.000020933836,0.000032803746,0.000004992573,0.000052220945,0.0000058504515,0.00027595044,0.000063964624,0.0020244527,0.9925364,0.0049491236],"study_design_scores_gemma":[0.000054820783,0.000031612308,0.0004125343,0.00016941542,0.000025343228,0.00018577336,0.000039985527,0.004050795,0.0008703042,0.011491911,0.9826142,0.000053325504],"about_ca_topic_score_codex":0.017617613,"about_ca_topic_score_gemma":0.023405455,"teacher_disagreement_score":0.10251616,"about_ca_system_score_codex":0.0026310107,"about_ca_system_score_gemma":0.0040231734,"threshold_uncertainty_score":0.3429507},"labels":[],"label_agreement":null},{"id":"W4386620668","doi":"10.1002/sta4.613","title":"On pairwise interaction multivariate Pareto models","year":2023,"lang":"en","type":"article","venue":"Stat","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pareto principle; Pairwise comparison; Multivariate statistics; Class (philosophy); Mathematics; Basis (linear algebra); Parametric statistics; Parametric model; Econometrics; Mathematical economics; Statistical physics; Computer science; Statistics; Artificial intelligence; Physics","score_opus":0.2719243919800507,"score_gpt":0.4067526109303313,"score_spread":0.13482821895028063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386620668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020420557,0.0010995602,0.9575087,0.0008221656,0.000061112696,0.000039328235,0.00029047026,0.00011683461,0.0196413],"genre_scores_gemma":[0.8537584,0.0056045083,0.10531263,0.0011711472,0.0007984246,0.0005182673,0.0010519719,0.00025363587,0.031530995],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976775,0.0011588744,0.00005518311,0.00029774717,0.0005222293,0.00028850915],"domain_scores_gemma":[0.9955889,0.0028266592,0.0005280071,0.00032205676,0.0004849364,0.00024939745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003948138,0.0014513998,0.0011609177,0.0020448256,0.0009204666,0.0018282519,0.0022461237,0.0018762328,0.0069723646],"category_scores_gemma":[0.009355664,0.00048782342,0.001471577,0.0021646328,0.0022412904,0.003088303,0.0028265964,0.0027495723,0.0013566712],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023213566,0.000027949365,0.00048277687,0.000055156124,0.00003346173,0.000102102014,0.00007917313,0.1169822,0.00045582507,0.87140816,0.00197905,0.008370883],"study_design_scores_gemma":[0.000008104963,0.000025858035,0.00027029918,0.00002757284,0.000012394451,0.00008021014,0.000031398584,0.3956868,0.00010748228,0.6014721,0.002256429,0.000021323207],"about_ca_topic_score_codex":0.0026631723,"about_ca_topic_score_gemma":0.0018156706,"teacher_disagreement_score":0.0069723646,"about_ca_system_score_codex":0.0013560958,"about_ca_system_score_gemma":0.0011895904,"threshold_uncertainty_score":0.023324907},"labels":[],"label_agreement":null},{"id":"W4386986716","doi":"10.3850/978-981-18-8071-1_p546-cd","title":"Return Periods of Extreme Events in the Changing Climate: LEYP Model","year":2023,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Return period; Climate change; Extreme weather; Environmental science; Reliability (semiconductor); Econometrics; Computer science; Extreme value theory; Pace; Climate model; Stochastic process; Poisson distribution; Climatology; Mathematics; Statistics; Geography","score_opus":0.21908609068841578,"score_gpt":0.35983375213186464,"score_spread":0.14074766144344886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386986716","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80125624,0.0006518743,0.14941236,0.005109703,0.00029050835,0.000055897497,0.0058267997,0.0009180293,0.03647859],"genre_scores_gemma":[0.9879665,0.00029164663,0.0030462136,0.00010592045,0.00006465908,0.000033207292,0.0008879122,0.00011268326,0.0074912487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998517,0.000046658213,0.000005757376,0.000041035386,0.000022895963,0.000031949643],"domain_scores_gemma":[0.9993907,0.0003005387,0.00010740203,0.00004645537,0.00007539854,0.00007940209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006095887,0.00031085493,0.0006250047,0.00038889793,0.00034705005,0.0012785778,0.0015476731,0.0013055663,0.004131017],"category_scores_gemma":[0.0025695967,0.00026156812,0.00064709596,0.000755447,0.00040510143,0.0009060511,0.0006784445,0.0013859089,0.00048467953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004477244,0.0000126587565,0.0020517278,0.00001844809,0.000025288566,0.00007448945,0.000020038395,0.9852904,0.0002709653,0.008372671,0.0015304898,0.002288029],"study_design_scores_gemma":[0.0000066849852,0.000008511403,0.0007204755,0.0000035211074,0.000010369937,0.000015722284,0.00001282518,0.9950466,0.00007493125,0.0036613098,0.00043160463,0.000007549032],"about_ca_topic_score_codex":0.017980702,"about_ca_topic_score_gemma":0.008290105,"teacher_disagreement_score":0.017980702,"about_ca_system_score_codex":0.0007610726,"about_ca_system_score_gemma":0.0007978114,"threshold_uncertainty_score":0.035752058},"labels":[],"label_agreement":null},{"id":"W4386988990","doi":"10.1002/eqe.4015","title":"Risk‐based optimization of concentrically braced tall timber buildings: Derivative free optimization algorithm","year":2023,"lang":"en","type":"article","venue":"Earthquake Engineering & Structural Dynamics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Limit (mathematics); Structural engineering; Engineering; Mathematical optimization; Optimization algorithm; Optimization problem; Algorithm; Computer science; Mathematics","score_opus":0.0166341865231726,"score_gpt":0.2610496430763205,"score_spread":0.2444154565531479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386988990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09735587,0.00022662106,0.8958727,0.00019280423,0.000021105756,0.00005926429,0.00008138038,0.00022956285,0.0059607136],"genre_scores_gemma":[0.8536853,0.00012498043,0.14137617,0.00007615502,0.000013238935,0.00017507128,0.00012085006,0.00009104268,0.0043371757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981385,0.00009007024,0.000006685315,0.000027566473,0.000036218204,0.000025641293],"domain_scores_gemma":[0.999273,0.00055051176,0.000051203617,0.000024881932,0.000074753334,0.00002562637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008704297,0.000660343,0.00091057946,0.00045888318,0.00021035632,0.00061355816,0.000587473,0.0009831341,0.0018187705],"category_scores_gemma":[0.0014305905,0.00043107598,0.00048829213,0.00040136796,0.0004998001,0.0003586747,0.00059611176,0.0005248549,0.00023709101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018604751,0.000014919785,0.00012030411,0.000012602563,0.00000685838,0.00001261223,0.0000071480786,0.9946056,0.00028683164,0.0008881381,0.000093857314,0.0039325124],"study_design_scores_gemma":[0.0000028407314,0.000010456165,0.00003540204,0.000001206657,0.0000012366346,0.0000015010988,0.0000019009285,0.99957496,0.00006137721,0.00026486712,0.000043466505,8.5136077e-7],"about_ca_topic_score_codex":0.004573289,"about_ca_topic_score_gemma":0.0031976495,"teacher_disagreement_score":0.004573289,"about_ca_system_score_codex":0.00046283207,"about_ca_system_score_gemma":0.0008504166,"threshold_uncertainty_score":0.009093344},"labels":[],"label_agreement":null},{"id":"W4387081103","doi":"10.1016/j.strusafe.2023.102391","title":"Soft Monte Carlo Simulation for imprecise probability estimation: A dimension reduction-based approach","year":2023,"lang":"en","type":"article","venue":"Structural Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Monte Carlo method; Random variable; Mathematical optimization; Mathematics; Dimension (graph theory); Benchmark (surveying); Reduction (mathematics); Interval (graph theory); Univariate; Upper and lower bounds; Probability distribution; Dimensionality reduction; Applied mathematics; Algorithm; Computer science; Statistics","score_opus":0.09070345815118433,"score_gpt":0.3525732052945648,"score_spread":0.2618697471433805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387081103","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021348859,0.000056996647,0.99687696,0.000070177506,0.00001051747,0.000017809753,0.00001915992,0.00010758289,0.0007059759],"genre_scores_gemma":[0.48552,0.00041963116,0.50944954,0.00031121966,0.00015654988,0.00042580022,0.0002578316,0.00037237175,0.0030870999],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755317,0.0011454548,0.0001041615,0.0002402905,0.00082044303,0.00013651613],"domain_scores_gemma":[0.98539746,0.011251676,0.0007160961,0.0013932842,0.0010326894,0.00020878036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003197882,0.0010908201,0.0027436074,0.0020955119,0.0008534453,0.002086849,0.0023789057,0.0017871547,0.0035766265],"category_scores_gemma":[0.016907303,0.0014950109,0.0021073993,0.0013866778,0.0021208124,0.002174184,0.0032031867,0.0028820017,0.00074896554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029050281,0.000019850477,0.00020981193,0.000026946434,0.000038972943,0.000027835393,0.000024516694,0.97067016,0.00040460803,0.020449443,0.00016684346,0.007932054],"study_design_scores_gemma":[0.0000018393557,0.0000037119917,0.00001843909,0.0000034559068,0.000003981022,0.000005962548,0.0000016062787,0.9935034,0.000105567306,0.00626258,0.00008641803,0.0000030775252],"about_ca_topic_score_codex":0.0041265227,"about_ca_topic_score_gemma":0.003207867,"teacher_disagreement_score":0.0041265227,"about_ca_system_score_codex":0.0012966692,"about_ca_system_score_gemma":0.0015563101,"threshold_uncertainty_score":0.016912222},"labels":[],"label_agreement":null},{"id":"W4387395192","doi":"10.1007/978-3-031-37003-8_28","title":"The Effect of Temporal Correlations on State Estimation Through Variational Bayesian Inference","year":2023,"lang":"en","type":"book-chapter","venue":"Conference proceedings of the Society for Experimental Mechanics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Covariance; Latent variable; Robustness (evolution); Diagonal; Inference; Bayesian inference; Mathematics; Covariance matrix; Gaussian; Algorithm; Posterior probability; Mathematical optimization; Applied mathematics; Computer science; Bayesian probability; Artificial intelligence; Statistics","score_opus":0.06107756173657767,"score_gpt":0.32777578898247933,"score_spread":0.26669822724590164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387395192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020709464,0.001078323,0.9733652,0.0010373939,0.00009489003,0.000014630952,0.00008130303,0.00017572327,0.0034430763],"genre_scores_gemma":[0.67444384,0.0025090643,0.31052178,0.00075269066,0.00040928132,0.0001405967,0.00044887912,0.0008325279,0.00994127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952408,0.0029546206,0.00019136402,0.000628663,0.0007559865,0.0002285902],"domain_scores_gemma":[0.90339756,0.09132489,0.0013057091,0.0023637095,0.0012639917,0.00034419002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011497582,0.00089013303,0.001493768,0.00071795797,0.0010331165,0.0026520642,0.0027920548,0.002633274,0.0027972853],"category_scores_gemma":[0.07425491,0.002276906,0.0013279844,0.0014183965,0.003338444,0.006931125,0.0036825885,0.004447594,0.00034286434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021518431,0.000046517172,0.0017632277,0.00013300683,0.00021141104,0.00013616271,0.00024341943,0.736318,0.0020173523,0.20275694,0.001953189,0.054205596],"study_design_scores_gemma":[0.000008296048,0.000013116949,0.0003067425,0.000013964997,0.000019966026,0.00002332456,0.000008692409,0.94883513,0.00055127864,0.0498504,0.00035361294,0.000015444051],"about_ca_topic_score_codex":0.013483565,"about_ca_topic_score_gemma":0.014129619,"teacher_disagreement_score":0.013483565,"about_ca_system_score_codex":0.0019013814,"about_ca_system_score_gemma":0.0016389763,"threshold_uncertainty_score":0.060805798},"labels":[],"label_agreement":null},{"id":"W4387986622","doi":"10.7712/120123.10717.20068","title":"SENSITIVITY TO DAMPING IN NONLINEAR DYNAMIC ANALYSIS","year":2023,"lang":"en","type":"article","venue":"COMPDYN Proceedings","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Tangent stiffness matrix; Sensitivity (control systems); Nonlinear system; Mathematical analysis; Tensor (intrinsic definition); Eigenvalues and eigenvectors; Matrix (chemical analysis); Damping matrix; Displacement (psychology); Stiffness matrix; Mathematics; Bilinear interpolation; Stiffness; Tangent; Control theory (sociology); Physics; Geometry; Materials science; Engineering; Computer science","score_opus":0.07588215944385095,"score_gpt":0.36528625267308074,"score_spread":0.2894040932292298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387986622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1610337,0.00065532397,0.8287142,0.0001839361,0.00004558473,0.00006676467,0.000065970286,0.0008661817,0.008368298],"genre_scores_gemma":[0.95985097,0.00032105306,0.037543338,0.00005419713,0.000013700346,0.000038467202,0.000045568977,0.00015730168,0.0019753678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985941,0.0005310195,0.000040468767,0.0001553882,0.00058444974,0.000094562645],"domain_scores_gemma":[0.9944455,0.004532113,0.00021308672,0.0004365839,0.00032755864,0.00004524214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018475034,0.00078440615,0.00053495617,0.0009802507,0.0002813385,0.0007700594,0.00034831325,0.00067236245,0.0017026805],"category_scores_gemma":[0.011630482,0.00039668562,0.00044935764,0.0004019538,0.00095568324,0.0008558754,0.0012398668,0.0007207419,0.0004249328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023030947,0.000055582605,0.0029253338,0.00023566566,0.000057725974,0.00024357167,0.00030182555,0.8788113,0.039656423,0.016345238,0.0003413813,0.0607957],"study_design_scores_gemma":[0.000003702575,0.000058313406,0.0007602837,0.000022100601,0.000009638184,0.00008841421,0.000027475124,0.9823658,0.009893154,0.006251583,0.0004959747,0.000023682596],"about_ca_topic_score_codex":0.0017358796,"about_ca_topic_score_gemma":0.0012910246,"teacher_disagreement_score":0.0018475034,"about_ca_system_score_codex":0.00052487897,"about_ca_system_score_gemma":0.0002734131,"threshold_uncertainty_score":0.009770632},"labels":[],"label_agreement":null},{"id":"W4388470092","doi":"10.2139/ssrn.4597400","title":"Differential Sensitivity in Discontinuous Models","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sensitivity (control systems); Engineering","score_opus":0.054519405128505415,"score_gpt":0.3062437891040276,"score_spread":0.2517243839755222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388470092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12927492,0.0018520844,0.8248953,0.002447676,0.00019740837,0.000048250942,0.00017838359,0.00020577735,0.040900204],"genre_scores_gemma":[0.9814109,0.0005482207,0.007416065,0.00016680673,0.00007581108,0.00003505233,0.00006957572,0.000040289924,0.010237146],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986411,0.00067758176,0.000046042496,0.00021048807,0.0002528257,0.00017186925],"domain_scores_gemma":[0.99246067,0.0058809696,0.0005751334,0.00027800817,0.00045963723,0.0003456253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023903253,0.0008302294,0.0014571957,0.0019105425,0.00060035725,0.0026860049,0.001126758,0.0017045124,0.0037960925],"category_scores_gemma":[0.012466144,0.00091017963,0.0012180953,0.00090504374,0.0030203837,0.00239272,0.0036661758,0.0019220667,0.0002565315],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045208348,0.00004160596,0.0010176755,0.000110242356,0.0000706074,0.00020691902,0.00013159348,0.35582733,0.00094215776,0.6354762,0.000888359,0.0052421805],"study_design_scores_gemma":[0.00000625364,0.000020352118,0.0002614393,0.00001797768,0.000018345585,0.00005182193,0.000031710424,0.7157555,0.00019261053,0.28303698,0.00059176073,0.000015319878],"about_ca_topic_score_codex":0.0027023698,"about_ca_topic_score_gemma":0.0011709053,"teacher_disagreement_score":0.0037960925,"about_ca_system_score_codex":0.002153004,"about_ca_system_score_gemma":0.0006759662,"threshold_uncertainty_score":0.015621245},"labels":[],"label_agreement":null},{"id":"W4388671260","doi":"10.1101/2023.11.10.566654","title":"A mathematical model of whole-body potassium regulation: Global parameter sensitivity analysis <sup>*</sup>","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sensitivity (control systems); Potassium; Function (biology); Transient (computer programming); Steady state (chemistry); Biological system; Electrolyte; Potassium channel; Mathematics; Computer science; Chemistry; Biophysics; Biology; Engineering; Cell biology","score_opus":0.06359375688790883,"score_gpt":0.28681825636746194,"score_spread":0.2232244994795531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388671260","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12801717,0.00041474597,0.863756,0.00061869965,0.00004787495,0.00006471083,0.00031235913,0.00018748715,0.0065808664],"genre_scores_gemma":[0.97398865,0.0003023412,0.021083746,0.00012473574,0.000021192613,0.00013036613,0.00014336254,0.000050747367,0.004154822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996407,0.00014056513,0.000014228902,0.0000870365,0.000075792115,0.0000417171],"domain_scores_gemma":[0.9983032,0.0011655788,0.00025258883,0.00005875956,0.00018220204,0.00003769991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012500522,0.0007192999,0.000726411,0.00045385232,0.0002241606,0.000835035,0.00070192385,0.0010377226,0.001558323],"category_scores_gemma":[0.0030527797,0.00026103822,0.0012714645,0.00031859128,0.0010380117,0.00068975874,0.0008384379,0.00083724863,0.00019551281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011677144,0.0000058261267,0.00027752187,0.000014665429,0.00001451402,0.000025818405,0.000010354102,0.99449795,0.0008017411,0.0036353825,0.00008021892,0.00062438316],"study_design_scores_gemma":[0.0000022084275,0.0000108038175,0.0001316119,0.0000022325105,0.000007467281,0.0000072102007,0.000004792638,0.9976527,0.00019526886,0.0018704586,0.00011173125,0.000003451346],"about_ca_topic_score_codex":0.005289702,"about_ca_topic_score_gemma":0.0017893509,"teacher_disagreement_score":0.005289702,"about_ca_system_score_codex":0.0008262253,"about_ca_system_score_gemma":0.00063528115,"threshold_uncertainty_score":0.010517836},"labels":[],"label_agreement":null},{"id":"W4388727542","doi":"10.1109/epeps58208.2023.10314878","title":"Efficient Uncertainty Quantification using sensitivity information in Least Squares SVM","year":2023,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Sensitivity (control systems); Support vector machine; Computer science; Least-squares function approximation; Least squares support vector machine; Data mining; Algorithm; Artificial intelligence; Machine learning; Pattern recognition (psychology); Mathematics; Statistics; Engineering; Electronic engineering","score_opus":0.13648659241290678,"score_gpt":0.3541513071939425,"score_spread":0.21766471478103572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388727542","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014480443,0.000053131887,0.9981184,0.000020659498,0.0000044554713,0.0000073824835,0.000009113169,0.00008976799,0.00024903208],"genre_scores_gemma":[0.4316629,0.00035447383,0.56615794,0.000088267334,0.000056091092,0.00013830823,0.00015328919,0.00018817346,0.0012006365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984516,0.00063739263,0.00008286694,0.00017722219,0.0005915575,0.000059338225],"domain_scores_gemma":[0.99736434,0.0019807196,0.00014937454,0.00016813427,0.0003033042,0.000034157638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022535995,0.0009497934,0.0011115373,0.0011018536,0.00042238765,0.0011192834,0.00078829407,0.0008346189,0.0013551803],"category_scores_gemma":[0.006903636,0.00052308117,0.0009002845,0.00072358246,0.00079231523,0.001588173,0.0013227774,0.0013411919,0.00038691342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000927169,0.000031461754,0.00034874657,0.00015906506,0.000073411866,0.00007416277,0.00007961741,0.8369856,0.012353751,0.033634108,0.0005920764,0.11557525],"study_design_scores_gemma":[0.000001653174,0.000012336293,0.000060035236,0.0000063476427,0.0000047930835,0.00001682504,0.000002962128,0.98956,0.002471736,0.0075618275,0.00029310715,0.000008315476],"about_ca_topic_score_codex":0.001175275,"about_ca_topic_score_gemma":0.0006908094,"teacher_disagreement_score":0.0022535995,"about_ca_system_score_codex":0.00063343294,"about_ca_system_score_gemma":0.00060032326,"threshold_uncertainty_score":0.011918366},"labels":[],"label_agreement":null},{"id":"W4388874110","doi":"10.1115/detc2023-114957","title":"Frequency Response Based Optimization of an Aircraft Hydraulic Pump Support Structure","year":2023,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Vibration; Bandwidth (computing); Frequency response; Reduction (mathematics); Finite element method; Optimal design; Frame (networking); Structural engineering; Computer science; Control theory (sociology); Acoustics; Engineering; Mathematics; Mechanical engineering; Physics","score_opus":0.05363125785321552,"score_gpt":0.3204203396553462,"score_spread":0.2667890818021307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388874110","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7345064,0.0001516278,0.2554524,0.00015651589,0.000023508,0.000057050966,0.00012569044,0.00029509168,0.0092317],"genre_scores_gemma":[0.9833808,0.000026035877,0.015280735,0.000010536715,0.0000018072705,0.000033954366,0.000032875858,0.000018018365,0.0012152536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987257,0.0000382675,0.000003620674,0.000017600287,0.00004306767,0.000024907455],"domain_scores_gemma":[0.9997774,0.00013387788,0.000029507424,0.00001223012,0.00004016907,0.000006859409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036216807,0.00047463522,0.00038479283,0.00038018526,0.00016360224,0.0004082723,0.00023991527,0.00054832216,0.0014876286],"category_scores_gemma":[0.00069183385,0.0003015553,0.0003621707,0.00012294638,0.0003156812,0.00021812806,0.00020756955,0.00023654388,0.00016946344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031567193,0.000017688604,0.0001994469,0.000018525656,0.000005364993,0.000020380303,0.000008055621,0.9892482,0.0064617814,0.00031013647,0.000060221406,0.003618538],"study_design_scores_gemma":[0.0000064198257,0.00006540427,0.00025144932,0.000002660202,0.0000052184955,0.0000048338047,0.0000072182115,0.9970861,0.0023075268,0.00010384004,0.00015665001,0.0000025707004],"about_ca_topic_score_codex":0.002596506,"about_ca_topic_score_gemma":0.0023797804,"teacher_disagreement_score":0.002596506,"about_ca_system_score_codex":0.00046338607,"about_ca_system_score_gemma":0.0005167287,"threshold_uncertainty_score":0.0051627755},"labels":[],"label_agreement":null},{"id":"W4388997075","doi":"10.1016/j.ress.2023.109849","title":"Reliability assessment of stochastic dynamical systems using physics informed neural network based PDEM","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Probability density function; Applied mathematics; Artificial neural network; Mathematics; Nonlinear system; Gaussian; Partial differential equation; Mathematical optimization; Control theory (sociology); Computer science; Mathematical analysis; Physics","score_opus":0.050713175910884,"score_gpt":0.32108461225446955,"score_spread":0.27037143634358557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388997075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06131028,0.000707302,0.9338824,0.0003723104,0.00004782831,0.00004619253,0.000112600326,0.0002133875,0.0033077707],"genre_scores_gemma":[0.9571667,0.00053471955,0.039930213,0.00010086272,0.000042633004,0.00013243621,0.0001857442,0.00004057469,0.0018659891],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971884,0.00008211322,0.00002164971,0.00006838822,0.00007754605,0.0000313156],"domain_scores_gemma":[0.99890316,0.00065425935,0.00014829062,0.00003735505,0.00021247163,0.00004458109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001036339,0.00081978017,0.0008123403,0.00070735277,0.00035511923,0.00073254417,0.0009562985,0.0010159482,0.0010667244],"category_scores_gemma":[0.0030709752,0.0005247466,0.0007712903,0.00033306182,0.00075955,0.0010423576,0.0009986021,0.0011328317,0.00010572879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011997808,0.000007423446,0.00090193795,0.000034241246,0.000016430116,0.000035524095,0.000017717208,0.99122787,0.0003579198,0.002600747,0.00009491251,0.004693228],"study_design_scores_gemma":[6.143465e-7,0.0000028439651,0.00007374846,0.0000019891868,0.0000014449107,0.0000028226839,0.0000015348188,0.9992471,0.000043896325,0.0005877623,0.00003482358,0.0000014399257],"about_ca_topic_score_codex":0.007744701,"about_ca_topic_score_gemma":0.0043974817,"teacher_disagreement_score":0.007744701,"about_ca_system_score_codex":0.00093674543,"about_ca_system_score_gemma":0.0010176755,"threshold_uncertainty_score":0.015399218},"labels":[],"label_agreement":null},{"id":"W4389126399","doi":"10.1080/10618600.2023.2289532","title":"Model-Based Smoothing with Integrated Wiener Processes and Overlapping Splines","year":2023,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Global Health Research; St. Michael's Hospital; University of Waterloo; University of Toronto","funders":"","keywords":"Smoothing; Computer science; Smoothing spline; Econometrics; Mathematics; Mathematical optimization; Algorithm; Statistics","score_opus":0.05858149059133981,"score_gpt":0.31366986845052763,"score_spread":0.2550883778591878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389126399","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004986782,0.00010245794,0.9944811,0.000055399516,0.000012077672,0.000006872108,0.000017739623,0.00006443996,0.00027308703],"genre_scores_gemma":[0.50511456,0.0008583098,0.48994058,0.00013943893,0.00012637433,0.00015429388,0.00027007362,0.000121822064,0.0032745842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973888,0.0011908242,0.00013263183,0.00048987765,0.0006219048,0.00017586738],"domain_scores_gemma":[0.99425125,0.003952774,0.00061786134,0.00067899964,0.0003939516,0.000105110026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007363034,0.00088032143,0.001445828,0.001524033,0.0005563094,0.0014251677,0.0020043536,0.0016676553,0.0012212697],"category_scores_gemma":[0.019019952,0.0008255096,0.0018368746,0.0017705695,0.001847127,0.0023717834,0.0018766408,0.0023722267,0.00029266466],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076390614,0.000034744222,0.0020452891,0.00008050602,0.00009486905,0.00009193304,0.00016443318,0.76716167,0.0014423628,0.18648055,0.00039245994,0.04193485],"study_design_scores_gemma":[0.0000061113114,0.000023460228,0.00026219783,0.000014377404,0.000011403918,0.0000211937,0.000009592316,0.95156413,0.00037444552,0.04719172,0.00050652475,0.000014811682],"about_ca_topic_score_codex":0.0057290187,"about_ca_topic_score_gemma":0.00427896,"teacher_disagreement_score":0.007363034,"about_ca_system_score_codex":0.0008495071,"about_ca_system_score_gemma":0.0014265786,"threshold_uncertainty_score":0.038939953},"labels":[],"label_agreement":null},{"id":"W4389132251","doi":"10.1016/j.compgeo.2023.105952","title":"Influence of data sampling on confidence in the calculation of reliability index for simple performance functions","year":2023,"lang":"en","type":"article","venue":"Computers and Geotechnics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Logarithm; Reliability (semiconductor); Mathematics; Range (aeronautics); Sample size determination; Confidence interval; Point estimation; Sample (material); Population; Index (typography); Limit (mathematics); Mathematical analysis; Computer science; Engineering; Physics","score_opus":0.17717958890867197,"score_gpt":0.37610938985978054,"score_spread":0.19892980095110857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389132251","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1706785,0.0014007998,0.82556814,0.00032358037,0.00007692542,0.000045523895,0.0001013442,0.00048647675,0.0013186584],"genre_scores_gemma":[0.93413,0.00027521665,0.06493875,0.000099530196,0.000065278604,0.00004328401,0.0000902191,0.00019655308,0.00016117812],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9764454,0.016818553,0.0010775946,0.0012933336,0.0038283644,0.00053681654],"domain_scores_gemma":[0.4663082,0.5105205,0.0058609433,0.010784964,0.005908196,0.00061713415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04231066,0.0006492578,0.0009389749,0.0014376807,0.0004426822,0.0016410266,0.0012944117,0.0011241613,0.0005576935],"category_scores_gemma":[0.29784417,0.0004774664,0.00089149526,0.0009819046,0.0020909898,0.0019587248,0.0016684041,0.0015315075,0.00011573457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026492106,0.00016480184,0.043329734,0.0009748471,0.0006776189,0.00090381765,0.0008959049,0.73347753,0.019248134,0.036146052,0.0011262173,0.16040617],"study_design_scores_gemma":[0.000035506764,0.00017913232,0.0060655996,0.00009331067,0.00010833202,0.00023295224,0.000035302255,0.97037023,0.015378595,0.0071488703,0.00029508953,0.000057068726],"about_ca_topic_score_codex":0.0023386022,"about_ca_topic_score_gemma":0.0014032605,"teacher_disagreement_score":0.04231066,"about_ca_system_score_codex":0.00075846113,"about_ca_system_score_gemma":0.0008510805,"threshold_uncertainty_score":0.22376281},"labels":[],"label_agreement":null},{"id":"W4389271838","doi":"10.46620/ursigass.2023.1475.hsko1928","title":"Efficient Uncertainty Quantification of Deterministic Wireless Channel Models Using Polynomial Chaos Expansion","year":2023,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Polynomial chaos; Wireless; Computer science; Channel (broadcasting); Uncertainty quantification; CHAOS (operating system); Polynomial; Polynomial expansion; Algorithm; Statistical physics; Mathematics; Telecommunications; Statistics; Physics; Monte Carlo method; Machine learning; Mathematical analysis; Computer security","score_opus":0.2160453271117972,"score_gpt":0.3597819085746029,"score_spread":0.1437365814628057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389271838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074597476,0.000060386566,0.9917665,0.00005301626,0.000005076044,0.000012744755,0.000026378471,0.000074904296,0.00054118765],"genre_scores_gemma":[0.8064713,0.00058678794,0.19051707,0.00006477663,0.00004243794,0.00015206193,0.0002102611,0.00009795074,0.0018574352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990515,0.00036789742,0.00003715005,0.000088123765,0.00038366675,0.00007165143],"domain_scores_gemma":[0.99735826,0.0019740185,0.00023547752,0.00016694116,0.00022573529,0.00003949881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016009742,0.0008122764,0.0008038664,0.00058933516,0.00037520155,0.000917584,0.00075712986,0.0006945868,0.0006487527],"category_scores_gemma":[0.005389895,0.00047325573,0.00078855926,0.00056532177,0.00093537336,0.0014179527,0.0014656159,0.0011681597,0.00016057827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018236937,0.000007520463,0.00012869838,0.000024601124,0.00001150695,0.000023553424,0.000021500027,0.9777664,0.0016098025,0.01274173,0.00012046382,0.007526096],"study_design_scores_gemma":[7.726515e-7,0.000004948529,0.000021162476,0.0000017194776,0.0000011207532,0.0000063509597,0.0000019321878,0.9971897,0.00037699833,0.0023175878,0.00007455567,0.000003184947],"about_ca_topic_score_codex":0.0028494815,"about_ca_topic_score_gemma":0.0018525814,"teacher_disagreement_score":0.0028494815,"about_ca_system_score_codex":0.0008032711,"about_ca_system_score_gemma":0.0012331383,"threshold_uncertainty_score":0.008466899},"labels":[],"label_agreement":null},{"id":"W4389486922","doi":"10.3390/jrfm16120509","title":"Monte Carlo Sensitivities Using the Absolute Measure-Valued Derivative Method","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Monte Carlo method; Measure (data warehouse); Sensitivity (control systems); Variance (accounting); Variance-based sensitivity analysis; Derivative (finance); Mathematics; Applied mathematics; Contrast (vision); Mathematical optimization; Probability density function; Quasi-Monte Carlo method; Computer science; Monte Carlo molecular modeling; Statistics; Markov chain Monte Carlo; One-way analysis of variance; Analysis of variance","score_opus":0.07852999520190679,"score_gpt":0.3307817654830018,"score_spread":0.252251770281095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389486922","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035316132,0.00011119885,0.99483156,0.000051818606,0.000018553337,0.00002462258,0.000015347614,0.00011790323,0.0012974316],"genre_scores_gemma":[0.3985163,0.00039996539,0.5970438,0.00017816309,0.00006073608,0.00017328952,0.000103525934,0.00025017202,0.0032740107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99796957,0.0010074981,0.00009178192,0.00019301231,0.0006519722,0.00008628686],"domain_scores_gemma":[0.99261206,0.005914573,0.0003117192,0.0003617558,0.00068478857,0.00011519596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004660643,0.0008247741,0.0009912612,0.0016518122,0.0004565548,0.0014736986,0.0011900234,0.0011704748,0.0029397083],"category_scores_gemma":[0.01626605,0.0005967253,0.0008340465,0.0009363332,0.0012234498,0.0018282353,0.0016020447,0.0017027248,0.0003949015],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000477692,0.000022501908,0.0005469002,0.00008044568,0.000045974164,0.000065079956,0.00005882642,0.8706246,0.0020851244,0.093204655,0.00045231264,0.03276582],"study_design_scores_gemma":[0.000004597179,0.000012058958,0.00008391765,0.000011489008,0.000005439412,0.000024541247,0.0000037604545,0.98308164,0.00075743074,0.015447647,0.0005577665,0.000009697115],"about_ca_topic_score_codex":0.002681029,"about_ca_topic_score_gemma":0.0015691874,"teacher_disagreement_score":0.004660643,"about_ca_system_score_codex":0.0014123593,"about_ca_system_score_gemma":0.00161686,"threshold_uncertainty_score":0.02464807},"labels":[],"label_agreement":null},{"id":"W4389551093","doi":"10.1080/08982112.2023.2286500","title":"Utilizing jackknife and bootstrap to understand tensile stress to failure of an epoxy resin","year":2023,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Jackknife resampling; Weibull distribution; Ultimate tensile strength; Percentile; Statistics; Reliability (semiconductor); Point estimation; Resampling; Mathematics; Weibull modulus; Stress (linguistics); Epoxy; Sample size determination; Replicate; Materials science; Composite material; Power (physics)","score_opus":0.2630767744072768,"score_gpt":0.41744897524886226,"score_spread":0.15437220084158548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389551093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13579918,0.00033721337,0.8616349,0.00008056481,0.000029445266,0.00012595674,0.0000960141,0.00034588078,0.0015507452],"genre_scores_gemma":[0.7344701,0.00025869496,0.26414648,0.00005848026,0.000019554798,0.0002475838,0.00023306841,0.00009044515,0.00047563185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958994,0.0017755203,0.00023272507,0.0005289945,0.0014090196,0.00015447532],"domain_scores_gemma":[0.9694268,0.022129465,0.0025728168,0.0023796347,0.0032967671,0.00019460157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011039081,0.00068712945,0.00083309895,0.0025286903,0.0004844238,0.0006368346,0.00095515116,0.00071159477,0.00091917475],"category_scores_gemma":[0.04992868,0.000290639,0.0006316746,0.0011244462,0.00076252496,0.0014071495,0.0006490252,0.00083655014,0.0002552672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083477603,0.00031053118,0.06698646,0.00079029356,0.00042357013,0.00085333106,0.0021240148,0.48555896,0.024104891,0.033139266,0.0017464398,0.3831275],"study_design_scores_gemma":[0.000031430576,0.0006626652,0.02633354,0.00011403235,0.000069783004,0.00049305335,0.000608416,0.9271147,0.015121485,0.026019376,0.0033324664,0.000099006254],"about_ca_topic_score_codex":0.002804688,"about_ca_topic_score_gemma":0.0027190824,"teacher_disagreement_score":0.011039081,"about_ca_system_score_codex":0.0006185616,"about_ca_system_score_gemma":0.00094885135,"threshold_uncertainty_score":0.0583809},"labels":[],"label_agreement":null},{"id":"W4390064206","doi":"10.1016/j.ijpvp.2023.105113","title":"Analyzing joint efficiency in storage tanks: A comparative study of API 650 standard and API 579 using finite element analysis for enhanced reliability","year":2023,"lang":"en","type":"article","venue":"International Journal of Pressure Vessels and Piping","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Concordia University","funders":"Chartered Institute of Management Accountants","keywords":"Finite element method; Reliability (semiconductor); Joint (building); Structural engineering; Engineering; Constraint (computer-aided design); Documentation; Mode (computer interface); Failure mode and effects analysis; Safety factor; Buckling; Stability (learning theory); Reliability engineering; Mechanical engineering; Computer science","score_opus":0.11693417963292854,"score_gpt":0.39122920448151055,"score_spread":0.27429502484858204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390064206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95378816,0.00009971314,0.043101054,0.000030220468,0.000004069567,0.000015724427,0.00013281596,0.00019205663,0.0026361188],"genre_scores_gemma":[0.995552,0.000032218413,0.0037458374,0.0000018545687,0.000001009833,0.0000065604418,0.000085233274,0.000028117864,0.00054706965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996964,0.000062417974,0.000018413082,0.000035765464,0.000143617,0.000043361404],"domain_scores_gemma":[0.99858296,0.0007698841,0.0001176838,0.00010827322,0.00039766458,0.00002341338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011415961,0.00041740033,0.0005463236,0.0010898038,0.00030426442,0.0006861515,0.0006272959,0.00051976467,0.0015018187],"category_scores_gemma":[0.0017493388,0.00027748622,0.0006050593,0.00091468863,0.00031828255,0.0008389022,0.00025476582,0.00026849488,0.0002510757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069057307,0.00021100695,0.03186232,0.0001750105,0.000104964,0.00018427694,0.00022799523,0.8651612,0.0455822,0.0026037076,0.00040796382,0.052788857],"study_design_scores_gemma":[0.000009997331,0.00021603638,0.018440507,0.0000067717874,0.00005493705,0.000044094813,0.00016005678,0.96485704,0.015339405,0.00047994533,0.00037504564,0.000016286307],"about_ca_topic_score_codex":0.0059157894,"about_ca_topic_score_gemma":0.006115357,"teacher_disagreement_score":0.0059157894,"about_ca_system_score_codex":0.00061322784,"about_ca_system_score_gemma":0.0006049558,"threshold_uncertainty_score":0.011762738},"labels":[],"label_agreement":null},{"id":"W4390431821","doi":"10.3808/jeil.202300118","title":"The Importance of Intelligent Colouring for Simulation Decomposition in Environmental Analysis","year":2023,"lang":"en","type":"article","venue":"Journal of Environmental Informatics Letters","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Queen's University","funders":"","keywords":"Computer science; Visual analytics; Visualization; Key (lock); Decomposition; Interdependence; Construct (python library); Analytics; Data science; Complex system; Contrast (vision); Data mining; Artificial intelligence","score_opus":0.040201448968555105,"score_gpt":0.3208645115037334,"score_spread":0.2806630625351783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390431821","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026688222,0.000098090495,0.994871,0.00022020326,0.000039253435,0.000032024705,0.000016763743,0.0005244658,0.001529422],"genre_scores_gemma":[0.08529215,0.00035461938,0.9130301,0.00011912186,0.000032889864,0.00008498176,0.00005655038,0.00034053903,0.0006890443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979513,0.0011756102,0.00014657786,0.00019496196,0.00043483212,0.00009669836],"domain_scores_gemma":[0.99179673,0.004777445,0.00043329023,0.001653196,0.00109251,0.00024688628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003951263,0.0012176475,0.00082186697,0.0013561066,0.00080490316,0.0034949055,0.001093894,0.000956006,0.003891557],"category_scores_gemma":[0.014418177,0.0007591307,0.0011407514,0.00072595326,0.002515162,0.0031677762,0.0027471085,0.0025789384,0.0010404444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005148719,0.00012075974,0.0018921247,0.0006728022,0.0000977147,0.00036440816,0.0018044012,0.3391777,0.07572291,0.24618566,0.004099424,0.32934713],"study_design_scores_gemma":[0.00006418412,0.00013975355,0.0007992632,0.00023435272,0.000047861347,0.00037053667,0.00025364463,0.75444126,0.040500887,0.171199,0.031777583,0.00017166979],"about_ca_topic_score_codex":0.0012252044,"about_ca_topic_score_gemma":0.0011513771,"teacher_disagreement_score":0.003951263,"about_ca_system_score_codex":0.0010243806,"about_ca_system_score_gemma":0.001078988,"threshold_uncertainty_score":0.020896554},"labels":[],"label_agreement":null},{"id":"W4390588069","doi":"10.1515/cmam-2022-0237","title":"Numerical Approximation of Gaussian Random Fields on Closed Surfaces","year":2024,"lang":"en","type":"article","venue":"Computational Methods in Applied Mathematics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Mathematics; Random field; Gaussian quadrature; Smoothness; Partial differential equation; Mathematical analysis; Gaussian random field; Gaussian; Sinc function; Applied mathematics; Integral equation; Finite element method; Numerical analysis; White noise; Gaussian process; Nyström method; Physics","score_opus":0.12379957004377101,"score_gpt":0.43301410787377353,"score_spread":0.3092145378300025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390588069","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19258368,0.00021846045,0.8028684,0.0002681695,0.000065880224,0.000037304704,0.000040581588,0.00020180315,0.0037156784],"genre_scores_gemma":[0.9367772,0.00013757877,0.06153263,0.000046206595,0.000023059045,0.000043330565,0.000057631096,0.00002549878,0.0013569003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939907,0.0002581438,0.000021754093,0.00007083839,0.00019048166,0.000059697435],"domain_scores_gemma":[0.9980393,0.0013251806,0.00014790629,0.0001374878,0.00027703567,0.00007308372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014801268,0.0005235815,0.0010399296,0.0007934716,0.0003380529,0.0012092746,0.0007113822,0.0015963659,0.0007364503],"category_scores_gemma":[0.0045875073,0.00023649518,0.00043952817,0.000491488,0.0017142547,0.0008961962,0.0009027244,0.00075737847,0.00011961317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004079442,0.00002503688,0.00036782236,0.000023161956,0.0000074605696,0.00005083217,0.00004716098,0.96735585,0.0025067285,0.026043849,0.00008733606,0.003444013],"study_design_scores_gemma":[0.0000020851076,0.000003562213,0.000018570505,0.000001276132,4.432326e-7,0.0000036277318,0.0000032101354,0.99832135,0.00024042165,0.0013637998,0.00003997809,0.0000017405401],"about_ca_topic_score_codex":0.0026332552,"about_ca_topic_score_gemma":0.0008392724,"teacher_disagreement_score":0.0026332552,"about_ca_system_score_codex":0.0007725054,"about_ca_system_score_gemma":0.00059894816,"threshold_uncertainty_score":0.007827759},"labels":[],"label_agreement":null},{"id":"W4390734476","doi":"10.2139/ssrn.4692050","title":"Computationally Efficient Simulation of Multivariate Wind Velocity Field Using a Low-Rank Representation of the Cross-Power Spectral Density Matrix","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Multivariate statistics; Rank (graph theory); Representation (politics); Spectral density; Field (mathematics); Matrix (chemical analysis); Power (physics); Mathematics; Statistical physics; Statistics; Physics; Combinatorics; Pure mathematics; Materials science; Political science","score_opus":0.04222611522345475,"score_gpt":0.37581810389320236,"score_spread":0.3335919886697476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390734476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12113762,0.00013373846,0.8685334,0.00050891953,0.00008543685,0.00007327704,0.00036212706,0.0015349219,0.0076304493],"genre_scores_gemma":[0.89377373,0.00006956296,0.10281018,0.00010533074,0.000043828477,0.00009572454,0.00033470147,0.00018936458,0.0025776285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974984,0.000091072354,0.000012728464,0.00003193558,0.00007072917,0.000043688073],"domain_scores_gemma":[0.99819404,0.0011843828,0.00013535476,0.00014547477,0.00023073706,0.000110030895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062650937,0.00052518246,0.00069213065,0.00036867222,0.0005274306,0.0010089547,0.0009104915,0.0011375546,0.0042689457],"category_scores_gemma":[0.0038583388,0.00045057194,0.00046203288,0.0005799952,0.0005328727,0.0010713531,0.00077318074,0.0010737348,0.00053362007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000528868,0.000024884419,0.0003797183,0.0000152486655,0.000008906716,0.00003852219,0.000020441723,0.9900932,0.0006944573,0.004769525,0.0004110572,0.0034912615],"study_design_scores_gemma":[0.000001995612,0.0000017958854,0.000015185544,3.6497912e-7,3.132335e-7,0.0000016748259,0.0000012625634,0.99950767,0.000066699504,0.00038065517,0.000021527689,8.5517917e-7],"about_ca_topic_score_codex":0.015914232,"about_ca_topic_score_gemma":0.013585869,"teacher_disagreement_score":0.015914232,"about_ca_system_score_codex":0.00067055435,"about_ca_system_score_gemma":0.0012423819,"threshold_uncertainty_score":0.03164321},"labels":[],"label_agreement":null},{"id":"W4390938897","doi":"10.1109/tcsi.2024.3350509","title":"Derivative-Enhanced Rational Polynomial Chaos for Uncertainty Quantification","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Polynomial chaos; Polynomial; Uncertainty quantification; Context (archaeology); Sensitivity (control systems); Key (lock); Computer science; CHAOS (operating system); Mathematical optimization; Algorithm; Applied mathematics; Mathematics; Machine learning; Electronic engineering; Statistics; Engineering","score_opus":0.08253712579110647,"score_gpt":0.3168171687773937,"score_spread":0.2342800429862872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390938897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062999534,0.00036703554,0.99036646,0.00014113741,0.00002858098,0.000014588509,0.000034033714,0.00009388795,0.0026543618],"genre_scores_gemma":[0.720964,0.0011286896,0.2737606,0.00015498325,0.00011384076,0.00007752433,0.000116617564,0.0001078421,0.0035758782],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991673,0.00024134955,0.000037042555,0.00011300968,0.00038189962,0.000059440594],"domain_scores_gemma":[0.998923,0.0006606385,0.00009814487,0.00012014472,0.0001656036,0.00003245323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00125384,0.00064478803,0.0007217808,0.0009224596,0.0004361404,0.000897185,0.0007615791,0.0005716104,0.001629723],"category_scores_gemma":[0.0034677265,0.00025650972,0.0006442137,0.0008677334,0.0012165154,0.001384426,0.0012463339,0.0014025886,0.0003212203],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008157563,0.000017752784,0.00032917707,0.00013371595,0.000031946478,0.0001156861,0.00010255987,0.46926263,0.011045854,0.47042292,0.0008911291,0.047565114],"study_design_scores_gemma":[0.0000030055312,0.000016194888,0.000047440943,0.0000059337294,0.000003987724,0.00002574856,0.000003885383,0.9489896,0.0015998071,0.04817117,0.0011220985,0.000011003909],"about_ca_topic_score_codex":0.0015230414,"about_ca_topic_score_gemma":0.0010091873,"teacher_disagreement_score":0.001629723,"about_ca_system_score_codex":0.0011138773,"about_ca_system_score_gemma":0.00066534936,"threshold_uncertainty_score":0.008081794},"labels":[],"label_agreement":null},{"id":"W4391102668","doi":"10.1029/2022wr033808","title":"An Improved Copula‐Based Framework for Efficient Global Sensitivity Analysis","year":2024,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Saskatchewan; University of Alberta","funders":"","keywords":"Identifiability; Sobol sequence; Sensitivity (control systems); Copula (linguistics); Dimension (graph theory); Mathematics; Mathematical optimization; Function (biology); Computer science; Variance-based sensitivity analysis; Variance (accounting); Econometrics; Applied mathematics; Statistics","score_opus":0.1427297753571669,"score_gpt":0.45360848965950656,"score_spread":0.31087871430233965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391102668","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016943397,0.0001244203,0.9967077,0.000047493308,0.000013596774,0.000028875018,0.00007477164,0.00016045097,0.0011484002],"genre_scores_gemma":[0.35350955,0.0008362281,0.6394873,0.00021298623,0.00014987425,0.0005971034,0.00067913387,0.0006871612,0.0038406483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808395,0.0010490762,0.000086164844,0.00025006922,0.00038358703,0.00014720172],"domain_scores_gemma":[0.9964966,0.0023192428,0.00019944503,0.00026168866,0.00063649414,0.00008654004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046747727,0.0020351936,0.0016545028,0.0028163067,0.00057689706,0.0018394224,0.0015045567,0.00093783136,0.005555349],"category_scores_gemma":[0.009303853,0.001015224,0.00260748,0.0017910362,0.0009537276,0.0015755766,0.0021911552,0.002460227,0.0007972942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020330503,0.00003359146,0.00041671607,0.000101503916,0.000096928154,0.00009852988,0.00005635714,0.90761,0.0013915224,0.0631645,0.0013809924,0.025629008],"study_design_scores_gemma":[0.0000018835796,0.0000065324216,0.00005430517,0.000006082519,0.0000066428584,0.0000065226914,0.000004339566,0.9896572,0.00014435833,0.009593458,0.00051378954,0.000004857123],"about_ca_topic_score_codex":0.011677734,"about_ca_topic_score_gemma":0.006506085,"teacher_disagreement_score":0.011677734,"about_ca_system_score_codex":0.0011655033,"about_ca_system_score_gemma":0.002237059,"threshold_uncertainty_score":0.024722874},"labels":[],"label_agreement":null},{"id":"W4391122544","doi":"10.1016/j.nucengdes.2023.112884","title":"A computational framework for probabilistic structural assessments of reactor components requiring complex code workflows","year":2024,"lang":"en","type":"article","venue":"Nuclear Engineering and Design","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nuclear Laboratories","funders":"Atomic Energy of Canada Limited","keywords":"Probabilistic logic; Piping; Reliability engineering; Computer science; Component (thermodynamics); Population; Workflow; Scalability; Engineering; Systems engineering; Database; Artificial intelligence; Mechanical engineering","score_opus":0.16342026039394292,"score_gpt":0.3708906551733282,"score_spread":0.20747039477938528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391122544","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037300873,0.00004679463,0.99351877,0.0001631198,0.00001964333,0.00004562475,0.00010630423,0.00026244466,0.0021070743],"genre_scores_gemma":[0.2562271,0.0002083542,0.7389403,0.00014865998,0.00011153929,0.00048654678,0.00045318354,0.0002934247,0.003130847],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998317,0.0005604455,0.000091147085,0.00023524264,0.00060902076,0.00018724673],"domain_scores_gemma":[0.9925271,0.0052581667,0.0004267281,0.0007143612,0.0007625322,0.00031095857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003832114,0.001107893,0.0016123456,0.0018559439,0.0015838321,0.0033821869,0.004611962,0.0024181446,0.007136779],"category_scores_gemma":[0.013884375,0.0012039876,0.0023642203,0.0014688805,0.0024664972,0.002510102,0.003429296,0.0027413135,0.00079643086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011008038,0.000019097612,0.00013436381,0.000020321917,0.00001136085,0.000026063195,0.00002141411,0.952587,0.00015312932,0.04329905,0.00027504485,0.003442188],"study_design_scores_gemma":[0.0000035054195,0.0000027488784,0.0000150782835,0.000003619675,0.0000023753125,0.00000401791,0.000003842255,0.98549867,0.000046271212,0.014204624,0.00021275076,0.0000025162678],"about_ca_topic_score_codex":0.019839112,"about_ca_topic_score_gemma":0.023134837,"teacher_disagreement_score":0.019839112,"about_ca_system_score_codex":0.0020159332,"about_ca_system_score_gemma":0.0047841705,"threshold_uncertainty_score":0.039447248},"labels":[],"label_agreement":null},{"id":"W4391124046","doi":"10.48550/arxiv.2401.10498","title":"Efficient Probabilistic Optimal Power Flow Assessment Using an Adaptive Stochastic Spectral Embedding Surrogate Model","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probabilistic logic; Embedding; Polynomial chaos; Monte Carlo method; Mathematical optimization; Computer science; Surrogate model; Uncertainty quantification; Partition (number theory); Flow (mathematics); Power (physics); Algorithm; Mathematics; Machine learning; Artificial intelligence","score_opus":0.19101210061141788,"score_gpt":0.29025867837078323,"score_spread":0.09924657775936535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391124046","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063762413,0.00002871122,0.9926844,0.000042059037,0.0000062038503,0.000010227659,0.000010976089,0.000047297137,0.0007938627],"genre_scores_gemma":[0.73305255,0.00018006888,0.263968,0.000057180845,0.000038474584,0.00013826085,0.00012399581,0.00007678748,0.002364655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958974,0.000178685,0.000015749802,0.00004199166,0.00015104165,0.000022742157],"domain_scores_gemma":[0.9993274,0.0004044314,0.000074722484,0.0000474372,0.00012170078,0.000024330571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008636284,0.0005294154,0.00063889177,0.0004417885,0.0002206762,0.000544309,0.0006356745,0.0006340511,0.0009927546],"category_scores_gemma":[0.0021242383,0.00028438045,0.0005095131,0.00035293086,0.0005175578,0.0010065957,0.0008137988,0.0007230079,0.00017107901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013386709,0.0000099260105,0.00011371456,0.000014612138,0.0000065298696,0.000016438948,0.000011926228,0.9831187,0.0010088974,0.00773515,0.00012837038,0.007822314],"study_design_scores_gemma":[5.6315406e-7,0.0000029960515,0.000008914947,6.847251e-7,4.4435183e-7,0.0000025173117,7.1052614e-7,0.9990657,0.00008812535,0.00077178277,0.000056666777,8.6557856e-7],"about_ca_topic_score_codex":0.001322089,"about_ca_topic_score_gemma":0.0011824961,"teacher_disagreement_score":0.001322089,"about_ca_system_score_codex":0.00030563757,"about_ca_system_score_gemma":0.0005897268,"threshold_uncertainty_score":0.004567325},"labels":[],"label_agreement":null},{"id":"W4391374402","doi":"10.1016/j.pnucene.2024.105082","title":"Time-variant consideration of parameters dependence-based reliability of passive systems: Synopsis and proposed framework","year":2024,"lang":"en","type":"article","venue":"Progress in Nuclear Energy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Reliability (semiconductor); Reliability engineering; Physics; Thermodynamics","score_opus":0.028310743829569263,"score_gpt":0.28922049283512274,"score_spread":0.26090974900555347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391374402","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042334315,0.010349934,0.97587866,0.000408956,0.00028707457,0.000030290754,0.00006584033,0.000074304735,0.008671564],"genre_scores_gemma":[0.7078313,0.05142436,0.2098978,0.000817412,0.005297413,0.00026829768,0.00034294368,0.00039390745,0.023726566],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927574,0.0001853444,0.00006271821,0.00020215898,0.00023073104,0.000043363085],"domain_scores_gemma":[0.9984579,0.0008300695,0.00012976672,0.00017022525,0.00037960752,0.000032526874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015579988,0.0013509516,0.0012502767,0.0013205186,0.000355847,0.0017086593,0.0025063988,0.0015112696,0.002060598],"category_scores_gemma":[0.0029192567,0.00069988356,0.0014032773,0.0015713082,0.0018686248,0.0026469447,0.0010928722,0.0023117915,0.0005228295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054955755,0.00005830143,0.00069406285,0.0010153783,0.00014021191,0.00047022133,0.00021078692,0.4504051,0.009332414,0.4709963,0.0025907573,0.06403143],"study_design_scores_gemma":[0.0000028490274,0.00007547811,0.00047825504,0.000083567764,0.00007427697,0.00021051595,0.000025538919,0.8598329,0.0011507748,0.130566,0.007466848,0.000032969165],"about_ca_topic_score_codex":0.0022053544,"about_ca_topic_score_gemma":0.0016405844,"teacher_disagreement_score":0.0025063988,"about_ca_system_score_codex":0.0009256136,"about_ca_system_score_gemma":0.00063095323,"threshold_uncertainty_score":0.008239627},"labels":[],"label_agreement":null},{"id":"W4391424882","doi":"10.1007/978-3-031-35471-7_11","title":"Time-Dependent Reliability Analysis of Degrading Structural Elements Using Stochastic FE and LSTM Learning","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Reliability (semiconductor); Artificial intelligence; Computer science; Physics; Thermodynamics","score_opus":0.031331702694705976,"score_gpt":0.28522062875343634,"score_spread":0.25388892605873037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391424882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10177386,0.00044647086,0.8952049,0.00013506609,0.000026006317,0.000021151476,0.00012250149,0.00057135004,0.0016987701],"genre_scores_gemma":[0.9472238,0.00019009126,0.05043524,0.000029340928,0.000022005353,0.000031145155,0.00018909732,0.0000763005,0.0018029758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998523,0.000032737476,0.000007832993,0.0000374927,0.00005108869,0.000018482991],"domain_scores_gemma":[0.9989617,0.0007038603,0.00009483013,0.000060984625,0.00016135086,0.000017278548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060500886,0.0006068404,0.00052730646,0.0005669076,0.00015890175,0.00039855888,0.0007308477,0.0007352785,0.0012050695],"category_scores_gemma":[0.0020857041,0.00040579037,0.0006187494,0.00040696765,0.00039169585,0.0007581547,0.00039669825,0.0006030084,0.00022002113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022690645,0.000010617835,0.00030008276,0.000023986446,0.000013370127,0.000026192547,0.000011018806,0.97932076,0.0020402367,0.0008849897,0.00014450097,0.017201493],"study_design_scores_gemma":[2.6986942e-7,0.000002820325,0.00009021369,9.864732e-7,0.0000013287771,0.000004037853,9.0216184e-7,0.9993674,0.00017813947,0.00033704238,0.0000160324,8.398766e-7],"about_ca_topic_score_codex":0.004706846,"about_ca_topic_score_gemma":0.006466286,"teacher_disagreement_score":0.004706846,"about_ca_system_score_codex":0.0006341841,"about_ca_system_score_gemma":0.00043797167,"threshold_uncertainty_score":0.0093589425},"labels":[],"label_agreement":null},{"id":"W4391424957","doi":"10.1007/978-3-031-35471-7_9","title":"Reliability Analysis of Structural Elements with Active Learning Kriging Using a New Learning Function: KO Function","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Kriging; Reliability (semiconductor); Function (biology); Reliability engineering; Computer science; Engineering; Machine learning; Physics; Biology; Cell biology; Thermodynamics","score_opus":0.030671356848962814,"score_gpt":0.2759410791451728,"score_spread":0.24526972229620997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391424957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007927018,0.00008664641,0.9911596,0.000028102539,0.000008091702,0.000008456401,0.0000143487605,0.00011271406,0.0006550436],"genre_scores_gemma":[0.6940343,0.00030704614,0.3001618,0.000037453083,0.000039783787,0.00012867987,0.00015649133,0.00030171458,0.004832774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995198,0.00016270365,0.000024465473,0.00006570544,0.00020205241,0.00002524457],"domain_scores_gemma":[0.9984529,0.0010231163,0.000101777645,0.00016009154,0.00024431356,0.000017737282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010824507,0.0007316414,0.00084439333,0.00074745505,0.00028993355,0.00066852075,0.0011719994,0.000980166,0.0011500042],"category_scores_gemma":[0.0030403172,0.0005831798,0.00094208715,0.0005009825,0.00079264364,0.001355126,0.00065925956,0.0010898733,0.00041354625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026876976,0.0000195266,0.00033057874,0.00006864919,0.000025005551,0.000016980657,0.000042166797,0.95651543,0.0046729445,0.005659353,0.00020685513,0.03241553],"study_design_scores_gemma":[8.1088547e-7,0.000006602516,0.00010803113,0.000003163535,0.000003672937,0.0000046921077,0.0000025311779,0.9977004,0.0008562771,0.0011899613,0.0001209837,0.0000028822835],"about_ca_topic_score_codex":0.002323673,"about_ca_topic_score_gemma":0.0029649634,"teacher_disagreement_score":0.002323673,"about_ca_system_score_codex":0.000567789,"about_ca_system_score_gemma":0.0006291764,"threshold_uncertainty_score":0.005724609},"labels":[],"label_agreement":null},{"id":"W4391611972","doi":"10.1007/s12206-024-0113-1","title":"Reliability evaluation of components with multiple failure modes based on mixture Weibull distribution using expectation maximization algorithm","year":2024,"lang":"en","type":"article","venue":"Journal of Mechanical Science and Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Weibull distribution; Expectation–maximization algorithm; Maximization; Particle swarm optimization; Mathematics; Reliability (semiconductor); Estimation theory; Mathematical optimization; Algorithm; Statistics; Applied mathematics; Maximum likelihood","score_opus":0.054339233129835865,"score_gpt":0.32448937937622413,"score_spread":0.2701501462463883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391611972","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043251537,0.00039419578,0.95551187,0.00004709551,0.000010137914,0.000026501482,0.000024380202,0.00019145831,0.0005427845],"genre_scores_gemma":[0.87741184,0.00027382126,0.1210184,0.000026130114,0.000022316,0.00008994961,0.00013057173,0.000069194306,0.00095790275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989768,0.00041161815,0.000057258123,0.00015042852,0.00032882756,0.000075037475],"domain_scores_gemma":[0.99764985,0.0015791457,0.00017318086,0.00009279698,0.00045045366,0.00005451259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025376738,0.0011133113,0.0017070433,0.0012414485,0.00036303542,0.00085282465,0.0012147485,0.00088608393,0.00090288575],"category_scores_gemma":[0.0042125043,0.00070946245,0.001399805,0.0008220777,0.0005343064,0.0012168805,0.00072255847,0.0005858188,0.00019217546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000970955,0.000025519692,0.0010000159,0.000072072165,0.0000732091,0.000045252706,0.000030086903,0.977098,0.002424068,0.0018525567,0.0001666178,0.017115599],"study_design_scores_gemma":[0.0000019382846,0.000013020421,0.00019472466,0.0000024040974,0.0000088358975,0.000010598709,0.0000031517955,0.99897766,0.00024508176,0.0005093626,0.000029824596,0.0000032587889],"about_ca_topic_score_codex":0.002511835,"about_ca_topic_score_gemma":0.0015758729,"teacher_disagreement_score":0.0025376738,"about_ca_system_score_codex":0.0007245081,"about_ca_system_score_gemma":0.0008141819,"threshold_uncertainty_score":0.013420641},"labels":[],"label_agreement":null},{"id":"W4391796518","doi":"10.1016/j.engstruct.2024.117597","title":"Finite element-based reliability analysis of reinforced concrete masonry walls under eccentric axial loading considering slenderness effects","year":2024,"lang":"en","type":"article","venue":"Engineering Structures","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Structural engineering; Masonry; Finite element method; Buckling; Eccentricity (behavior); Limit state design; Reliability (semiconductor); Engineering; Geotechnical engineering; Computer science; Physics","score_opus":0.023616150918752455,"score_gpt":0.2824494034454404,"score_spread":0.25883325252668793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391796518","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78929996,0.00032347543,0.2062821,0.00010709884,0.000023791537,0.000021046459,0.00006814125,0.00018697082,0.003687451],"genre_scores_gemma":[0.99404305,0.000050910763,0.0053677703,0.000004889986,0.000003113138,0.000008478107,0.000028382574,0.000013506951,0.00048009763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997936,0.0000739736,0.000009703437,0.000024661666,0.00006946752,0.000028702969],"domain_scores_gemma":[0.9988772,0.00069273054,0.00014189542,0.00006865718,0.00018501429,0.000034414883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075350865,0.0005079219,0.00075565756,0.000957495,0.00027837817,0.0004313148,0.00084038585,0.0009677611,0.0007931155],"category_scores_gemma":[0.001708801,0.0005572086,0.0005732671,0.000340492,0.0007071542,0.00048507375,0.00040700662,0.00033626045,0.00014905051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037526777,0.000019520767,0.00086597196,0.000023174123,0.000013213892,0.0000741521,0.000030907308,0.9914723,0.0038887733,0.00075625913,0.00004742911,0.0027707377],"study_design_scores_gemma":[0.0000010761073,0.000010642232,0.00037996625,0.0000023541788,0.0000043483997,0.0000071717063,0.000006252172,0.99904925,0.00039952644,0.00011661701,0.000020549842,0.0000022115125],"about_ca_topic_score_codex":0.0035891694,"about_ca_topic_score_gemma":0.0030923984,"teacher_disagreement_score":0.0035891694,"about_ca_system_score_codex":0.00037438638,"about_ca_system_score_gemma":0.00048013247,"threshold_uncertainty_score":0.0071365833},"labels":[],"label_agreement":null},{"id":"W4391982742","doi":"10.37247/paam3ed.3.23.11","title":"Eigenvalues and Eigenvectors for a Hermitian Gaussian Operator: Role of the Schrödinger-Robertson Uncertainty Relation","year":2022,"lang":"en","type":"book-chapter","venue":"Vide Leaf, Hyderabad eBooks","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Eigenvalues and eigenvectors; Hermitian matrix; Operator (biology); Relation (database); Schrödinger's cat; Gaussian; Mathematical physics; Mathematics; Pure mathematics; Physics; Quantum mechanics; Computer science; Biology","score_opus":0.04916733061098035,"score_gpt":0.2783785058787205,"score_spread":0.22921117526774012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391982742","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032435376,0.014827622,0.45727885,0.0055652615,0.0025207405,0.000058126978,0.000348078,0.00044834556,0.48651752],"genre_scores_gemma":[0.5117688,0.012711511,0.17406462,0.001558904,0.0025777598,0.00014475043,0.00030485127,0.0007217278,0.2961471],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996166,0.000107644235,0.000017828139,0.00008090055,0.00014479039,0.00003215771],"domain_scores_gemma":[0.9995733,0.00026518517,0.000029833815,0.000041187588,0.00005884672,0.000031716765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007425765,0.00058754324,0.0006152376,0.00087965233,0.00074317865,0.0030196141,0.000692479,0.0013172545,0.0073526483],"category_scores_gemma":[0.001347379,0.00045168507,0.0003926973,0.0009755247,0.003224076,0.0024714496,0.00075173035,0.002038654,0.0017588305],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044948356,0.0000033392525,0.000013664651,0.0000165922,0.0000015785521,0.000016091744,0.00006440659,0.00070641475,0.00053105975,0.99163777,0.0022803552,0.0047241724],"study_design_scores_gemma":[0.0000019609956,0.0000052979512,0.00007143591,0.0000090570775,0.0000013824144,0.000053115276,0.000034479737,0.004429806,0.000222447,0.98651266,0.008648247,0.000010104748],"about_ca_topic_score_codex":0.0005408994,"about_ca_topic_score_gemma":0.00045602606,"teacher_disagreement_score":0.0073526483,"about_ca_system_score_codex":0.00061045995,"about_ca_system_score_gemma":0.0005452987,"threshold_uncertainty_score":0.024597049},"labels":[],"label_agreement":null},{"id":"W4392063755","doi":"10.3997/2214-4609.202379022","title":"How Thin is a Thin Bed? an Uncertainty Perspective","year":2023,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Perspective (graphical); Computer science; Artificial intelligence","score_opus":0.12382698101757937,"score_gpt":0.3607156862318824,"score_spread":0.236888705214303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392063755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047859192,0.009912402,0.8493885,0.024739666,0.0008660762,0.00006553413,0.00037730995,0.00019083371,0.0666005],"genre_scores_gemma":[0.898658,0.007215797,0.07858877,0.0016990681,0.0013571354,0.00010916069,0.00022744056,0.00022125719,0.011923548],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969869,0.0008669115,0.00013685146,0.00054796063,0.00108061,0.00038082444],"domain_scores_gemma":[0.9869421,0.00874757,0.0010772087,0.0010317129,0.001536569,0.0006648609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039735115,0.00080662186,0.0021803945,0.0024500694,0.0019840053,0.0065296907,0.003543261,0.0047519943,0.0064985314],"category_scores_gemma":[0.019918961,0.0014854621,0.00128848,0.0015958317,0.010003984,0.016412698,0.0038943565,0.0042966576,0.0006671933],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049355334,0.000022646116,0.0004922764,0.00014501443,0.000037892787,0.00018772707,0.00016546297,0.05019893,0.0006767594,0.93214804,0.0017981231,0.014077832],"study_design_scores_gemma":[0.000005777035,0.000032676176,0.00034958785,0.000088034554,0.000022532771,0.0001775361,0.00031772302,0.071661934,0.00052228733,0.922035,0.0047516,0.00003539673],"about_ca_topic_score_codex":0.00349828,"about_ca_topic_score_gemma":0.0015818893,"teacher_disagreement_score":0.0065296907,"about_ca_system_score_codex":0.0017054715,"about_ca_system_score_gemma":0.0009971097,"threshold_uncertainty_score":0.02173978},"labels":[],"label_agreement":null},{"id":"W4392242141","doi":"10.1142/s0218539324500074","title":"Time-Variant Reliability for Systems with Non-monotonic Limit-State Functions","year":2024,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Monotonic function; Reliability (semiconductor); Limit (mathematics); Reliability engineering; Limit state design; State (computer science); Computer science; Mathematics; Engineering; Structural engineering; Physics; Algorithm; Mathematical analysis","score_opus":0.03852672259165902,"score_gpt":0.3244799068469326,"score_spread":0.2859531842552736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392242141","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02730965,0.00031798842,0.966218,0.00015194187,0.00002137757,0.00001459177,0.000031581494,0.00015045554,0.0057844035],"genre_scores_gemma":[0.9254133,0.0009768035,0.06949862,0.00007297346,0.000039183935,0.00007002887,0.00008812796,0.00008800609,0.0037529827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997253,0.000079657315,0.000010112737,0.00003284307,0.00012282119,0.000029229985],"domain_scores_gemma":[0.9991371,0.00056246313,0.00008110471,0.000103151375,0.00009803745,0.000018144896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000829315,0.0004016667,0.0003042824,0.0005945842,0.00022683975,0.00094557443,0.00045488751,0.0004308603,0.0018113315],"category_scores_gemma":[0.0029005143,0.00016251853,0.0005016811,0.00035556027,0.0010813974,0.00093644025,0.0005023307,0.0009036108,0.00029271154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003554108,0.000013240137,0.0008850009,0.000076077005,0.000016748932,0.00018774676,0.0001582057,0.74840224,0.0060972306,0.22026876,0.00047399002,0.02338518],"study_design_scores_gemma":[0.0000015025674,0.000016841806,0.00022546467,0.0000067490914,0.0000030777157,0.000057809713,0.000017509901,0.9550748,0.0008590972,0.04293337,0.0007972147,0.0000065612803],"about_ca_topic_score_codex":0.0013591108,"about_ca_topic_score_gemma":0.0007126436,"teacher_disagreement_score":0.0018113315,"about_ca_system_score_codex":0.000584025,"about_ca_system_score_gemma":0.00042862355,"threshold_uncertainty_score":0.006059468},"labels":[],"label_agreement":null},{"id":"W4392652551","doi":"10.5194/egusphere-egu24-19540","title":"Uncertainty estimation of conductive thin plates parameters through a Bayesian approach","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"iNano Medical (Canada)","funders":"","keywords":"Bayesian probability; Estimation; Bayes estimator; Electrical conductor; Computer science; Artificial intelligence; Mathematics; Econometrics; Statistics; Materials science; Engineering; Composite material","score_opus":0.13183330385777325,"score_gpt":0.3588794191146164,"score_spread":0.22704611525684315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392652551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016891425,0.00027905242,0.9812708,0.00014513625,0.000012328584,0.000046380683,0.00013405946,0.0001710526,0.0010498127],"genre_scores_gemma":[0.5537284,0.001107239,0.43953735,0.00018355656,0.00013719659,0.00030721212,0.0011764454,0.00016045183,0.0036621343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990847,0.00035444612,0.00005104338,0.000171823,0.00024542987,0.000092501075],"domain_scores_gemma":[0.9953897,0.003523832,0.0003267824,0.00013432831,0.00050880335,0.000116470634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002839912,0.00087887736,0.0012818176,0.0025532818,0.00074378087,0.0017795034,0.0018751374,0.0015822945,0.0025185836],"category_scores_gemma":[0.00896391,0.0013066715,0.0012720841,0.0011918197,0.001207704,0.0017787251,0.0015682642,0.0015097664,0.00050829386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007889313,0.000035398352,0.001731301,0.000071507726,0.00006845828,0.00011462068,0.000068033936,0.94302225,0.0011244097,0.014131336,0.0004175309,0.03913632],"study_design_scores_gemma":[0.000005492506,0.000007380444,0.00024901627,0.000014578494,0.000009536406,0.00001532601,0.000008853272,0.9924947,0.0002344797,0.0066823238,0.00026608954,0.000012302118],"about_ca_topic_score_codex":0.01482324,"about_ca_topic_score_gemma":0.012829587,"teacher_disagreement_score":0.01482324,"about_ca_system_score_codex":0.0010141588,"about_ca_system_score_gemma":0.0018685822,"threshold_uncertainty_score":0.0294739},"labels":[],"label_agreement":null},{"id":"W4393004895","doi":"10.1561/0200000113","title":"Predictive Global Sensitivity Analysis: Foundational Concepts, Tools, and Applications","year":2024,"lang":"en","type":"article","venue":"Foundations and Trends® in Technology Information and Operations Management","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Gilead Sciences (Canada)","funders":"","keywords":"Sensitivity (control systems); Computer science; Data science; Management science; Engineering","score_opus":0.02582304636302766,"score_gpt":0.3378309512395788,"score_spread":0.3120079048765511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393004895","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00096310297,0.0046902006,0.98798263,0.0010300011,0.00010469963,0.00004852131,0.00012284177,0.00029546497,0.0047624684],"genre_scores_gemma":[0.2529714,0.033746853,0.7045469,0.0012270915,0.0016044696,0.0008437268,0.00051471696,0.0005287841,0.004015972],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956672,0.0020332993,0.00021854293,0.00056917063,0.0013458251,0.00016592085],"domain_scores_gemma":[0.98739606,0.010266813,0.00063873176,0.00083332235,0.0007297743,0.00013530302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008797173,0.003027966,0.0019051553,0.005604278,0.0008027515,0.0043900935,0.0028933643,0.0025231314,0.0033968494],"category_scores_gemma":[0.01768828,0.0012765061,0.002488127,0.0047482974,0.006063801,0.005058647,0.0036632784,0.005929734,0.00079952565],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026133514,0.00007110332,0.0010107801,0.00069777784,0.00015438041,0.00013023661,0.00025909353,0.29736885,0.00078059814,0.59071445,0.0049271286,0.10385953],"study_design_scores_gemma":[0.000007795554,0.00003285781,0.0002904495,0.0003862137,0.00003193258,0.0000969777,0.00007913753,0.3080846,0.0007541629,0.6739852,0.016189598,0.000061013576],"about_ca_topic_score_codex":0.0035689468,"about_ca_topic_score_gemma":0.001232908,"teacher_disagreement_score":0.008797173,"about_ca_system_score_codex":0.0022125936,"about_ca_system_score_gemma":0.002221436,"threshold_uncertainty_score":0.046524405},"labels":[],"label_agreement":null},{"id":"W4393291811","doi":"10.1061/ajrua6.rueng-1034","title":"Metric Systems for Performance Evaluation of Active Learning Kriging Configurations for Reliability Analysis","year":2024,"lang":"en","type":"article","venue":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Metric (unit); Kriging; Computer science; Reliability (semiconductor); Reliability engineering; Machine learning; Artificial intelligence; Engineering; Operations management; Physics","score_opus":0.0416758247615118,"score_gpt":0.3151130710443866,"score_spread":0.2734372462828748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393291811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16273583,0.00060426834,0.83010757,0.00009566512,0.00005087136,0.00034011135,0.0003820355,0.0014384743,0.004245106],"genre_scores_gemma":[0.7552655,0.00015570961,0.24314705,0.000026918724,0.00001072303,0.00038572255,0.00047572586,0.000115865725,0.0004168192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99248683,0.0044319937,0.0005046006,0.0004865843,0.0019093489,0.00018062817],"domain_scores_gemma":[0.9875251,0.006566879,0.0013158878,0.0016213116,0.0028304805,0.00014035426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010577758,0.0016772234,0.0011629111,0.0035579011,0.0005161918,0.0010721618,0.0012533303,0.0008131353,0.0012358822],"category_scores_gemma":[0.030350244,0.00034216113,0.00066699623,0.0028993355,0.0007385869,0.0015751341,0.0012734751,0.00094905973,0.00036195063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047463615,0.0001951794,0.007951265,0.00039248748,0.00019746316,0.0000741028,0.00023077901,0.80985427,0.009706977,0.015544499,0.0011214282,0.15425697],"study_design_scores_gemma":[0.00002173751,0.00065247656,0.0031322606,0.00003893159,0.0000308266,0.00006384807,0.0000729499,0.98154724,0.009324591,0.0040171067,0.001042514,0.00005556196],"about_ca_topic_score_codex":0.0017997058,"about_ca_topic_score_gemma":0.0018093401,"teacher_disagreement_score":0.010577758,"about_ca_system_score_codex":0.0010670124,"about_ca_system_score_gemma":0.0008988185,"threshold_uncertainty_score":0.055941164},"labels":[],"label_agreement":null},{"id":"W4393942902","doi":"10.1137/22m1524989","title":"Subsampling of Parametric Models with Bifidelity Boosting","year":2024,"lang":"en","type":"article","venue":"SIAM/ASA Journal on Uncertainty Quantification","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Air Force Office of Scientific Research; U.S. Department of Energy","keywords":"Boosting (machine learning); Computer science; Sketch; Leverage (statistics); Regression; Machine learning; Fidelity; Parametric statistics; Data mining; Residual; Artificial intelligence; Algorithm; Mathematics; Statistics","score_opus":0.22410383171903517,"score_gpt":0.37904507855285396,"score_spread":0.1549412468338188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393942902","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046151383,0.0001449092,0.99437296,0.00008809503,0.000016338394,0.000026795282,0.000024784114,0.00017074589,0.00054018473],"genre_scores_gemma":[0.40791562,0.00052936614,0.5870063,0.00035971744,0.00013654264,0.00033203352,0.0004332983,0.0002524072,0.0030347903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843854,0.0007666145,0.00005682727,0.0001724229,0.0004771826,0.00008845167],"domain_scores_gemma":[0.9944746,0.0037125053,0.0004479706,0.00067378406,0.0005071943,0.00018399251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005625062,0.0014132054,0.0014228718,0.00096462015,0.00047751528,0.0012701215,0.001721273,0.001757454,0.002167363],"category_scores_gemma":[0.0178498,0.0010319734,0.0012505221,0.000610218,0.0014041809,0.0013967089,0.0032236502,0.0019983258,0.000793922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008352039,0.00004705385,0.00107317,0.00013378533,0.000046541103,0.00008434501,0.00009966279,0.9128869,0.0029581892,0.039246414,0.0011619404,0.04217842],"study_design_scores_gemma":[0.0000045199718,0.00001994655,0.00006578173,0.000010796951,0.000003385751,0.000014443894,0.0000052161968,0.9913914,0.0005055302,0.007475161,0.0004976967,0.000006061145],"about_ca_topic_score_codex":0.0018012826,"about_ca_topic_score_gemma":0.0018085463,"teacher_disagreement_score":0.005625062,"about_ca_system_score_codex":0.0007616665,"about_ca_system_score_gemma":0.00093258714,"threshold_uncertainty_score":0.029748559},"labels":[],"label_agreement":null},{"id":"W4394746005","doi":"10.62913/engj.v47i1.975","title":"Critical Evaluation of Equivalent Moment Factor Procedures for Laterally Unsupported Beams","year":2010,"lang":"en","type":"article","venue":"Engineering Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Golder Associates (Canada); University of Alberta","funders":"","keywords":"Moment (physics); Bending moment; Mathematics; Shear and moment diagram; Point (geometry); Square (algebra); Moment distribution method; Statistics; Structural engineering; Bending; Geometry; Physics; Engineering; Classical mechanics; Bending stiffness","score_opus":0.11103972939163617,"score_gpt":0.3938440677685861,"score_spread":0.28280433837694996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394746005","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029563254,0.0001458012,0.96717674,0.000020087002,0.000015594038,0.00009713768,0.000026646405,0.0002469075,0.0027078562],"genre_scores_gemma":[0.36248043,0.00030148277,0.6349599,0.000027783444,0.000020573098,0.00019583371,0.00011715263,0.00026241923,0.0016344287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99706984,0.00079229864,0.00016887208,0.00015617117,0.0016860119,0.00012674947],"domain_scores_gemma":[0.9903668,0.0058925482,0.0008930343,0.00054856925,0.0022169868,0.00008211884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042727883,0.00079501903,0.0006009042,0.00272565,0.0004740877,0.00088351587,0.0010915981,0.00056842825,0.0033389598],"category_scores_gemma":[0.018922275,0.0003201844,0.00072095444,0.00084730494,0.0009996826,0.0014247951,0.00085730787,0.0006451849,0.0003748038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004332233,0.00015322867,0.005084734,0.00064182054,0.00010657353,0.00026470618,0.0007693386,0.35773706,0.06526588,0.10791622,0.0014752907,0.4601519],"study_design_scores_gemma":[0.000039927214,0.0003164106,0.0021750722,0.00009700154,0.00004616134,0.000273485,0.00020899985,0.91596067,0.061902937,0.013541474,0.005355792,0.00008210789],"about_ca_topic_score_codex":0.0016117694,"about_ca_topic_score_gemma":0.00267347,"teacher_disagreement_score":0.0042727883,"about_ca_system_score_codex":0.00084172626,"about_ca_system_score_gemma":0.0013775863,"threshold_uncertainty_score":0.022596955},"labels":[],"label_agreement":null},{"id":"W4394897192","doi":"10.2139/ssrn.4797881","title":"Structural Integrity Assessment of Candu Pressure Tubes Using Sobol Indices for Global Sensitivity Analysis","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Atomic Energy (Canada); Canadian Nuclear Laboratories","funders":"","keywords":"Sobol sequence; Download; Sensitivity (control systems); Computer science; Structural integrity; Reliability engineering; Engineering; Operating system; Structural engineering","score_opus":0.07013215204164472,"score_gpt":0.4010528858550732,"score_spread":0.33092073381342846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394897192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18691537,0.0003562121,0.80351585,0.00018809299,0.000032894208,0.000085262596,0.00013934229,0.00037528336,0.008391615],"genre_scores_gemma":[0.949777,0.00020327614,0.047847934,0.00002913725,0.00001777473,0.00009224781,0.00009370953,0.00009474843,0.0018441542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913174,0.0003033744,0.000024574929,0.00013747194,0.00033349515,0.00006937368],"domain_scores_gemma":[0.99803144,0.0011458313,0.00018829806,0.00019810532,0.00036908657,0.00006717144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002108268,0.0012599143,0.001036689,0.0016825253,0.0006463025,0.0020480547,0.0008254097,0.0015282086,0.0018190969],"category_scores_gemma":[0.005122968,0.0004330497,0.0008856243,0.000628065,0.0013293745,0.0016040991,0.0017222614,0.0010407532,0.0002266536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024219415,0.00007393109,0.0028595096,0.00015969951,0.00006542764,0.00017063922,0.000119149925,0.8965324,0.024502046,0.024757935,0.00049770524,0.05001924],"study_design_scores_gemma":[0.0000028888696,0.00006355023,0.0006473428,0.00001097894,0.000013803019,0.000024671159,0.000022488426,0.98827726,0.006118372,0.004570701,0.00023293331,0.000015058743],"about_ca_topic_score_codex":0.0013133235,"about_ca_topic_score_gemma":0.0010481012,"teacher_disagreement_score":0.002108268,"about_ca_system_score_codex":0.000804148,"about_ca_system_score_gemma":0.00078384875,"threshold_uncertainty_score":0.011149704},"labels":[],"label_agreement":null},{"id":"W4394936111","doi":"10.1016/j.strusafe.2024.102474","title":"Development of methods of structural reliability","year":2024,"lang":"en","type":"article","venue":"Structural Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":63,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Calgary","funders":"","keywords":"Structural reliability; Reliability (semiconductor); Reliability engineering; Computer science; Engineering; Structural engineering; Forensic engineering; Probabilistic logic","score_opus":0.09115977976002816,"score_gpt":0.42935593301526054,"score_spread":0.3381961532552324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394936111","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001608856,0.0001786182,0.99865484,0.000033133878,0.000033772245,0.00001707005,0.000014248638,0.000038246053,0.0008691232],"genre_scores_gemma":[0.019036321,0.00075820176,0.9764925,0.00006244892,0.00013924985,0.00024935554,0.0000853998,0.00012883836,0.0030476183],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99699414,0.0013732252,0.00015312401,0.0003768755,0.0010269507,0.000075645396],"domain_scores_gemma":[0.991456,0.0050343773,0.00024726414,0.0009873603,0.0021551927,0.00011983763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005220779,0.001436289,0.0012708318,0.0030014594,0.0007140406,0.0014349007,0.0028576427,0.0013821844,0.003924749],"category_scores_gemma":[0.017427031,0.0012071519,0.00231326,0.0011457447,0.002033604,0.0023049607,0.0022445067,0.0042953766,0.002193353],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002088049,0.000046979763,0.00048116222,0.0003963999,0.000099344965,0.000047369274,0.00024163896,0.0856114,0.00321411,0.72896355,0.0033068014,0.17757031],"study_design_scores_gemma":[0.000027340448,0.00007177993,0.00034214125,0.00018742359,0.000038503185,0.0001171589,0.000039498147,0.49144343,0.0037197568,0.45731872,0.04664943,0.000044785957],"about_ca_topic_score_codex":0.002222083,"about_ca_topic_score_gemma":0.0018403954,"teacher_disagreement_score":0.005220779,"about_ca_system_score_codex":0.0014485829,"about_ca_system_score_gemma":0.0022938345,"threshold_uncertainty_score":0.02761048},"labels":[],"label_agreement":null},{"id":"W4394978569","doi":"10.1007/s11538-024-01288-y","title":"Untangling the Molecular Interactions Underlying Intracellular Phase Separation Using Combined Global Sensitivity Analyses","year":2024,"lang":"en","type":"article","venue":"Bulletin of Mathematical Biology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of North Carolina at Chapel Hill","keywords":"Separation (statistics); Sensitivity (control systems); Phase (matter); Intracellular; Biological system; Chemistry; Chemical physics; Biology; Mathematics; Statistics; Biochemistry; Engineering","score_opus":0.23904277941462287,"score_gpt":0.4890455656023689,"score_spread":0.250002786187746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394978569","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6776145,0.00069559045,0.31416458,0.00038322745,0.000043933705,0.00020326211,0.0006039303,0.0007096927,0.0055813245],"genre_scores_gemma":[0.9781877,0.00021687617,0.020519892,0.000056628378,0.0000089585765,0.00012357728,0.00017349877,0.000046655743,0.00066618697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954057,0.00015107758,0.000026747337,0.00007938598,0.00013313555,0.000069161506],"domain_scores_gemma":[0.997661,0.0018963872,0.00016666758,0.000117983895,0.000121434125,0.000036626294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001712189,0.0010469645,0.0007705387,0.0011361906,0.0002880064,0.0007705211,0.00046412542,0.00060243654,0.0013235972],"category_scores_gemma":[0.0028235337,0.00030007947,0.0014349861,0.00042692843,0.00061825954,0.00068995473,0.0009310872,0.0009207973,0.0001168762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014239528,0.00012761637,0.005523426,0.00032926898,0.00021981074,0.0003027181,0.00015482368,0.8841247,0.086259104,0.007542853,0.00035662108,0.014916616],"study_design_scores_gemma":[0.0000036956203,0.00007671453,0.0014624633,0.00000936589,0.000027960103,0.00003264331,0.000036962072,0.97687393,0.019150129,0.0020131955,0.00029082297,0.000022068116],"about_ca_topic_score_codex":0.0026663863,"about_ca_topic_score_gemma":0.0015025466,"teacher_disagreement_score":0.0026663863,"about_ca_system_score_codex":0.0007107198,"about_ca_system_score_gemma":0.00048490037,"threshold_uncertainty_score":0.009055018},"labels":[],"label_agreement":null},{"id":"W4395471280","doi":"10.2139/ssrn.4804375","title":"Optimization of the Generalized Covariance Estimator in Noncausal Processes","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Covariance; Estimator; Estimation of covariance matrices; Mathematics; Law of total covariance; Applied mathematics; Matérn covariance function; Mathematical optimization; Covariance intersection; Computer science; Statistics","score_opus":0.03204648867701856,"score_gpt":0.3115201389440634,"score_spread":0.27947365026704485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395471280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005941935,0.000133181,0.9930189,0.00018311605,0.00001930342,0.000011671059,0.00003985243,0.00007927801,0.00057271204],"genre_scores_gemma":[0.46764016,0.00082253234,0.52361315,0.00025277413,0.00019399161,0.00020724817,0.0005304817,0.00046752693,0.006272113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979983,0.0011080146,0.000070093156,0.0003224974,0.00036489347,0.00013625207],"domain_scores_gemma":[0.99524033,0.0037652624,0.00032131554,0.0002110352,0.00036952802,0.00009251231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034524915,0.0010108025,0.0017366525,0.0006786388,0.00037414805,0.001754856,0.0010938832,0.0014755629,0.0017700511],"category_scores_gemma":[0.013479107,0.0010636359,0.00086347247,0.00090342225,0.0015463731,0.0019593532,0.001893068,0.0015070603,0.00046847944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025665868,0.000057509606,0.00071166304,0.000267613,0.00013568229,0.00009656779,0.000083266816,0.8359209,0.00502355,0.103499874,0.0014924668,0.05245423],"study_design_scores_gemma":[0.00002206407,0.000029034887,0.000229946,0.000012908628,0.000013636439,0.000019809026,0.0000065873373,0.976233,0.0008766038,0.022117708,0.00042377462,0.000015076352],"about_ca_topic_score_codex":0.0033202814,"about_ca_topic_score_gemma":0.0037628228,"teacher_disagreement_score":0.0034524915,"about_ca_system_score_codex":0.0011034312,"about_ca_system_score_gemma":0.0027643105,"threshold_uncertainty_score":0.01825875},"labels":[],"label_agreement":null},{"id":"W4396640371","doi":"10.1016/j.jcp.2024.113063","title":"Residual vector and solution mode analysis using semi-supervised machine learning for mesh modification and CFD stability improvement","year":2024,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Ansys","keywords":"Jacobian matrix and determinant; Residual; Principal component analysis; Eigenvalues and eigenvectors; Finite element method; Stability (learning theory); Computer science; Finite volume method; Algorithm; Outlier; Support vector machine; Mathematical optimization; Mathematics; Applied mathematics; Artificial intelligence; Engineering; Machine learning; Physics","score_opus":0.1118340652656017,"score_gpt":0.36101398102565974,"score_spread":0.24917991576005805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396640371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023060767,0.00005546377,0.97550696,0.00004861185,0.000021002343,0.000025098809,0.0000301319,0.0008001722,0.00045168144],"genre_scores_gemma":[0.54045725,0.00007330063,0.45578903,0.00006683628,0.00003263179,0.00012559247,0.00028346205,0.00032246957,0.0028494599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999549,0.00013929616,0.00003155612,0.00009269475,0.00015254591,0.000034904235],"domain_scores_gemma":[0.99774915,0.0010228175,0.00020754774,0.00030323755,0.00066489354,0.000052355615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013690718,0.0006470023,0.00077118777,0.00069063035,0.00035134045,0.00054799195,0.0010766371,0.00085001026,0.0022784066],"category_scores_gemma":[0.003776969,0.0003459146,0.00073106016,0.0003559617,0.0005625319,0.000976435,0.00081199163,0.0011833677,0.00059466524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003514839,0.000304156,0.0018813445,0.00015797028,0.000082692444,0.000045225894,0.00015941239,0.54395753,0.025441205,0.005510932,0.0021257093,0.41998237],"study_design_scores_gemma":[0.000001929709,0.0000116809315,0.00007371271,0.0000011311256,0.0000016621096,0.00000310629,0.0000022735553,0.99857795,0.0009980584,0.00025633752,0.00007000149,0.000002046474],"about_ca_topic_score_codex":0.0024935934,"about_ca_topic_score_gemma":0.0033326312,"teacher_disagreement_score":0.0024935934,"about_ca_system_score_codex":0.0003069566,"about_ca_system_score_gemma":0.0008791156,"threshold_uncertainty_score":0.007622063},"labels":[],"label_agreement":null},{"id":"W4396993480","doi":"10.2139/ssrn.4829504","title":"An Efficient method to Simulate Diffusion Bridges","year":2024,"lang":"fr","type":"article","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Diffusion; Computer science; Physics; Thermodynamics","score_opus":0.02863992755972715,"score_gpt":0.36177989453781334,"score_spread":0.3331399669780862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396993480","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0137034785,0.000041427826,0.9825959,0.00008495487,0.00004697841,0.000060100567,0.0000810172,0.00046797868,0.0029181244],"genre_scores_gemma":[0.3893907,0.000109345005,0.60346913,0.00012209467,0.000051626597,0.00044082935,0.0002401699,0.00031111581,0.005864942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996375,0.0001070717,0.000017844328,0.000049559072,0.00014711762,0.000040785817],"domain_scores_gemma":[0.9977725,0.0014323969,0.00010987497,0.00025927628,0.00031226614,0.00011366329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010208421,0.00061762315,0.00089944864,0.0008782821,0.0006750234,0.00075891876,0.00175886,0.0019940007,0.007011915],"category_scores_gemma":[0.0050304895,0.0004976483,0.0007167032,0.0007493776,0.0007263709,0.0009869796,0.0013735514,0.0012976268,0.00088703376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000922482,0.00006436315,0.0004563976,0.000052307114,0.000031904332,0.000081305945,0.00004298929,0.93345934,0.003240651,0.034310173,0.00085846876,0.027309837],"study_design_scores_gemma":[0.000011365126,0.0000069661505,0.00001638363,0.0000017372596,0.0000026594637,0.0000087739845,0.0000019014723,0.9954738,0.00038759326,0.0038468284,0.00023944455,0.00000259219],"about_ca_topic_score_codex":0.004449142,"about_ca_topic_score_gemma":0.004302071,"teacher_disagreement_score":0.007011915,"about_ca_system_score_codex":0.0006920377,"about_ca_system_score_gemma":0.0011871018,"threshold_uncertainty_score":0.02345723},"labels":[],"label_agreement":null},{"id":"W4398172107","doi":"10.1007/s00180-024-01507-z","title":"Advancements in Rényi entropy and divergence estimation for model assessment","year":2024,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Divergence (linguistics); Estimation; Econometrics; Entropy estimation; Mathematics; Statistics; Computer science; Artificial intelligence; Estimator; Engineering; Philosophy","score_opus":0.0889429360870464,"score_gpt":0.4218571900454962,"score_spread":0.3329142539584498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398172107","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021346582,0.005371517,0.9890891,0.00093460374,0.00021568463,0.00001888154,0.000047041365,0.00009915804,0.002089332],"genre_scores_gemma":[0.2832062,0.02470918,0.68240803,0.0008992013,0.0038530391,0.00022194712,0.000341026,0.00044056078,0.003920791],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9883875,0.006017277,0.00075668073,0.0013158598,0.003362434,0.00016013334],"domain_scores_gemma":[0.9394622,0.047818013,0.0019077813,0.0052708974,0.004962915,0.00057815824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019996231,0.0021371464,0.0027244745,0.0041168267,0.0006443728,0.0040928638,0.0029260768,0.0022580815,0.0020791178],"category_scores_gemma":[0.06565218,0.0010094744,0.0021794331,0.003775892,0.0036296318,0.007899579,0.0046330923,0.0068292185,0.0008703193],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088591114,0.000097761505,0.0018567378,0.00051798066,0.0002399906,0.00005555407,0.0001506339,0.11652842,0.0018422456,0.6817399,0.0019330858,0.19494896],"study_design_scores_gemma":[0.000010842274,0.000085934145,0.0006796165,0.00013045015,0.00005685938,0.00008656024,0.00002943772,0.64095265,0.0018465243,0.34677318,0.009272718,0.00007523647],"about_ca_topic_score_codex":0.0017615476,"about_ca_topic_score_gemma":0.0011634252,"teacher_disagreement_score":0.019996231,"about_ca_system_score_codex":0.0019766921,"about_ca_system_score_gemma":0.002020522,"threshold_uncertainty_score":0.105751395},"labels":[],"label_agreement":null},{"id":"W4400366544","doi":"10.3390/s24134349","title":"Probabilistic Analysis of Critical Speed Values of a Rotating Machine as a Function of the Change of Dynamic Parameters","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Kultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR","keywords":"Probabilistic logic; Monte Carlo method; Rotor (electric); Vibration; Bearing (navigation); Helicopter rotor; Engineering; Critical speed; Stability (learning theory); Control theory (sociology); Computer science; Mechanical engineering; Mathematics; Physics; Artificial intelligence; Machine learning","score_opus":0.07927064364646644,"score_gpt":0.35570069218278483,"score_spread":0.27643004853631836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400366544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4529535,0.00028440746,0.5433049,0.00019361371,0.000015331896,0.000046213045,0.0001726713,0.00037140222,0.0026579332],"genre_scores_gemma":[0.99349505,0.000050479797,0.006185448,0.0000069988573,0.0000037665986,0.000012451743,0.000046241734,0.000012022497,0.00018763893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949586,0.000117874435,0.000018809254,0.000107425934,0.00020977088,0.000050154853],"domain_scores_gemma":[0.9940785,0.004275773,0.000890722,0.00030552826,0.0003849042,0.00006462776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015051235,0.00036952127,0.0003653578,0.0010630863,0.00033825534,0.0005307951,0.00044335515,0.00051224494,0.0007302201],"category_scores_gemma":[0.007456111,0.00040685304,0.00042974745,0.00048564185,0.0007671933,0.00074951147,0.000386593,0.00058690965,0.00008870448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004709909,0.000017702832,0.0025387502,0.000023204164,0.000018194789,0.0000496457,0.000034504137,0.9842036,0.003601332,0.004267935,0.00007626848,0.0051217764],"study_design_scores_gemma":[0.0000013407354,0.000026130101,0.0014234803,0.0000027507692,0.0000056020936,0.000035161356,0.0000073734823,0.9953673,0.0015744731,0.0014439974,0.00010332073,0.000009139443],"about_ca_topic_score_codex":0.0017297823,"about_ca_topic_score_gemma":0.0017462298,"teacher_disagreement_score":0.0017297823,"about_ca_system_score_codex":0.000586995,"about_ca_system_score_gemma":0.00046190497,"threshold_uncertainty_score":0.007959962},"labels":[],"label_agreement":null},{"id":"W4400787272","doi":"10.1137/23m1561968","title":"Square Root LASSO: Well-Posedness, Lipschitz Stability, and the Tuning Trade-Off","year":2024,"lang":"en","type":"article","venue":"SIAM Journal on Optimization","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Concordia University; Institut de Valorisation des Données; Centre de Recherches Mathématiques","keywords":"Mathematics; Lipschitz continuity; Lasso (programming language); Stability (learning theory); Square root; Square (algebra); Applied mathematics; Mathematical optimization; Mathematical analysis; Geometry; Computer science","score_opus":0.04309997475809929,"score_gpt":0.29497667689339835,"score_spread":0.25187670213529906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400787272","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01689122,0.0018871628,0.97631866,0.0016771308,0.000108024804,0.000022949873,0.000091928196,0.00025307262,0.002749929],"genre_scores_gemma":[0.75093937,0.0023386402,0.23702025,0.0011060811,0.0005911176,0.00023469704,0.0002903713,0.000486126,0.0069932747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967231,0.0017708504,0.00013685964,0.0005528229,0.0006539734,0.00016240677],"domain_scores_gemma":[0.9862563,0.011341902,0.00072724844,0.00075253315,0.0006761749,0.00024586206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007670849,0.0013815463,0.0015125774,0.00061883935,0.00055550295,0.002260216,0.001269653,0.0027236782,0.0021132142],"category_scores_gemma":[0.0321009,0.000713091,0.0005507843,0.0006596027,0.002868694,0.0036277939,0.002146033,0.0027667063,0.0005008648],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000964119,0.00019469729,0.003121899,0.00067439605,0.00030699975,0.00037695214,0.00025410816,0.581497,0.017558338,0.1870952,0.0094084935,0.1985478],"study_design_scores_gemma":[0.000032485947,0.000109651584,0.00039285197,0.00003502059,0.00002006232,0.000099300974,0.000026785743,0.93738306,0.0020136926,0.05897442,0.00088411005,0.000028575912],"about_ca_topic_score_codex":0.001007391,"about_ca_topic_score_gemma":0.0008755383,"teacher_disagreement_score":0.007670849,"about_ca_system_score_codex":0.0005732712,"about_ca_system_score_gemma":0.0010628541,"threshold_uncertainty_score":0.040567815},"labels":[],"label_agreement":null},{"id":"W4401453054","doi":"10.1109/piers62282.2024.10618476","title":"Efficient Uncertainty Quantification with Subspace Pursuit for FDTD Based Microwave Circuit Models","year":2024,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Finite-difference time-domain method; Subspace topology; Microwave; Computer science; Microwave imaging; Electronic engineering; Algorithm; Artificial intelligence; Telecommunications; Physics; Engineering; Optics","score_opus":0.15293690605381974,"score_gpt":0.3296260338014416,"score_spread":0.17668912774762188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401453054","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006520503,0.00007107559,0.99269485,0.00005091906,0.0000034021864,0.000010294555,0.000021020245,0.00006528448,0.0005625293],"genre_scores_gemma":[0.6270341,0.00046444483,0.36945987,0.00006097462,0.000031893087,0.0001805671,0.00017927216,0.00007658485,0.0025123267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969804,0.00012520832,0.000013512791,0.000028071103,0.00011242242,0.000022878106],"domain_scores_gemma":[0.99927527,0.00052207935,0.00006754679,0.000049885075,0.00006809773,0.000017065317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008471213,0.0005170494,0.0006735213,0.00047708672,0.0003145984,0.0006577759,0.0004949671,0.0006513759,0.0007588494],"category_scores_gemma":[0.0017547866,0.00038382947,0.0005211463,0.00047884585,0.0006846756,0.0008012471,0.0008473708,0.0007941274,0.00014384353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024009996,0.000012687093,0.00014117638,0.000036187932,0.000014639259,0.000021734644,0.000030782703,0.9605594,0.0022403365,0.020537084,0.0001924901,0.016189393],"study_design_scores_gemma":[6.558828e-7,0.0000029545802,0.000010956125,0.0000010321693,5.8172196e-7,0.000003501895,0.0000012624821,0.9975654,0.00026380352,0.0020493523,0.000099118406,0.0000014642519],"about_ca_topic_score_codex":0.0022147442,"about_ca_topic_score_gemma":0.0014178616,"teacher_disagreement_score":0.0022147442,"about_ca_system_score_codex":0.0005693818,"about_ca_system_score_gemma":0.0006858282,"threshold_uncertainty_score":0.0044800043},"labels":[],"label_agreement":null},{"id":"W4401670426","doi":"10.1016/j.nucengdes.2024.113499","title":"Structural integrity assessment of CANDU pressure tubes using Sobol indices for global sensitivity analysis","year":2024,"lang":"en","type":"article","venue":"Nuclear Engineering and Design","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nuclear Laboratories","funders":"Atomic Energy of Canada Limited","keywords":"Sobol sequence; Sensitivity (control systems); Structural integrity; Materials science; Nuclear engineering; Structural engineering; Engineering; Reliability engineering","score_opus":0.08983179180064922,"score_gpt":0.36101829730053325,"score_spread":0.27118650549988405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401670426","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4712404,0.000292342,0.51682085,0.00016281457,0.000024754516,0.00010178022,0.00015878897,0.00041931026,0.010778879],"genre_scores_gemma":[0.97411704,0.00009220334,0.024609668,0.000014820673,0.0000058265914,0.000052274252,0.000059041096,0.00004408407,0.0010050209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994355,0.000196688,0.00001568315,0.000073446485,0.00022915797,0.00004951056],"domain_scores_gemma":[0.99886286,0.0006546576,0.00010718297,0.00010516832,0.00023423128,0.000035896457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016590386,0.00093090086,0.00071508967,0.0013786593,0.00057397975,0.0014178219,0.0006386583,0.0010884191,0.0012306443],"category_scores_gemma":[0.0031002583,0.00036233608,0.0007596684,0.00049317913,0.0009022859,0.0011509532,0.0011556982,0.00067347643,0.00014954337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017212288,0.000058138554,0.0029292146,0.00008585539,0.000043502132,0.000113510185,0.00007514524,0.9378191,0.021148462,0.0075933035,0.00024819566,0.029713448],"study_design_scores_gemma":[0.0000027685226,0.00006796985,0.0008564894,0.0000068640365,0.0000112064145,0.000017803754,0.00002329362,0.99039596,0.007101999,0.0013330067,0.00017147088,0.000011163832],"about_ca_topic_score_codex":0.0016455397,"about_ca_topic_score_gemma":0.0017058052,"teacher_disagreement_score":0.0016590386,"about_ca_system_score_codex":0.0007447093,"about_ca_system_score_gemma":0.0006522655,"threshold_uncertainty_score":0.008773923},"labels":[],"label_agreement":null},{"id":"W4402217410","doi":"10.1016/j.cie.2024.110536","title":"Robust inference for an interval-monitored step-stress experiment with competing risks for failure with an application to capacitor data","year":2024,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Ministerio de Universidades; Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Reliability engineering; Interval (graph theory); Computer science; Stress (linguistics); Engineering; Artificial intelligence; Mathematics","score_opus":0.27266498387247334,"score_gpt":0.3712985966494697,"score_spread":0.09863361277699634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402217410","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019669062,0.0001695356,0.9795814,0.00008425308,0.000013073507,0.000056430792,0.00009836216,0.00013972764,0.00018817978],"genre_scores_gemma":[0.59206915,0.00048375322,0.40429297,0.00019118912,0.00008664648,0.000498497,0.0007136684,0.00009008141,0.0015739821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9894539,0.0068614697,0.00037317208,0.0017989998,0.001205379,0.0003070429],"domain_scores_gemma":[0.84454024,0.14042911,0.005508727,0.0066329306,0.0022985644,0.00059040525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03364477,0.001119037,0.0019171706,0.0015516173,0.00048359108,0.0015889179,0.0031361547,0.002480537,0.001591841],"category_scores_gemma":[0.095639236,0.0007304988,0.0020562557,0.0013257281,0.0032571414,0.0023066215,0.0020564124,0.0028715027,0.00021734207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000820831,0.00023635343,0.01370662,0.00048993283,0.00093817373,0.00047190176,0.00034137355,0.70826906,0.0067581236,0.18832202,0.00080662186,0.07883891],"study_design_scores_gemma":[0.00004490628,0.00016460048,0.0020052902,0.00002261437,0.00006489443,0.000079154874,0.000021796634,0.9508693,0.0012538593,0.045002203,0.00043417886,0.000037148355],"about_ca_topic_score_codex":0.0022099402,"about_ca_topic_score_gemma":0.0011664046,"teacher_disagreement_score":0.03364477,"about_ca_system_score_codex":0.001165903,"about_ca_system_score_gemma":0.0012219325,"threshold_uncertainty_score":0.17793268},"labels":[],"label_agreement":null},{"id":"W4402287087","doi":"10.21203/rs.3.rs-4857963/v1","title":"Surrogate-based Sensitivity Analysis of Facet Optical Coatings Produced without and with in-situ Design Re-optimization","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Sobol sequence; Robustness (evolution); Fabrication; Materials science; Facet (psychology); Coating; Photonics; Sensitivity (control systems); Wavelength; Reflectivity; Waveguide; Computer science; Optics; Optoelectronics; Electronic engineering; Nanotechnology; Engineering; Physics","score_opus":0.17921993324992924,"score_gpt":0.4237994900450128,"score_spread":0.24457955679508359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402287087","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6493768,0.000446521,0.33960956,0.0002275236,0.00006230366,0.000059582086,0.00055961596,0.0002826711,0.009375456],"genre_scores_gemma":[0.98822135,0.00006847101,0.010772198,0.000019002257,0.000003671284,0.000018175604,0.00013297981,0.000034150573,0.0007299188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907875,0.00026221518,0.000027059568,0.00011245901,0.00043077068,0.000088739886],"domain_scores_gemma":[0.99795425,0.0013833778,0.00019072815,0.0002078069,0.0002334449,0.000030420557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015554596,0.0005120258,0.0004863166,0.0005032955,0.00021145488,0.00082180824,0.00042715986,0.00082217634,0.0013200691],"category_scores_gemma":[0.0036792082,0.00031861346,0.0007689331,0.00037309038,0.00058411044,0.000570129,0.00067065755,0.00058054476,0.00014503225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029157312,0.00006850015,0.0011268263,0.00015202626,0.000057000765,0.00009176619,0.00003991149,0.9220342,0.06512149,0.0026779817,0.00026316376,0.008075602],"study_design_scores_gemma":[0.000005615355,0.00008059239,0.000978191,0.00000770012,0.000011426607,0.000033833163,0.000010126811,0.95741916,0.040163215,0.001068106,0.00021121078,0.000010770193],"about_ca_topic_score_codex":0.00090038916,"about_ca_topic_score_gemma":0.00080072426,"teacher_disagreement_score":0.0015554596,"about_ca_system_score_codex":0.0007261425,"about_ca_system_score_gemma":0.0003416432,"threshold_uncertainty_score":0.008226156},"labels":[],"label_agreement":null},{"id":"W4402395793","doi":"10.1007/s13171-024-00370-w","title":"On Sufficiency and Ancillarity","year":2024,"lang":"en","type":"article","venue":"Sankhya A","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Business","score_opus":0.08485132905213244,"score_gpt":0.3660165460920027,"score_spread":0.28116521703987024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402395793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0497829,0.00654987,0.788162,0.012590972,0.00091008283,0.00014670179,0.0006110826,0.00023388883,0.14101252],"genre_scores_gemma":[0.86843306,0.0041416967,0.10561881,0.0048380997,0.0020467213,0.0006309756,0.0008036227,0.00025989098,0.0132272225],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9897872,0.005622442,0.0006705354,0.0015375086,0.0017238996,0.0006584163],"domain_scores_gemma":[0.9076384,0.07749083,0.0026463778,0.0046504703,0.006566481,0.0010074795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015924122,0.0013938254,0.002659187,0.0047116335,0.0026485135,0.0036102952,0.0023232994,0.0031018918,0.009511464],"category_scores_gemma":[0.06870127,0.0013968267,0.002255453,0.0026784483,0.010201122,0.011333079,0.006315211,0.007094833,0.0010182584],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004799559,0.000016007785,0.00037296014,0.000105220126,0.000019403069,0.00011036389,0.00012582407,0.0016665986,0.00026527845,0.99108243,0.0018399159,0.0043479963],"study_design_scores_gemma":[0.0000112769985,0.000018713683,0.00017771377,0.000060314906,0.000014707235,0.00013730429,0.000041367417,0.007483389,0.00022266143,0.98928577,0.0025337776,0.000013077943],"about_ca_topic_score_codex":0.0009457732,"about_ca_topic_score_gemma":0.0006745466,"teacher_disagreement_score":0.015924122,"about_ca_system_score_codex":0.0016329366,"about_ca_system_score_gemma":0.0010916283,"threshold_uncertainty_score":0.08421582},"labels":[],"label_agreement":null},{"id":"W4402757601","doi":"10.1007/978-3-031-61347-0","title":"Sharp Inequalities for Ordered Random Variables in Statistics and Reliability","year":2024,"lang":"en","type":"book","venue":"Frontiers in probability and the statistical sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Statistics; Inequality; Reliability (semiconductor); Mathematics; Random variable; Econometrics; Psychology; Physics; Mathematical analysis","score_opus":0.05816150815464568,"score_gpt":0.321093276675979,"score_spread":0.2629317685213333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402757601","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033420299,0.06395524,0.6843727,0.0065539377,0.003433394,0.00007226499,0.0005152805,0.00043450226,0.23732063],"genre_scores_gemma":[0.21161209,0.09531043,0.3608907,0.0062972177,0.014381517,0.0006752063,0.0014897923,0.0014518718,0.30789122],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988745,0.00033009835,0.00005550238,0.00014802146,0.0005215441,0.000070296745],"domain_scores_gemma":[0.9953219,0.0037371689,0.00013347334,0.00026172865,0.00045915015,0.000086543776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023493795,0.0019912433,0.0013463226,0.0019674224,0.000615217,0.0031668926,0.0013220777,0.0012846091,0.009827419],"category_scores_gemma":[0.008555163,0.000827846,0.0008207948,0.0029280013,0.0035003503,0.004779278,0.0011612307,0.006375875,0.003592334],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020075728,0.000012831468,0.00004189499,0.0001500243,0.000014502263,0.000042119213,0.00010526936,0.0030439403,0.00050544407,0.93664795,0.030816348,0.028599618],"study_design_scores_gemma":[0.0000054107004,0.000012766818,0.0000846736,0.000046907076,0.000007969356,0.000052275158,0.000016623415,0.0046751373,0.00018502442,0.9540288,0.040874526,0.000009908938],"about_ca_topic_score_codex":0.0013428144,"about_ca_topic_score_gemma":0.0012232949,"teacher_disagreement_score":0.009827419,"about_ca_system_score_codex":0.001849541,"about_ca_system_score_gemma":0.0009968693,"threshold_uncertainty_score":0.032875955},"labels":[],"label_agreement":null},{"id":"W4402827737","doi":"10.1007/978-3-031-61531-3_7","title":"A Novel Approach for Random Field Incorporation in Stochastic Finite Element Simulation of Existing Structures: Application in Marine Structures","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Finite element method; Field (mathematics); Element (criminal law); Computer science; Random field; Engineering; Mathematics; Structural engineering; Pure mathematics; Statistics; Political science","score_opus":0.061430255789417416,"score_gpt":0.305297111594725,"score_spread":0.2438668558053076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402827737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002132022,0.00004049169,0.9957628,0.000038700207,0.00004808755,0.000027794598,0.000022341514,0.00026380466,0.0016639252],"genre_scores_gemma":[0.0920353,0.00021593866,0.89840066,0.00018726363,0.00009457253,0.00022509192,0.00012473126,0.0006106941,0.008105659],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996271,0.00008269932,0.000020067304,0.000068119116,0.00017736366,0.000024727004],"domain_scores_gemma":[0.9994222,0.00025557424,0.000058572063,0.00009927335,0.00012726411,0.00003713719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066686026,0.0007587449,0.0009200086,0.0006380341,0.0005158957,0.0009403868,0.0020913868,0.002208064,0.0038701964],"category_scores_gemma":[0.0016185042,0.00071335846,0.0010865083,0.00053058384,0.00071777764,0.0010866832,0.0016446211,0.001391482,0.0012350108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006967618,0.0002844018,0.000365051,0.00018764012,0.000093640076,0.00022658835,0.00012524083,0.8157377,0.035715703,0.06553016,0.0019230349,0.07974108],"study_design_scores_gemma":[0.0000039665792,0.000016150316,0.00002886754,0.0000051230504,0.000004910719,0.000030113975,0.0000037936454,0.9956934,0.0011618574,0.0019687493,0.0010764076,0.0000067359088],"about_ca_topic_score_codex":0.0014939479,"about_ca_topic_score_gemma":0.0025713805,"teacher_disagreement_score":0.0038701964,"about_ca_system_score_codex":0.00040856664,"about_ca_system_score_gemma":0.0006330138,"threshold_uncertainty_score":0.0129470825},"labels":[],"label_agreement":null},{"id":"W4403127322","doi":"10.1109/pesgm51994.2024.10688503","title":"A Bayesian Method to Infer Parameters in Power Flow Models Using Linear Sensitivities","year":2024,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Bayesian probability; Power flow; Linear model; Power (physics); Statistics; Data mining; Econometrics; Artificial intelligence; Machine learning; Mathematics; Electric power system","score_opus":0.16326098609461673,"score_gpt":0.4004375610047809,"score_spread":0.23717657491016417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403127322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007002173,0.000047242425,0.99873656,0.00003822395,0.0000049174478,0.000010895435,0.0000191528,0.0000892949,0.00035355368],"genre_scores_gemma":[0.19864196,0.00055049243,0.7965008,0.0002229844,0.00013627643,0.00031484416,0.00039372113,0.00023974419,0.00299911],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978258,0.001083755,0.00008683839,0.0003143667,0.00060535356,0.00008392828],"domain_scores_gemma":[0.9942901,0.0044763447,0.0004129861,0.00030408037,0.00042982725,0.00008675883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004210231,0.0015351439,0.0014437586,0.002272044,0.0008288682,0.0013568288,0.0018319939,0.0011523762,0.0027860922],"category_scores_gemma":[0.017398492,0.0015422616,0.0014362911,0.0013636131,0.001437654,0.002301752,0.0020418328,0.0025824818,0.0008080617],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005076514,0.000056525878,0.00080386375,0.00010341708,0.00013552638,0.00007335251,0.00010868115,0.8554019,0.0015689464,0.05591703,0.0011639994,0.08461606],"study_design_scores_gemma":[0.000007460541,0.0000149218,0.000119609205,0.000018461848,0.000014158376,0.000027564218,0.0000060635143,0.97003305,0.0005250729,0.028356796,0.0008593884,0.000017492683],"about_ca_topic_score_codex":0.007648287,"about_ca_topic_score_gemma":0.007964372,"teacher_disagreement_score":0.007648287,"about_ca_system_score_codex":0.0011908181,"about_ca_system_score_gemma":0.002205881,"threshold_uncertainty_score":0.02226609},"labels":[],"label_agreement":null},{"id":"W4403132674","doi":"10.1214/24-ba1469","title":"Fast Power Curve Approximation for Posterior Analyses","year":2024,"lang":"es","type":"article","venue":"Bayesian Analysis","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; McGill University","funders":"","keywords":"Mathematics; Econometrics; Applied mathematics; Computer science; Statistics","score_opus":0.06587582818423708,"score_gpt":0.37802685980630074,"score_spread":0.31215103162206365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403132674","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005280526,0.00012438063,0.99853027,0.000064039654,0.0000111185145,0.00002797824,0.000026917625,0.00016429537,0.00052299374],"genre_scores_gemma":[0.0944293,0.00089485943,0.8999685,0.00027285868,0.00014094444,0.0007199915,0.00037039796,0.00066241645,0.0025408338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9908488,0.004670519,0.00037817674,0.0009835799,0.002770081,0.00034880696],"domain_scores_gemma":[0.92885464,0.06040667,0.0018114522,0.004803834,0.0037493054,0.00037416632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017310029,0.0020734328,0.002272856,0.00341185,0.00085557654,0.003256968,0.0029491268,0.0021551147,0.009239788],"category_scores_gemma":[0.13694704,0.0014731011,0.0018979324,0.0028910583,0.0022427484,0.005334312,0.0036243827,0.006276567,0.0027469336],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002671796,0.000082655344,0.0019969717,0.00042686466,0.00020865435,0.00026209222,0.00044006613,0.40824682,0.003364779,0.36069998,0.0046292106,0.2193748],"study_design_scores_gemma":[0.000036797166,0.000045524706,0.0002713209,0.00007924491,0.000028112167,0.000101162535,0.00003585,0.788275,0.0015422322,0.20496185,0.0046010655,0.000021862437],"about_ca_topic_score_codex":0.0036366638,"about_ca_topic_score_gemma":0.0019765857,"teacher_disagreement_score":0.017310029,"about_ca_system_score_codex":0.0021087725,"about_ca_system_score_gemma":0.0026872784,"threshold_uncertainty_score":0.091545224},"labels":[],"label_agreement":null},{"id":"W4403148220","doi":"10.1016/j.anucene.2024.110930","title":"Framework for the correct treatment of model input parameters for Bayesian updating problems in nuclear engineering","year":2024,"lang":"en","type":"article","venue":"Annals of Nuclear Energy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Bayesian probability; Computer science; Applied mathematics; Algorithm; Mathematics; Artificial intelligence","score_opus":0.18706061431756582,"score_gpt":0.36519916471054503,"score_spread":0.1781385503929792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403148220","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023797229,0.00013556886,0.9985342,0.00031342203,0.000036786005,0.000011956672,0.000027321788,0.00004442909,0.0006582501],"genre_scores_gemma":[0.09383487,0.0012347106,0.89723617,0.00081069034,0.0007134885,0.00051384175,0.0002679633,0.00039596594,0.004992366],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99434495,0.0034059882,0.00030348613,0.00045374557,0.0012858056,0.00020616982],"domain_scores_gemma":[0.9859416,0.00944647,0.00070518017,0.0014460337,0.0021447435,0.00031597278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013418649,0.0019786702,0.0029593278,0.0020296972,0.0010644869,0.0033322226,0.004325998,0.004595172,0.005079283],"category_scores_gemma":[0.03703766,0.0015608602,0.0023603288,0.0016729023,0.003716869,0.0044488483,0.003944498,0.007879558,0.0014453289],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026290936,0.00004820968,0.00015667424,0.00013820191,0.000068820365,0.00007184379,0.000108166525,0.1693738,0.00057601626,0.80175614,0.0026644352,0.02501138],"study_design_scores_gemma":[0.00002880419,0.0000236622,0.00007089801,0.00004838482,0.000032648823,0.000041331296,0.000012033899,0.6245956,0.00027907142,0.3706151,0.0042237425,0.000028704097],"about_ca_topic_score_codex":0.005273184,"about_ca_topic_score_gemma":0.004689802,"teacher_disagreement_score":0.013418649,"about_ca_system_score_codex":0.0019708495,"about_ca_system_score_gemma":0.003997509,"threshold_uncertainty_score":0.07096541},"labels":[],"label_agreement":null},{"id":"W4403365197","doi":"10.48550/arxiv.2410.07974","title":"Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Air Force Office of Scientific Research; Institut de Valorisation des Données; Toyota Research Institute; U.S. Department of Energy; Max-Planck-Gesellschaft; Deutscher Akademischer Austauschdienst; National Science Foundation","keywords":"Lagrangian; Sample (material); Path (computing); Sampling (signal processing); Transition (genetics); Mathematics; Statistical physics; Applied mathematics; Mathematical optimization; Physics; Computer science","score_opus":0.19289724749989742,"score_gpt":0.24606922450251456,"score_spread":0.05317197700261714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403365197","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019412446,0.00014987757,0.99667525,0.00021961475,0.000019520045,0.000026110696,0.000039943247,0.00005923209,0.0008692865],"genre_scores_gemma":[0.34271416,0.00070220063,0.64707816,0.00057385676,0.00017972813,0.00059010135,0.00041662736,0.00036739456,0.0073778005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888927,0.00057609804,0.000037911308,0.00013704356,0.00025774355,0.00010188061],"domain_scores_gemma":[0.9959341,0.0031967645,0.0002077364,0.00019313148,0.00029283628,0.00017525286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039183805,0.0010749733,0.0019338938,0.0011906129,0.00059657893,0.0012566593,0.0025725767,0.001887314,0.002861342],"category_scores_gemma":[0.009646572,0.0010218682,0.0011737305,0.0009717705,0.0021468122,0.0015956288,0.0030394355,0.002853705,0.00042663876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005024469,0.00004386576,0.00058928534,0.000106819665,0.000050588227,0.00006178176,0.00006769698,0.83289534,0.00066552544,0.14441866,0.001540281,0.01951001],"study_design_scores_gemma":[0.000007960863,0.000011201891,0.000029717417,0.000009106117,0.0000029700318,0.0000059143135,0.0000037066816,0.9776053,0.00010622353,0.02174734,0.00046663423,0.0000039958522],"about_ca_topic_score_codex":0.0059757736,"about_ca_topic_score_gemma":0.00588579,"teacher_disagreement_score":0.0059757736,"about_ca_system_score_codex":0.0018736325,"about_ca_system_score_gemma":0.0029468855,"threshold_uncertainty_score":0.020722628},"labels":[],"label_agreement":null},{"id":"W4404009343","doi":"10.1061/jbenf2.beeng-6697","title":"Recommendations for Active-Learning Kriging Reliability Analysis of Bridge Structures","year":2024,"lang":"en","type":"article","venue":"Journal of Bridge Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Bridge (graph theory); Kriging; Reliability (semiconductor); Structural reliability; Structural engineering; Engineering; Civil engineering; Forensic engineering; Construction engineering; Reliability engineering; Computer science; Machine learning; Artificial intelligence; Physics","score_opus":0.0876456878379476,"score_gpt":0.367028029619585,"score_spread":0.27938234178163734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404009343","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036666196,0.007904653,0.8997454,0.017922908,0.0008445246,0.0015745944,0.004084791,0.005939219,0.025317697],"genre_scores_gemma":[0.09194644,0.0034088802,0.894462,0.0007457724,0.00014388318,0.0008935475,0.0023458246,0.000548671,0.00550501],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99285406,0.0035137595,0.00065054756,0.0002885945,0.0024804263,0.00021266458],"domain_scores_gemma":[0.9611358,0.0124481125,0.0017966236,0.0026231494,0.02112615,0.00087011413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012500273,0.0018543891,0.001190127,0.0034481024,0.00072935596,0.001609459,0.0040104724,0.0025950058,0.0061471076],"category_scores_gemma":[0.051610783,0.0010781283,0.0009232866,0.0019512437,0.0006455591,0.0018619887,0.0011645699,0.002471926,0.0060782307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031353123,0.0006917163,0.009566933,0.0017149524,0.00013453032,0.00034065705,0.0004672448,0.25232607,0.012911428,0.009024913,0.1538922,0.5586158],"study_design_scores_gemma":[0.00046465397,0.0009083712,0.01507242,0.0055197184,0.00035924272,0.00044616906,0.0019127271,0.7101787,0.029064696,0.046784855,0.18875815,0.00053024676],"about_ca_topic_score_codex":0.010300884,"about_ca_topic_score_gemma":0.026880637,"teacher_disagreement_score":0.012500273,"about_ca_system_score_codex":0.0014621546,"about_ca_system_score_gemma":0.0042412407,"threshold_uncertainty_score":0.066108525},"labels":[],"label_agreement":null},{"id":"W4404075406","doi":"10.1002/psp4.13256","title":"Global sensitivity analysis of Open Systems Pharmacology Suite physiologically based pharmacokinetic models","year":2024,"lang":"en","type":"article","venue":"CPT Pharmacometrics & Systems Pharmacology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Bayer","keywords":"Physiologically based pharmacokinetic modelling; Sobol sequence; Sensitivity (control systems); Parametric statistics; Computer science; Suite; Pharmacokinetics; Parametric model; Pharmacology; Mathematics; Statistics; Engineering; Medicine","score_opus":0.17897910999179253,"score_gpt":0.4432367166416438,"score_spread":0.2642576066498513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404075406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09120063,0.0009866656,0.8845608,0.0005991299,0.00008540495,0.000326183,0.002205313,0.0020059145,0.018029839],"genre_scores_gemma":[0.87977177,0.0008017149,0.11082459,0.00022560116,0.000045200202,0.00079775613,0.0021558004,0.000496915,0.0048806476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99560237,0.0026921565,0.00014333964,0.00035043008,0.0010093012,0.0002023747],"domain_scores_gemma":[0.98729485,0.010575453,0.00051982905,0.0005228973,0.0009804111,0.00010650744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008265858,0.0014334325,0.0012332499,0.0017658485,0.00045934485,0.0017369168,0.0011258916,0.0010148654,0.0053942525],"category_scores_gemma":[0.014826319,0.000568425,0.0022130103,0.0009888286,0.0006689187,0.000997474,0.001723275,0.0015126625,0.00049487205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003879853,0.000023650098,0.00065922947,0.00010504947,0.00008773178,0.000061486135,0.000025522031,0.97998637,0.0007556893,0.008676361,0.00085934077,0.0087206755],"study_design_scores_gemma":[0.000008849604,0.00006013628,0.00042470402,0.00002424485,0.00003457376,0.0000284447,0.000016808111,0.9875994,0.0013079584,0.008713587,0.0017640406,0.00001735569],"about_ca_topic_score_codex":0.007534302,"about_ca_topic_score_gemma":0.00427971,"teacher_disagreement_score":0.008265858,"about_ca_system_score_codex":0.0017143632,"about_ca_system_score_gemma":0.001807603,"threshold_uncertainty_score":0.043714523},"labels":[],"label_agreement":null},{"id":"W4404181752","doi":"10.1016/j.prostr.2024.09.274","title":"Random Finite Element Reliability Assessment of Existing Concrete Structures – Case Studies and Research Direction","year":2024,"lang":"en","type":"article","venue":"Procedia Structural Integrity","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"FuelCell Energy (Canada); Dalhousie University","funders":"","keywords":"Finite element method; Reliability (semiconductor); Structural engineering; Reliability engineering; Structural reliability; Engineering; Computer science; Materials science; Physics; Artificial intelligence","score_opus":0.2761760205200929,"score_gpt":0.503508320505408,"score_spread":0.22733229998531507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404181752","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6787358,0.0012399805,0.3110259,0.000607649,0.00001743922,0.000182676,0.00023877122,0.00021372046,0.00773811],"genre_scores_gemma":[0.9425893,0.00032033896,0.05600961,0.000017759367,0.0000059006165,0.00003277844,0.00008479634,0.0000132412115,0.00092629786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99756914,0.0015317274,0.00009180172,0.00017892191,0.00051233126,0.000116030875],"domain_scores_gemma":[0.99129933,0.0067456984,0.0004127871,0.0005900755,0.0008454658,0.000106611544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003933288,0.00050147943,0.0005641427,0.0018483802,0.00027648432,0.00064290664,0.0013151553,0.0012554749,0.0011315111],"category_scores_gemma":[0.007514734,0.00040997783,0.0007216928,0.0008501099,0.0009362668,0.0008114838,0.0003990101,0.00044386555,0.00017761266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054687844,0.00020171818,0.006014232,0.00009344817,0.000034234952,0.00022756068,0.0000970745,0.9636612,0.0018580606,0.0032752461,0.0001829637,0.024299499],"study_design_scores_gemma":[0.0000061462515,0.000111074594,0.001564184,0.000019622423,0.000011740697,0.0001319947,0.000098949255,0.9934489,0.00242842,0.0016411402,0.0005230359,0.000014831967],"about_ca_topic_score_codex":0.008291661,"about_ca_topic_score_gemma":0.0119511215,"teacher_disagreement_score":0.008291661,"about_ca_system_score_codex":0.00106916,"about_ca_system_score_gemma":0.0005916883,"threshold_uncertainty_score":0.020801485},"labels":[],"label_agreement":null},{"id":"W4404609311","doi":"10.1109/epeps61853.2024.10754199","title":"Gradient-Based Method to Find Solution for Rational Polynomial Chaos Coefficients for Uncertainty Quantification","year":2024,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Polynomial chaos; CHAOS (operating system); Polynomial; Applied mathematics; Computer science; Mathematics; Mathematical optimization; Algorithm; Statistics; Mathematical analysis","score_opus":0.15634276277991063,"score_gpt":0.41870082796339636,"score_spread":0.2623580651834857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404609311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019900782,0.00006705061,0.99700886,0.000043230397,0.0000145855365,0.000020419777,0.0000075947833,0.00008702668,0.0007611769],"genre_scores_gemma":[0.2014837,0.00032974468,0.79394734,0.00009593852,0.000046509474,0.00024271231,0.00009834429,0.0001654717,0.003590283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997371,0.000083962426,0.000012779678,0.00003171174,0.00011288654,0.000021665535],"domain_scores_gemma":[0.99961925,0.00019529472,0.000028642295,0.00002136982,0.00012155453,0.000013906177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006866876,0.0007165475,0.00066623924,0.0006966478,0.0003936754,0.00050474016,0.0006460746,0.0007668056,0.0025233943],"category_scores_gemma":[0.00205237,0.00029640438,0.0005031281,0.00060114486,0.00051444414,0.00072211755,0.0006186727,0.0011326321,0.0005909909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008294463,0.00006788758,0.0006215158,0.00020789712,0.000049129572,0.00010464446,0.0001302099,0.7611676,0.014945213,0.0744641,0.0030164921,0.14514233],"study_design_scores_gemma":[0.0000040167724,0.000008448564,0.000029690687,0.000003307417,0.0000023263462,0.000011571863,0.0000028733255,0.99663526,0.00069452636,0.0019726045,0.0006317128,0.0000037599484],"about_ca_topic_score_codex":0.003123016,"about_ca_topic_score_gemma":0.0027006445,"teacher_disagreement_score":0.003123016,"about_ca_system_score_codex":0.00047919923,"about_ca_system_score_gemma":0.0011997431,"threshold_uncertainty_score":0.008441567},"labels":[],"label_agreement":null},{"id":"W4404769895","doi":"10.1007/s11075-024-01986-7","title":"Explicit-implicit methods for stochastic susceptible-infected-recovered model","year":2024,"lang":"en","type":"article","venue":"Numerical Algorithms","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; National Natural Science Foundation of China","keywords":"Theory of computation; Mathematics; Applied mathematics; Calculus (dental); Mathematical economics; Algorithm; Medicine","score_opus":0.11949634076554967,"score_gpt":0.43646319936855477,"score_spread":0.3169668586030051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404769895","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009208672,0.0005228443,0.9870046,0.00041104128,0.00007868372,0.000030691164,0.00007528753,0.00008994536,0.0025782883],"genre_scores_gemma":[0.66574067,0.0012229765,0.30718845,0.00044989094,0.00027105527,0.00045347546,0.00041195113,0.00034413606,0.0239175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99939644,0.00027640437,0.0000292665,0.00007878028,0.00014778982,0.00007137174],"domain_scores_gemma":[0.99626476,0.0024885389,0.000389796,0.00020332869,0.00047827585,0.0001753321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023661577,0.0010026275,0.0017993066,0.0009517043,0.000661787,0.0013547281,0.0029822292,0.0029417034,0.0031888115],"category_scores_gemma":[0.008304308,0.00091422204,0.0010800239,0.0008505507,0.0022682801,0.0021223095,0.0030460127,0.0024433264,0.00037396813],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027332308,0.000024830735,0.00028129868,0.0000717173,0.00002336804,0.000048420123,0.000046890753,0.9340206,0.00035472942,0.06044176,0.00040412278,0.004255026],"study_design_scores_gemma":[0.0000034487766,0.0000019724887,0.0000132007945,0.000003887913,0.0000018008029,0.0000044251165,0.0000027260533,0.99302626,0.000040657746,0.0067409794,0.00015798205,0.000002651362],"about_ca_topic_score_codex":0.010862042,"about_ca_topic_score_gemma":0.00857111,"teacher_disagreement_score":0.010862042,"about_ca_system_score_codex":0.001483873,"about_ca_system_score_gemma":0.002671058,"threshold_uncertainty_score":0.021597683},"labels":[],"label_agreement":null},{"id":"W4404793216","doi":"10.1016/j.engstruct.2024.119345","title":"Reliability analysis of timber columns under fire load using numerical models with equivalent section temperature","year":2024,"lang":"en","type":"article","venue":"Engineering Structures","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Section (typography); Structural engineering; Engineering; Fire resistance; Reliability engineering; Materials science; Computer science; Composite material; Physics","score_opus":0.04500587842145836,"score_gpt":0.3091110485780916,"score_spread":0.2641051701566332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404793216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32652983,0.00035781055,0.66913617,0.00008469577,0.00002678767,0.000047218986,0.0002642937,0.00038291162,0.0031703582],"genre_scores_gemma":[0.9721086,0.00017479972,0.02638603,0.000012163279,0.000008101595,0.000055888693,0.00017365887,0.00003378035,0.0010470304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977,0.000059342165,0.0000127409385,0.000047298752,0.000084968226,0.000025635547],"domain_scores_gemma":[0.9993747,0.00029071767,0.00011996303,0.0000750839,0.00012168901,0.000017885599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047504148,0.00049313024,0.00040484197,0.0005614343,0.00023060146,0.0004990867,0.0006403457,0.0006211944,0.00080000854],"category_scores_gemma":[0.0012171993,0.00032862593,0.0008029179,0.00036226652,0.00038983123,0.0005076412,0.00030551286,0.00043751663,0.00019925892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009737009,0.00000744355,0.0006522554,0.000011142647,0.000006436053,0.000020759257,0.000013008167,0.9947089,0.0019802663,0.00031288274,0.000031320622,0.0022458807],"study_design_scores_gemma":[5.306626e-7,0.000006920825,0.00027631668,0.0000015993248,0.000002333996,0.000008217546,0.0000030427693,0.999046,0.00047507902,0.00012963133,0.000048288588,0.0000019995528],"about_ca_topic_score_codex":0.006523942,"about_ca_topic_score_gemma":0.0057575735,"teacher_disagreement_score":0.006523942,"about_ca_system_score_codex":0.00045578467,"about_ca_system_score_gemma":0.00045385776,"threshold_uncertainty_score":0.012971938},"labels":[],"label_agreement":null},{"id":"W4404986528","doi":"10.48550/arxiv.2411.15499","title":"Asymmetric Errors","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Banff International Research Station for Mathematical Innovation and Discovery; U.S. Department of Energy","keywords":"Computer science","score_opus":0.21496563466570437,"score_gpt":0.24940675366341392,"score_spread":0.03444111899770955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404986528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032786927,0.00026111823,0.97334486,0.0006987147,0.0012229062,0.00020855202,0.001188899,0.0030713924,0.016724937],"genre_scores_gemma":[0.1330573,0.00082991226,0.8116549,0.0017334906,0.0019815424,0.0009527313,0.003237061,0.008701576,0.037851427],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9624014,0.006741904,0.003785172,0.0070207505,0.018061612,0.0019891246],"domain_scores_gemma":[0.919636,0.019332122,0.00650386,0.033617284,0.019635716,0.0012749963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015646227,0.003350032,0.002189072,0.0073611243,0.0036753295,0.006883882,0.0057420796,0.003430163,0.039779667],"category_scores_gemma":[0.11948485,0.0014844686,0.003170114,0.0064984704,0.0034488332,0.0080398815,0.011232485,0.007024024,0.02137998],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005647023,0.00012479558,0.0045145755,0.0005815709,0.00027232725,0.00096805865,0.00085396535,0.0194422,0.010037817,0.48174796,0.046664007,0.43422797],"study_design_scores_gemma":[0.00013699582,0.00016898538,0.0023615053,0.0005221371,0.0002798307,0.001906747,0.0003278952,0.06905754,0.047930237,0.62649596,0.25049034,0.00032185199],"about_ca_topic_score_codex":0.0017551958,"about_ca_topic_score_gemma":0.0014978774,"teacher_disagreement_score":0.039779667,"about_ca_system_score_codex":0.0015764349,"about_ca_system_score_gemma":0.003097087,"threshold_uncertainty_score":0.13307625},"labels":[],"label_agreement":null},{"id":"W4405287396","doi":"10.1016/j.ejor.2024.12.008","title":"Differential quantile-based sensitivity in discontinuous models","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Sensitivity (control systems); Quantile; Computer science; Differential (mechanical device); Econometrics; Mathematical optimization; Mathematics; Physics","score_opus":0.29282658539964224,"score_gpt":0.43266200402375077,"score_spread":0.13983541862410853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405287396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056651533,0.00054988905,0.93766195,0.00046479885,0.00003755011,0.000028000763,0.00013556001,0.00018625165,0.0042845234],"genre_scores_gemma":[0.96676016,0.00050372427,0.029072223,0.00013046213,0.000032978853,0.000050066817,0.00009791358,0.000085772306,0.0032666542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99856395,0.0007222163,0.00006310801,0.00021959846,0.0002640638,0.00016711571],"domain_scores_gemma":[0.9917908,0.006482072,0.0006332667,0.0004963339,0.00039059142,0.00020695504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040299026,0.000882611,0.00096682383,0.0013576696,0.00035419516,0.0019614599,0.0009911838,0.0011230547,0.0024044823],"category_scores_gemma":[0.01675539,0.0005897504,0.0013951635,0.0008051298,0.0020416472,0.0021879028,0.0023915751,0.0018925213,0.00014701468],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023590033,0.0000166023,0.0013346216,0.000061854094,0.00004484233,0.00015949347,0.00010415025,0.8248524,0.0011489303,0.1670177,0.00032180225,0.004913914],"study_design_scores_gemma":[0.00000255166,0.000011671722,0.0002951915,0.000011633105,0.000010383608,0.000033661858,0.000014048593,0.9227033,0.00032632728,0.07628609,0.00029534442,0.000009657058],"about_ca_topic_score_codex":0.00311424,"about_ca_topic_score_gemma":0.0012577903,"teacher_disagreement_score":0.0040299026,"about_ca_system_score_codex":0.0018168957,"about_ca_system_score_gemma":0.0008226004,"threshold_uncertainty_score":0.021312416},"labels":[],"label_agreement":null},{"id":"W4405740845","doi":"10.1016/j.strusafe.2024.102569","title":"Stochastic modelling of non-stationary and dependent weather extremes for structural reliability analysis in the changing climate","year":2024,"lang":"en","type":"article","venue":"Structural Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Structural reliability; Environmental science; Climate change; Climatology; Econometrics; Meteorology; Computer science; Mathematics; Statistics; Geography; Geology; Physics","score_opus":0.0522720842641937,"score_gpt":0.32769529819800763,"score_spread":0.2754232139338139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405740845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06286634,0.00021166923,0.9350395,0.00020818361,0.00003004517,0.000021147038,0.000078084195,0.00007003795,0.0014749208],"genre_scores_gemma":[0.97198457,0.0005140625,0.025137365,0.00004031071,0.000052567204,0.00006052711,0.00011886513,0.000024807274,0.0020669003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992267,0.00036087382,0.000029908375,0.000107704705,0.00017538376,0.000099460405],"domain_scores_gemma":[0.9985233,0.00092322537,0.00028905462,0.00009043127,0.00011865152,0.00005537778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017522534,0.00047268157,0.00054391613,0.0005029145,0.000324615,0.00073158735,0.0012778649,0.0009143388,0.0007715976],"category_scores_gemma":[0.0042110723,0.00037590507,0.00077090156,0.0006152,0.0011003772,0.00094633107,0.0007890347,0.0013098664,0.00013064653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008540754,0.000009137298,0.0006419617,0.000009925276,0.000011856793,0.00003569705,0.00002679717,0.9705521,0.00060478266,0.025891222,0.0000905847,0.002117279],"study_design_scores_gemma":[0.0000012100927,0.0000058369255,0.00020568987,0.0000013799647,0.0000024849955,0.0000053068625,0.000004337755,0.99451005,0.00006086162,0.00507485,0.00012475079,0.0000032092853],"about_ca_topic_score_codex":0.010557431,"about_ca_topic_score_gemma":0.012065965,"teacher_disagreement_score":0.010557431,"about_ca_system_score_codex":0.00093640096,"about_ca_system_score_gemma":0.0008715093,"threshold_uncertainty_score":0.020991981},"labels":[],"label_agreement":null},{"id":"W4405746808","doi":"10.1016/j.net.2024.103408","title":"An optimal procedure for fragility analysis of nuclear containment structures under internal pressure","year":2024,"lang":"en","type":"article","venue":"Nuclear Engineering and Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Korea Institute of Energy Technology Evaluation and Planning; Ministry of Trade, Industry and Energy; National Research Foundation of Korea; Ministry of Education","keywords":"Fragility; Internal pressure; Containment (computer programming); Nuclear engineering; Risk analysis (engineering); Computer science; Engineering; Medicine; Physics; Thermodynamics","score_opus":0.018158848169689837,"score_gpt":0.2927118432400768,"score_spread":0.274552995070387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405746808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053293817,0.000039036815,0.9939534,0.000018713588,0.0000046592468,0.000029100776,0.00001801973,0.0001372756,0.00047031668],"genre_scores_gemma":[0.19161728,0.00011547096,0.8072113,0.000028381859,0.000018784382,0.00023196932,0.000119429096,0.0001058508,0.0005515343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994035,0.0001618656,0.000031532545,0.0001257304,0.00022561132,0.000051705505],"domain_scores_gemma":[0.998862,0.0007041916,0.00011338617,0.000102355894,0.00019519779,0.000022867434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010289154,0.0007156743,0.00071737386,0.0011785564,0.00047861537,0.00046207118,0.00078047084,0.0007115792,0.0022897013],"category_scores_gemma":[0.0041194237,0.00046447618,0.000828916,0.000469152,0.0006544561,0.0006462165,0.00069593283,0.0009581225,0.0004460793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010352011,0.000085924956,0.0009257188,0.00025485686,0.000045717905,0.0001333974,0.00011697881,0.76143533,0.02775208,0.020022178,0.0009823093,0.18814209],"study_design_scores_gemma":[0.000010909965,0.00004607016,0.0003022901,0.000008988613,0.000008915755,0.000043891374,0.00001131833,0.989837,0.00439232,0.004673077,0.0006538495,0.000011451593],"about_ca_topic_score_codex":0.0027312785,"about_ca_topic_score_gemma":0.002972506,"teacher_disagreement_score":0.0027312785,"about_ca_system_score_codex":0.0004654872,"about_ca_system_score_gemma":0.0018083518,"threshold_uncertainty_score":0.0076598525},"labels":[],"label_agreement":null},{"id":"W4405855203","doi":"10.1016/j.aap.2024.107903","title":"Quantifying learning algorithm uncertainties in autonomous driving systems: Enhancing safety through Polynomial Chaos Expansion and High Definition maps","year":2024,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Polynomial chaos; CHAOS (operating system); Algorithm; Computer science; Poison control; Polynomial; Engineering; Mathematics; Computer security; Medical emergency; Medicine; Monte Carlo method; Statistics","score_opus":0.06288564394510299,"score_gpt":0.33078157207827663,"score_spread":0.26789592813317364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405855203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08947131,0.00012403655,0.90869135,0.00019015794,0.0000195971,0.00002130018,0.000022326827,0.000090679525,0.0013692428],"genre_scores_gemma":[0.9710127,0.000094228526,0.02819174,0.000023331162,0.000015122744,0.000015898942,0.000017803695,0.000024259922,0.00060486223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994223,0.00019456513,0.000023677421,0.00009635853,0.00019536597,0.00006769875],"domain_scores_gemma":[0.99645656,0.0025260572,0.00033962767,0.00020886154,0.0003893702,0.000079499456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014972314,0.0005512662,0.0004915667,0.0005887075,0.00036262654,0.0010231116,0.0005758497,0.00067216065,0.0006595652],"category_scores_gemma":[0.0073161884,0.0002379849,0.00038340234,0.00035523743,0.0011563066,0.0021696475,0.0015888921,0.0010156449,0.000083262756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071360766,0.000024891551,0.0007659387,0.00004463194,0.0000179553,0.000026275578,0.000078456054,0.9569959,0.0030793943,0.018337041,0.00010117514,0.020456925],"study_design_scores_gemma":[0.0000013118414,0.000022384585,0.00018839224,0.000002466068,0.000002795711,0.0000063286557,0.000006522503,0.99355316,0.0008540329,0.0052965744,0.0000617793,0.0000041665826],"about_ca_topic_score_codex":0.0021907883,"about_ca_topic_score_gemma":0.0014968066,"teacher_disagreement_score":0.0021907883,"about_ca_system_score_codex":0.00076799537,"about_ca_system_score_gemma":0.0008112504,"threshold_uncertainty_score":0.007918179},"labels":[],"label_agreement":null},{"id":"W4406485035","doi":"10.1016/0304-3908(75)90032-6","title":"10.1016/0304-3908(75)90032-6","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tower; Reliability (semiconductor); Meteorology; Environmental science; Reliability engineering; Engineering; Marine engineering; Structural engineering; Geography; Physics","score_opus":0.030280321429722177,"score_gpt":0.23690287944731644,"score_spread":0.20662255801759427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406485035","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00068105216,0.0003311326,0.001271536,0.00044170118,0.00030351512,0.00012628511,0.0008991158,0.0011026451,0.994843],"genre_scores_gemma":[0.0011421395,0.00018351032,0.0005355843,0.00022504275,0.00004970849,0.000059893646,0.00044797393,0.00021948527,0.99713683],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99923134,0.000052387808,0.000061581275,0.0002811949,0.00019450273,0.00017902562],"domain_scores_gemma":[0.9978073,0.0005657293,0.00013680481,0.0003185947,0.0005642201,0.00060740276],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012151131,0.00275642,0.0016099518,0.0024973738,0.0023566857,0.0033343278,0.002978857,0.004839897,0.98697335],"category_scores_gemma":[0.001785235,0.00091586774,0.0011971,0.0026516204,0.0023912406,0.0042881756,0.0028480403,0.0022802392,0.9925598],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047888127,0.000275277,0.0013384143,0.00058003684,0.00004836289,0.00034739918,0.00015381293,0.00080295856,0.0034526717,0.0077028098,0.36924508,0.61557436],"study_design_scores_gemma":[0.00005905789,0.00013215547,0.00082730845,0.00029915886,0.000016822207,0.00033399212,0.00016332412,0.00046493558,0.00062806596,0.0009773576,0.99607337,0.000024647534],"about_ca_topic_score_codex":0.0060525774,"about_ca_topic_score_gemma":0.0044044554,"teacher_disagreement_score":0.013026655,"about_ca_system_score_codex":0.0013695459,"about_ca_system_score_gemma":0.0010829144,"threshold_uncertainty_score":0.018580914},"labels":[],"label_agreement":null},{"id":"W4406530948","doi":"10.1016/0967-0653(93)90076-j","title":"10.1016/0967-0653(93)90076-j","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sensitivity (control systems); Environmental science; Mathematics; Engineering","score_opus":0.028270138867503937,"score_gpt":0.23085580309925952,"score_spread":0.20258566423175559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406530948","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006292378,0.00038774608,0.0013432909,0.00046781477,0.0003605268,0.0001278212,0.00072358275,0.0008802369,0.99507976],"genre_scores_gemma":[0.0010841688,0.00025174321,0.0006814887,0.00026646652,0.00006130793,0.00006706827,0.00045207748,0.0001990027,0.9969367],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993631,0.000048972124,0.000057805162,0.00022087016,0.00016470204,0.00014452607],"domain_scores_gemma":[0.99811244,0.00047454704,0.00011333653,0.00023978019,0.00045755017,0.0006023855],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0011757134,0.002431474,0.0014064461,0.0024337105,0.0023805504,0.0035118088,0.002933381,0.00488278,0.98518074],"category_scores_gemma":[0.0016635171,0.0009692421,0.0011641745,0.00227836,0.0024648693,0.005022875,0.002686733,0.0022666696,0.9901793],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004729746,0.0002754802,0.0013913413,0.0006321884,0.00004701087,0.00035611851,0.00015452037,0.0007232931,0.0032893769,0.007625489,0.3742432,0.610789],"study_design_scores_gemma":[0.000055720157,0.00013915467,0.00089359266,0.00030852738,0.000018676057,0.0004346098,0.00019378631,0.00037114424,0.0005068494,0.0011205266,0.9959317,0.000025681908],"about_ca_topic_score_codex":0.005131518,"about_ca_topic_score_gemma":0.004383508,"teacher_disagreement_score":0.014819264,"about_ca_system_score_codex":0.0010291105,"about_ca_system_score_gemma":0.0010244792,"threshold_uncertainty_score":0.021137774},"labels":[],"label_agreement":null},{"id":"W4406691752","doi":"10.1016/j.compgeo.2025.107087","title":"Bayesian updating of geotechnical parameters with polynomial chaos Kriging model and Gibbs sampling","year":2025,"lang":"en","type":"article","venue":"Computers and Geotechnics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Key Research and Development Program of China; Beijing University of Technology","keywords":"Kriging; Polynomial chaos; Gibbs sampling; Bayesian probability; Sampling (signal processing); Geotechnical engineering; Polynomial; Mathematics; Geology; Applied mathematics; Statistics; Engineering; Monte Carlo method; Mathematical analysis","score_opus":0.04691442621150353,"score_gpt":0.3085923402734893,"score_spread":0.26167791406198576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406691752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043904632,0.00015143288,0.9546611,0.00018423729,0.000024062541,0.000028845272,0.000099723526,0.00016137482,0.00078459916],"genre_scores_gemma":[0.88278127,0.00030994677,0.11365113,0.00007487594,0.00004784443,0.00009572122,0.0003761421,0.00013899119,0.0025240707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894613,0.00043927706,0.00006172867,0.00021414667,0.00024597152,0.00009278429],"domain_scores_gemma":[0.9955042,0.003031624,0.00049488974,0.0003922021,0.00048406186,0.00009309733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024244725,0.000558,0.0016775795,0.0011168545,0.00065018045,0.0012387849,0.0018904678,0.0013217403,0.0011859664],"category_scores_gemma":[0.012182278,0.0012060008,0.0008281277,0.0015058309,0.0015140834,0.0021301808,0.0012499945,0.0014356511,0.00030954924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046106168,0.000013158366,0.00082774804,0.000028399336,0.000025470841,0.000021920727,0.00004270065,0.9759822,0.000334315,0.0146228615,0.0001741164,0.007880878],"study_design_scores_gemma":[0.000005088746,0.000003958738,0.00017985042,0.0000030288907,0.000004560714,0.0000060026855,0.0000025053791,0.99392974,0.00013170454,0.005618413,0.000108024804,0.0000070583105],"about_ca_topic_score_codex":0.022186784,"about_ca_topic_score_gemma":0.023430988,"teacher_disagreement_score":0.022186784,"about_ca_system_score_codex":0.0014975958,"about_ca_system_score_gemma":0.0018438224,"threshold_uncertainty_score":0.044115305},"labels":[],"label_agreement":null},{"id":"W4406809879","doi":"10.1177/87552930241307624","title":"Impact of parameter selection on seismic loss and recovery time estimates: A variance‐based sensitivity analysis","year":2025,"lang":"en","type":"article","venue":"Earthquake Spectra","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Division of Civil, Mechanical and Manufacturing Innovation; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Sensitivity (control systems); Selection (genetic algorithm); Variance (accounting); Statistics; Geology; Econometrics; Seismology; Environmental science; Computer science; Mathematics; Engineering; Economics; Machine learning","score_opus":0.025760253792034036,"score_gpt":0.31704800441652764,"score_spread":0.2912877506244936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406809879","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84574234,0.0003440082,0.15044622,0.00034895586,0.000019849918,0.0001248213,0.0003513706,0.00015392,0.0024685136],"genre_scores_gemma":[0.99433774,0.000052307536,0.0053226864,0.000022334381,0.000002380367,0.000033613233,0.00009189026,0.0000106025145,0.00012651166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961986,0.0027679398,0.00010926188,0.00023347088,0.00048430572,0.00020648856],"domain_scores_gemma":[0.9687526,0.028443975,0.00094987877,0.0009249222,0.0008492373,0.00007940595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009363389,0.0010738663,0.0007083444,0.0011979938,0.00037522105,0.0010639055,0.00064701866,0.0009961829,0.0007775168],"category_scores_gemma":[0.021398848,0.0006534209,0.0015767914,0.00060821156,0.0007029886,0.0010256119,0.000974319,0.0011373125,0.00007727451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011090722,0.000050314848,0.002763676,0.000026006315,0.00012841278,0.000055901997,0.000022695662,0.9920437,0.0012398316,0.0009685828,0.00006982707,0.0025202234],"study_design_scores_gemma":[0.000016447788,0.00018684778,0.002990236,0.00001543963,0.00009670248,0.000040176343,0.00005048709,0.99186826,0.0028025084,0.0017684733,0.00013194213,0.000032453736],"about_ca_topic_score_codex":0.0044956114,"about_ca_topic_score_gemma":0.0021420962,"teacher_disagreement_score":0.009363389,"about_ca_system_score_codex":0.0009468028,"about_ca_system_score_gemma":0.00074497575,"threshold_uncertainty_score":0.049518883},"labels":[],"label_agreement":null},{"id":"W4407035833","doi":"10.33993/jnaat541-1434","title":"Some simple full-range inverse-normal approximations","year":2025,"lang":"en","type":"article","venue":"Journal of Numerical Analysis and Approximation Theory","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Approximation error; Range (aeronautics); Mathematics; Inverse; Simple (philosophy); Approximations of π; Function (biology); Constant (computer programming); Error function; Applied mathematics; Mathematical analysis; Algorithm; Computer science; Geometry","score_opus":0.02980272599678139,"score_gpt":0.3038267216426017,"score_spread":0.2740239956458203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407035833","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016761458,0.0004434107,0.9874386,0.0001548158,0.00012516984,0.000029243738,0.00007656691,0.000323414,0.009732706],"genre_scores_gemma":[0.105783544,0.0017264023,0.86763173,0.00034352788,0.00020398066,0.00019030938,0.00031308082,0.0004210465,0.023386305],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984491,0.00039108042,0.000076212964,0.00018886398,0.000779421,0.0001153568],"domain_scores_gemma":[0.99839896,0.00071046554,0.00009530223,0.00033834908,0.00041113375,0.000045846762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027314564,0.0010649018,0.0007759975,0.0013246445,0.0005206227,0.0015544343,0.0019115044,0.0012727348,0.010805985],"category_scores_gemma":[0.012076003,0.0005498103,0.0010234967,0.0011033913,0.00082947506,0.0024107425,0.001317188,0.0021704629,0.006022745],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001301048,0.000068672896,0.00087152317,0.0002461937,0.000033012806,0.0003076007,0.00024380349,0.23312332,0.0049218694,0.55920154,0.01595843,0.18489401],"study_design_scores_gemma":[0.000017765866,0.000040752704,0.00023669239,0.00007904949,0.000017120907,0.0004274402,0.000044932665,0.80387366,0.0028455725,0.16329817,0.029067146,0.000051663155],"about_ca_topic_score_codex":0.003268451,"about_ca_topic_score_gemma":0.0038030038,"teacher_disagreement_score":0.010805985,"about_ca_system_score_codex":0.0011244714,"about_ca_system_score_gemma":0.0007981798,"threshold_uncertainty_score":0.03614968},"labels":[],"label_agreement":null},{"id":"W4407240179","doi":"10.1115/1.4067867","title":"Sensitivity Analysis Based on the Fisher Information Matrix Applied to Systems With Random Design Inputs","year":2025,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Engineering and Physical Sciences Research Council","keywords":"Fisher information; Sensitivity (control systems); Computer science; Matrix (chemical analysis); Random matrix; Algorithm; Mathematics; Statistics; Engineering; Physics; Eigenvalues and eigenvectors; Electronic engineering; Materials science","score_opus":0.046252409617047886,"score_gpt":0.2922370502981545,"score_spread":0.24598464068110662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407240179","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14122264,0.0004336419,0.8527321,0.00030288938,0.000036084293,0.00014576488,0.000121367564,0.00023034304,0.004775216],"genre_scores_gemma":[0.9862147,0.00012279772,0.01304122,0.00003083296,0.000010416009,0.00006627827,0.00003411271,0.000016675398,0.00046299843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978217,0.0010028123,0.00008199101,0.00019053067,0.0007245739,0.00017854545],"domain_scores_gemma":[0.99186605,0.006867444,0.00050813775,0.0002498049,0.00043042796,0.00007814143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005614137,0.00089546165,0.0010137763,0.0021548644,0.00045995697,0.0010093133,0.00049263483,0.0007785436,0.0013206946],"category_scores_gemma":[0.015897304,0.0005998451,0.0010462776,0.0006951314,0.0015270166,0.0009150905,0.0010870917,0.0006962715,0.00007639251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003215419,0.000009991186,0.0004791776,0.00003437067,0.000036584282,0.000061864026,0.000028649656,0.9886052,0.0013625325,0.0068318956,0.00005216309,0.0024655284],"study_design_scores_gemma":[0.0000017635791,0.000020717085,0.00039980406,0.000007324095,0.000008315434,0.000013678254,0.0000069819985,0.99514425,0.00061988714,0.003712735,0.000055253207,0.000009305226],"about_ca_topic_score_codex":0.0035939782,"about_ca_topic_score_gemma":0.0016158543,"teacher_disagreement_score":0.005614137,"about_ca_system_score_codex":0.0016387681,"about_ca_system_score_gemma":0.0007657982,"threshold_uncertainty_score":0.029690742},"labels":[],"label_agreement":null},{"id":"W4407334470","doi":"10.1177/1748006x251314506","title":"Robust inference and model selection for data from one-shot devices under cyclic accelerated life-tests with an application to a test of CSP solder joints","year":2025,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Inference; Soldering; One shot; Selection (genetic algorithm); Test (biology); Computer science; Model selection; Reliability engineering; Engineering drawing; Engineering; Artificial intelligence; Materials science; Mechanical engineering; Composite material; Geology","score_opus":0.15210944091594922,"score_gpt":0.34818249347216357,"score_spread":0.19607305255621435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407334470","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015951054,0.00015098065,0.9835187,0.00009654729,0.000007870327,0.000021612288,0.00005009906,0.00007660578,0.00012660064],"genre_scores_gemma":[0.6257733,0.0005718593,0.37139043,0.00015325882,0.00009270174,0.0003274147,0.0007254325,0.00008801728,0.00087758666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937668,0.004173988,0.00023840972,0.0007259726,0.00094345585,0.00015143682],"domain_scores_gemma":[0.94302595,0.050577328,0.0022386995,0.0023437866,0.0015740782,0.00024017887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018826945,0.0011172774,0.0013915893,0.0015309798,0.0004178717,0.0012281904,0.0019558046,0.0016187932,0.0006377854],"category_scores_gemma":[0.06918073,0.0005311706,0.0012837737,0.001035768,0.0019785122,0.0016947824,0.0016971446,0.0021838436,0.00016171442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022836069,0.00010777853,0.0059398427,0.00020146609,0.00036539015,0.00029769834,0.00020503529,0.8480274,0.0046185614,0.059610076,0.0005057786,0.07989266],"study_design_scores_gemma":[0.00001752325,0.00007126624,0.00083400786,0.000013263192,0.000019388202,0.000049113733,0.000014643946,0.96897596,0.00104077,0.02870674,0.00023382566,0.000023419892],"about_ca_topic_score_codex":0.0019863397,"about_ca_topic_score_gemma":0.0013077927,"teacher_disagreement_score":0.018826945,"about_ca_system_score_codex":0.00076036877,"about_ca_system_score_gemma":0.0013292865,"threshold_uncertainty_score":0.09956759},"labels":[],"label_agreement":null},{"id":"W4407413675","doi":"10.2514/6.2025-0584","title":"Adaptive Gaussian Process Surrogate Models for Efficient Uncertainty Quantification of the Flutter Boundary","year":2025,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Toronto","funders":"","keywords":"Gaussian process; Computer science; Flutter; Process (computing); Boundary (topology); Uncertainty quantification; Gaussian; Mathematical optimization; Mathematics; Machine learning; Engineering; Aerodynamics; Physics; Aerospace engineering; Mathematical analysis","score_opus":0.08745096371123615,"score_gpt":0.3443277698750854,"score_spread":0.2568768061638492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407413675","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007516656,0.00003759165,0.9919367,0.00003688612,0.000005177794,0.00001691471,0.000016990145,0.00005666077,0.00037647202],"genre_scores_gemma":[0.6776661,0.0001539553,0.32039225,0.000071008355,0.000022466245,0.0002643176,0.0001685753,0.00005597544,0.0012053181],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819237,0.00083458924,0.000072672054,0.00019564408,0.0006015381,0.00010312541],"domain_scores_gemma":[0.9937291,0.0040277736,0.00080570264,0.00055189925,0.0007500899,0.00013545615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004677942,0.00087846,0.0009451137,0.0008180462,0.00028404553,0.0008748723,0.0011670997,0.0011588096,0.0012722403],"category_scores_gemma":[0.014123298,0.0005833583,0.0008118581,0.0005840526,0.0010175597,0.001328033,0.0013108448,0.0012392864,0.00020795864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034497072,0.000018032877,0.00038679477,0.000016839664,0.000010383842,0.000015900745,0.000017764092,0.9802807,0.0008571688,0.010675291,0.00009636424,0.0075901346],"study_design_scores_gemma":[0.000002371556,0.000013465438,0.00003686855,0.0000021637459,0.0000011807851,0.0000039847696,0.0000011363344,0.99746954,0.00027383454,0.0021144187,0.000078985744,0.0000020483142],"about_ca_topic_score_codex":0.0016691159,"about_ca_topic_score_gemma":0.0013385051,"teacher_disagreement_score":0.004677942,"about_ca_system_score_codex":0.0007742193,"about_ca_system_score_gemma":0.0012283145,"threshold_uncertainty_score":0.024739563},"labels":[],"label_agreement":null},{"id":"W4407625186","doi":"10.1038/s41598-025-89625-6","title":"Surrogate sensitivity analysis of facet optical coatings produced without and with in situ design reoptimization","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Sobol sequence; Facet (psychology); Fabrication; Coating; Materials science; Robustness (evolution); Photonics; Sensitivity (control systems); Wavelength; Reflectivity; Computer science; Optoelectronics; Optics; Nanotechnology; Electronic engineering; Physics; Engineering; Chemistry","score_opus":0.043231763457014925,"score_gpt":0.3092880016296032,"score_spread":0.2660562381725883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407625186","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6649334,0.0004327965,0.33006015,0.00015333151,0.000028633734,0.00006492687,0.0002719122,0.00017506699,0.003879779],"genre_scores_gemma":[0.979229,0.000111913374,0.01988019,0.000016245398,0.0000027459403,0.00003727069,0.00012064032,0.000022501927,0.00057945057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934715,0.00023804387,0.000026301213,0.000069764596,0.00024512893,0.000073477546],"domain_scores_gemma":[0.99716836,0.002118384,0.00027726946,0.00015480525,0.0002552799,0.000025835174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016187669,0.0005135875,0.00046154598,0.00045522518,0.00014824119,0.0005202477,0.00027662038,0.00046814885,0.0006975686],"category_scores_gemma":[0.0041679936,0.00024222916,0.0005422847,0.00029571427,0.0004329593,0.00029674993,0.00039958983,0.0004407134,0.00007706654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111263274,0.000033432727,0.0007694413,0.0000947205,0.000039439616,0.000053365562,0.000028416987,0.9631644,0.027583718,0.0018372436,0.000115662835,0.006168935],"study_design_scores_gemma":[0.000002988611,0.00008490737,0.0005054248,0.0000038431076,0.000006697699,0.000014823785,0.000007121183,0.97939974,0.019264976,0.00056838733,0.0001330598,0.000008096183],"about_ca_topic_score_codex":0.0009495015,"about_ca_topic_score_gemma":0.00073627976,"teacher_disagreement_score":0.0016187669,"about_ca_system_score_codex":0.0006391034,"about_ca_system_score_gemma":0.00041625634,"threshold_uncertainty_score":0.008561015},"labels":[],"label_agreement":null},{"id":"W4407918076","doi":"10.1139/tcsme-2024-0102","title":"Uncertainty-based reliability analysis of cutters via improved Bayesian prior distribution","year":2025,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Bayesian probability; Reliability engineering; Computer science; Distribution (mathematics); Statistics; Mathematics; Artificial intelligence; Engineering; Physics","score_opus":0.016220147915355997,"score_gpt":0.26295291831373085,"score_spread":0.24673277039837485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407918076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009460773,0.00026028726,0.98919886,0.0000622203,0.0000071054046,0.000020215217,0.000042823172,0.000115956944,0.00083173777],"genre_scores_gemma":[0.65920025,0.0014904375,0.33530948,0.000120951416,0.00008584956,0.00021254881,0.00056074,0.00017239917,0.0028473493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781287,0.0006999407,0.00008462336,0.00032195292,0.0009772491,0.000103355145],"domain_scores_gemma":[0.99322814,0.004592692,0.0004914603,0.00041208338,0.0011984261,0.000077193785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004001977,0.00074184407,0.0011923476,0.0021220238,0.00037328905,0.0011530366,0.001307347,0.0008339206,0.0017104116],"category_scores_gemma":[0.013691152,0.0006412526,0.000988668,0.0012450467,0.00096057437,0.0021940437,0.0011558551,0.0013838513,0.00033886812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000868223,0.000030538504,0.0017128019,0.00010951361,0.000068026115,0.00008187685,0.000111256035,0.9102911,0.0035657468,0.02746684,0.0006815345,0.055794034],"study_design_scores_gemma":[0.000003804625,0.0000140150205,0.00044659694,0.0000109558305,0.000011572106,0.000026308055,0.0000062139816,0.9912077,0.00064964255,0.0072558695,0.0003551098,0.000012131476],"about_ca_topic_score_codex":0.0047667813,"about_ca_topic_score_gemma":0.003696539,"teacher_disagreement_score":0.0047667813,"about_ca_system_score_codex":0.0010945502,"about_ca_system_score_gemma":0.001119277,"threshold_uncertainty_score":0.021164715},"labels":[],"label_agreement":null},{"id":"W4408133923","doi":"10.1016/j.ress.2025.110966","title":"A new multiple stochastic Kriging model for active learning surrogate-assisted reliability analysis","year":2025,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Kriging; Surrogate model; Reliability (semiconductor); Reliability engineering; Computer science; Machine learning; Mathematical optimization; Mathematics; Engineering","score_opus":0.031184380386972813,"score_gpt":0.2969319440954838,"score_spread":0.265747563708511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408133923","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036783435,0.00007279297,0.995376,0.00005132382,0.000016839003,0.000011824027,0.00004934592,0.00015185776,0.00059165375],"genre_scores_gemma":[0.6164653,0.00040731617,0.37310627,0.0001656034,0.000089341964,0.00028488875,0.00062532624,0.00025646787,0.008599499],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999316,0.00023090234,0.000040731193,0.000120343044,0.00023873961,0.00005329369],"domain_scores_gemma":[0.9990602,0.00044872012,0.000114780414,0.000082998624,0.00024922666,0.000044010925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011310898,0.0007550231,0.0014474165,0.0005419842,0.0003720497,0.0009790359,0.0023999263,0.0016302243,0.0017761549],"category_scores_gemma":[0.00222151,0.0007275452,0.0011470508,0.00087101874,0.0005911052,0.0013840806,0.0010055356,0.0017584739,0.00066013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021518841,0.000017514061,0.00012189255,0.000023747423,0.000016870934,0.000014564238,0.000011541484,0.98608774,0.00089696056,0.0026915967,0.00022320206,0.0098729115],"study_design_scores_gemma":[9.550132e-7,0.000003672686,0.000016398186,9.299289e-7,0.0000016288418,0.00000206983,3.6270413e-7,0.99949396,0.00007629447,0.00031294423,0.000089190464,0.0000016263831],"about_ca_topic_score_codex":0.006455503,"about_ca_topic_score_gemma":0.008602639,"teacher_disagreement_score":0.006455503,"about_ca_system_score_codex":0.0005821166,"about_ca_system_score_gemma":0.0010984448,"threshold_uncertainty_score":0.01283586},"labels":[],"label_agreement":null},{"id":"W4408512362","doi":"10.2139/ssrn.5182808","title":"A Computationally Efficient Multi-Model Method for Uncertain Dynamical Systems","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; University of Waterloo","funders":"","keywords":"Computer science; Dynamical systems theory; Physics","score_opus":0.08065798207053318,"score_gpt":0.3997232200391618,"score_spread":0.3190652379686286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408512362","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00087479944,0.000057653288,0.9982091,0.000048209087,0.000022564416,0.000015618114,0.000023605397,0.000115974064,0.00063259364],"genre_scores_gemma":[0.23955753,0.0002289014,0.7531772,0.00019567645,0.00013449928,0.0003826764,0.00023450893,0.0003414119,0.0057475273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961853,0.00014242672,0.00001918731,0.0000647634,0.00012160801,0.000033363678],"domain_scores_gemma":[0.9989328,0.0006900682,0.00007189129,0.00009951653,0.00014519795,0.000060549504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011135469,0.0010406035,0.0015644013,0.00070087437,0.00062670856,0.0010199546,0.0015691284,0.001599111,0.007161358],"category_scores_gemma":[0.0026954676,0.0007533377,0.0011806323,0.0006278998,0.0006299095,0.0013742337,0.0021721458,0.002172518,0.0015279673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010679116,0.00004937203,0.00015006003,0.00015049486,0.00005969637,0.00008785515,0.000035401692,0.8937274,0.0022115025,0.029548125,0.0014795847,0.07239376],"study_design_scores_gemma":[0.000005427801,0.000008280719,0.0000098460405,0.0000031609338,0.000002825841,0.0000069167086,0.0000016794144,0.9949216,0.00012110138,0.0045734053,0.00034308885,0.0000026505438],"about_ca_topic_score_codex":0.003949862,"about_ca_topic_score_gemma":0.003802021,"teacher_disagreement_score":0.007161358,"about_ca_system_score_codex":0.000594372,"about_ca_system_score_gemma":0.0012333608,"threshold_uncertainty_score":0.023957074},"labels":[],"label_agreement":null},{"id":"W4409060286","doi":"10.1016/b978-0-44-314153-9.00010-6","title":"Introduction","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science","score_opus":0.03917176351360193,"score_gpt":0.28766054865521046,"score_spread":0.24848878514160852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409060286","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029358332,0.0026498165,0.005093352,0.0016753458,0.0028594558,0.00009340033,0.0014792575,0.00080673303,0.98504907],"genre_scores_gemma":[0.0007119386,0.0013327156,0.0013792561,0.00053251005,0.00039463132,0.000041696163,0.0009207404,0.00020080902,0.9944857],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994436,0.000045684425,0.000020174135,0.000113932074,0.0003261388,0.000050572777],"domain_scores_gemma":[0.99942786,0.00008433202,0.000020376026,0.00009295509,0.00027379734,0.000100624464],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00054731366,0.0010589346,0.0006446319,0.0019514033,0.0011669196,0.00402091,0.001467358,0.0018062682,0.57250863],"category_scores_gemma":[0.0016905166,0.0003825004,0.0005622799,0.0017617968,0.0005380525,0.0029242854,0.0021560253,0.0016902207,0.50005335],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020853298,0.0000384164,0.00008823499,0.00016983607,0.0000024853587,0.000050524868,0.000097138785,0.00021406075,0.0005270132,0.035618074,0.6972493,0.26592398],"study_design_scores_gemma":[0.000001324672,0.00000473828,0.000058861733,0.00005765882,8.527835e-7,0.000032517855,0.000022000078,0.000034499466,0.00007953373,0.0038324315,0.9958734,0.0000022923884],"about_ca_topic_score_codex":0.0026145095,"about_ca_topic_score_gemma":0.0043304944,"teacher_disagreement_score":0.42749137,"about_ca_system_score_codex":0.0011708122,"about_ca_system_score_gemma":0.001607831,"threshold_uncertainty_score":0.6097646},"labels":[],"label_agreement":null},{"id":"W4409176069","doi":"10.1016/j.jmva.2025.105444","title":"Hoeffding decomposition of functions of random dependent variables","year":2025,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université du Québec à Montréal","funders":"Artificial and Natural Intelligence Toulouse Institute; Agence Nationale de la Recherche","keywords":"Mathematics; Decomposition; Random variable; Applied mathematics; Statistical physics; Discrete mathematics; Statistics","score_opus":0.03588672441699648,"score_gpt":0.3527272723117638,"score_spread":0.31684054789476734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409176069","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071158097,0.00039502655,0.98769844,0.00011011374,0.00004878869,0.00002736432,0.00012310287,0.00008277103,0.004398491],"genre_scores_gemma":[0.3241805,0.006157636,0.61377966,0.00039898473,0.00052731746,0.00051037566,0.0013110951,0.0008360024,0.052298438],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99757653,0.0010079656,0.00014250698,0.00025942814,0.00073175726,0.000281914],"domain_scores_gemma":[0.9894911,0.0067889392,0.00062770245,0.0012155906,0.0015628661,0.00031377297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006498553,0.002149122,0.0015708752,0.003516514,0.00055097963,0.002383985,0.0018207895,0.0012142557,0.005656619],"category_scores_gemma":[0.016337937,0.0010310992,0.0015509378,0.0015957541,0.0024277016,0.0032617766,0.0011708963,0.0024917545,0.0013327599],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006718794,0.00005062324,0.00052902725,0.00020578457,0.000055409673,0.0001820834,0.00019061915,0.120564066,0.0032033327,0.83802766,0.001978706,0.03494544],"study_design_scores_gemma":[0.000011205865,0.00003713902,0.00053798396,0.000056377517,0.00003566104,0.00010979503,0.000032195134,0.63416946,0.0020560662,0.35990882,0.0030136877,0.000031601252],"about_ca_topic_score_codex":0.0042019566,"about_ca_topic_score_gemma":0.0033284733,"teacher_disagreement_score":0.006498553,"about_ca_system_score_codex":0.0016334809,"about_ca_system_score_gemma":0.0023042974,"threshold_uncertainty_score":0.034368098},"labels":[],"label_agreement":null},{"id":"W4409606836","doi":"10.3390/e27040441","title":"A Constrained Talagrand Transportation Inequality with Applications to Rate-Distortion-Perception Theory","year":2025,"lang":"en","type":"article","venue":"Entropy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Connection (principal bundle); Randomness; Mathematics; Inequality; Kullback–Leibler divergence; Distortion (music); Rate distortion; Divergence (linguistics); Gaussian; Applied mathematics; Statistical physics; Computer science; Mathematical analysis; Statistics; Physics; Geometry; Quantum mechanics; Telecommunications","score_opus":0.03113817167025833,"score_gpt":0.32115232896692486,"score_spread":0.2900141572966665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409606836","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005866359,0.0012962497,0.97451276,0.0017237305,0.00013366633,0.000036635225,0.000252291,0.000053467575,0.016124766],"genre_scores_gemma":[0.68210065,0.006241567,0.29371426,0.001396648,0.0009804677,0.00064914126,0.0006836906,0.00032165897,0.013912022],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99521166,0.0019989966,0.00023190833,0.00090978324,0.001254261,0.00039335538],"domain_scores_gemma":[0.9802069,0.014313906,0.001339686,0.0018072899,0.0018448055,0.00048734137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076685594,0.0015626402,0.0016852467,0.0025923105,0.00130937,0.004737871,0.0034294133,0.0021017492,0.008814591],"category_scores_gemma":[0.02964596,0.00057210965,0.001859786,0.003289438,0.005423272,0.010277304,0.005415343,0.004279348,0.001121695],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023168534,0.000011302947,0.00017154514,0.00006301471,0.000023621931,0.00004920436,0.00006569469,0.021968752,0.00043438122,0.9676546,0.0012729146,0.008261812],"study_design_scores_gemma":[0.000005793745,0.000032419433,0.00024985906,0.000037763934,0.000011459139,0.00006575835,0.000033319495,0.1300455,0.00039767707,0.8662441,0.0028442754,0.00003211215],"about_ca_topic_score_codex":0.0029186446,"about_ca_topic_score_gemma":0.0013955968,"teacher_disagreement_score":0.008814591,"about_ca_system_score_codex":0.00459879,"about_ca_system_score_gemma":0.0017765649,"threshold_uncertainty_score":0.040555656},"labels":[],"label_agreement":null},{"id":"W4409799849","doi":"10.11159/icgre25.158","title":"Optimizing Reliability Analysis of Unsaturated Slopes through Polynomial Chaos Expansion over the Crude Monte Carlo Simulations","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Civil, Structural, and Environmental Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Education, India","keywords":"Monte Carlo method; Polynomial chaos; CHAOS (operating system); Reliability (semiconductor); Computer science; Polynomial; Statistical physics; Applied mathematics; Mathematics; Statistics; Physics; Thermodynamics; Mathematical analysis","score_opus":0.01518351705181845,"score_gpt":0.2586301838220254,"score_spread":0.24344666677020696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409799849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.108989194,0.00032604387,0.88751453,0.00010447985,0.00001320344,0.000059556824,0.000057516958,0.00032949002,0.0026059938],"genre_scores_gemma":[0.9226319,0.00017162085,0.07638717,0.00002313779,0.000011584292,0.00007793084,0.000070954266,0.00006124462,0.000564569],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995284,0.00018833965,0.000019209927,0.00006087125,0.00015385739,0.000049422317],"domain_scores_gemma":[0.9984054,0.0010208519,0.00016742016,0.00009658122,0.00025483064,0.00005488399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012875617,0.0006578555,0.0005927159,0.0005930035,0.00028460502,0.00051847025,0.0006879685,0.000468358,0.0006746121],"category_scores_gemma":[0.0038847055,0.0003322129,0.00046895928,0.00037767555,0.0004866518,0.00052700547,0.00072633143,0.00059952185,0.00012965963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010173283,0.000005027622,0.00032644963,0.000012067197,0.000004754232,0.000011447941,0.000008722783,0.99481803,0.00061470416,0.0009451351,0.00004081179,0.0032026102],"study_design_scores_gemma":[7.2051574e-7,0.000004916071,0.00005157595,0.000001042645,0.0000012966278,0.0000022070521,0.0000011092094,0.9995353,0.00015854681,0.00020628472,0.000036220135,7.9522783e-7],"about_ca_topic_score_codex":0.004824094,"about_ca_topic_score_gemma":0.003731497,"teacher_disagreement_score":0.004824094,"about_ca_system_score_codex":0.0006747771,"about_ca_system_score_gemma":0.0009784438,"threshold_uncertainty_score":0.009592056},"labels":[],"label_agreement":null},{"id":"W4409965309","doi":"10.1016/j.engstruct.2025.120435","title":"A Gaussian Process surrogate approach for analyzing parameter uncertainty in mechanics-based structural finite element models","year":2025,"lang":"en","type":"article","venue":"Engineering Structures","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Agencia Nacional de Investigación y Desarrollo; Universidad de los Andes","keywords":"Finite element method; Gaussian process; Process (computing); Applied mathematics; Surrogate model; Structural mechanics; Gaussian; Structural engineering; Computer science; Mathematics; Statistical physics; Mathematical optimization; Engineering; Physics","score_opus":0.04316928130520949,"score_gpt":0.31936747608033267,"score_spread":0.2761981947751232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409965309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035030532,0.00005632228,0.9954724,0.00005184546,0.000011811151,0.000009707752,0.000025921407,0.000069078276,0.00079999],"genre_scores_gemma":[0.6647782,0.00057650957,0.3288694,0.00018810004,0.00008559633,0.00021770102,0.0004683062,0.00018428022,0.004631987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989717,0.00040040837,0.00004803972,0.000082330655,0.00044204787,0.000055564244],"domain_scores_gemma":[0.99828726,0.0010442876,0.00014855522,0.0001713038,0.00029312985,0.000055505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020868946,0.000672349,0.0011980913,0.0010486711,0.00042009357,0.0010913357,0.0011441158,0.0019362699,0.0012244426],"category_scores_gemma":[0.0059235035,0.0006722916,0.0011132064,0.0011912409,0.0009519074,0.0012602366,0.0015168396,0.0010912436,0.00039886922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022757507,0.000029916091,0.00016678474,0.000030296515,0.000018242486,0.000036376172,0.000019991478,0.96620184,0.0014927642,0.021754181,0.00022589831,0.010000936],"study_design_scores_gemma":[9.574476e-7,0.0000062830686,0.000022141832,0.0000019609158,0.000001386287,0.000005340468,0.0000013407081,0.9967405,0.00014060474,0.002958734,0.000119034856,0.0000017541834],"about_ca_topic_score_codex":0.0012930022,"about_ca_topic_score_gemma":0.001502086,"teacher_disagreement_score":0.0020868946,"about_ca_system_score_codex":0.00043135718,"about_ca_system_score_gemma":0.0009673449,"threshold_uncertainty_score":0.011036694},"labels":[],"label_agreement":null},{"id":"W4410322529","doi":"10.1017/jfm.2025.343","title":"The implications of a fluid yield stress","year":2025,"lang":"en","type":"article","venue":"Journal of Fluid Mechanics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Yield (engineering); Stress (linguistics); Mechanics; Materials science; Physics; Composite material","score_opus":0.06447267237935113,"score_gpt":0.33515504272007995,"score_spread":0.27068237034072884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410322529","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5117292,0.0038422882,0.35660574,0.029266888,0.0016231844,0.00007733528,0.00019044019,0.00025384227,0.09641107],"genre_scores_gemma":[0.99016565,0.0009770392,0.0049799928,0.0004299791,0.00041989004,0.000022340411,0.000014961043,0.000024005187,0.0029661215],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991059,0.00023802384,0.000048576287,0.0001571598,0.00031764928,0.00013266531],"domain_scores_gemma":[0.99684614,0.0015739652,0.0005910486,0.0002839582,0.00041959854,0.00028524335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017942454,0.0005505531,0.000509964,0.00057168823,0.0014893273,0.00228754,0.0005747506,0.0017136184,0.0018256285],"category_scores_gemma":[0.009572152,0.00030682026,0.00039409538,0.00033552138,0.006131114,0.0037286633,0.0022744806,0.0018194141,0.00018487674],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006898486,0.000074028874,0.0025310318,0.000051121082,0.0000143130865,0.000596998,0.00032997454,0.033520415,0.005497143,0.9403576,0.00143438,0.015523921],"study_design_scores_gemma":[0.00001581753,0.00016537213,0.0029108545,0.000039998766,0.000014498021,0.0003533976,0.00034034517,0.09226217,0.0028857256,0.8959287,0.005029273,0.00005391875],"about_ca_topic_score_codex":0.0011240408,"about_ca_topic_score_gemma":0.00050134293,"teacher_disagreement_score":0.00228754,"about_ca_system_score_codex":0.0008263348,"about_ca_system_score_gemma":0.0008071686,"threshold_uncertainty_score":0.009489},"labels":[],"label_agreement":null},{"id":"W4410742951","doi":"10.1061/ajrua6.rueng-1562","title":"LUB: A Novel Adaptive Kriging Framework Incorporating Lower and Upper Bound Analysis for Enhanced Structural Reliability-Based Design Optimization","year":2025,"lang":"en","type":"article","venue":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Kriging; Reliability (semiconductor); Upper and lower bounds; Computer science; Reliability engineering; Structural reliability; Mathematical optimization; Mathematics; Engineering; Artificial intelligence; Machine learning; Physics","score_opus":0.023312098978249665,"score_gpt":0.2773658421561214,"score_spread":0.25405374317787177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410742951","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013428758,0.0000642414,0.9977452,0.000025772924,0.000005158726,0.000013207735,0.000021285518,0.00020466757,0.00057752925],"genre_scores_gemma":[0.2420239,0.00040182122,0.7542086,0.00012505794,0.00004172418,0.00031433237,0.00029154785,0.00036274633,0.002230209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992964,0.00020756946,0.000028713988,0.00007849268,0.00032492576,0.00006379633],"domain_scores_gemma":[0.99923706,0.00035397284,0.000103951075,0.00008234892,0.00018894635,0.000033746524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00164993,0.0012612663,0.0011449013,0.0012198037,0.00036662523,0.0009168451,0.0018684141,0.00094818434,0.001597278],"category_scores_gemma":[0.0030944394,0.0007058396,0.0008407339,0.0007802994,0.00079025066,0.0009456313,0.0015552982,0.0012831851,0.0005796037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000147079945,0.0000133408685,0.00022819337,0.000050366067,0.000015844593,0.000018108938,0.000023782373,0.9737951,0.0016174059,0.006280012,0.0003672856,0.017575832],"study_design_scores_gemma":[0.0000025807715,0.000009090279,0.000026470836,0.0000050981043,0.0000029347539,0.000004059047,0.0000021904164,0.99761295,0.00022280191,0.0016783852,0.0004301134,0.0000032538],"about_ca_topic_score_codex":0.0077318023,"about_ca_topic_score_gemma":0.009041813,"teacher_disagreement_score":0.0077318023,"about_ca_system_score_codex":0.00079647714,"about_ca_system_score_gemma":0.0020432207,"threshold_uncertainty_score":0.015373588},"labels":[],"label_agreement":null},{"id":"W4410964945","doi":"10.1002/we.70037","title":"Simulation Analysis and Safety Risk Assessment of a Wind Turbine Blade Failure Event","year":2025,"lang":"en","type":"article","venue":"Wind Energy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute","funders":"","keywords":"Debris; Turbine blade; Turbine; Marine engineering; Engineering; Event (particle physics); Environmental science; Forensic engineering; Meteorology; Aerospace engineering; Physics","score_opus":0.01813786638992326,"score_gpt":0.32076633595858184,"score_spread":0.3026284695686586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410964945","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9355807,0.00009481212,0.059591245,0.00016276762,0.000020071906,0.00013325766,0.00027224465,0.00014371563,0.0040012905],"genre_scores_gemma":[0.99610066,0.000030130977,0.003315451,0.0000080763,0.0000020824843,0.000029087314,0.0000946669,0.0000033538781,0.00041642852],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995022,0.00023228436,0.000029155597,0.000060617076,0.000104140905,0.000071625596],"domain_scores_gemma":[0.99626344,0.002804446,0.00035848224,0.00013684963,0.00033787437,0.000098983095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017305064,0.0005604623,0.00047383172,0.00096458336,0.00036831197,0.00064340746,0.00056733354,0.0009435927,0.001437407],"category_scores_gemma":[0.0039068586,0.00030810563,0.00068096956,0.00037359452,0.0005512092,0.00040364853,0.0005103336,0.000550599,0.00012284397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003401403,0.000016653235,0.001640985,0.000005262885,0.0000100176985,0.00002589001,0.000007942507,0.99707735,0.00016654801,0.00035434208,0.000029068891,0.0006319602],"study_design_scores_gemma":[0.0000056317895,0.000054811855,0.0004952285,0.000002049718,0.000005231169,0.000007540092,0.000011834523,0.9989768,0.00017415434,0.0002243316,0.00003944288,0.0000029655123],"about_ca_topic_score_codex":0.014883778,"about_ca_topic_score_gemma":0.00765504,"teacher_disagreement_score":0.014883778,"about_ca_system_score_codex":0.0013130527,"about_ca_system_score_gemma":0.00088782824,"threshold_uncertainty_score":0.029594302},"labels":[],"label_agreement":null},{"id":"W4411442034","doi":"10.1007/978-3-662-69359-9_661","title":"Strong Approximations in Probability and Statistics","year":2025,"lang":"en","type":"book-chapter","venue":"International Encyclopedia of Statistical Science","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Statistics; Probability and statistics; Mathematics; Statistical physics; Econometrics; Physics","score_opus":0.045822628499798214,"score_gpt":0.3359893696755272,"score_spread":0.290166741175729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411442034","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017354984,0.025609989,0.843331,0.0035845442,0.0027510808,0.000030148276,0.00018611005,0.0005575461,0.12221412],"genre_scores_gemma":[0.18972512,0.06429706,0.42475483,0.0033889585,0.0133160995,0.0004274919,0.0009537547,0.0012563859,0.30188027],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99809784,0.00078372966,0.00007146496,0.00019068812,0.00079499313,0.00006122476],"domain_scores_gemma":[0.9946483,0.0039461944,0.00013894142,0.0006224898,0.0005408254,0.000103257524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026729563,0.001415378,0.0016266622,0.0016631427,0.0005659946,0.0035283607,0.0011623878,0.0015619143,0.009642541],"category_scores_gemma":[0.00960918,0.0007932369,0.00072547124,0.0024625068,0.0033949534,0.0041027144,0.001707669,0.005229973,0.004952956],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000104658075,0.000011431124,0.000053417858,0.00012269018,0.000012625727,0.000033720145,0.0000671439,0.004165247,0.0002167808,0.9307938,0.021424593,0.04308802],"study_design_scores_gemma":[0.00000415538,0.000011818906,0.000077603516,0.000055233024,0.000006967405,0.000070681315,0.000014678892,0.018598277,0.00022893211,0.9276905,0.053232186,0.000008972359],"about_ca_topic_score_codex":0.00095490686,"about_ca_topic_score_gemma":0.0007513529,"teacher_disagreement_score":0.009642541,"about_ca_system_score_codex":0.0015134679,"about_ca_system_score_gemma":0.0011686205,"threshold_uncertainty_score":0.032257497},"labels":[],"label_agreement":null},{"id":"W4411977321","doi":"10.1121/10.0037081","title":"A hybrid methodology for uncertainty analysis of vibration response in fluid-filled pipes","year":2025,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Smiths Detection (Canada)","funders":"National Natural Science Foundation of China","keywords":"Vibration; Pipeline (software); Transformation (genetics); Frequency response; Noise (video); Computer science; Pipeline transport; Variance (accounting); Acoustics; Uncertainty quantification; Control theory (sociology); Engineering; Mechanical engineering; Physics","score_opus":0.07222856592498732,"score_gpt":0.36835126043781197,"score_spread":0.29612269451282464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411977321","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012672804,0.000019821651,0.99855214,0.0000067441765,0.0000024380129,0.000005857777,0.000004646938,0.000023623914,0.000117391304],"genre_scores_gemma":[0.47717565,0.00028801392,0.5204406,0.00004985942,0.000048466685,0.00021902483,0.00011692804,0.000076916556,0.0015845754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999215,0.00024633354,0.000043695487,0.00012890244,0.00032666026,0.000039388753],"domain_scores_gemma":[0.9986387,0.00092385523,0.00012750729,0.00008536896,0.00020166725,0.000022969145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014166753,0.0007207079,0.0006293623,0.00091603346,0.0002512735,0.0005966076,0.0006400525,0.0006837125,0.00091724837],"category_scores_gemma":[0.0033288004,0.00033174545,0.00079942786,0.0005364477,0.0007299341,0.0009177763,0.0010045565,0.0007413279,0.00016193955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003678473,0.000026535734,0.00049844716,0.0001332831,0.00005390067,0.00005653975,0.000060076716,0.8939139,0.010965891,0.023481138,0.000165393,0.07060803],"study_design_scores_gemma":[0.0000019689749,0.000025027672,0.00008827797,0.000004108849,0.0000042306433,0.000017259865,0.0000039745264,0.9953656,0.0012717454,0.0028998544,0.0003114499,0.0000065792615],"about_ca_topic_score_codex":0.0010025458,"about_ca_topic_score_gemma":0.00064832415,"teacher_disagreement_score":0.0014166753,"about_ca_system_score_codex":0.00033161198,"about_ca_system_score_gemma":0.00064922404,"threshold_uncertainty_score":0.007492125},"labels":[],"label_agreement":null},{"id":"W4412045734","doi":"10.1016/j.jprocont.2025.103493","title":"Robust PINN modeling via sensitivity-based adaptive sampling: Integration of optimal sensor placement and structural uncertainty handling","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Mitacs","keywords":"Sensitivity (control systems); Adaptive sampling; Sampling (signal processing); Computer science; Control theory (sociology); Mathematical optimization; Mathematics; Engineering; Artificial intelligence; Electronic engineering; Statistics; Monte Carlo method; Control (management)","score_opus":0.08513528541155314,"score_gpt":0.3353344606637946,"score_spread":0.25019917525224145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412045734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012679732,0.00009333131,0.9861609,0.00009051608,0.000014382192,0.000020457734,0.000019605628,0.00009479239,0.0008262942],"genre_scores_gemma":[0.9038916,0.00024035113,0.094388135,0.00011093588,0.000030548334,0.00011329297,0.000079806494,0.000042001837,0.0011032843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995608,0.00014702133,0.000024058598,0.000109802655,0.00012273612,0.000035533318],"domain_scores_gemma":[0.9987822,0.00075056386,0.00016811257,0.00011882567,0.00014571856,0.000034531076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012564127,0.0008161064,0.0007105278,0.0003839471,0.0002381152,0.00058657303,0.001133779,0.00080208015,0.0008131004],"category_scores_gemma":[0.004597515,0.0005062611,0.00053060736,0.00034505973,0.00082721555,0.001355081,0.0013819832,0.0010920729,0.00010880744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022645134,0.000010783888,0.00028256804,0.000024683244,0.000011641029,0.00002362103,0.000020529511,0.98391706,0.0013346839,0.0045259916,0.000095346484,0.009730531],"study_design_scores_gemma":[7.9411575e-7,0.0000057361317,0.000026004094,0.0000015896142,0.0000011078062,0.000003579915,0.0000010156052,0.9987526,0.00023945005,0.00092753535,0.00003921045,0.0000013904811],"about_ca_topic_score_codex":0.0027133361,"about_ca_topic_score_gemma":0.002134701,"teacher_disagreement_score":0.0027133361,"about_ca_system_score_codex":0.000590281,"about_ca_system_score_gemma":0.00083903334,"threshold_uncertainty_score":0.0066446066},"labels":[],"label_agreement":null},{"id":"W4412485520","doi":"10.2514/6.2025-3314","title":"Dragonfly CFD Validation and Uncertainty Quantification","year":2025,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computational fluid dynamics; Computer science; Uncertainty quantification; Aerospace engineering; Engineering; Machine learning","score_opus":0.08660742451414889,"score_gpt":0.3697496425979775,"score_spread":0.2831422180838286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412485520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2901951,0.000121277844,0.6942466,0.00029814252,0.00008548707,0.00022689687,0.0013160387,0.0023713969,0.011139041],"genre_scores_gemma":[0.88523245,0.00006509322,0.11167324,0.00005335516,0.000008473316,0.00019931636,0.00096500554,0.00017000179,0.0016330845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892104,0.00022997132,0.0000636591,0.00014371706,0.00055295986,0.000088602],"domain_scores_gemma":[0.99742115,0.0010198333,0.00023681304,0.00054275687,0.00073179824,0.000047561174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002575973,0.00076769135,0.00038632483,0.0010390071,0.0007916028,0.0011446308,0.00072186714,0.0006216831,0.0022534614],"category_scores_gemma":[0.0071241735,0.00025454658,0.0005074324,0.00036885432,0.00069971685,0.00082444877,0.0012253748,0.00092594227,0.00032963377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013159875,0.000100042416,0.0073491195,0.0000870902,0.00002862412,0.00016002903,0.00017536774,0.89079696,0.023414053,0.012102081,0.0017963444,0.063858785],"study_design_scores_gemma":[0.000009030164,0.00006104962,0.0016311378,0.000024446026,0.000004810101,0.000049827682,0.00003727567,0.9545531,0.038770355,0.0020782086,0.002756809,0.000023872653],"about_ca_topic_score_codex":0.0048418012,"about_ca_topic_score_gemma":0.003914422,"teacher_disagreement_score":0.0048418012,"about_ca_system_score_codex":0.0008305392,"about_ca_system_score_gemma":0.0016496661,"threshold_uncertainty_score":0.013623238},"labels":[],"label_agreement":null},{"id":"W4412486952","doi":"10.2514/6.2025-3474","title":"Multi-Fidelity Constrained Bayesian Optimization with Application to Aircraft Wing Design","year":2025,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Bayesian optimization; Computer science; Wing; Fidelity; Bayesian probability; Aeronautics; Aerospace engineering; Artificial intelligence; Engineering","score_opus":0.04734433952924825,"score_gpt":0.3212576408291148,"score_spread":0.2739133012998665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412486952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042245206,0.00019822564,0.9939658,0.00014662607,0.000011671831,0.000018737555,0.000021991553,0.000080324775,0.0013320806],"genre_scores_gemma":[0.4273812,0.000602453,0.56935394,0.00014891294,0.00004915894,0.00021098896,0.00011811121,0.00012679363,0.002008418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935466,0.0002778085,0.000021842829,0.000055990953,0.00024610423,0.000043570217],"domain_scores_gemma":[0.9981329,0.0012902466,0.000210398,0.000096432836,0.00019681154,0.000073296214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018065949,0.0007293146,0.00094192696,0.0007543697,0.0005225856,0.0008598099,0.0008568619,0.0013738314,0.0018156612],"category_scores_gemma":[0.0048453403,0.0006455518,0.0007131189,0.0006184832,0.0008305372,0.0008900605,0.0014365565,0.0013884539,0.0002693903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016460755,0.00001733255,0.00027503335,0.00004363899,0.000011846056,0.000021050015,0.000018297502,0.9780644,0.00085273426,0.009781733,0.00019704376,0.010700398],"study_design_scores_gemma":[0.000003255674,0.0000069242487,0.000033238768,0.0000065233353,0.0000016803249,0.0000073387905,0.0000024104988,0.99702114,0.00026355375,0.0023307174,0.0003201598,0.0000029574635],"about_ca_topic_score_codex":0.0043312795,"about_ca_topic_score_gemma":0.0040628053,"teacher_disagreement_score":0.0043312795,"about_ca_system_score_codex":0.000772494,"about_ca_system_score_gemma":0.001170321,"threshold_uncertainty_score":0.0095543265},"labels":[],"label_agreement":null},{"id":"W4412710320","doi":"10.1016/j.ress.2025.111467","title":"Multi-fidelity modelling for uncertainty quantification of timber beam-column connections exposed to standard fire","year":2025,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Column (typography); Fidelity; High fidelity; Uncertainty quantification; Engineering; Environmental science; Computer science; Forensic engineering; Structural engineering; Machine learning; Connection (principal bundle); Telecommunications","score_opus":0.06253877276431374,"score_gpt":0.31621239954271924,"score_spread":0.2536736267784055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412710320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19907199,0.00015607347,0.798515,0.000069807094,0.000012595577,0.00003500437,0.00013794402,0.00019404327,0.0018075113],"genre_scores_gemma":[0.9838974,0.00006814548,0.015395821,0.000009299325,0.0000040318164,0.00004342526,0.00007656594,0.000012002643,0.00049334444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960095,0.000106263964,0.000024982486,0.00007470117,0.0001472495,0.000045793782],"domain_scores_gemma":[0.9990779,0.0005245462,0.00018066909,0.00008018082,0.00011344443,0.000023146114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009329119,0.00046237517,0.00040707685,0.0005397669,0.0002550402,0.0004960163,0.0006755563,0.00077929074,0.0007204226],"category_scores_gemma":[0.0023214014,0.00038862642,0.0006217939,0.00024731326,0.0004594244,0.00077130494,0.0005611858,0.0006578627,0.00008267356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007492417,0.0000051114507,0.00047048987,0.0000066571793,0.000004129553,0.0000133513795,0.000009098235,0.9960401,0.0007639493,0.00045949325,0.000018078546,0.0022020936],"study_design_scores_gemma":[3.2180336e-7,0.000004195284,0.00021182586,0.0000015409689,0.0000012753096,0.0000030246385,0.0000014470569,0.999273,0.00029511825,0.00017695894,0.00002992317,0.0000014658356],"about_ca_topic_score_codex":0.006810285,"about_ca_topic_score_gemma":0.0053217052,"teacher_disagreement_score":0.006810285,"about_ca_system_score_codex":0.0006078957,"about_ca_system_score_gemma":0.00043905393,"threshold_uncertainty_score":0.013541281},"labels":[],"label_agreement":null},{"id":"W4413044107","doi":"10.1016/j.ress.2025.111484","title":"Lifetime analysis of circular k-out-of-n: G balanced systems in a shock environment","year":2025,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Kyonggi University","keywords":"Shock (circulatory); Computer science; Mathematics; Reliability engineering; Engineering; Medicine","score_opus":0.017366525865037834,"score_gpt":0.2573075088691865,"score_spread":0.23994098300414868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413044107","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88404495,0.00039385623,0.106616005,0.0003109251,0.000030281883,0.000038056973,0.00013636657,0.00012522581,0.0083042905],"genre_scores_gemma":[0.99731296,0.00003871021,0.0010825221,0.000012814344,0.0000029428077,0.000005865603,0.00002669455,0.0000076587,0.0015097258],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998795,0.000019664507,0.000004264266,0.000020950136,0.000025253776,0.000050326453],"domain_scores_gemma":[0.99932444,0.0002772644,0.00012505952,0.000041299645,0.00015963735,0.00007234956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051163015,0.00024068209,0.0003820671,0.00039983867,0.00044666667,0.00047260817,0.0005127914,0.0006720018,0.0023423433],"category_scores_gemma":[0.0016238017,0.00015080937,0.0002585637,0.00025656747,0.00067133835,0.0006901218,0.0005642701,0.00027103236,0.00022883677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031038042,0.000043592758,0.0024699748,0.000059157544,0.000029784347,0.00017577937,0.00014297359,0.96216524,0.011690234,0.014758805,0.00061391183,0.007540145],"study_design_scores_gemma":[0.0000033987624,0.000046996658,0.0006303111,0.0000036281153,0.0000069264297,0.00002043603,0.00003390234,0.9971118,0.00066860317,0.001347053,0.00012211206,0.0000048379784],"about_ca_topic_score_codex":0.0044308486,"about_ca_topic_score_gemma":0.002739712,"teacher_disagreement_score":0.0044308486,"about_ca_system_score_codex":0.00077743526,"about_ca_system_score_gemma":0.00049411436,"threshold_uncertainty_score":0.008810163},"labels":[],"label_agreement":null},{"id":"W4413121938","doi":"10.1109/ims40360.2025.11104042","title":"Tensor Train Optimization for Polynomial Chaos for High Dimensional Uncertainty Quantification","year":2025,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Polynomial chaos; CHAOS (operating system); Tensor (intrinsic definition); Polynomial; Computer science; Uncertainty quantification; Mathematical optimization; Applied mathematics; Algorithm; Mathematics; Mathematical analysis; Geometry; Machine learning; Monte Carlo method; Statistics","score_opus":0.0819060505188244,"score_gpt":0.3539389795763053,"score_spread":0.27203292905748094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413121938","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020204543,0.00007692706,0.997042,0.00007258422,0.000012863749,0.000013363994,0.000024055831,0.00008638014,0.0006514134],"genre_scores_gemma":[0.31427583,0.000728171,0.67812264,0.00013211316,0.00012068314,0.0002060594,0.0003167278,0.00029922463,0.0057985582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949205,0.00020475956,0.000023480714,0.0000619676,0.00017692568,0.0000407613],"domain_scores_gemma":[0.9991542,0.00044060638,0.00008754694,0.00010157695,0.0001723184,0.000043705037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012469111,0.0012088208,0.00091905525,0.0007304682,0.00052588654,0.0008651917,0.0007538114,0.00083968753,0.0026855795],"category_scores_gemma":[0.002650637,0.0003497259,0.0007417384,0.0008934623,0.0009430453,0.0015122758,0.001316867,0.0015767878,0.000593677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065352695,0.000037167447,0.0002797867,0.00012306777,0.00004863422,0.000052805546,0.000051399096,0.8288393,0.0064089126,0.08908091,0.0023820186,0.07263058],"study_design_scores_gemma":[0.0000010308732,0.000007378256,0.000015006293,0.0000018166769,0.0000016107704,0.0000044240396,0.000001658311,0.9943593,0.00034719007,0.004910895,0.00034698515,0.0000027057283],"about_ca_topic_score_codex":0.0033290822,"about_ca_topic_score_gemma":0.0033078715,"teacher_disagreement_score":0.0033290822,"about_ca_system_score_codex":0.0010161763,"about_ca_system_score_gemma":0.0011665545,"threshold_uncertainty_score":0.0089841485},"labels":[],"label_agreement":null},{"id":"W4413186690","doi":"10.1139/cjce-2024-0569","title":"Notional lateral loads in a second-order P-Delta analysis: perturbation or real equivalent effect?","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Fredericton; University of New Brunswick","funders":"","keywords":"Notional amount; Perturbation (astronomy); Delta; Structural engineering; Mathematics; Physics; Mechanics; Engineering; Aerospace engineering; Economics","score_opus":0.026838472938160763,"score_gpt":0.285785306353612,"score_spread":0.2589468334154512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413186690","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027648339,0.00078795315,0.91758734,0.013773585,0.0018327083,0.000048749607,0.000098096825,0.00032100416,0.037902284],"genre_scores_gemma":[0.81526715,0.0009935537,0.16632426,0.003579414,0.0011608677,0.00010839837,0.00006711359,0.00044273402,0.012056475],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974491,0.00090883084,0.00014024627,0.00034961695,0.000992007,0.00016020506],"domain_scores_gemma":[0.9949303,0.0028442526,0.0003371736,0.0010274749,0.00073522184,0.0001254468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003461242,0.0008210466,0.00069189287,0.00072327326,0.000655245,0.0020602166,0.002450903,0.0021879945,0.0051615667],"category_scores_gemma":[0.014219958,0.00046184883,0.0011815666,0.0004197397,0.0076678703,0.0056206095,0.0021575296,0.005111197,0.00078814494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008301589,0.000020854304,0.00058155326,0.00011549458,0.000024776486,0.00026290017,0.0004457582,0.03499322,0.0019060434,0.93558306,0.00353376,0.022449551],"study_design_scores_gemma":[0.000022290178,0.000082583494,0.00082565116,0.00014283226,0.000023290748,0.00019664851,0.00030699137,0.12787364,0.0020988446,0.8507963,0.017566182,0.000064679734],"about_ca_topic_score_codex":0.0028907794,"about_ca_topic_score_gemma":0.0028240306,"teacher_disagreement_score":0.0051615667,"about_ca_system_score_codex":0.0011921187,"about_ca_system_score_gemma":0.0011925327,"threshold_uncertainty_score":0.018305004},"labels":[],"label_agreement":null},{"id":"W4413288341","doi":"10.1214/25-ejs2405","title":"An analysis of precision in estimation with the stochastic EM algorithm","year":2025,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Estimation; Expectation–maximization algorithm; Algorithm; Statistics; Maximum likelihood","score_opus":0.01446069452024128,"score_gpt":0.32040418191396186,"score_spread":0.30594348739372057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413288341","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011567675,0.0017578178,0.9839002,0.00069375907,0.000054009142,0.000047153546,0.000057846235,0.00017420204,0.0017473557],"genre_scores_gemma":[0.5460285,0.0027781902,0.44592413,0.0007813813,0.00043428785,0.00036214097,0.00037464418,0.00045964308,0.002856991],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9794907,0.011442949,0.0011407831,0.0027367922,0.004393403,0.000795404],"domain_scores_gemma":[0.7331376,0.23771523,0.0069434964,0.0137973735,0.0076320767,0.0007742338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.048465505,0.0016886516,0.0027199774,0.0028530892,0.0013703823,0.0035723415,0.004126958,0.0035633384,0.002015679],"category_scores_gemma":[0.25493848,0.0017576329,0.0016174559,0.002971421,0.0051311646,0.0073902635,0.006198116,0.0038662842,0.00048896257],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054191326,0.000060414357,0.0074211364,0.0004834294,0.0005300105,0.00033185876,0.0004935129,0.66992617,0.0019557655,0.26073632,0.0015627916,0.055956703],"study_design_scores_gemma":[0.00004572352,0.00013697402,0.0014413369,0.00019951948,0.00009901073,0.00026679056,0.000057213627,0.8815727,0.002172212,0.112341695,0.0015963987,0.00007040504],"about_ca_topic_score_codex":0.0026081859,"about_ca_topic_score_gemma":0.001563997,"teacher_disagreement_score":0.048465505,"about_ca_system_score_codex":0.0026084662,"about_ca_system_score_gemma":0.0021970242,"threshold_uncertainty_score":0.25631315},"labels":[],"label_agreement":null},{"id":"W4413362818","doi":"10.1016/j.soildyn.2025.109738","title":"Active sparse polynomial chaos expansion for reliability analysis of underground structures considering the spatial variability of soil properties","year":2025,"lang":"en","type":"article","venue":"Soil Dynamics and Earthquake Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Key Research and Development Program of China; Beijing University of Technology","keywords":"Polynomial chaos; Reliability (semiconductor); CHAOS (operating system); Applied mathematics; Mathematics; Polynomial; Soil science; Geotechnical engineering; Geology; Environmental science; Computer science; Statistics; Mathematical analysis; Physics; Monte Carlo method","score_opus":0.02835335350406908,"score_gpt":0.2626706943612063,"score_spread":0.23431734085713724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413362818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.098119915,0.00031169344,0.8997772,0.00017638059,0.000022164297,0.000012302944,0.000061137376,0.00007669457,0.0014425801],"genre_scores_gemma":[0.9770486,0.00033928367,0.020606687,0.000019888977,0.00003379824,0.000028483368,0.00007708963,0.000036573227,0.0018096095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998964,0.000039672428,0.0000032528242,0.000013277807,0.00003146199,0.00001593501],"domain_scores_gemma":[0.99928856,0.00047094678,0.00007621044,0.000034474262,0.000105540246,0.000024215688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003872025,0.00039756464,0.0005217196,0.00045939873,0.00019189384,0.00037019403,0.000422406,0.00038417964,0.00051673903],"category_scores_gemma":[0.0017600876,0.00023556982,0.00043563035,0.00038035,0.00045860352,0.00055612065,0.00043952963,0.0006026059,0.000087008084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027441898,0.000010694159,0.00043188265,0.00003274248,0.000017956034,0.00003639714,0.00003299396,0.9712406,0.0021086887,0.018086474,0.00026703396,0.0077070612],"study_design_scores_gemma":[3.2566226e-7,0.0000019062794,0.00003974136,4.672688e-7,8.5636094e-7,0.000001779242,0.0000011159491,0.9989207,0.000048753278,0.0009605961,0.00002302551,6.8584967e-7],"about_ca_topic_score_codex":0.0032949208,"about_ca_topic_score_gemma":0.0024052707,"teacher_disagreement_score":0.0032949208,"about_ca_system_score_codex":0.00038503463,"about_ca_system_score_gemma":0.0004223333,"threshold_uncertainty_score":0.0065514445},"labels":[],"label_agreement":null},{"id":"W4413578831","doi":"10.1080/15397734.2025.2549469","title":"Support-vector-machine-regression assisted methodology for the design-for-reliability of tapered composite tubes","year":2025,"lang":"en","type":"article","venue":"Mechanics Based Design of Structures and Machines","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Support vector machine; Composite number; Reliability (semiconductor); Reliability engineering; Structural engineering; Engineering; Computer science; Mechanical engineering; Composite material; Materials science; Engineering drawing; Machine learning; Physics; Power (physics)","score_opus":0.1257649665616389,"score_gpt":0.3701321608380117,"score_spread":0.2443671942763728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413578831","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010157307,0.00015087923,0.98854625,0.000025660001,0.000006896265,0.000033706026,0.000023502429,0.00046508526,0.0005907598],"genre_scores_gemma":[0.47136936,0.00023646861,0.5262911,0.00004210649,0.00001663816,0.00033758982,0.00017358919,0.0001227278,0.0014103827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995746,0.00014527696,0.000020448346,0.000056740697,0.00016964832,0.000033212822],"domain_scores_gemma":[0.9990408,0.00048883975,0.0001548478,0.000055442208,0.00023688516,0.00002319701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012120964,0.00084479136,0.0006329299,0.000633276,0.00019716132,0.00042199146,0.00070910255,0.00059528847,0.0015975093],"category_scores_gemma":[0.002041719,0.000363168,0.000664924,0.00034598072,0.0003059046,0.0002995615,0.00044012585,0.0007721198,0.0003792788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026744246,0.000020077197,0.00040923024,0.00007336772,0.000018596606,0.000039416154,0.000022216736,0.95463437,0.0038418064,0.0022551315,0.00021100415,0.038448058],"study_design_scores_gemma":[0.0000013049292,0.000018713234,0.00004059915,0.0000028507848,0.0000023322814,0.0000056395447,0.0000019831623,0.99876726,0.00065469637,0.00032177533,0.00018135579,0.0000015644196],"about_ca_topic_score_codex":0.0020197057,"about_ca_topic_score_gemma":0.0017694893,"teacher_disagreement_score":0.0020197057,"about_ca_system_score_codex":0.00039965194,"about_ca_system_score_gemma":0.0010771265,"threshold_uncertainty_score":0.006410241},"labels":[],"label_agreement":null},{"id":"W4413864196","doi":"10.1016/j.enbuild.2025.116360","title":"Characterizing climate change-induced degradation and its impacts on serviceability and indoor environment improvement using runtime data","year":2025,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Serviceability (structure); Environmental science; Climate change; Degradation (telecommunications); Computer science; Environmental resource management; Engineering; Civil engineering; Geology; Telecommunications","score_opus":0.11455622104421889,"score_gpt":0.3243677865230151,"score_spread":0.20981156547879623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413864196","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9638727,0.00014904949,0.027430246,0.00014893725,0.00003650856,0.000025354233,0.00523332,0.0007625808,0.002341261],"genre_scores_gemma":[0.9928357,0.00004437543,0.0034798367,0.000011126405,0.000008800041,0.000008749564,0.0032862364,0.00006638582,0.0002587802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99953353,0.000082215454,0.000026278938,0.0001297604,0.0001343318,0.00009385521],"domain_scores_gemma":[0.99852175,0.000548439,0.00018266337,0.00031440903,0.00034891698,0.00008381222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063486747,0.00048699268,0.00038537945,0.00059850217,0.00021341229,0.00083112775,0.0005044823,0.00042609454,0.0009900673],"category_scores_gemma":[0.0030604824,0.0001971117,0.0005254661,0.001018649,0.00032584934,0.00089483004,0.0004195221,0.0005036158,0.00038363138],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047532414,0.00020510613,0.13552362,0.00013394786,0.00014865614,0.000121731086,0.000096740325,0.8163105,0.011436738,0.0013968449,0.0017783473,0.03237239],"study_design_scores_gemma":[0.000007925428,0.00008336572,0.07202232,0.00000786976,0.000035312903,0.00005263725,0.00008751337,0.91926205,0.0063963565,0.0010229378,0.0010015795,0.000020095355],"about_ca_topic_score_codex":0.012787544,"about_ca_topic_score_gemma":0.018862268,"teacher_disagreement_score":0.012787544,"about_ca_system_score_codex":0.00060207146,"about_ca_system_score_gemma":0.00058457773,"threshold_uncertainty_score":0.025426209},"labels":[],"label_agreement":null},{"id":"W4414268152","doi":"10.1080/27690911.2025.2555674","title":"Statistical inverse inference for solving the first kind of nonlinear Fredholm integral equation","year":2025,"lang":"en","type":"article","venue":"Applied Mathematics in Science and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Mahasarakham University","keywords":"Markov chain Monte Carlo; Integral equation; Nonlinear system; Fredholm integral equation; Inverse problem; Bayesian probability; Statistical inference; Inverse; Bayesian inference","score_opus":0.07048832201516862,"score_gpt":0.3308372601064702,"score_spread":0.26034893809130155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414268152","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002977744,0.0001046424,0.99607205,0.00015013752,0.000013002002,0.000015159535,0.000017347953,0.000034185872,0.0006158022],"genre_scores_gemma":[0.33699444,0.0009986883,0.6574375,0.00028426916,0.00016552708,0.00036100825,0.00023196382,0.00013208893,0.003394449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990268,0.00037213665,0.00004209869,0.00015089916,0.00035668368,0.000051519484],"domain_scores_gemma":[0.9927556,0.005923597,0.0005615863,0.00019329254,0.00047301518,0.00009294376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003192043,0.00069692306,0.00097613357,0.0009902295,0.0005246319,0.001046802,0.0011166311,0.0012830048,0.0013041482],"category_scores_gemma":[0.014082106,0.0006005493,0.0008635951,0.0007834493,0.0022348196,0.0013212331,0.0014697235,0.0018085815,0.00022284918],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003561163,0.000050185638,0.0010985045,0.00022289746,0.00006512422,0.000082158454,0.000098834345,0.77369845,0.0023835846,0.1889578,0.00084992655,0.03245694],"study_design_scores_gemma":[0.000003973477,0.0000053330764,0.00008241379,0.000007599684,0.0000031611992,0.0000145567365,0.0000043680952,0.97358114,0.00030180055,0.02572028,0.00026887533,0.000006544593],"about_ca_topic_score_codex":0.0043445346,"about_ca_topic_score_gemma":0.0036517582,"teacher_disagreement_score":0.0043445346,"about_ca_system_score_codex":0.0009555607,"about_ca_system_score_gemma":0.0023354858,"threshold_uncertainty_score":0.016881287},"labels":[],"label_agreement":null},{"id":"W4414621179","doi":"10.3390/mca30050106","title":"High-Performance Simulation of Generalized Tempered Stable Random Variates: Exact and Numerical Methods for Heavy-Tailed Data","year":2025,"lang":"en","type":"article","venue":"Mathematical and Computational Applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Stable distribution; Inversion (geology); Fourier transform; Distribution (mathematics); Series (stratigraphy); Exponential function; Numerical analysis; Time series; Stability (learning theory)","score_opus":0.09628627980499271,"score_gpt":0.4092471900606585,"score_spread":0.3129609102556658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414621179","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018168295,0.00017757046,0.97932154,0.00012663283,0.000037282392,0.00003704041,0.000046546447,0.00034423292,0.0017407633],"genre_scores_gemma":[0.66379136,0.00036177284,0.33298132,0.00013953345,0.000052606196,0.00022615104,0.00019727049,0.0002339195,0.0020161476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993106,0.0003224943,0.000039958293,0.000069227994,0.00020478741,0.000052947802],"domain_scores_gemma":[0.9946924,0.003679098,0.0003491723,0.00054647226,0.0006001969,0.00013263049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027386204,0.00060703163,0.00076663867,0.0007350028,0.00053115794,0.0011058258,0.0013451857,0.001204186,0.0023160032],"category_scores_gemma":[0.011462533,0.00031510447,0.0007012589,0.00071875786,0.0010514255,0.0013101976,0.0011117524,0.0014168554,0.00044333088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034265056,0.00002594788,0.00086564774,0.000032937674,0.000017062734,0.000040130613,0.000049153812,0.959202,0.00075431017,0.02730441,0.0003064169,0.011367699],"study_design_scores_gemma":[0.0000018563072,0.0000027733752,0.000018467641,0.0000021754254,7.4440993e-7,0.000004068325,0.0000021909616,0.9977119,0.00010774438,0.0020462824,0.000100120975,0.000001717624],"about_ca_topic_score_codex":0.0064800796,"about_ca_topic_score_gemma":0.0036957157,"teacher_disagreement_score":0.0064800796,"about_ca_system_score_codex":0.00079698086,"about_ca_system_score_gemma":0.0010725121,"threshold_uncertainty_score":0.014483392},"labels":[],"label_agreement":null},{"id":"W4414909223","doi":"10.1007/s44248-025-00067-x","title":"A comparative case study on the performance of global sensitivity analysis methods on digit classification","year":2025,"lang":"en","type":"article","venue":"Discover Data","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sensitivity (control systems); MNIST database; Key (lock); Deep learning; Digit recognition","score_opus":0.3825434191462059,"score_gpt":0.5072527023867561,"score_spread":0.12470928324055025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414909223","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5882167,0.0024283084,0.39413652,0.0011931908,0.00011801082,0.000590839,0.00044617933,0.0006086881,0.012261533],"genre_scores_gemma":[0.9101187,0.000335725,0.08806735,0.00013866379,0.000026647604,0.0001643667,0.00019355757,0.00005349841,0.0009013432],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.990655,0.0058624824,0.00041983442,0.0009241856,0.0017111212,0.00042742427],"domain_scores_gemma":[0.91904974,0.070550755,0.0020253656,0.0038896159,0.0040267357,0.00045779895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019176608,0.001642923,0.0011160711,0.0028201498,0.0007653357,0.001691279,0.001168493,0.0019524428,0.0018759561],"category_scores_gemma":[0.040786512,0.00031208337,0.0011962104,0.0022401412,0.0015286745,0.0017808125,0.0018702637,0.001742073,0.00022099941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010617116,0.00042091988,0.018700266,0.0006614456,0.00033629523,0.0005661306,0.0005190463,0.8396661,0.0046433248,0.016217548,0.0022976438,0.11490946],"study_design_scores_gemma":[0.000064898144,0.0006981561,0.0059477687,0.00009766328,0.000084827785,0.0003077333,0.00056259416,0.96568674,0.010181548,0.014532254,0.0017729116,0.00006282955],"about_ca_topic_score_codex":0.0031074525,"about_ca_topic_score_gemma":0.0036871592,"teacher_disagreement_score":0.019176608,"about_ca_system_score_codex":0.0021234956,"about_ca_system_score_gemma":0.000985834,"threshold_uncertainty_score":0.10141677},"labels":[],"label_agreement":null},{"id":"W4415313183","doi":"10.1016/j.orl.2025.107384","title":"Robust confidence bands for stochastic processes using simulation","year":2025,"lang":"en","type":"article","venue":"Operations Research Letters","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Higher Education Commision, Pakistan; Government of Canada","keywords":"Stochastic optimization; Sample (material); Robust optimization; Stochastic process; Baseline (sea); Confidence interval; Stochastic modelling; Sample mean and sample covariance","score_opus":0.4345626140957993,"score_gpt":0.49836194601376044,"score_spread":0.06379933191796111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415313183","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029839203,0.00021517844,0.9958424,0.00010871537,0.000022610835,0.000019106552,0.000032677595,0.00015720872,0.0006181421],"genre_scores_gemma":[0.6050628,0.0013837275,0.3879477,0.0002466621,0.00023603355,0.000642299,0.00070642214,0.0004731689,0.0033011872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9883541,0.0070144264,0.0005641197,0.0012679555,0.0020508221,0.00074854627],"domain_scores_gemma":[0.888639,0.095182575,0.0050888862,0.00599901,0.004297775,0.00079287286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022305503,0.0019346412,0.0032926542,0.004159004,0.00079764606,0.005229831,0.0030528521,0.003082883,0.0046652867],"category_scores_gemma":[0.11777235,0.0018668682,0.0024130808,0.0023377747,0.004245585,0.0054471716,0.004542037,0.004468384,0.0008981905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028739465,0.00005739983,0.0004726379,0.00017201705,0.00016080748,0.000035244226,0.0000786155,0.7318099,0.000899632,0.2412645,0.0007623669,0.023999445],"study_design_scores_gemma":[0.000021148817,0.000020827867,0.00010977509,0.000042877255,0.00001602209,0.000012387739,0.000006360627,0.9198921,0.00047188444,0.078967996,0.00041850767,0.000020058025],"about_ca_topic_score_codex":0.0028622905,"about_ca_topic_score_gemma":0.00090691523,"teacher_disagreement_score":0.022305503,"about_ca_system_score_codex":0.002051091,"about_ca_system_score_gemma":0.0023487268,"threshold_uncertainty_score":0.11796421},"labels":[],"label_agreement":null},{"id":"W4415438364","doi":"10.1007/978-3-031-97435-9_25","title":"Reliability-Based Risk Matrix for Evaluating Existing Structures—Format and Real-Life Case Studies","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nortel (Canada); Dalhousie University","funders":"","keywords":"Process (computing); Limit (mathematics); Matrix (chemical analysis); Risk assessment; Event (particle physics); Risk management","score_opus":0.1015498778738899,"score_gpt":0.38403681922824523,"score_spread":0.2824869413543553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415438364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070337065,0.0008647028,0.9102228,0.00017933101,0.0000468324,0.00017747855,0.0008344313,0.00058293337,0.016754463],"genre_scores_gemma":[0.622923,0.0007516669,0.36599502,0.000029353296,0.00003482477,0.00020191172,0.0009994245,0.00018901544,0.008875745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851805,0.00058591925,0.0000711897,0.00011009852,0.0006681537,0.000046588113],"domain_scores_gemma":[0.99615014,0.0025604167,0.0002294905,0.00019990839,0.000818601,0.00004145077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030572657,0.00089932216,0.0006324795,0.002736013,0.00035563263,0.0018061721,0.0014817916,0.0007104737,0.0055861995],"category_scores_gemma":[0.008309945,0.00029067334,0.0006292807,0.0018207664,0.00045943639,0.0018072805,0.0005522644,0.00058042904,0.00068673474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023396246,0.00016709017,0.0031853002,0.00030623327,0.00008224298,0.00029343602,0.00015911835,0.7358321,0.0066839103,0.03592416,0.00458542,0.21254706],"study_design_scores_gemma":[0.000012628034,0.00021286757,0.0017040289,0.000043067837,0.00003985149,0.00030640856,0.00011327089,0.9749322,0.0029518665,0.017882211,0.0017672267,0.000034377204],"about_ca_topic_score_codex":0.0023529462,"about_ca_topic_score_gemma":0.0028709169,"teacher_disagreement_score":0.0055861995,"about_ca_system_score_codex":0.0011206954,"about_ca_system_score_gemma":0.0004969512,"threshold_uncertainty_score":0.018687725},"labels":[],"label_agreement":null},{"id":"W4415475671","doi":"10.1016/j.oceaneng.2025.122951","title":"A summary of the Lucy Ashton resistance prediction workshop","year":2025,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Community Sector Council Newfoundland and Labrador","funders":"Kongsberg Maritime; National Supercomputer Centre, Linköpings Universitet; Energimyndigheten; Vetenskapsrådet; Hrvatska Zaklada za Znanost; Linköpings Universitet","keywords":"Froude number; Computational fluid dynamics; Resistance (ecology); Constant (computer programming); Computer simulation","score_opus":0.025799157029652464,"score_gpt":0.267466697311949,"score_spread":0.24166754028229653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415475671","genre_codex":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18565756,0.03916522,0.22753277,0.0836376,0.05079768,0.007265569,0.028878996,0.009837819,0.36722672],"genre_scores_gemma":[0.2676819,0.030322108,0.069640495,0.0061602048,0.011045234,0.0030438856,0.04076752,0.0027339535,0.5686047],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950086,0.0006148081,0.00025465118,0.000783782,0.0027460593,0.00059215265],"domain_scores_gemma":[0.99072963,0.0007411433,0.0002416297,0.0005300057,0.0060197036,0.0017379149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012106202,0.0024635107,0.0010908055,0.0021821568,0.0021648349,0.00572595,0.0024640851,0.002446157,0.026189977],"category_scores_gemma":[0.008513654,0.00082524924,0.0014356251,0.0016822154,0.0006153531,0.0025890183,0.003651428,0.0028997753,0.019711401],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011331729,0.0012858697,0.0048792595,0.0012373968,0.00010655706,0.0006345358,0.001036893,0.018410496,0.01666084,0.004990675,0.48950693,0.4601174],"study_design_scores_gemma":[0.00013284513,0.001937707,0.012555444,0.0010105636,0.00009442093,0.00021980802,0.0012311854,0.013808994,0.019845264,0.0028944653,0.9460267,0.00024262317],"about_ca_topic_score_codex":0.007447527,"about_ca_topic_score_gemma":0.0071137655,"teacher_disagreement_score":0.026189977,"about_ca_system_score_codex":0.0034076856,"about_ca_system_score_gemma":0.0052327467,"threshold_uncertainty_score":0.08761424},"labels":[],"label_agreement":null},{"id":"W4415517820","doi":"10.1016/j.cma.2025.118486","title":"Goal-oriented calibration of a model and its modeling errors","year":2025,"lang":"en","type":"article","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Calibration; Process (computing); Range (aeronautics); Simple (philosophy); Mathematical model; Process modeling","score_opus":0.07121285581418824,"score_gpt":0.3520941003758736,"score_spread":0.28088124456168534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415517820","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007228755,0.00002305623,0.9911848,0.00005984598,0.000015966927,0.000015025712,0.000022982204,0.00027395296,0.0011756563],"genre_scores_gemma":[0.7451342,0.00013634766,0.25223565,0.00014515032,0.0000256994,0.00013935122,0.0002669103,0.00028244837,0.0016342811],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978955,0.00068791636,0.0000787277,0.00045693186,0.0007465203,0.00013439295],"domain_scores_gemma":[0.99693274,0.00074968726,0.00044002564,0.00087374164,0.00091458764,0.00008905546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029142199,0.001076873,0.00073213415,0.0008693914,0.00050541514,0.001748429,0.0015318416,0.0014492073,0.0014258988],"category_scores_gemma":[0.011329826,0.0007196722,0.0009927351,0.0005667979,0.00097154564,0.0015994916,0.0027860377,0.0021186552,0.00080137386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010391498,0.000092143324,0.0012451991,0.000091629816,0.000078974925,0.000055561453,0.0001473036,0.89345366,0.010309751,0.045828506,0.00078130583,0.04781202],"study_design_scores_gemma":[0.0000131528095,0.000060750503,0.0006191663,0.000027634453,0.000019856174,0.00004151553,0.00002913368,0.9631163,0.007556442,0.026952483,0.0015418214,0.000021739901],"about_ca_topic_score_codex":0.0022503135,"about_ca_topic_score_gemma":0.0015434016,"teacher_disagreement_score":0.0029142199,"about_ca_system_score_codex":0.00096040365,"about_ca_system_score_gemma":0.0021785705,"threshold_uncertainty_score":0.015412033},"labels":[],"label_agreement":null},{"id":"W4415532558","doi":"10.1016/j.ress.2025.111782","title":"Sequential surrogate modeling for inverse design with conformal inference-based uncertainty quantification","year":2025,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Uncertainty quantification; Surrogate model; Inverse; Key (lock); Sequential analysis; Filter (signal processing); Uncertainty analysis; Robustness (evolution)","score_opus":0.08205350306348919,"score_gpt":0.3150593916324906,"score_spread":0.2330058885690014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415532558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018594858,0.00005818368,0.9972951,0.000039543793,0.000008410083,0.00001101181,0.00002701708,0.00006648531,0.00063484453],"genre_scores_gemma":[0.54274213,0.00040402228,0.45214954,0.00015547262,0.00008644726,0.00027833213,0.00043055243,0.00024094312,0.003512616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858344,0.0006107987,0.000058926806,0.00019182736,0.00047544565,0.00007964043],"domain_scores_gemma":[0.99650085,0.0023171974,0.00029925583,0.00040081498,0.00039967292,0.000082306266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027942944,0.001094018,0.0015230676,0.00083601585,0.0003834471,0.0013828784,0.0012662435,0.0009981599,0.0030679607],"category_scores_gemma":[0.009693213,0.0011060844,0.00144173,0.00087070296,0.0012730822,0.0014579514,0.0020480743,0.0017722313,0.00045287155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045930512,0.000018294286,0.00021288423,0.000053577485,0.000030348137,0.00002838164,0.000029311599,0.9475863,0.0009628356,0.035137907,0.00036286152,0.015531468],"study_design_scores_gemma":[0.0000036862716,0.000011961633,0.000026519574,0.0000044354347,0.0000039645197,0.000006255627,0.0000016353864,0.9877747,0.00023502426,0.011662829,0.00026611984,0.0000028906518],"about_ca_topic_score_codex":0.0029089607,"about_ca_topic_score_gemma":0.00282825,"teacher_disagreement_score":0.0030679607,"about_ca_system_score_codex":0.00094516465,"about_ca_system_score_gemma":0.0015591892,"threshold_uncertainty_score":0.014777839},"labels":[],"label_agreement":null},{"id":"W4415707768","doi":"10.1109/tmtt.2025.3623960","title":"Tensor Train Newton Optimization for Polynomial Chaos for High-Dimensional Uncertainty Quantification","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Polynomial chaos; Uncertainty quantification; Convergence (economics); Tensor (intrinsic definition); Monte Carlo method; Newton's method; Gradient descent; Polynomial; Optimization problem; Transmission line","score_opus":0.03187074823895479,"score_gpt":0.30978568571701787,"score_spread":0.27791493747806306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415707768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003970804,0.00009028778,0.99476874,0.000082623184,0.00001683793,0.000014378846,0.00002343541,0.00009980859,0.000933116],"genre_scores_gemma":[0.3404075,0.00051647256,0.65327096,0.000108006396,0.00007081821,0.00016564952,0.0002285375,0.00027618316,0.0049558724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995608,0.00018162349,0.000019134104,0.000053439795,0.00015391226,0.00003101353],"domain_scores_gemma":[0.9991003,0.00050951604,0.00008842239,0.00008107998,0.00018644032,0.000034363104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010711026,0.0009841204,0.00066420925,0.00052271783,0.0005158628,0.0007274744,0.0006055866,0.0006496778,0.0017794212],"category_scores_gemma":[0.0031016055,0.00036548826,0.00060673256,0.0005993073,0.0008914545,0.0010870171,0.00091784936,0.0012913985,0.0004133303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042292755,0.000018597024,0.00037263494,0.00009989146,0.00003386542,0.000051253362,0.00007110668,0.8908068,0.0036720675,0.06155711,0.0014703715,0.04180407],"study_design_scores_gemma":[0.0000011498132,0.000005256194,0.00002136895,0.000001740458,0.0000011214266,0.0000047528706,0.0000021463482,0.9959455,0.00028233213,0.003367908,0.00036425565,0.0000025174224],"about_ca_topic_score_codex":0.0048538735,"about_ca_topic_score_gemma":0.0050201374,"teacher_disagreement_score":0.0048538735,"about_ca_system_score_codex":0.0009374293,"about_ca_system_score_gemma":0.0014549376,"threshold_uncertainty_score":0.009651244},"labels":[],"label_agreement":null},{"id":"W5535040","doi":"10.1007/978-1-4612-1250-8_18","title":"Tables of Design Data","year":2000,"lang":"en","type":"book-chapter","venue":"Mechanical engineering series","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Computer science","score_opus":0.14633401307755103,"score_gpt":0.291017626176521,"score_spread":0.14468361309896996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W5535040","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025061406,0.004253359,0.13368136,0.0010026633,0.0016538511,0.0005100977,0.62229574,0.01664905,0.21744773],"genre_scores_gemma":[0.029891914,0.005025293,0.2009491,0.0015285795,0.00047489806,0.0017783475,0.5798521,0.005447621,0.17505214],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99878126,0.0002968689,0.00015476285,0.0002340632,0.00047035204,0.00006272096],"domain_scores_gemma":[0.99282783,0.003925554,0.00042386,0.0013353148,0.00137719,0.000110311594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011758971,0.0016507062,0.00093834,0.005235426,0.00052484946,0.0035573146,0.0015512338,0.00083595136,0.2889321],"category_scores_gemma":[0.012669452,0.0009258413,0.0006612398,0.008396672,0.0003000228,0.002199729,0.00074046437,0.0010822506,0.18247278],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013642009,0.000046156987,0.0006542197,0.0010002819,0.000028264807,0.00007429917,0.00005573312,0.0040908586,0.0008768104,0.034132358,0.71597433,0.24293025],"study_design_scores_gemma":[0.000044479744,0.000035039135,0.00047686094,0.00026255377,0.000017112561,0.00015627469,0.000040855462,0.0031986516,0.0012103609,0.040840264,0.9536943,0.000023344503],"about_ca_topic_score_codex":0.0014157224,"about_ca_topic_score_gemma":0.0026099726,"teacher_disagreement_score":0.2889321,"about_ca_system_score_codex":0.0009235471,"about_ca_system_score_gemma":0.0014293916,"threshold_uncertainty_score":0.9665742},"labels":[],"label_agreement":null},{"id":"W602447159","doi":"10.1142/5456","title":"Elements of Applied Probability for Engineering, Mathematics and Systems Science","year":2004,"lang":"en","type":"book","venue":"WORLD SCIENTIFIC eBooks","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Science and engineering; Engineering mathematics; Mathematics; Mathematics education; Calculus (dental); Computer science; Applied mathematics; Engineering; Engineering ethics","score_opus":0.08094866877409998,"score_gpt":0.2969765969925687,"score_spread":0.2160279282184687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W602447159","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012050598,0.100438096,0.39413595,0.010545567,0.011871886,0.00019948625,0.00064092595,0.00089069764,0.48007244],"genre_scores_gemma":[0.039962977,0.07774378,0.13936356,0.0045055686,0.018786144,0.00080643536,0.00068896485,0.0010287166,0.7171139],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99781895,0.00039164477,0.00015048232,0.00027199864,0.0012995735,0.000067404304],"domain_scores_gemma":[0.99705815,0.0018350871,0.000094214905,0.00028170517,0.00063282886,0.000098023615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017567715,0.0016317198,0.0016177801,0.002472108,0.001131738,0.0045592613,0.0012014221,0.0020498547,0.019541925],"category_scores_gemma":[0.005931699,0.0009830006,0.00076400145,0.0035561111,0.0043575633,0.0055098594,0.0013267247,0.006160668,0.013566265],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015079587,0.000028026434,0.00009535029,0.00043368453,0.000024757646,0.00006594934,0.00033415877,0.0017905713,0.0006846812,0.7370701,0.14916344,0.11029414],"study_design_scores_gemma":[0.0000057807147,0.000016863727,0.0003005492,0.00012961714,0.0000118757,0.00021646259,0.000055433262,0.0030038084,0.000334387,0.4867614,0.50914735,0.000016461207],"about_ca_topic_score_codex":0.0016464023,"about_ca_topic_score_gemma":0.0018695374,"teacher_disagreement_score":0.019541925,"about_ca_system_score_codex":0.0024797844,"about_ca_system_score_gemma":0.0020720067,"threshold_uncertainty_score":0.065374255},"labels":[],"label_agreement":null},{"id":"W6888907036","doi":"10.22725/icasp13.087","title":"Optimal Sample Size Determination based on Bayesian Reliability and Value of Information","year":2019,"lang":"en","type":"article","venue":"Seoul National University Open Repository (Seoul National University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Empirical probability; Bayesian probability; Random variable; Probabilistic logic; Probability distribution; Sample (material)","score_opus":0.019063960656901975,"score_gpt":0.25036385933422545,"score_spread":0.23129989867732348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6888907036","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008643043,0.00017259695,0.9903503,0.00012537335,0.00001185559,0.000055973356,0.000021170035,0.000095809315,0.00052377424],"genre_scores_gemma":[0.5512173,0.00060529757,0.44555938,0.0002724754,0.00013066243,0.00069212937,0.0002672777,0.00016167558,0.0010937259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9937914,0.002907792,0.00037360867,0.0010725543,0.0015081571,0.00034648436],"domain_scores_gemma":[0.96511,0.028841572,0.001253389,0.0010723168,0.0033243683,0.00039832108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009011303,0.0011135065,0.0021493798,0.0016492632,0.0007411438,0.0012818585,0.0017380425,0.0014074435,0.0014893429],"category_scores_gemma":[0.051007543,0.0009272493,0.0009786983,0.0008299573,0.0018892194,0.0030435824,0.001838696,0.001689741,0.00026068778],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068680616,0.00016368086,0.004473668,0.0005148647,0.00017494819,0.000356169,0.00041740088,0.76121444,0.012624055,0.09740926,0.001503547,0.120461226],"study_design_scores_gemma":[0.000034107507,0.000081353355,0.00063508784,0.000039385075,0.000025446818,0.000077039964,0.00002769287,0.9710259,0.002920039,0.02469712,0.00040788826,0.000028951827],"about_ca_topic_score_codex":0.0022176418,"about_ca_topic_score_gemma":0.0019305145,"teacher_disagreement_score":0.009011303,"about_ca_system_score_codex":0.0015298802,"about_ca_system_score_gemma":0.0022000712,"threshold_uncertainty_score":0.047656894},"labels":[],"label_agreement":null},{"id":"W6912872233","doi":"10.5281/zenodo.6845852","title":"Improved Calibration of Building Models using Approximate Bayesian Calibration and Neural Networks","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Calibration; Artificial neural network; Bayesian probability; Monte Carlo method; Sensitivity (control systems); Energy (signal processing); Function (biology)","score_opus":0.0851745353442337,"score_gpt":0.28132425883422885,"score_spread":0.19614972348999515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912872233","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010351129,0.00008316397,0.98607975,0.00013718891,0.000025839572,0.00002770215,0.00018983323,0.0005235922,0.0025818218],"genre_scores_gemma":[0.6880314,0.00026953523,0.3051167,0.00022205748,0.00004989534,0.0002407211,0.0015771127,0.0006794278,0.0038131988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804246,0.0009048023,0.000095562835,0.00035224855,0.0005176035,0.000087327935],"domain_scores_gemma":[0.996088,0.001938993,0.00039440847,0.0006623505,0.0008453837,0.0000708551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003701848,0.0012202087,0.001047665,0.0013473443,0.00054459466,0.001689117,0.0018734159,0.0013824136,0.0042007137],"category_scores_gemma":[0.014361538,0.0011366582,0.0013322579,0.0014341916,0.000765475,0.0021365706,0.0020748866,0.0024347457,0.0011169405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013375762,0.000008072539,0.0003096089,0.000015897156,0.000016668326,0.000006076167,0.000016851818,0.9880261,0.00028186312,0.0021433,0.00025643455,0.008905787],"study_design_scores_gemma":[0.000003055416,0.000003595864,0.00015638616,0.000008397937,0.0000029828107,0.0000045968295,0.0000041905414,0.99595547,0.00020054351,0.0033413942,0.0003122351,0.0000072894377],"about_ca_topic_score_codex":0.015164191,"about_ca_topic_score_gemma":0.013321482,"teacher_disagreement_score":0.015164191,"about_ca_system_score_codex":0.001723019,"about_ca_system_score_gemma":0.0016917766,"threshold_uncertainty_score":0.030151844},"labels":[],"label_agreement":null},{"id":"W6920308296","doi":"10.60692/4k6n3-5vd63","title":"Stochastic process design kits for photonic circuits based on polynomial chaos augmented macro-modelling","year":2018,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Polynomial chaos; Photonics; Collocation (remote sensing); Stochastic process; Block (permutation group theory); Process (computing); Electronic circuit; Photonic integrated circuit; Class (philosophy)","score_opus":0.1445232149529586,"score_gpt":0.29069457206177723,"score_spread":0.14617135710881862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920308296","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009728729,0.000032294673,0.9887149,0.00003692151,0.000009338328,0.00002712858,0.000030960044,0.00017091932,0.0012487622],"genre_scores_gemma":[0.52627766,0.00016916715,0.4705557,0.00003349894,0.000013575478,0.00025896385,0.0001243404,0.000071263836,0.0024958367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996673,0.00006957609,0.000015761136,0.000031835938,0.00019488158,0.000020743168],"domain_scores_gemma":[0.9997322,0.00011260943,0.000043741835,0.000058603197,0.000044428634,0.000008366287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045234003,0.00038149787,0.00036033386,0.00029556543,0.0002735015,0.00054052344,0.00059220736,0.00045480469,0.0012158187],"category_scores_gemma":[0.00089076214,0.0003088488,0.00050372793,0.00019340523,0.00044927115,0.0004523827,0.00053593813,0.00068646215,0.00030789655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022102407,0.000022421862,0.00025563687,0.000043983182,0.0000107025,0.000038128434,0.000035603625,0.91566795,0.018529264,0.04938162,0.00020704238,0.015785467],"study_design_scores_gemma":[0.0000029317252,0.000016930537,0.00004359084,0.000002401251,0.0000024310605,0.000010933208,0.0000018924924,0.9921558,0.0034584037,0.0032735253,0.0010275114,0.0000036071226],"about_ca_topic_score_codex":0.0009192104,"about_ca_topic_score_gemma":0.001229797,"teacher_disagreement_score":0.0012158187,"about_ca_system_score_codex":0.00049171154,"about_ca_system_score_gemma":0.00082978373,"threshold_uncertainty_score":0.0040673018},"labels":[],"label_agreement":null},{"id":"W6923603177","doi":"10.14288/1.0176904","title":"The Cumberland News","year":2012,"lang":"en","type":"article","venue":"Open Collections","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"News bureau; News media; Postal service; Newspaper","score_opus":0.11705258106543724,"score_gpt":0.36489263442188064,"score_spread":0.24784005335644338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6923603177","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012933282,0.04139435,0.0011765852,0.09428431,0.073130324,0.00014718827,0.004043779,0.0008423443,0.7836878],"genre_scores_gemma":[0.0043069166,0.013334885,0.0005837932,0.020499116,0.011036852,0.000071494316,0.0017098181,0.0003377623,0.94811934],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969246,0.00027226948,0.00009662771,0.00039176777,0.0019865755,0.0003282096],"domain_scores_gemma":[0.9923058,0.0016037439,0.00035817188,0.00079853507,0.0030392,0.0018945195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027528086,0.0012047517,0.0007564606,0.0023140647,0.0042485166,0.011292274,0.0013778375,0.0033405486,0.2283715],"category_scores_gemma":[0.011558547,0.0004948508,0.0005521065,0.0025273545,0.0018228499,0.003064617,0.0022345847,0.005616672,0.09193532],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022081076,0.000011718818,0.00006124244,0.000051820378,0.0000021330484,0.000040785762,0.000022283566,0.000022451077,0.000059267426,0.002018131,0.9757218,0.02196614],"study_design_scores_gemma":[0.0000024338544,0.000003560153,0.00018266648,0.00008341559,0.000001359544,0.000014985101,0.000021672568,0.0000132233845,0.000051347346,0.00021244984,0.99940956,0.000003311769],"about_ca_topic_score_codex":0.033965666,"about_ca_topic_score_gemma":0.092908606,"teacher_disagreement_score":0.96603435,"about_ca_system_score_codex":0.0056205057,"about_ca_system_score_gemma":0.0078000966,"threshold_uncertainty_score":0.7639788},"labels":[],"label_agreement":null},{"id":"W6926756995","doi":"10.25384/sage.21076487","title":"Supplemental Material - Tailoring Strength Training Prescriptions for People with Rheumatoid Arthritis: A Scoping Review","year":2022,"lang":"en","type":"dataset","venue":"Sage Journals Data","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Research Canada","funders":"","keywords":"Medical prescription; Strength training; Training (meteorology); Alternative medicine; Exercise prescription","score_opus":0.16076826838156805,"score_gpt":0.3787225551822698,"score_spread":0.21795428680070178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6926756995","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022081948,0.00034879404,0.00022380413,0.00022067694,0.000041440133,0.00024536444,0.99760747,0.00016625178,0.000925361],"genre_scores_gemma":[0.0030416998,0.0009168938,0.003959174,0.00082997506,0.00008806022,0.0054493863,0.98150635,0.00016425445,0.0040441654],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972172,0.0007271307,0.00093667494,0.0003459245,0.00060628843,0.0001668768],"domain_scores_gemma":[0.9706542,0.01883814,0.003634583,0.0012400141,0.0050537526,0.00057926914],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0034304094,0.0010168441,0.0014374513,0.0052123247,0.000510895,0.0021068589,0.0014197464,0.0018498087,0.36752132],"category_scores_gemma":[0.040251184,0.00063547253,0.0023531737,0.006902279,0.00022697367,0.001170365,0.0016964226,0.0011119848,0.05389288],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029579602,0.00009321571,0.0032192809,0.033886474,0.00030379175,0.00004618171,0.000057264635,0.0003734011,0.00015025737,0.00077286724,0.9428207,0.017980667],"study_design_scores_gemma":[0.0022119794,0.00015587283,0.022792475,0.019405026,0.0007979489,0.00021505053,0.00013944288,0.0006254315,0.0004969688,0.0033328366,0.9497453,0.000081624916],"about_ca_topic_score_codex":0.0071143317,"about_ca_topic_score_gemma":0.02605918,"teacher_disagreement_score":0.36752132,"about_ca_system_score_codex":0.0015530436,"about_ca_system_score_gemma":0.004890186,"threshold_uncertainty_score":0.9021541},"labels":[],"label_agreement":null},{"id":"W6929496238","doi":"10.5061/dryad.qrfj6q5j3","title":"Data from: A test of Haldane’s rule in Neodiprion sawflies and implications for the evolution of postzygotic isolation in haplodiploids","year":2023,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Haplodiploidy; Reproductive isolation; Sterility; Hybrid; Hybrid zone; Ploidy; Genetic algorithm; Isolation (microbiology)","score_opus":0.21905825136119458,"score_gpt":0.40579851902234515,"score_spread":0.18674026766115057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929496238","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9846956,0.00018004686,0.0013399886,0.000294535,0.000047952934,0.000035701923,0.006546646,0.00006241776,0.006797046],"genre_scores_gemma":[0.98826164,0.00007499163,0.0016111091,0.00032383396,0.0000131262605,0.0000807499,0.008479859,0.00005353229,0.0011012201],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99845874,0.00020427261,0.00020978236,0.0005806947,0.0004179491,0.00012862025],"domain_scores_gemma":[0.9928076,0.0032678877,0.0012800802,0.0013129027,0.00081395835,0.00051760976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016104362,0.00017822559,0.0005379464,0.0009058948,0.0007873624,0.0008389569,0.000888129,0.00074551906,0.009713369],"category_scores_gemma":[0.0063852193,0.00019386053,0.0005789034,0.00082905503,0.000923896,0.0007317171,0.00087937835,0.0010298918,0.0015778503],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002890157,0.00033563952,0.8243428,0.00059228047,0.0008110092,0.0013350607,0.0021809225,0.0007779353,0.12745008,0.0015896903,0.004511226,0.033183265],"study_design_scores_gemma":[0.00010375401,0.00040125917,0.9829175,0.000056082117,0.00013284381,0.0006031242,0.0005688342,0.0013449983,0.0064781387,0.0005604974,0.00680402,0.000028974291],"about_ca_topic_score_codex":0.0031219383,"about_ca_topic_score_gemma":0.0044550006,"teacher_disagreement_score":0.009713369,"about_ca_system_score_codex":0.000380652,"about_ca_system_score_gemma":0.00028224228,"threshold_uncertainty_score":0.032494485},"labels":[],"label_agreement":null},{"id":"W6930112927","doi":"10.5281/zenodo.11828542","title":"Hilti hit hy 150 zulassung pdf","year":2024,"lang":"de","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Washer; Power (physics); Element (criminal law)","score_opus":0.06577878327478807,"score_gpt":0.281620118229368,"score_spread":0.2158413349545799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930112927","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00055204786,0.00042497585,0.0020756775,0.0003639562,0.00080153527,0.0005109473,0.015600373,0.017832862,0.9618375],"genre_scores_gemma":[0.0019351493,0.00029647164,0.00089446775,0.00028577252,0.00015686244,0.00017087218,0.009189961,0.004469516,0.982601],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993411,0.00003935902,0.00002678723,0.00009181441,0.0004024629,0.00009846651],"domain_scores_gemma":[0.99818027,0.00023635633,0.000077880155,0.00038236135,0.000780818,0.0003424412],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006716074,0.0011661641,0.00096924725,0.0026081696,0.0011453365,0.004522327,0.0018293922,0.001888351,0.9442096],"category_scores_gemma":[0.0025403136,0.00089956506,0.0006668253,0.0016894653,0.00030510302,0.0028331734,0.002842761,0.0016781626,0.92711717],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030199531,0.00004627392,0.00006862807,0.000097825585,0.0000025416625,0.00002833474,0.000018104907,0.000030177764,0.0004494487,0.00068723824,0.95639527,0.042145967],"study_design_scores_gemma":[0.000021197742,0.00003147437,0.00043648505,0.000054710716,0.0000029691635,0.000070209164,0.000026164562,0.000075409895,0.0005950758,0.00033205014,0.9983448,0.000009425361],"about_ca_topic_score_codex":0.002180715,"about_ca_topic_score_gemma":0.002730946,"teacher_disagreement_score":0.055790424,"about_ca_system_score_codex":0.0008010963,"about_ca_system_score_gemma":0.0009850196,"threshold_uncertainty_score":0.07957816},"labels":[],"label_agreement":null},{"id":"W6931962864","doi":"10.5683/sp3/34t8ch","title":"Lac Gosselin (West) Quebec. 1:50,000. Map Sheet 031O14, ed. 1, 1960","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Georeference; General partnership; Raster graphics; Natural (archaeology); Aerial photography; Government (linguistics); Digital mapping; Vector map; Geographic information system","score_opus":0.04763697334726633,"score_gpt":0.3119251679931064,"score_spread":0.26428819464584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931962864","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000084756895,0.00007793359,0.00003938627,0.00003217144,0.000014362882,0.000005037554,0.99794453,0.00017122684,0.0016306655],"genre_scores_gemma":[0.0007734368,0.00012381043,0.00020104928,0.0000333623,0.0000058603136,0.00002922566,0.9938645,0.00010246348,0.004866319],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993362,0.000044900182,0.00003659386,0.00019865831,0.00021035314,0.00017326459],"domain_scores_gemma":[0.99789524,0.00017360086,0.00012611736,0.00030767478,0.0012789998,0.0002183225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005637181,0.0021887713,0.0014409552,0.0039326474,0.0015264353,0.003499153,0.0022734497,0.0009262206,0.13871561],"category_scores_gemma":[0.003460607,0.0006949774,0.0007505428,0.013841085,0.00050400075,0.0011232456,0.0009590737,0.0014308909,0.11156531],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001837154,0.000004878598,0.00068799395,0.00014704627,0.000010346209,0.00001002722,0.000014329864,0.000114329225,0.00002732951,0.00022808794,0.99555355,0.0031836613],"study_design_scores_gemma":[0.00005825799,0.000004650846,0.009388872,0.00027430625,0.000012754973,0.000021549311,0.000093815055,0.0003022271,0.00012398478,0.00036940185,0.98932487,0.000025470503],"about_ca_topic_score_codex":0.9445131,"about_ca_topic_score_gemma":0.9675262,"teacher_disagreement_score":0.13871561,"about_ca_system_score_codex":0.009912364,"about_ca_system_score_gemma":0.014495627,"threshold_uncertainty_score":0.46404994},"labels":[],"label_agreement":null},{"id":"W6932020888","doi":"10.5683/sp3/jwf7k2","title":"Replication Data for: Large-scale methane controlled release study","year":2024,"lang":"en","type":"dataset","venue":"Borealis","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Methane; Transect; Mixing ratio; Wind direction; Landfill gas; Wind speed; Hydrology (agriculture); Metadata","score_opus":0.1237648281684592,"score_gpt":0.399250212361123,"score_spread":0.27548538419266383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6932020888","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005151416,0.00007415843,0.0036099134,0.00075581885,0.00082007184,0.0025748746,0.9626763,0.0017520553,0.022585543],"genre_scores_gemma":[0.022519646,0.00014940657,0.013625917,0.00095797324,0.00022585658,0.012622337,0.9155568,0.0019533057,0.032388654],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9874982,0.0023419573,0.0018438083,0.0019231485,0.00574544,0.0006474574],"domain_scores_gemma":[0.9203318,0.015570691,0.0042697005,0.027296614,0.030408703,0.00212257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011713819,0.001021676,0.0011743697,0.0022340692,0.0019507154,0.0026930547,0.0023377747,0.0015803807,0.19439232],"category_scores_gemma":[0.058731988,0.0007570715,0.0012023803,0.0045715896,0.0007898776,0.0020230426,0.0024921373,0.0020340502,0.103728876],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019532533,0.00043156446,0.009131978,0.00095284346,0.0001229183,0.00020053436,0.00030667786,0.0005178756,0.001992379,0.0020666553,0.946294,0.036029294],"study_design_scores_gemma":[0.0011887852,0.0006170146,0.03465663,0.0005006837,0.00009376124,0.00012330092,0.0005590284,0.00054290605,0.002494095,0.0017179254,0.95738465,0.000121254176],"about_ca_topic_score_codex":0.018389938,"about_ca_topic_score_gemma":0.021813372,"teacher_disagreement_score":0.19439232,"about_ca_system_score_codex":0.0015642985,"about_ca_system_score_gemma":0.0076045054,"threshold_uncertainty_score":0.6503071},"labels":[],"label_agreement":null},{"id":"W6948637615","doi":"10.5061/dryad.ngf1vhhx8","title":"Data from: Paternal hatching care regulates the timing, synchrony, and success of hatching in a coral reef fish","year":2022,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Gestational period; TSG101; Nucleofection; Paraphernalia; Population; Proteogenomics","score_opus":0.22230108411094565,"score_gpt":0.4023191342735198,"score_spread":0.18001805016257413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948637615","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031209427,0.00025788828,0.0004152098,0.00019176406,0.00003306263,0.00002676023,0.9947699,0.0003830712,0.0008014254],"genre_scores_gemma":[0.008219088,0.00013503253,0.0011888333,0.00012322566,0.000011693835,0.0002449425,0.9890063,0.000087729284,0.0009831756],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99886,0.00035345272,0.0001168095,0.0003740068,0.00019394005,0.00010172529],"domain_scores_gemma":[0.9969223,0.0018266086,0.0003182299,0.00045545568,0.0003305377,0.00014684415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019927428,0.0016313918,0.0010584086,0.0010345692,0.00060576265,0.0013097548,0.0021903233,0.0018047128,0.032863546],"category_scores_gemma":[0.010075385,0.00038791797,0.001542208,0.0018688107,0.00051167986,0.00069303723,0.0012433454,0.0012029795,0.021534394],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005457153,0.00016842227,0.018833097,0.0035582627,0.00046752338,0.0001522422,0.000103465514,0.00637584,0.00042926756,0.0012886374,0.95485234,0.013225128],"study_design_scores_gemma":[0.0024065592,0.00024683852,0.073081546,0.0011357631,0.00045133417,0.00037256652,0.00026956975,0.00972296,0.0013395252,0.005746008,0.9050761,0.00015132067],"about_ca_topic_score_codex":0.016399309,"about_ca_topic_score_gemma":0.03419823,"teacher_disagreement_score":0.032863546,"about_ca_system_score_codex":0.0009919774,"about_ca_system_score_gemma":0.0014741324,"threshold_uncertainty_score":0.109939575},"labels":[],"label_agreement":null},{"id":"W6967579280","doi":"10.5281/zenodo.12376876","title":"Iso 13585 pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Liquation; Quality (philosophy); Subpoena; Work (physics); Scope (computer science); Interchangeability","score_opus":0.08389329812263657,"score_gpt":0.30185936792815976,"score_spread":0.2179660698055232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6967579280","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005999044,0.0014453795,0.010140505,0.00064235256,0.0026780905,0.0006532586,0.01321582,0.006572679,0.964052],"genre_scores_gemma":[0.0044564675,0.002449978,0.006987229,0.000779106,0.0004959927,0.00039058755,0.020546475,0.003752824,0.9601415],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99512273,0.00024351537,0.00030301642,0.00031641763,0.003725757,0.00028858712],"domain_scores_gemma":[0.9930218,0.00027442875,0.00020312541,0.0006319299,0.005598696,0.00027009012],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00264348,0.0023411524,0.0012979568,0.0065394323,0.0015663317,0.007568343,0.0045569856,0.0031770412,0.56013197],"category_scores_gemma":[0.006765658,0.0012198002,0.0014950574,0.005247113,0.0011798993,0.0052007632,0.0028077464,0.002328644,0.62056196],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012206102,0.00009598147,0.00012930014,0.0008497027,0.0000094869,0.00010841185,0.00007134084,0.00044742847,0.00397311,0.007933586,0.7973903,0.18886933],"study_design_scores_gemma":[0.000009293484,0.000025253252,0.00020086423,0.00010087073,0.0000049709156,0.000055549186,0.000029204188,0.000064962405,0.001187361,0.0006925551,0.99761957,0.000009428624],"about_ca_topic_score_codex":0.009132733,"about_ca_topic_score_gemma":0.0076533454,"teacher_disagreement_score":0.43986803,"about_ca_system_score_codex":0.0025557799,"about_ca_system_score_gemma":0.0045705005,"threshold_uncertainty_score":0.62741834},"labels":[],"label_agreement":null},{"id":"W6968576313","doi":"10.5281/zenodo.3742477","title":"The definition of Steed Variance/ Covariance and their applications","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Normalization (sociology); Covariance; Standard deviation; Covariance and correlation; Random variable; Central moment; Variance (accounting); Covariance matrix; Hurst exponent; Variable (mathematics)","score_opus":0.1301313013124654,"score_gpt":0.2765325026100522,"score_spread":0.1464012012975868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6968576313","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033079695,0.0073798536,0.9754809,0.0010217492,0.0007139513,0.000044687175,0.00029127643,0.0002192683,0.011540378],"genre_scores_gemma":[0.27596992,0.026110549,0.6657189,0.0029493745,0.0058826287,0.00087708567,0.00097472593,0.0012242452,0.020292567],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995061,0.0014449703,0.00040265868,0.0011504481,0.0017058644,0.00023497862],"domain_scores_gemma":[0.9938706,0.0032708705,0.0007127866,0.0006720962,0.0012880106,0.0001856097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004905855,0.0016015947,0.0016179302,0.0032345653,0.0010377814,0.0042485977,0.0018018666,0.0020347445,0.004136729],"category_scores_gemma":[0.016808484,0.00087666645,0.0019172309,0.0042045433,0.004492746,0.0053977454,0.0032690242,0.004351925,0.0018623051],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023214376,0.00001410804,0.0005181789,0.0001322059,0.000057120567,0.00011522308,0.00012800179,0.013294997,0.0005926306,0.94261277,0.0030429605,0.03946857],"study_design_scores_gemma":[0.00000787114,0.000030761264,0.00049575715,0.00009860845,0.000027988717,0.00032923007,0.00005711827,0.05931062,0.0006185077,0.9054209,0.03353586,0.00006685794],"about_ca_topic_score_codex":0.0035633217,"about_ca_topic_score_gemma":0.0013837742,"teacher_disagreement_score":0.004905855,"about_ca_system_score_codex":0.002006351,"about_ca_system_score_gemma":0.00160604,"threshold_uncertainty_score":0.025944948},"labels":[],"label_agreement":null},{"id":"W6986793186","doi":"","title":"Quantifying the effects of uncertainty in building simulation","year":2002,"lang":"en","type":"dissertation","venue":"NPARC","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Monte Carlo method; Uncertainty analysis; Factorial; Uncertainty quantification; Function (biology); Design of experiments; Sensitivity analysis; Work (physics)","score_opus":0.08475040992054696,"score_gpt":0.36703640647777624,"score_spread":0.2822859965572293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6986793186","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14463727,0.0017139463,0.8440509,0.0004700756,0.00008725063,0.00014234577,0.0001441108,0.00033482234,0.008419371],"genre_scores_gemma":[0.87891316,0.000784818,0.119309366,0.00007657612,0.000047658417,0.00014572158,0.00011638742,0.00011907261,0.00048715406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9801675,0.010353015,0.00076402625,0.0011264068,0.0069629564,0.0006261251],"domain_scores_gemma":[0.8405544,0.14188339,0.0051905736,0.0071991202,0.00464126,0.00053124916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018996177,0.0012720688,0.0010560111,0.002159479,0.0008490867,0.0032420577,0.001571612,0.0013446657,0.0010440378],"category_scores_gemma":[0.101763666,0.0009944594,0.0010234875,0.001733443,0.002513984,0.00462493,0.0036190243,0.0017094689,0.0001574747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013050047,0.000047438043,0.007477439,0.00025004565,0.00014914214,0.0000673503,0.0003735941,0.90332407,0.0018266963,0.039221615,0.0002495392,0.04688258],"study_design_scores_gemma":[0.00001492709,0.00019439589,0.0040936465,0.00013960345,0.00009695487,0.00009096045,0.00019571476,0.9329514,0.005350069,0.05404112,0.0027445187,0.000086777036],"about_ca_topic_score_codex":0.0048279734,"about_ca_topic_score_gemma":0.0030991074,"teacher_disagreement_score":0.018996177,"about_ca_system_score_codex":0.0030269227,"about_ca_system_score_gemma":0.0016871612,"threshold_uncertainty_score":0.100462615},"labels":[],"label_agreement":null},{"id":"W6987526031","doi":"","title":"System-level Structural Reliability of Bridges","year":2010,"lang":"en","type":"dissertation","venue":"Library and Archives Canada (Government of Canada)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Girder; Redundancy (engineering); Structural reliability; Structural system; Bridge (graph theory); Reliability (semiconductor); Deck","score_opus":0.011734381591630146,"score_gpt":0.1995266994401546,"score_spread":0.18779231784852446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6987526031","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6859545,0.00040032415,0.29298583,0.00015572984,0.00001653585,0.000036095134,0.0002154557,0.00028078997,0.019954717],"genre_scores_gemma":[0.99419796,0.000081936065,0.004564541,0.0000064466203,0.0000036369813,0.000015559004,0.000078027515,0.000010295315,0.0010415482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996068,0.00007703424,0.000012040739,0.00005572463,0.00019536736,0.00005299113],"domain_scores_gemma":[0.99936765,0.00021057735,0.00006826607,0.00008852824,0.00025159842,0.000013335781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006668078,0.00015741464,0.00021203331,0.00033845406,0.00013875701,0.00056895264,0.00026884093,0.00028389256,0.0017090394],"category_scores_gemma":[0.0025670365,0.00012279829,0.00019865339,0.00026253503,0.00037471508,0.00041133704,0.00025801404,0.00021852441,0.00027207288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007170537,0.000028283948,0.00851814,0.000107148626,0.000054577526,0.0001203431,0.0001866269,0.8689154,0.030222945,0.054502744,0.00065989076,0.036612347],"study_design_scores_gemma":[0.000009120197,0.00014203305,0.008927641,0.000016443637,0.000024083374,0.0000770951,0.00005048542,0.9582553,0.007124099,0.023791375,0.0015679147,0.000014393339],"about_ca_topic_score_codex":0.001918516,"about_ca_topic_score_gemma":0.0016521512,"teacher_disagreement_score":0.001918516,"about_ca_system_score_codex":0.0005775556,"about_ca_system_score_gemma":0.0004167042,"threshold_uncertainty_score":0.005717337},"labels":[],"label_agreement":null},{"id":"W6987721699","doi":"","title":"Uncertainty evaluation for functional kriging: an application to the Canadian temperature data set","year":2015,"lang":"en","type":"article","venue":"IRIS UNIMORE (University of Modena and Reggio Emilia)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Liquation; Diafiltration; Emperipolesis; Triacetin; Demotion","score_opus":0.3024865550826776,"score_gpt":0.35518232030420044,"score_spread":0.05269576522152286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6987721699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24469149,0.0013581151,0.74578226,0.0004889986,0.000047524656,0.00017697051,0.002305946,0.0016502248,0.0034984904],"genre_scores_gemma":[0.7590219,0.00050292653,0.23762643,0.00003846865,0.000013523535,0.00007362551,0.0013724193,0.0002732916,0.0010774595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911445,0.00032440497,0.000056525696,0.00009395858,0.00032807843,0.00008245504],"domain_scores_gemma":[0.9971004,0.0019048275,0.000075390206,0.00013741151,0.0007316473,0.000050399576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035902967,0.000789761,0.0010457136,0.0017102209,0.0010276219,0.0012100097,0.0010390803,0.0006531884,0.0010022536],"category_scores_gemma":[0.011137401,0.00035905515,0.0007932954,0.0022530293,0.0004699438,0.00060417905,0.0007141396,0.0007233091,0.00011136928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100214645,0.000031323707,0.0047855536,0.00017677782,0.000074093485,0.000067248606,0.00013056774,0.8773583,0.0016652024,0.0042727552,0.0011230906,0.11021487],"study_design_scores_gemma":[0.0000052739188,0.000011103046,0.0021843554,0.000011932023,0.0000093606395,0.000014079578,0.000044291006,0.9947836,0.0007397698,0.0016016078,0.000580213,0.000014346816],"about_ca_topic_score_codex":0.5288388,"about_ca_topic_score_gemma":0.53320086,"teacher_disagreement_score":0.4711612,"about_ca_system_score_codex":0.0028966032,"about_ca_system_score_gemma":0.0053648376,"threshold_uncertainty_score":0.9478719},"labels":[],"label_agreement":null},{"id":"W6996059401","doi":"","title":"Reliability and Statistical Analyses of Reinforced Concrete Beams Considering Shear-moment Interaction","year":2011,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Beam (structure); Reinforced concrete; Reliability (semiconductor); Shear (geology); Bending moment; Bending; Flexural strength","score_opus":0.3093583800818636,"score_gpt":0.39278200457080326,"score_spread":0.08342362448893964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996059401","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91953814,0.00024274841,0.07813297,0.000020130952,0.000007814394,0.000026075993,0.00023355486,0.000107838394,0.0016907174],"genre_scores_gemma":[0.99213964,0.00010413926,0.006993307,0.0000037015266,0.0000051565626,0.00002369116,0.00024308868,0.000008465392,0.00047880446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992698,0.00014551327,0.000034367033,0.00009347605,0.00039846232,0.000058347945],"domain_scores_gemma":[0.99725336,0.0013132187,0.0004952466,0.0002429769,0.00065863796,0.00003660218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001783022,0.00042496747,0.00027348768,0.0012080801,0.00017339329,0.00023168666,0.00033116652,0.00021905199,0.00062117697],"category_scores_gemma":[0.0035641617,0.00019970284,0.00047505414,0.00061246095,0.00035286185,0.00024038854,0.00020344365,0.00021625232,0.00015596808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001851416,0.00004615396,0.082058005,0.0001043197,0.00015277191,0.00030234276,0.00021509518,0.7936785,0.07349644,0.00475671,0.00030670708,0.0446979],"study_design_scores_gemma":[0.0000075774647,0.00029509675,0.08050604,0.000019837751,0.00009083775,0.00015303536,0.00006760719,0.88026696,0.036189165,0.0014721794,0.00089421426,0.000037452923],"about_ca_topic_score_codex":0.0045671957,"about_ca_topic_score_gemma":0.005755647,"teacher_disagreement_score":0.0045671957,"about_ca_system_score_codex":0.0004643403,"about_ca_system_score_gemma":0.0006928338,"threshold_uncertainty_score":0.009429693},"labels":[],"label_agreement":null},{"id":"W6996921254","doi":"","title":"Strathcona Solar Initiatives details 140-kW community center project in Ontario","year":2015,"lang":"en","type":"other","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Center (category theory); Government (linguistics); Community center; Work (physics)","score_opus":0.248050078478351,"score_gpt":0.368691300101607,"score_spread":0.120641221623256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996921254","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011093745,0.0006141807,0.009994678,0.004178267,0.00047489366,0.0005634158,0.013211585,0.0021266723,0.95774263],"genre_scores_gemma":[0.049118128,0.0004080812,0.004787776,0.00032842212,0.000053395655,0.000116314266,0.0056684813,0.0008953512,0.938624],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99874395,0.000074387346,0.000010644695,0.000100927995,0.0008136833,0.00025654302],"domain_scores_gemma":[0.998256,0.00007000843,0.000034959292,0.00013328515,0.0010863213,0.00041932554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013839877,0.0006633745,0.00043219107,0.00064573204,0.0041503645,0.0024553346,0.0010011842,0.0010176911,0.16749473],"category_scores_gemma":[0.0012856092,0.00040172986,0.0004709005,0.0008767266,0.0009257037,0.0009339601,0.0017110548,0.0010413973,0.032378692],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031110385,0.00014254733,0.0037480206,0.000177093,0.000021889338,0.0001385136,0.00032718404,0.0026222356,0.0027285847,0.028956937,0.86399174,0.09683413],"study_design_scores_gemma":[0.00004724462,0.00004417858,0.0050564823,0.000040868093,0.0000072883363,0.000025296566,0.0002511406,0.0021771656,0.0011334491,0.0028509994,0.9883505,0.000015306357],"about_ca_topic_score_codex":0.7662178,"about_ca_topic_score_gemma":0.93743104,"teacher_disagreement_score":0.23378217,"about_ca_system_score_codex":0.015959825,"about_ca_system_score_gemma":0.04067133,"threshold_uncertainty_score":0.5603257},"labels":[],"label_agreement":null},{"id":"W7017291023","doi":"","title":"Analysis of Parametric Uncertainty Linked to Behavior of Fatigue of Electricity Transmission Cables","year":2019,"lang":"en","type":"other","venue":"Mecánica Computacional (Asociación Argentina de Mecánica Computacional)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Parametric statistics; Transmission (telecommunications); Electricity; Electric power transmission; Power transmission; Control theory (sociology)","score_opus":0.06781397896174028,"score_gpt":0.3468518113705277,"score_spread":0.2790378324087874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7017291023","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95191467,0.00020089072,0.044617295,0.00005323262,0.000007471844,0.000026366537,0.0003021424,0.00018574658,0.0026921632],"genre_scores_gemma":[0.99886864,0.000020742413,0.00086702954,0.0000014936858,0.0000015126773,0.0000074149752,0.0000700951,0.000009836417,0.0001530987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994692,0.00015439578,0.000027382968,0.000094742056,0.00017908294,0.00007515047],"domain_scores_gemma":[0.9906743,0.007259438,0.0008561363,0.00061601266,0.0005192236,0.00007492278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014265324,0.00039626533,0.0002682771,0.0010749834,0.00020544895,0.0003631491,0.00046987075,0.00055090216,0.0013252591],"category_scores_gemma":[0.010138133,0.00017991355,0.00039988363,0.00055535283,0.0004081544,0.0004246645,0.00040797857,0.00042359697,0.00010439455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036374026,0.00010534477,0.027586775,0.0001453281,0.000098636156,0.00040810741,0.00026079224,0.89988613,0.023205733,0.002300451,0.00031319642,0.04532573],"study_design_scores_gemma":[0.000003403533,0.00018368829,0.02346628,0.000015599511,0.000024741697,0.00016400518,0.00006035439,0.96828985,0.0069014393,0.0005697667,0.00029316545,0.000027649254],"about_ca_topic_score_codex":0.001236253,"about_ca_topic_score_gemma":0.00065885816,"teacher_disagreement_score":0.0014265324,"about_ca_system_score_codex":0.0003037222,"about_ca_system_score_gemma":0.00015237104,"threshold_uncertainty_score":0.0075443387},"labels":[],"label_agreement":null},{"id":"W7023674144","doi":"","title":"Open water resistance experiment uncertainty analysis","year":2002,"lang":"en","type":"report","venue":"NPARC","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Error analysis; Uncertainty analysis; Value (mathematics); Observational error; Measurement uncertainty; Systematic error; Set (abstract data type); Sensitivity analysis","score_opus":0.18940562513839448,"score_gpt":0.3899246250252812,"score_spread":0.2005189998868867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7023674144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1220214,0.00044219082,0.8044413,0.0004654254,0.00029880577,0.0027007766,0.006250859,0.0020337198,0.06134559],"genre_scores_gemma":[0.7739191,0.00053021667,0.1966504,0.00031533805,0.000078428675,0.004306243,0.005237342,0.0005587252,0.01840422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9855137,0.0027053824,0.0005460297,0.001859986,0.008773698,0.00060112064],"domain_scores_gemma":[0.9736293,0.014601089,0.0013184751,0.0047623967,0.0053496337,0.00033917624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072246124,0.0009751236,0.0011373032,0.0016104586,0.0011010404,0.0014716324,0.001925695,0.0012592353,0.011172324],"category_scores_gemma":[0.026257271,0.00035162427,0.0010746615,0.0015551813,0.0013610038,0.002213893,0.0017179864,0.0018173921,0.0013272169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030881537,0.0017646268,0.00958135,0.0015668789,0.0005742821,0.00052833086,0.0005979952,0.43816575,0.0729942,0.15484859,0.019974088,0.29631582],"study_design_scores_gemma":[0.00040716885,0.0027800011,0.0139344735,0.00028812274,0.00046071265,0.00038544493,0.0005543662,0.523521,0.256326,0.11183216,0.08918129,0.00032942832],"about_ca_topic_score_codex":0.0029394622,"about_ca_topic_score_gemma":0.0024918693,"teacher_disagreement_score":0.011172324,"about_ca_system_score_codex":0.0014186531,"about_ca_system_score_gemma":0.0020555535,"threshold_uncertainty_score":0.03820789},"labels":[],"label_agreement":null},{"id":"W7027250905","doi":"","title":"Circuit uncertainty quantification in time domain using sensitivity integrated stochastic collocation method","year":2020,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Uncertainty quantification; Sensitivity (control systems); Collocation (remote sensing); Time domain; Uncertainty analysis; Domain (mathematical analysis); Measurement uncertainty","score_opus":0.08126894715687018,"score_gpt":0.326778540295414,"score_spread":0.2455095931385438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027250905","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036412189,0.00005116049,0.99507767,0.000022568556,0.0000107808655,0.0000084841795,0.00001656038,0.000075429816,0.0010960477],"genre_scores_gemma":[0.6806055,0.00036626586,0.31447074,0.000074512864,0.00004016667,0.000095391246,0.00018678016,0.00016331763,0.0039973976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967086,0.00008886626,0.00001590873,0.00005733617,0.00014795647,0.000019084398],"domain_scores_gemma":[0.9993011,0.00037480754,0.00006013537,0.00006756412,0.00017863102,0.000017693734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005067118,0.00052266754,0.0005063091,0.0004371827,0.0002712358,0.0007664593,0.00050698203,0.00050845084,0.0017374008],"category_scores_gemma":[0.0016515495,0.00029562603,0.00056937203,0.00041933777,0.00045898813,0.00092354964,0.00087228155,0.0008279001,0.00028654194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023926044,0.000014639735,0.00027953566,0.00006410744,0.000030011326,0.00004771504,0.000056061646,0.93583995,0.007275668,0.018883064,0.0005818705,0.036903627],"study_design_scores_gemma":[7.046143e-7,0.0000050447766,0.00006191435,0.000003172968,0.0000026583293,0.00000706207,0.0000036665394,0.9971962,0.00056927826,0.0018882795,0.00025916038,0.0000029029318],"about_ca_topic_score_codex":0.002314982,"about_ca_topic_score_gemma":0.0019050319,"teacher_disagreement_score":0.002314982,"about_ca_system_score_codex":0.0004697718,"about_ca_system_score_gemma":0.000543341,"threshold_uncertainty_score":0.0058121085},"labels":[],"label_agreement":null},{"id":"W7036393902","doi":"","title":"Chapitre 28. Professeur à l’Université Laval de Montréal","year":2020,"lang":"fr","type":"other","venue":"Cairn.info","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Work (physics); Component (thermodynamics)","score_opus":0.029965888287619414,"score_gpt":0.24679972521394766,"score_spread":0.21683383692632824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036393902","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019171182,0.05644601,0.016636623,0.03065888,0.008925803,0.00021977573,0.018492395,0.0033247948,0.8633787],"genre_scores_gemma":[0.009736696,0.015225535,0.004447973,0.0012400094,0.0017917971,0.00009391838,0.0041188803,0.0010093335,0.9623358],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974235,0.00025543632,0.00007512765,0.0004625021,0.0014446019,0.00033878497],"domain_scores_gemma":[0.9968389,0.0007967701,0.00011258473,0.00031682913,0.001365567,0.0005693192],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020151772,0.0019871092,0.0015842649,0.0035794997,0.0028034,0.008777325,0.0021680312,0.0029526805,0.46545103],"category_scores_gemma":[0.004644832,0.0008391173,0.0009373775,0.0055242116,0.0018242673,0.002897664,0.0018423612,0.0029052044,0.1932053],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048285423,0.000031805637,0.00037421487,0.00022135422,0.00001372538,0.000105313506,0.00017383938,0.0005920764,0.00041177165,0.045424614,0.8598932,0.09270973],"study_design_scores_gemma":[0.000010504232,0.000007579123,0.000638876,0.00015949828,0.000003816573,0.00006384731,0.00007144282,0.00033007486,0.00021835642,0.00352634,0.9949569,0.0000128793745],"about_ca_topic_score_codex":0.42278886,"about_ca_topic_score_gemma":0.42828572,"teacher_disagreement_score":0.46545103,"about_ca_system_score_codex":0.014036229,"about_ca_system_score_gemma":0.014998078,"threshold_uncertainty_score":0.8406559},"labels":[],"label_agreement":null},{"id":"W7042953050","doi":"","title":"Probabilistic Modelling of Soil Shear Strength by Maximum Entropy Quantile Functions","year":2023,"lang":"en","type":"article","venue":"Trinity's Access to Research Output (TARA) (Trinity College Dublin)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Northwestern University","keywords":"Quantile; Principle of maximum entropy; Probability density function; Quantile function; Cumulative distribution function; Akaike information criterion; Log-normal distribution; Probability distribution; Random variable","score_opus":0.3546220310637346,"score_gpt":0.4273258649112744,"score_spread":0.07270383384753981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7042953050","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042949658,0.00014707209,0.9552412,0.000109924185,0.000010222232,0.000033065684,0.0001522657,0.00016911936,0.0011875373],"genre_scores_gemma":[0.9598379,0.00024954844,0.037656657,0.000032428292,0.000022323247,0.00008210797,0.00017251066,0.000058210106,0.0018883419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994816,0.00019402136,0.00002463989,0.00010251916,0.00011768247,0.00007948887],"domain_scores_gemma":[0.9983381,0.0011407254,0.00026185715,0.0000730518,0.0001454719,0.000040889183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020552892,0.00064153655,0.0007320832,0.00087638793,0.00036825053,0.0009995855,0.0014738336,0.00093612407,0.0010885784],"category_scores_gemma":[0.0042865956,0.00066630245,0.0009514521,0.0009360464,0.0010762084,0.0013203978,0.0007819463,0.0009287255,0.00019151802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001242266,0.000006552347,0.000721415,0.000008419985,0.000008810649,0.000023097213,0.000017957858,0.9908845,0.0002713013,0.0054872595,0.000051355386,0.002506924],"study_design_scores_gemma":[9.3683707e-7,0.0000023262292,0.00019771299,0.0000014161246,0.000001633862,0.0000039123174,0.0000021234496,0.99769294,0.000073795796,0.001972475,0.000047926824,0.000002902146],"about_ca_topic_score_codex":0.009578751,"about_ca_topic_score_gemma":0.004902427,"teacher_disagreement_score":0.009578751,"about_ca_system_score_codex":0.0012872374,"about_ca_system_score_gemma":0.0006345532,"threshold_uncertainty_score":0.019046009},"labels":[],"label_agreement":null},{"id":"W7070893371","doi":"","title":"Poorly-informative priors in geotechnical risk analysis","year":2023,"lang":"en","type":"article","venue":"Trinity's Access to Research Output (TARA) (Trinity College Dublin)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Hydro (Canada)","funders":"","keywords":"Vagueness; Prior probability; Context (archaeology); Bayesian probability; Uncertainty quantification; Domain (mathematical analysis); Hazard; Term (time)","score_opus":0.40413807322039463,"score_gpt":0.5138787550290018,"score_spread":0.10974068180860719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7070893371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008851246,0.0038931689,0.9682597,0.008319334,0.000214561,0.000042627576,0.00009355151,0.00016699912,0.010158858],"genre_scores_gemma":[0.5284502,0.006952075,0.45496324,0.0027847458,0.001415703,0.0002520306,0.00025582206,0.0004199014,0.0045062853],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9674326,0.021507071,0.0016100181,0.0037117586,0.005077678,0.00066092587],"domain_scores_gemma":[0.8617894,0.118590854,0.0057165027,0.006586328,0.0058125365,0.0015043243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05114752,0.0021259412,0.0022007306,0.0046496955,0.0018667955,0.0083776675,0.0034419503,0.0049881237,0.0027342155],"category_scores_gemma":[0.16139835,0.0022185629,0.0018311788,0.0031682025,0.019573644,0.017910006,0.0069536828,0.012942476,0.0008745706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036272613,0.000027053098,0.0011652505,0.00017148872,0.00010006455,0.00014923856,0.0014965595,0.07263333,0.00037179128,0.8945657,0.0017291864,0.027554067],"study_design_scores_gemma":[0.000004327268,0.000008096105,0.00029566025,0.00010936207,0.000013293798,0.00003866508,0.00008409137,0.032862853,0.00017389475,0.96394336,0.0024270257,0.00003958551],"about_ca_topic_score_codex":0.0052768267,"about_ca_topic_score_gemma":0.0030762793,"teacher_disagreement_score":0.05114752,"about_ca_system_score_codex":0.0059172027,"about_ca_system_score_gemma":0.0023911998,"threshold_uncertainty_score":0.2704972},"labels":[],"label_agreement":null},{"id":"W7095138709","doi":"","title":"FACULTÉ DES SCIENCES ET GÉNIE","year":2015,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Work (physics); Context (archaeology); Government (linguistics); Natural (archaeology)","score_opus":0.7255351322801468,"score_gpt":0.4983949620656476,"score_spread":0.22714017021449917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095138709","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071414113,0.044748034,0.0031995256,0.048489608,0.009424313,0.00015435918,0.003827419,0.00049767963,0.8825177],"genre_scores_gemma":[0.04533261,0.017250914,0.0026748183,0.003496448,0.0016325167,0.0001577623,0.0013483879,0.00020077675,0.9279058],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973732,0.00039082672,0.00013403366,0.00046162022,0.0011291734,0.00051114435],"domain_scores_gemma":[0.99666387,0.00043234177,0.0002415857,0.0003804464,0.0010594492,0.0012222896],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019903367,0.0010437155,0.0013058734,0.0014422293,0.0033028822,0.0058655827,0.00094458717,0.0024416244,0.251589],"category_scores_gemma":[0.0060199737,0.0002717527,0.00050615857,0.0018281997,0.002215719,0.0022192472,0.0047638463,0.002968464,0.0986647],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022954322,0.0001132484,0.0041479277,0.0005583305,0.000024401576,0.00037373352,0.001556819,0.00046107252,0.0011594137,0.18613246,0.49505395,0.31018904],"study_design_scores_gemma":[0.000005395633,0.00002336387,0.0019056094,0.000089861285,0.0000017285291,0.00010236735,0.00016219876,0.00003439688,0.00014975318,0.0031095862,0.9944084,0.0000074446166],"about_ca_topic_score_codex":0.01573048,"about_ca_topic_score_gemma":0.022662545,"teacher_disagreement_score":0.748411,"about_ca_system_score_codex":0.005424864,"about_ca_system_score_gemma":0.009217388,"threshold_uncertainty_score":0.84164906},"labels":[],"label_agreement":null},{"id":"W7099886350","doi":"","title":"Executive Summary Smart Social Policy –“Making Work Pay”","year":2002,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Earnings; Payroll; Work (physics); Punitive damages; Productivity; Quarter (Canadian coin); Employability; Quality (philosophy)","score_opus":0.1066070160734092,"score_gpt":0.33851586091066593,"score_spread":0.23190884483725674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099886350","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019014244,0.012896019,0.0013678002,0.360594,0.09504039,0.0010771295,0.008997605,0.0010530255,0.51707256],"genre_scores_gemma":[0.011556198,0.013488689,0.001998297,0.1410622,0.027068686,0.0011877514,0.0054148966,0.00036357137,0.7978596],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99749976,0.00043423058,0.00016847011,0.00027764583,0.0011008657,0.0005189371],"domain_scores_gemma":[0.99230677,0.0015303075,0.00043699564,0.00055460015,0.0037596477,0.0014116379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005268604,0.0016129952,0.0008797382,0.0013623234,0.0018020476,0.006543767,0.0022300887,0.012901179,0.07288019],"category_scores_gemma":[0.009237066,0.00050612574,0.00089883944,0.0016746122,0.0009978071,0.003393792,0.0024660584,0.008452498,0.052756183],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020819163,0.000015358044,0.00006749135,0.00007267767,0.0000028368752,0.000016529659,0.000019219815,0.0000702616,0.00005889328,0.0038694337,0.9820947,0.013691845],"study_design_scores_gemma":[0.000013673846,0.000020081146,0.0005860093,0.00016563882,0.0000049308114,0.000007677637,0.000052734948,0.000058610567,0.00007845212,0.0010447721,0.9979594,0.000008039053],"about_ca_topic_score_codex":0.008918192,"about_ca_topic_score_gemma":0.009851312,"teacher_disagreement_score":0.07288019,"about_ca_system_score_codex":0.0029018316,"about_ca_system_score_gemma":0.010457652,"threshold_uncertainty_score":0.24380851},"labels":[],"label_agreement":null},{"id":"W7103411732","doi":"","title":"Uncertainty evaluation for functional kriging: an application to the Canadian temperature data set","year":2015,"lang":"en","type":"article","venue":"IRIS UNIMORE (University of Modena and Reggio Emilia)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Liquation; Diafiltration; Emperipolesis; Triacetin; Demotion","score_opus":0.3024865550826776,"score_gpt":0.35518232030420044,"score_spread":0.05269576522152286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7103411732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24469149,0.0013581151,0.74578226,0.0004889986,0.000047524656,0.00017697051,0.002305946,0.0016502248,0.0034984904],"genre_scores_gemma":[0.7590219,0.00050292653,0.23762643,0.00003846865,0.000013523535,0.00007362551,0.0013724193,0.0002732916,0.0010774595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911445,0.00032440497,0.000056525696,0.00009395858,0.00032807843,0.00008245504],"domain_scores_gemma":[0.9971004,0.0019048275,0.000075390206,0.00013741151,0.0007316473,0.000050399576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035902967,0.000789761,0.0010457136,0.0017102209,0.0010276219,0.0012100097,0.0010390803,0.0006531884,0.0010022536],"category_scores_gemma":[0.011137401,0.00035905515,0.0007932954,0.0022530293,0.0004699438,0.00060417905,0.0007141396,0.0007233091,0.00011136928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100214645,0.000031323707,0.0047855536,0.00017677782,0.000074093485,0.000067248606,0.00013056774,0.8773583,0.0016652024,0.0042727552,0.0011230906,0.11021487],"study_design_scores_gemma":[0.0000052739188,0.000011103046,0.0021843554,0.000011932023,0.0000093606395,0.000014079578,0.000044291006,0.9947836,0.0007397698,0.0016016078,0.000580213,0.000014346816],"about_ca_topic_score_codex":0.5288388,"about_ca_topic_score_gemma":0.53320086,"teacher_disagreement_score":0.4711612,"about_ca_system_score_codex":0.0028966032,"about_ca_system_score_gemma":0.0053648376,"threshold_uncertainty_score":0.9478719},"labels":[],"label_agreement":null},{"id":"W7115074596","doi":"10.1016/j.nahs.2025.101670","title":"A new framework for bounding reachability probabilities of continuous-time stochastic systems","year":2025,"lang":"en","type":"article","venue":"Nonlinear Analysis Hybrid Systems","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute of Software, Chinese Academy of Sciences; Chinese Academy of Sciences; National Research Foundation Singapore; Canadian Anesthesiologists' Society","keywords":"Reachability; Bounding overwatch; Complement (music); Upper and lower bounds; Reachability problem; Set (abstract data type); Partial differential equation; Stochastic process","score_opus":0.044923905533259804,"score_gpt":0.3420248129813498,"score_spread":0.29710090744809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115074596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00062391773,0.00015593639,0.9982681,0.000048512324,0.000031933712,0.000013789615,0.000026632093,0.00006975596,0.0007613875],"genre_scores_gemma":[0.38631263,0.0027848626,0.5964427,0.0005096793,0.0012125351,0.0006943481,0.00052959507,0.00045221747,0.01106142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99585575,0.001108938,0.00022322673,0.0007784923,0.0016792575,0.00035442706],"domain_scores_gemma":[0.99080676,0.006324068,0.00066270796,0.0007220817,0.0010973172,0.00038702128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006881402,0.0032040775,0.002652271,0.0034873758,0.0011811885,0.0036809721,0.004473121,0.0022982561,0.0040174415],"category_scores_gemma":[0.015961481,0.0014357496,0.0033295173,0.0019324946,0.0033804514,0.0055340175,0.0043563265,0.005017623,0.0007331493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004312097,0.00004877428,0.00030907305,0.0001605926,0.00011585554,0.00012758191,0.00007628289,0.61093295,0.0024048227,0.3686033,0.0012734178,0.015904268],"study_design_scores_gemma":[0.0000051385346,0.00002331118,0.00007176645,0.000018871076,0.000023609595,0.000030708143,0.0000055253545,0.93447536,0.00033137607,0.0638107,0.0011897231,0.000013832891],"about_ca_topic_score_codex":0.005067309,"about_ca_topic_score_gemma":0.0045293993,"teacher_disagreement_score":0.006881402,"about_ca_system_score_codex":0.002732957,"about_ca_system_score_gemma":0.002888974,"threshold_uncertainty_score":0.03639275},"labels":[],"label_agreement":null},{"id":"W7116989396","doi":"10.1080/15397734.2025.2601219","title":"Surrogate model-assisted reliability-based design of tapered composite tubes considering global buckling failure","year":2025,"lang":"en","type":"article","venue":"Mechanics Based Design of Structures and Machines","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Buckling; Composite number; Surrogate model; Finite element method; Failure mode and effects analysis; Stability (learning theory)","score_opus":0.046503525459846005,"score_gpt":0.30219774010528494,"score_spread":0.25569421464543896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116989396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08505973,0.00014836714,0.910605,0.00007189476,0.000014118411,0.000041104264,0.000035841793,0.00025163474,0.0037722685],"genre_scores_gemma":[0.93358475,0.00009073645,0.065190814,0.000021606766,0.0000055941236,0.00007907332,0.00006251028,0.000034681034,0.00093034364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996191,0.0001528989,0.000011665099,0.000031904525,0.00015341141,0.000030952575],"domain_scores_gemma":[0.9992766,0.0003270713,0.00012874107,0.00007296889,0.00015518331,0.000039406284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007861333,0.0004470145,0.00060259557,0.000489306,0.00020153825,0.00055509806,0.0005605435,0.0006384395,0.00082028843],"category_scores_gemma":[0.0016847005,0.00035875713,0.00052173325,0.00022303688,0.00042544148,0.00045015677,0.00055812026,0.00043365898,0.00017932644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014921968,0.0000074131417,0.00014385044,0.000010021116,0.0000047350536,0.000017491377,0.0000070862347,0.99397546,0.0016291658,0.0010542959,0.000034711262,0.003100897],"study_design_scores_gemma":[7.3134186e-7,0.000015431717,0.000032141517,0.0000011336797,9.850238e-7,0.0000049561368,0.0000010389737,0.99929917,0.00032652364,0.0002600938,0.000056733395,0.000001043248],"about_ca_topic_score_codex":0.0005582445,"about_ca_topic_score_gemma":0.0006263171,"teacher_disagreement_score":0.00082028843,"about_ca_system_score_codex":0.0003769646,"about_ca_system_score_gemma":0.00071186945,"threshold_uncertainty_score":0.004157543},"labels":[],"label_agreement":null},{"id":"W7132909975","doi":"","title":"Reliability-based structural optimization with fatigue crack initiation constraint","year":2007,"lang":"","type":"dissertation","venue":"TSpace","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bank of Canada; Library and Archives Canada","funders":"","keywords":"Probabilistic logic; Cantilever; Constraint (computer-aided design); Probabilistic design; Reliability (semiconductor); Monte Carlo method; Optimization problem; Surrogate model; Nonlinear system","score_opus":0.06615334852633974,"score_gpt":0.39098277689618977,"score_spread":0.32482942836985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132909975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013060541,0.000106695625,0.984202,0.00006283687,0.000007995169,0.00002963373,0.000024714896,0.00009699167,0.002408585],"genre_scores_gemma":[0.6205596,0.00030725152,0.3746684,0.00007314948,0.00002663526,0.00044395783,0.00014264749,0.00016243012,0.0036159875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996203,0.00012932393,0.00001314938,0.00006831186,0.00012738272,0.00004154854],"domain_scores_gemma":[0.99933773,0.00038333153,0.00008675178,0.000040350482,0.00013509963,0.00001678728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010743963,0.0008341534,0.0007635873,0.00068173546,0.00027692487,0.00042418853,0.00072276575,0.0008280152,0.0015239444],"category_scores_gemma":[0.0017662368,0.0006134687,0.00089768355,0.000436589,0.00054236816,0.000510398,0.00064777845,0.0006863509,0.00027086627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005815913,0.0000054723887,0.000102783044,0.000016909291,0.0000062233635,0.000009347758,0.000008123999,0.9928255,0.00073547877,0.0025762012,0.000062541214,0.0036457086],"study_design_scores_gemma":[0.0000017810473,0.000009287717,0.00003749177,0.0000016933412,0.0000023502248,0.0000037405396,0.0000014642675,0.9989697,0.00014336883,0.00071141345,0.00011646791,0.0000013100222],"about_ca_topic_score_codex":0.0024898886,"about_ca_topic_score_gemma":0.0024676402,"teacher_disagreement_score":0.0024898886,"about_ca_system_score_codex":0.00061082374,"about_ca_system_score_gemma":0.0010872621,"threshold_uncertainty_score":0.005682051},"labels":[],"label_agreement":null},{"id":"W7133358969","doi":"10.1109/edaps66187.2025.11411723","title":"Uncertainty Quantification Using Riemannian Tensor Train Completion for Polynomial Chaos","year":2025,"lang":"","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Polynomial chaos; Initialization; Curse of dimensionality; Uncertainty quantification; Tensor (intrinsic definition); Polynomial; CHAOS (operating system); Sampling (signal processing); Computational complexity theory","score_opus":0.19121542031294897,"score_gpt":0.41122008880319216,"score_spread":0.2200046684902432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133358969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036070298,0.000047499907,0.9956358,0.000067938745,0.000009254091,0.000017678738,0.000030459954,0.0001444486,0.00043974287],"genre_scores_gemma":[0.3895509,0.00035415697,0.60555625,0.00011595943,0.00008592245,0.00016415463,0.00038481306,0.00035907674,0.0034287418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883205,0.00046441646,0.00006680512,0.00015910406,0.00040354743,0.000074163196],"domain_scores_gemma":[0.9974988,0.0010373992,0.00029463868,0.00040181944,0.00063701946,0.00013027266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022402862,0.0010250913,0.00084301503,0.0010216521,0.0005873414,0.0010843694,0.0010926323,0.0007110826,0.0018139828],"category_scores_gemma":[0.0064123794,0.0003920115,0.00084321183,0.0007569524,0.0014438198,0.002007352,0.0018079507,0.001998656,0.00049090886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008581778,0.000045999783,0.0007476659,0.00014563,0.000071462455,0.00008529249,0.00015156229,0.7501254,0.0086304275,0.12453021,0.0027692004,0.11261141],"study_design_scores_gemma":[0.0000016048194,0.000012851927,0.00006114139,0.0000030453307,0.0000017542886,0.000009785942,0.0000038953494,0.99000007,0.0009262543,0.008516974,0.0004550331,0.0000075276976],"about_ca_topic_score_codex":0.0062573208,"about_ca_topic_score_gemma":0.0048799636,"teacher_disagreement_score":0.0062573208,"about_ca_system_score_codex":0.0012945924,"about_ca_system_score_gemma":0.001899051,"threshold_uncertainty_score":0.012441814},"labels":[],"label_agreement":null},{"id":"W7163649434","doi":"10.13182/t130-38157","title":"Uncertainty Quantification in Predictor-Corrector Quasi-Static Monte Carlo Transient Simulation","year":2022,"lang":"","type":"article","venue":"Transactions of the American Nuclear Society","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Uncertainty quantification; Monte Carlo method; Uncertainty analysis; Transient (computer programming); Measurement uncertainty; Monte Carlo method in statistical physics","score_opus":0.0405086694288359,"score_gpt":0.2956044899977389,"score_spread":0.255095820568903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7163649434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013802389,0.00018895185,0.9830499,0.00022031777,0.00002861416,0.00002567502,0.000052739684,0.00028533462,0.0023460188],"genre_scores_gemma":[0.8981913,0.00027658953,0.098908216,0.00010689175,0.000045685978,0.00014495426,0.0001250967,0.00018656996,0.0020146626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869114,0.0005899708,0.00005278998,0.000104353545,0.00045113955,0.00011067771],"domain_scores_gemma":[0.9935546,0.0047678766,0.00043264768,0.00042550286,0.00069504406,0.000124395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034077454,0.0006400467,0.001437123,0.0009774095,0.0008248582,0.0020420027,0.0016003532,0.0015519238,0.0018561699],"category_scores_gemma":[0.013991609,0.0008616023,0.0005816643,0.0008413463,0.0018909421,0.0019124681,0.0017108978,0.0014941869,0.00024460963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025627683,0.000009219123,0.00013632604,0.000022228269,0.000009560232,0.00001039121,0.000014121685,0.9844956,0.00020829806,0.011845466,0.000081830614,0.0031413801],"study_design_scores_gemma":[0.0000014567514,0.0000024418644,0.000018935361,0.0000027946817,0.0000011722922,0.0000013323979,0.0000011200319,0.9967969,0.00010531585,0.003024071,0.00004289304,0.0000016108337],"about_ca_topic_score_codex":0.009201924,"about_ca_topic_score_gemma":0.00534408,"teacher_disagreement_score":0.009201924,"about_ca_system_score_codex":0.0015152105,"about_ca_system_score_gemma":0.0021650614,"threshold_uncertainty_score":0.018296719},"labels":[],"label_agreement":null},{"id":"W74097125","doi":"10.23940/ijpe.10.2.p123.mag","title":"Performability-Based Design Optimization of Dynamic systems","year":2010,"lang":"en","type":"article","venue":"International Journal of Performability Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Reliability engineering; Engineering","score_opus":0.03478166115955999,"score_gpt":0.31011159587058656,"score_spread":0.27532993471102657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W74097125","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03959823,0.00023463531,0.9481727,0.00025024463,0.000037158778,0.000082416445,0.000051795574,0.00013514653,0.011437684],"genre_scores_gemma":[0.95197517,0.00021430982,0.043438613,0.0000572854,0.00004020086,0.00016874344,0.00009580644,0.000080428516,0.003929527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991721,0.00031969414,0.000029764898,0.00009894278,0.0002558478,0.0001237265],"domain_scores_gemma":[0.9991435,0.00046230858,0.0001236635,0.000054768898,0.00016944697,0.00004629525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020864576,0.0016898111,0.0012862858,0.0010378532,0.00051214633,0.0012510491,0.0008021188,0.0010214584,0.002460892],"category_scores_gemma":[0.0033584184,0.0005832918,0.0008735812,0.00051589473,0.0009518312,0.0008862722,0.0013206739,0.0006622573,0.00032981895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022925726,0.000016688637,0.00008637946,0.000029623478,0.000013568011,0.000010157688,0.00001371626,0.9884774,0.00080784835,0.0055323867,0.00008139036,0.0049077705],"study_design_scores_gemma":[0.000006484209,0.00004457405,0.00008696444,0.000004220152,0.0000075300104,0.0000040710365,0.0000049203677,0.995742,0.00031641228,0.0035885358,0.00019139923,0.0000028711827],"about_ca_topic_score_codex":0.0024495078,"about_ca_topic_score_gemma":0.0018683847,"teacher_disagreement_score":0.002460892,"about_ca_system_score_codex":0.0010524964,"about_ca_system_score_gemma":0.0011125226,"threshold_uncertainty_score":0.0110343695},"labels":[],"label_agreement":null},{"id":"W793791935","doi":"10.1016/j.apm.2015.06.009","title":"Efficient numerical simulation method for evaluations of global sensitivity analysis with parameter uncertainty","year":2015,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Uncertainty quantification; Realization (probability); Variance (accounting); Sampling (signal processing); Sensitivity (control systems); Set (abstract data type); Construct (python library); Uncertainty analysis; Function (biology); Computer science; Probability density function; Importance sampling; Measurement uncertainty; Mathematics; Algorithm; Propagation of uncertainty; Mathematical optimization; Monte Carlo method; Statistics; Machine learning; Engineering","score_opus":0.18346940501868922,"score_gpt":0.4137982262383677,"score_spread":0.2303288212196785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W793791935","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025919615,0.00004572283,0.99540496,0.00002255102,0.000016984342,0.000028969725,0.000034012304,0.00026713512,0.0015876001],"genre_scores_gemma":[0.24795918,0.00018388616,0.7472666,0.000073488874,0.000033757507,0.0005203571,0.00021386065,0.00039720567,0.0033515512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994142,0.00023085787,0.000032075797,0.000044544133,0.0002366072,0.000041791303],"domain_scores_gemma":[0.9983046,0.0010695795,0.00008440507,0.00015907208,0.00034080117,0.000041467112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001435077,0.00089669874,0.001221068,0.0010943532,0.0006621942,0.00065572944,0.0013545862,0.0011039732,0.0048949285],"category_scores_gemma":[0.0033982254,0.0005976719,0.0009487912,0.0008926079,0.0005726222,0.0007316055,0.0011108399,0.0012637236,0.0007142176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046428868,0.00004661072,0.00016732342,0.00007044911,0.00003629939,0.000048188824,0.000034046145,0.9540361,0.004205817,0.01294135,0.000656503,0.027710795],"study_design_scores_gemma":[0.000005062526,0.000005611759,0.00001986076,0.0000031657767,0.000003875019,0.000007895515,0.0000015965563,0.9976915,0.00045846685,0.0013610858,0.0004391146,0.0000027850094],"about_ca_topic_score_codex":0.0041240742,"about_ca_topic_score_gemma":0.0035005447,"teacher_disagreement_score":0.0048949285,"about_ca_system_score_codex":0.00056177395,"about_ca_system_score_gemma":0.0012944156,"threshold_uncertainty_score":0.016375184},"labels":[],"label_agreement":null},{"id":"W835815097","doi":"10.1007/s40435-015-0195-9","title":"Fractional order derivative aero-servo-viscoelasticity","year":2015,"lang":"en","type":"article","venue":"International Journal of Dynamics and Control","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"University of Illinois at Urbana-Champaign; National Centre for Supercomputing Applications","keywords":"Fractional calculus; Viscoelasticity; Mathematics; Mathematical analysis; Convolution (computer science); Laplace transform; Derivative (finance); Flutter; Divergence (linguistics); Applied mathematics; Physics; Computer science; Mechanics","score_opus":0.04533893097272151,"score_gpt":0.32973594461820316,"score_spread":0.28439701364548164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W835815097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35199472,0.00092383195,0.62167794,0.00048864714,0.000389423,0.000045588662,0.00010599621,0.00067501666,0.02369888],"genre_scores_gemma":[0.9874936,0.000109111315,0.008979859,0.000024798497,0.0000168193,0.0000066747816,0.000014259686,0.000011965049,0.003342886],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992025,0.000009732621,0.0000043846358,0.000016406742,0.000039900773,0.000009211471],"domain_scores_gemma":[0.9998398,0.000052747422,0.00003335929,0.00003093937,0.000033013937,0.000010146374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001488913,0.00019342698,0.00022358578,0.0002118183,0.0002755643,0.00041420455,0.00021242163,0.00041124882,0.0016970476],"category_scores_gemma":[0.0006083597,0.00008768008,0.00020471703,0.00014992458,0.00022748607,0.00036226545,0.00033700856,0.00027968173,0.0001507444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063100964,0.00017978388,0.0030606033,0.00048185626,0.00006988134,0.0008666657,0.00023912013,0.3123533,0.45618087,0.058261592,0.0011279762,0.16654736],"study_design_scores_gemma":[0.00001732285,0.00023994489,0.003410098,0.000018358205,0.000027932574,0.00037053772,0.000021669184,0.94714665,0.035630383,0.00814005,0.0049563996,0.000020617847],"about_ca_topic_score_codex":0.00038174252,"about_ca_topic_score_gemma":0.00043436105,"teacher_disagreement_score":0.0016970476,"about_ca_system_score_codex":0.00021390468,"about_ca_system_score_gemma":0.00017835258,"threshold_uncertainty_score":0.0056771636},"labels":[],"label_agreement":null},{"id":"W841994648","doi":"","title":"LIKELIHOOD-BASED INFERENTIAL METHODS FOR SOME FLEXIBLE CURE RATE MODELS","year":2014,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Statistics; Maximum likelihood; Econometrics; Computer science; Mathematics","score_opus":0.06769496850596923,"score_gpt":0.3286094080807715,"score_spread":0.26091443957480226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W841994648","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018281778,0.00025075252,0.99742585,0.00010016303,0.000008749836,0.000048470378,0.0000446536,0.00008228075,0.00021083627],"genre_scores_gemma":[0.15951818,0.0012425436,0.8356245,0.00022747835,0.00017535168,0.0008395748,0.0005649929,0.00014905397,0.0016583053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98444206,0.011582918,0.00073503325,0.0014761256,0.0014829125,0.0002810111],"domain_scores_gemma":[0.92107725,0.071913116,0.002592226,0.0024552394,0.0016130795,0.00034915327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0269751,0.0013310702,0.0020509495,0.0036464185,0.0007408416,0.0020041734,0.0031924967,0.0017739765,0.0027556587],"category_scores_gemma":[0.08342965,0.0008086344,0.0021437113,0.0025311317,0.0021342137,0.0027904252,0.0031528987,0.0043129185,0.0005286411],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017267159,0.00017934134,0.004468706,0.0005749131,0.0002920365,0.0007676426,0.0009500969,0.42604196,0.0015273241,0.3505814,0.001687117,0.21275668],"study_design_scores_gemma":[0.00002243161,0.000050936924,0.0004724787,0.000045875488,0.00003033194,0.00016642397,0.000053362102,0.87406546,0.00050030125,0.12315722,0.0014025448,0.000032643307],"about_ca_topic_score_codex":0.0014946538,"about_ca_topic_score_gemma":0.0011916981,"teacher_disagreement_score":0.0269751,"about_ca_system_score_codex":0.0013068919,"about_ca_system_score_gemma":0.0018174622,"threshold_uncertainty_score":0.14265966},"labels":[],"label_agreement":null},{"id":"W9321275","doi":"10.1007/1-4020-4891-2_63","title":"Time-Variant Reliability Analysis for Series Systems With Log-Normal Vector Response","year":2007,"lang":"en","type":"book-chapter","venue":"Solid mechanics and its applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Series (stratigraphy); Reliability (semiconductor); Computer science; Reliability engineering; Engineering; Biology; Physics","score_opus":0.0560302646246323,"score_gpt":0.30021772113027495,"score_spread":0.24418745650564266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W9321275","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004615708,0.0016630008,0.9903728,0.00014558742,0.00007617232,0.00001076665,0.000044122342,0.00017371206,0.002898023],"genre_scores_gemma":[0.73270994,0.010442991,0.21472237,0.00023154927,0.0010297248,0.00015405557,0.0006554912,0.0005665526,0.039487295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955004,0.00011319505,0.000022961816,0.00008862618,0.00020147681,0.000023602439],"domain_scores_gemma":[0.998389,0.0010369495,0.00010980287,0.0001346506,0.00031231964,0.000017248594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012244921,0.0011888705,0.001039264,0.0011070531,0.00023012134,0.0009738086,0.0010338535,0.000841533,0.002851958],"category_scores_gemma":[0.0039182873,0.00042560213,0.00088248483,0.001043703,0.000952277,0.0016391269,0.00044404584,0.0014467693,0.0006309442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039792976,0.0000396696,0.0005667971,0.00028664293,0.00009884839,0.00016194963,0.00015611721,0.68385065,0.008584535,0.20030062,0.0060170502,0.09989729],"study_design_scores_gemma":[0.0000013046184,0.000019005282,0.00024672717,0.000010748517,0.000013725632,0.000069517766,0.000010316126,0.9280257,0.000650054,0.06959708,0.0013450737,0.000010663141],"about_ca_topic_score_codex":0.0015994597,"about_ca_topic_score_gemma":0.0011410372,"teacher_disagreement_score":0.002851958,"about_ca_system_score_codex":0.00066085765,"about_ca_system_score_gemma":0.00038102176,"threshold_uncertainty_score":0.009540796},"labels":[],"label_agreement":null}]}