{"meta":{"query_hash":"763eca5e0b98","filters":{"venue":"Journal of Process Control"},"cohort_total":169,"direct_labels_cover":0,"predictions_cover":169,"exported":169,"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/763eca5e0b98","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Process+Control"},"results":[{"id":"W1197831148","doi":"10.1016/j.jprocont.2015.07.002","title":"Robust multivariable estimation and control in an epitaxial thin film growth process under uncertainty","year":2015,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Solidification and crystal growth phenomena","field":"Materials Science","cited_by":39,"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":"Estimator; Multivariable calculus; Surface roughness; Monte Carlo method; Parametric statistics; Surface finish; Control theory (sociology); Computer science; Mathematics; Materials science; Engineering; Statistics; Artificial intelligence; Control (management); Control engineering","score_opus":0.03442063953067794,"score_gpt":0.2862608276913659,"score_spread":0.251840188160688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1197831148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18522109,0.00043905186,0.81064236,0.00063419034,0.00006343379,0.000041715153,0.00004954667,0.00014432872,0.0027643056],"genre_scores_gemma":[0.99126446,0.000106578416,0.0073500164,0.00002705684,0.000015162509,0.00002462985,0.000021328635,0.0000135795235,0.0011773591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995938,0.00012379867,0.000019663108,0.00010129953,0.0001052797,0.000056173438],"domain_scores_gemma":[0.99833965,0.0011416366,0.0002205371,0.000047190428,0.00022643243,0.000024530122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093649194,0.0006354595,0.0008588777,0.0002974056,0.0005209543,0.0011543982,0.0004852118,0.000936862,0.00055065204],"category_scores_gemma":[0.0036149344,0.00050865515,0.00049432047,0.00037496304,0.0007188233,0.00073942106,0.00094307587,0.0008515867,0.000053314263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017659889,0.000048520717,0.0005954511,0.00010478366,0.00005650639,0.00013247578,0.00011068648,0.9631093,0.014226774,0.0066188,0.00019694905,0.014623114],"study_design_scores_gemma":[0.0000046232394,0.000034543165,0.00027263563,0.0000015734911,0.0000054697985,0.0000043195946,0.000006154389,0.998033,0.0010644774,0.0005121202,0.000055964603,0.0000050475323],"about_ca_topic_score_codex":0.013226544,"about_ca_topic_score_gemma":0.006567092,"teacher_disagreement_score":0.013226544,"about_ca_system_score_codex":0.0007080019,"about_ca_system_score_gemma":0.0007863228,"threshold_uncertainty_score":0.026299119},"labels":[],"label_agreement":null},{"id":"W1684160175","doi":"10.1016/j.jprocont.2015.08.013","title":"Optimal continuous-time state estimation for linear finite and infinite-dimensional chemical process systems with state constraints","year":2015,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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 Alberta","funders":"","keywords":"Estimator; State (computer science); Mathematical optimization; Process (computing); Constraint (computer-aided design); Process state; State estimator; Dissipative system; Continuous stirred-tank reactor; Mathematics; Computer science; Applied mathematics; Control theory (sociology); Algorithm; Engineering","score_opus":0.006750881057735581,"score_gpt":0.2303783005188191,"score_spread":0.22362741946108353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1684160175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0461604,0.00060726044,0.95019525,0.00044159024,0.000067639274,0.00004813995,0.000120799195,0.00025416585,0.0021047804],"genre_scores_gemma":[0.96487516,0.00029511916,0.03238143,0.00008277693,0.000060256654,0.00009903936,0.00020351716,0.00004022842,0.001962508],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903274,0.000281484,0.00007211672,0.00027260935,0.00019094192,0.00015007627],"domain_scores_gemma":[0.99512625,0.0038525532,0.0003899507,0.00013841278,0.00041586746,0.000076943455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018917314,0.0013332514,0.001997073,0.0006801452,0.00075452036,0.0021480343,0.0010047101,0.0020428456,0.0018045607],"category_scores_gemma":[0.007883461,0.0011021124,0.0008853977,0.0007159222,0.001853482,0.0021682954,0.0015495406,0.0018896393,0.00026965907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017194495,0.000047009333,0.00033188143,0.00012511075,0.000051384737,0.000040474984,0.00007748829,0.9753567,0.0010410012,0.0045272424,0.0002914391,0.017938426],"study_design_scores_gemma":[0.000007798544,0.000014247262,0.0001041779,0.0000045433703,0.0000050708745,0.0000027902088,0.0000046202726,0.9984528,0.00028715245,0.0010602156,0.000051121693,0.000005504697],"about_ca_topic_score_codex":0.022496168,"about_ca_topic_score_gemma":0.01349714,"teacher_disagreement_score":0.022496168,"about_ca_system_score_codex":0.0014264692,"about_ca_system_score_gemma":0.0026161615,"threshold_uncertainty_score":0.044730425},"labels":[],"label_agreement":null},{"id":"W1965833531","doi":"10.1016/j.jprocont.2009.07.011","title":"Fault detection and diagnosis in process data using one-class support vector machines","year":2009,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":364,"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":"University of Alberta","keywords":"Fault detection and isolation; Support vector machine; Pattern recognition (psychology); Artificial intelligence; Benchmark (surveying); Principal component analysis; Computer science; Feature selection; Feature vector; False alarm; Feature (linguistics); Fault (geology); Constant false alarm rate; Process (computing); Data mining","score_opus":0.019388778471370976,"score_gpt":0.2780280444186275,"score_spread":0.2586392659472565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965833531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093486175,0.0005438287,0.90390307,0.00019540079,0.00011769433,0.00006137398,0.00013839219,0.0010747055,0.00047938322],"genre_scores_gemma":[0.9216223,0.00019116825,0.07737583,0.000039124476,0.00006410324,0.000059239854,0.00019234467,0.000021043043,0.00043483826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987362,0.00028702064,0.00016690466,0.00027487922,0.00040424208,0.00013079499],"domain_scores_gemma":[0.9950949,0.0033282156,0.00047637057,0.00032805433,0.0006889754,0.00008350396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014919768,0.0009818802,0.0014406877,0.0011726264,0.00042960796,0.0010473985,0.0008799684,0.0010190737,0.00081259926],"category_scores_gemma":[0.008459498,0.000250611,0.0006406705,0.0008429318,0.0005360771,0.0012355213,0.00054413365,0.0014706951,0.00025519784],"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":[0.0016184783,0.00044516393,0.0049418,0.00041248862,0.00014962319,0.00030937576,0.00015990983,0.17730911,0.01603403,0.0038920662,0.001429797,0.7932981],"study_design_scores_gemma":[0.000018612036,0.00010574445,0.0008630702,0.000008292235,0.000015124106,0.00007106541,0.000015394453,0.9927874,0.004243,0.0016785286,0.00018305484,0.0000106407315],"about_ca_topic_score_codex":0.0020664542,"about_ca_topic_score_gemma":0.0011470179,"teacher_disagreement_score":0.0020664542,"about_ca_system_score_codex":0.0004369923,"about_ca_system_score_gemma":0.000595566,"threshold_uncertainty_score":0.007890403},"labels":[],"label_agreement":null},{"id":"W1965877603","doi":"10.1016/j.jprocont.2007.07.009","title":"Identification from step responses with transient initial conditions","year":2007,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","cited_by":60,"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":"Transient (computer programming); Two step; Process (computing); Transient response; Identification (biology); Control theory (sociology); Step response; Computer science; System identification; Steady state (chemistry); Algorithm; Iterative and incremental development; Control engineering; Engineering; Mathematics; Data modeling; Applied mathematics; Artificial intelligence; Control (management)","score_opus":0.0074878351099177095,"score_gpt":0.2573148725084366,"score_spread":0.2498270373985189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965877603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039312318,0.00010850716,0.9564545,0.000039909257,0.00004868134,0.0001006556,0.00008559305,0.00091334514,0.00293653],"genre_scores_gemma":[0.9112824,0.00024989774,0.081109464,0.000061642684,0.000018426348,0.00021866529,0.0003383134,0.00014979522,0.0065713925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977404,0.00005073473,0.000012625514,0.000035007182,0.00009356461,0.00003404022],"domain_scores_gemma":[0.9986759,0.0009093744,0.000058387166,0.0001648799,0.00017453893,0.000016976717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000517599,0.0008245112,0.0006517041,0.0005405641,0.0002669819,0.00064311933,0.0004384017,0.0010833163,0.004040888],"category_scores_gemma":[0.003979024,0.00046937613,0.00041754637,0.0004244403,0.0004473943,0.0007754154,0.00078450853,0.0009395633,0.0012132925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014148167,0.0001648744,0.0011601431,0.0011337596,0.00016488414,0.00084424706,0.0005439265,0.5836558,0.103820145,0.018486911,0.0023913581,0.2862191],"study_design_scores_gemma":[0.000027715554,0.00012571708,0.0009904448,0.000043642707,0.000018676179,0.00015855506,0.00004880792,0.96099037,0.030986825,0.0054712244,0.0011168459,0.000021216556],"about_ca_topic_score_codex":0.0009564616,"about_ca_topic_score_gemma":0.0008724935,"teacher_disagreement_score":0.004040888,"about_ca_system_score_codex":0.00020973447,"about_ca_system_score_gemma":0.00036172918,"threshold_uncertainty_score":0.013518155},"labels":[],"label_agreement":null},{"id":"W1966491970","doi":"10.1016/s0959-1524(02)00122-1","title":"Sensor selection for model-based real-time optimization: relating design of experiments and design cost","year":2003,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"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":"Metric (unit); Selection (genetic algorithm); Process (computing); Mathematical optimization; Computer science; Engineering; Industrial engineering; Reliability engineering; Operations research; Control engineering; Machine learning; Operations management; Mathematics","score_opus":0.018588865676067642,"score_gpt":0.256012836447609,"score_spread":0.23742397077154137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966491970","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04231183,0.0004428741,0.9557551,0.00020347635,0.000027504433,0.00010255397,0.000038108923,0.0003200775,0.00079854066],"genre_scores_gemma":[0.8604901,0.00021344339,0.13846634,0.00009952429,0.000023693485,0.0001685082,0.000060028004,0.00008474547,0.00039362223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967217,0.0022209766,0.00013949527,0.00033225922,0.00045823396,0.00012733511],"domain_scores_gemma":[0.98608834,0.010740232,0.001266501,0.000913748,0.0008319441,0.0001592637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073153228,0.0017171231,0.002446008,0.0007974246,0.00033397408,0.0012658294,0.0011991353,0.0014879883,0.0012914514],"category_scores_gemma":[0.020530693,0.0009229427,0.00089897605,0.00051583257,0.0012668853,0.002592932,0.0009903852,0.0010122398,0.00021394546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018900263,0.0006006339,0.00093864667,0.0005563269,0.00019058747,0.00006115464,0.00006518675,0.86692625,0.032969944,0.0075951535,0.00044924073,0.08775686],"study_design_scores_gemma":[0.00010331635,0.00039560776,0.00036036575,0.0000095110745,0.00003930578,0.000017350209,0.000007282949,0.9835444,0.011660879,0.0036875822,0.00015876346,0.00001573036],"about_ca_topic_score_codex":0.000785585,"about_ca_topic_score_gemma":0.0007890716,"teacher_disagreement_score":0.0073153228,"about_ca_system_score_codex":0.0008245426,"about_ca_system_score_gemma":0.0012516076,"threshold_uncertainty_score":0.038687587},"labels":[],"label_agreement":null},{"id":"W1967356337","doi":"10.1016/j.jprocont.2013.05.003","title":"Measurement-based optimization of batch and repetitive processes using an integrated two-layer architecture","year":2013,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":7,"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":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Agentúra na Podporu Výskumu a Vývoja; McMaster University","keywords":"Layer (electronics); Architecture; Process engineering; Computer science; Computer architecture; Engineering; Materials science; Nanotechnology","score_opus":0.013185885879848697,"score_gpt":0.23695598447967067,"score_spread":0.22377009859982197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967356337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.149039,0.00033509426,0.84532225,0.00019139657,0.000055449527,0.000073275645,0.00004251726,0.0013398735,0.0036011238],"genre_scores_gemma":[0.93075436,0.000057481717,0.067906134,0.000034745994,0.000014701326,0.00006291477,0.000038681086,0.00004362773,0.0010874466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994861,0.00011301554,0.000029713408,0.00011840946,0.00017394726,0.000078846344],"domain_scores_gemma":[0.99954176,0.0001990772,0.000062442195,0.0000555668,0.00011467158,0.000026550257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009116508,0.0009396999,0.0011178396,0.00028071261,0.00048013814,0.0013660268,0.001432671,0.0010195465,0.0010130686],"category_scores_gemma":[0.0012632344,0.0006710914,0.0006967593,0.00038396253,0.00043119743,0.0011272959,0.0009828447,0.000910639,0.0001901149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037403672,0.0001806348,0.00076960487,0.00007682069,0.00008183191,0.00004764372,0.000054126704,0.92558616,0.026494766,0.0025076123,0.00028659496,0.04354014],"study_design_scores_gemma":[0.0000073127117,0.000028316925,0.00009260807,9.708639e-7,0.000005393089,0.0000023355742,0.0000013634004,0.99790215,0.0017271271,0.00017886645,0.000050045233,0.0000035630337],"about_ca_topic_score_codex":0.0061525046,"about_ca_topic_score_gemma":0.0061860476,"teacher_disagreement_score":0.0061525046,"about_ca_system_score_codex":0.00073691574,"about_ca_system_score_gemma":0.0015115745,"threshold_uncertainty_score":0.0122333765},"labels":[],"label_agreement":null},{"id":"W1969419131","doi":"10.1016/s0959-1524(01)00002-6","title":"Model predictive control using an extended ARMarkov model","year":2002,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"","keywords":"Model predictive control; Control theory (sociology); Controller (irrigation); Markov chain; Consistency (knowledge bases); Markov process; Computer science; Identification (biology); Markov model; Process (computing); Mathematics; Control (management); Artificial intelligence; Machine learning; Statistics","score_opus":0.020762857762902532,"score_gpt":0.2476948779197096,"score_spread":0.22693202015680705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969419131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030804934,0.00056205736,0.96179086,0.00026919297,0.00014761844,0.000037833535,0.00010706592,0.0003999062,0.0058804806],"genre_scores_gemma":[0.96779335,0.0003663021,0.026889177,0.000080288664,0.000089988614,0.000104377476,0.00017888627,0.000033359393,0.004464197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995677,0.00014674192,0.00003484406,0.00009537885,0.000103417704,0.000051993506],"domain_scores_gemma":[0.99910516,0.0005261585,0.0000955857,0.000078987185,0.00017188929,0.000022171493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010937024,0.0008465887,0.0017607698,0.00040154552,0.00047856153,0.0015860687,0.0011526495,0.0012206585,0.0023437813],"category_scores_gemma":[0.0022465717,0.0005473535,0.0007464667,0.0006095364,0.00069891673,0.001970028,0.001153298,0.0016908547,0.0005153777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018071891,0.000049415932,0.00019371732,0.00008536704,0.00006963004,0.00005960182,0.000036474677,0.9602022,0.0011421068,0.013233222,0.0005173649,0.024230085],"study_design_scores_gemma":[0.000009264286,0.000024161975,0.00006015886,0.0000024494245,0.0000064182486,0.0000034286948,0.0000011936484,0.99649316,0.00012896633,0.0031439436,0.00012243706,0.0000044836943],"about_ca_topic_score_codex":0.004476395,"about_ca_topic_score_gemma":0.0031019978,"teacher_disagreement_score":0.004476395,"about_ca_system_score_codex":0.00041447056,"about_ca_system_score_gemma":0.0007001808,"threshold_uncertainty_score":0.008900702},"labels":[],"label_agreement":null},{"id":"W1970621495","doi":"10.1016/j.jprocont.2009.10.004","title":"Detection and quantification of valve stiction by the method of unknown input estimation","year":2009,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Stiction; Control theory (sociology); Estimation; Computer science; Mathematics; Materials science; Engineering; Artificial intelligence; Microelectromechanical systems; Control (management)","score_opus":0.007352080264471618,"score_gpt":0.26393336108866794,"score_spread":0.25658128082419634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970621495","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035025053,0.000503562,0.9635889,0.00006188625,0.000039155842,0.000017544606,0.000032152428,0.00029642717,0.00043526368],"genre_scores_gemma":[0.7790208,0.00035575512,0.21952894,0.00003682192,0.0000430436,0.00004045929,0.00006456694,0.000035227047,0.0008744693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994407,0.00018581057,0.000026371594,0.000087749104,0.00021970975,0.000039707746],"domain_scores_gemma":[0.99812657,0.0011360722,0.00022077998,0.00013254717,0.00034802768,0.00003595826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007288449,0.0003584324,0.0007514732,0.0007536066,0.00022451482,0.00069134455,0.00049118034,0.00094183587,0.0004795049],"category_scores_gemma":[0.0034167077,0.00026940694,0.00031983264,0.00031592057,0.0005317843,0.00070111826,0.0005648758,0.0006125531,0.00013942692],"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":[0.00084354106,0.00016988633,0.0061702225,0.00070532673,0.0001886048,0.00017521628,0.00027089275,0.16780835,0.27666566,0.012789211,0.0011691434,0.533044],"study_design_scores_gemma":[0.000012126362,0.00009132563,0.0024207758,0.00001585903,0.000018392955,0.00013908399,0.00000910942,0.9682962,0.027275467,0.0011181466,0.0005636438,0.000039911065],"about_ca_topic_score_codex":0.0007608544,"about_ca_topic_score_gemma":0.0007532315,"teacher_disagreement_score":0.00094183587,"about_ca_system_score_codex":0.0002461019,"about_ca_system_score_gemma":0.00043676616,"threshold_uncertainty_score":0.0038545728},"labels":[],"label_agreement":null},{"id":"W1972864165","doi":"10.1016/j.jprocont.2013.03.011","title":"A switching robust model predictive control approach for nonlinear systems","year":2013,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Ministry of Knowledge Economy","keywords":"Model predictive control; Control theory (sociology); Nonlinear system; Lyapunov function; Continuous stirred-tank reactor; Scheme (mathematics); Stability (learning theory); Control (management); Computer science; Dynamic programming; Control engineering; Engineering; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning","score_opus":0.008660982814741489,"score_gpt":0.21071950076412546,"score_spread":0.20205851794938398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972864165","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065882,0.00037436633,0.9864966,0.00012272183,0.0001161682,0.00003388473,0.000039582672,0.0003893035,0.005839004],"genre_scores_gemma":[0.88081896,0.00066455896,0.10857866,0.00018344574,0.0002617165,0.00023053927,0.00021524065,0.00015939928,0.008887479],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997397,0.00007087051,0.000015904185,0.000048841954,0.000098193545,0.000026511765],"domain_scores_gemma":[0.9997389,0.00014194794,0.000023203169,0.00003195579,0.000055123266,0.000008789671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006011426,0.000818061,0.001117517,0.00048710665,0.00039951917,0.000991299,0.0012078189,0.0008774574,0.00414232],"category_scores_gemma":[0.00084828725,0.00042357537,0.0007830917,0.00047908892,0.00052119396,0.00076454005,0.00090623973,0.001102619,0.00059155724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012727766,0.00007144724,0.00011645453,0.00018366547,0.000089062145,0.0001165851,0.0000720228,0.8511414,0.0051166117,0.029552432,0.0019159749,0.11149704],"study_design_scores_gemma":[0.000004982787,0.000023455827,0.000029069734,0.0000024549854,0.0000067963365,0.0000050798812,0.0000017767572,0.99659854,0.00023436183,0.0027377654,0.00035252707,0.0000030907804],"about_ca_topic_score_codex":0.0032116063,"about_ca_topic_score_gemma":0.0023978038,"teacher_disagreement_score":0.00414232,"about_ca_system_score_codex":0.0004018241,"about_ca_system_score_gemma":0.0004948935,"threshold_uncertainty_score":0.013857424},"labels":[],"label_agreement":null},{"id":"W1973037882","doi":"10.1016/j.jprocont.2011.01.012","title":"Lyapunov stability of economically oriented NMPC for cyclic processes","year":2011,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":196,"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":"Lyapunov function; Control theory (sociology); Model predictive control; Stability (learning theory); Nonlinear system; Swing; Pressure swing adsorption; Computer science; Electricity; Lyapunov stability; Steady state (chemistry); Mathematical optimization; Mathematics; Engineering; Control (management)","score_opus":0.014966992960922954,"score_gpt":0.21931931313785966,"score_spread":0.2043523201769367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973037882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33540666,0.00048131868,0.62888604,0.00066504563,0.00009073913,0.00007404146,0.00013573031,0.00014762205,0.0341128],"genre_scores_gemma":[0.99114424,0.00013950258,0.005060618,0.000029986828,0.000017195796,0.000037565307,0.00003551934,0.000017290187,0.003517995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978787,0.00008212719,0.000005936731,0.000027431643,0.00006190949,0.000034764227],"domain_scores_gemma":[0.9993774,0.00030778788,0.00010795595,0.000030391046,0.0001470611,0.00002947602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006655263,0.00072139164,0.0004376553,0.00043957762,0.00042364968,0.00093834894,0.0004009729,0.00050892046,0.0020429445],"category_scores_gemma":[0.0019329893,0.00022377794,0.00036494667,0.00026094762,0.0007255835,0.0004025079,0.0007035174,0.0005890516,0.00020331315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013539655,0.000042626238,0.0004851517,0.00010790102,0.000048891223,0.00018567698,0.00014187947,0.8146802,0.00941555,0.16390909,0.0008248384,0.010022762],"study_design_scores_gemma":[0.0000084046615,0.00004304502,0.00013155819,0.0000057250786,0.0000046165833,0.000012112943,0.000014323696,0.980183,0.00053499045,0.018766554,0.00029010078,0.000005557145],"about_ca_topic_score_codex":0.0037022333,"about_ca_topic_score_gemma":0.0023262226,"teacher_disagreement_score":0.0037022333,"about_ca_system_score_codex":0.00092238135,"about_ca_system_score_gemma":0.00093771884,"threshold_uncertainty_score":0.0073613524},"labels":[],"label_agreement":null},{"id":"W1976590516","doi":"10.1016/s0959-1524(01)00043-9","title":"Issues in performance diagnostics of model-based controllers","year":2002,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":99,"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":"MIMO; Process (computing); Task (project management); Control theory (sociology); Key (lock); Control engineering; Nonlinear system; Computer science; Controller (irrigation); Disturbance (geology); Engineering; Control (management); Artificial intelligence; Systems engineering; Channel (broadcasting)","score_opus":0.009650032165954751,"score_gpt":0.22389336768508775,"score_spread":0.214243335519133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976590516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04057976,0.0035674956,0.94191647,0.005851917,0.0003422378,0.000053274583,0.000091075206,0.001433486,0.006164347],"genre_scores_gemma":[0.94479585,0.0006466008,0.05281885,0.00041229057,0.00031330733,0.000034248464,0.00007683141,0.00012347467,0.0007784373],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99093455,0.0042477897,0.00052847207,0.0007376095,0.0030461322,0.0005052856],"domain_scores_gemma":[0.9346107,0.0531155,0.0020661207,0.005509535,0.004367488,0.00033077848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01123008,0.001033591,0.001628907,0.0015637663,0.00088182243,0.0044080284,0.00251769,0.0045118635,0.0019887034],"category_scores_gemma":[0.10619086,0.00081104826,0.000697111,0.0008872734,0.002171724,0.0060094944,0.0019753114,0.002243135,0.00046657736],"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":[0.0007684964,0.00028524883,0.007508373,0.000491532,0.00013693843,0.000623639,0.0006516592,0.33515272,0.008117583,0.33522594,0.006354349,0.30468354],"study_design_scores_gemma":[0.00005285551,0.00019885579,0.0013700555,0.00009584397,0.000033234737,0.00049271056,0.00012053119,0.7727929,0.008100463,0.21502589,0.0016633265,0.000053316417],"about_ca_topic_score_codex":0.0018189775,"about_ca_topic_score_gemma":0.00072592456,"teacher_disagreement_score":0.01123008,"about_ca_system_score_codex":0.001149266,"about_ca_system_score_gemma":0.0011446385,"threshold_uncertainty_score":0.05939108},"labels":[],"label_agreement":null},{"id":"W1978795938","doi":"10.1016/j.jprocont.2010.10.017","title":"Suboptimal flowrate estimators and their application to the design of measurement strategies","year":2010,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"Initialization; Estimator; Generalization; Computer science; Mathematical optimization; Variance (accounting); Process (computing); Process engineering; Algorithm; Data mining; Mathematics; Engineering; Statistics","score_opus":0.008588072251210438,"score_gpt":0.21674790592846216,"score_spread":0.20815983367725172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978795938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00467421,0.00027564232,0.9944792,0.00007024547,0.000025195686,0.000014589364,0.000012518302,0.00007731771,0.00037097634],"genre_scores_gemma":[0.47541055,0.001289392,0.5203672,0.00014482805,0.00018387604,0.00022320016,0.00011720996,0.000112475,0.0021513468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906474,0.00047669793,0.00006986124,0.00012261205,0.00020158864,0.000064436455],"domain_scores_gemma":[0.9918562,0.0065561496,0.00050717156,0.00034066208,0.00063685584,0.00010302612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039589973,0.0009249741,0.0015243421,0.0009820916,0.00049910217,0.0017184631,0.0007877819,0.0016569826,0.0011697976],"category_scores_gemma":[0.024295961,0.0008992399,0.00058061443,0.0006289839,0.0011590138,0.0014739009,0.0010400891,0.001292919,0.00024119852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002625395,0.0000613016,0.00072616414,0.00014728916,0.00007144787,0.00006315037,0.00015133478,0.8043849,0.0046436777,0.07624626,0.00083093357,0.11241099],"study_design_scores_gemma":[0.00001658238,0.00003659419,0.00009217798,0.000013697599,0.000008118407,0.000013611576,0.00000371354,0.9888493,0.00090539054,0.009737608,0.00031281525,0.000010511191],"about_ca_topic_score_codex":0.0030207525,"about_ca_topic_score_gemma":0.0024320798,"teacher_disagreement_score":0.0039589973,"about_ca_system_score_codex":0.0008782617,"about_ca_system_score_gemma":0.0017429513,"threshold_uncertainty_score":0.020937443},"labels":[],"label_agreement":null},{"id":"W1980202627","doi":"10.1016/j.jprocont.2014.11.004","title":"Optimal design and operation of energy systems under uncertainty","year":2014,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Process Optimization and Integration","field":"Engineering","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":"Queen's University","funders":"","keywords":"Solver; Mathematical optimization; Stochastic programming; Piecewise; Nonlinear programming; Integer programming; Fractional programming; Decomposition; Separable space; Integer (computer science); Optimization problem; Nonlinear system; Computer science; Mathematics","score_opus":0.006880820777796669,"score_gpt":0.20916154170452192,"score_spread":0.20228072092672525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980202627","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14963764,0.0015670434,0.8280811,0.0015331323,0.00014029688,0.00012609012,0.00016856223,0.00014839489,0.01859771],"genre_scores_gemma":[0.9868602,0.00029456438,0.011183971,0.0000333271,0.000029206432,0.00006654492,0.000038134604,0.000021305414,0.0014727475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999137,0.00041018473,0.00003937655,0.000103526254,0.0001868081,0.00012318153],"domain_scores_gemma":[0.99879825,0.00088620983,0.00012279545,0.000035983037,0.00012208712,0.000034792574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017531468,0.00069768593,0.001511656,0.0006048745,0.00055825454,0.0022032368,0.0007511188,0.0013574623,0.0014121649],"category_scores_gemma":[0.004289055,0.0009997017,0.0005895172,0.00066213903,0.0014009125,0.0016020965,0.001125758,0.00092549203,0.00016102707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050834984,0.000014565703,0.00007365567,0.00002643741,0.000016294793,0.00001953705,0.000017483262,0.98826015,0.00038512656,0.007987245,0.000094674106,0.003053995],"study_design_scores_gemma":[0.0000136048875,0.000022934342,0.000056743556,0.0000034410389,0.0000045871743,0.0000026736802,0.000009111974,0.99176157,0.00018745127,0.0077840756,0.00014929092,0.000004529972],"about_ca_topic_score_codex":0.0056767915,"about_ca_topic_score_gemma":0.0033021725,"teacher_disagreement_score":0.0056767915,"about_ca_system_score_codex":0.001194425,"about_ca_system_score_gemma":0.0015550628,"threshold_uncertainty_score":0.01128751},"labels":[],"label_agreement":null},{"id":"W1980699135","doi":"10.1016/j.jprocont.2005.11.002","title":"Performance monitoring of SISO control loops subject to LTV disturbance dynamics: An improved LTI benchmark","year":2006,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":19,"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":"Control theory (sociology); Disturbance (geology); Benchmark (surveying); Controller (irrigation); Process (computing); Univariate; LTI system theory; Mathematics; Linear system; Computer science; Control (management); Statistics; Multivariate statistics; Artificial intelligence","score_opus":0.003182856963550106,"score_gpt":0.2079703881976795,"score_spread":0.20478753123412938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980699135","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":[0.5049541,0.00062179036,0.48393926,0.00041966548,0.00011097706,0.000095819225,0.00039441878,0.0013322391,0.008131626],"genre_scores_gemma":[0.9956418,0.000030545376,0.0039353953,0.000014067194,0.000007396888,0.00001581543,0.0000998002,0.000015435047,0.0002397073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932075,0.00023162442,0.00004668554,0.00011960472,0.00017056175,0.000110802],"domain_scores_gemma":[0.99774927,0.0012256869,0.00033785394,0.0001884133,0.00042498056,0.00007377758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015337821,0.0009379145,0.0009190623,0.0005931805,0.00033642608,0.0009212032,0.00053259113,0.00082481303,0.00097533735],"category_scores_gemma":[0.0052873045,0.00015756782,0.00023488599,0.00042638322,0.00035271925,0.00069092785,0.000543414,0.0005221743,0.00010369015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015859865,0.00018415379,0.0021024377,0.00028565878,0.00006995738,0.00015867951,0.000085065934,0.9296867,0.021253645,0.0047598677,0.0010950431,0.03873274],"study_design_scores_gemma":[0.000016337786,0.00012197132,0.0008760355,0.0000056346444,0.000006964057,0.000008866145,0.0000064370706,0.9949184,0.0033048198,0.0006596711,0.000070034686,0.0000048240217],"about_ca_topic_score_codex":0.002884106,"about_ca_topic_score_gemma":0.0015959091,"teacher_disagreement_score":0.002884106,"about_ca_system_score_codex":0.0005140388,"about_ca_system_score_gemma":0.0005390283,"threshold_uncertainty_score":0.008111477},"labels":[],"label_agreement":null},{"id":"W1980944302","doi":"10.1016/j.jprocont.2007.02.001","title":"Nonlinear system identification and control of chemical processes using fast orthogonal search","year":2007,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","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":"Queen's University; University of Ontario Institute of Technology","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonlinear system; Control theory (sociology); Linearization; Controller (irrigation); System identification; Chemical process; Identification (biology); Process (computing); Inverse; Nonlinear control; Control system; Process control; Nonlinear system identification; Computer science; Inverse system; Mathematics; Control (management); Engineering; Artificial intelligence; Data modeling; Physics","score_opus":0.009960057358899285,"score_gpt":0.2501679591636727,"score_spread":0.2402079018047734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980944302","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030271737,0.00036704369,0.9677728,0.00010138505,0.00005275536,0.000041916323,0.000020507792,0.00016269519,0.0012091743],"genre_scores_gemma":[0.69388735,0.00051265844,0.3023756,0.000050006278,0.000042636693,0.0002308431,0.00007778494,0.000048080943,0.0027750847],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963605,0.00014689576,0.000026592143,0.000043217227,0.00011183108,0.000035413934],"domain_scores_gemma":[0.99926966,0.00040935638,0.00009544858,0.00006243591,0.00014763119,0.000015502359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012703617,0.00048651738,0.000836521,0.0004227702,0.0005349143,0.00060734205,0.00043077796,0.0006217625,0.0008842978],"category_scores_gemma":[0.0025071166,0.0004028522,0.00048107596,0.00046329157,0.00059865025,0.0008740812,0.0007672438,0.00070961966,0.00023897673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003759977,0.00012935798,0.00053016644,0.00022609692,0.000103190825,0.00006208416,0.00010044938,0.807799,0.018416855,0.019115357,0.00060406065,0.15253733],"study_design_scores_gemma":[0.000010976378,0.000021939142,0.00007236076,0.0000021659332,0.000004209428,0.000007577071,0.0000030460685,0.997136,0.0014604087,0.0011290945,0.00014868987,0.0000034807838],"about_ca_topic_score_codex":0.0033705859,"about_ca_topic_score_gemma":0.0030149766,"teacher_disagreement_score":0.0033705859,"about_ca_system_score_codex":0.0003133788,"about_ca_system_score_gemma":0.001179988,"threshold_uncertainty_score":0.0067184567},"labels":[],"label_agreement":null},{"id":"W1981815611","doi":"10.1016/j.jprocont.2015.03.002","title":"Kalman filter based fault detection for two-dimensional systems","year":2015,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"Laurentian University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fault detection and isolation; Residual; Kalman filter; Fault (geology); Control theory (sociology); Computer science; State (computer science); Algorithm; Extended Kalman filter; Filter (signal processing); Mathematics; Artificial intelligence; Computer vision","score_opus":0.0138221682848891,"score_gpt":0.2467203218985029,"score_spread":0.2328981536136138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981815611","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035629723,0.0004732869,0.96213526,0.00015109654,0.00008773048,0.000018733825,0.000058895293,0.0006040076,0.000841435],"genre_scores_gemma":[0.93912864,0.0003785379,0.05786159,0.00006208219,0.000046952817,0.000052479656,0.00014292685,0.000028938768,0.0022978883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962723,0.00006890323,0.00003534506,0.00008736664,0.00012836627,0.000052780935],"domain_scores_gemma":[0.99896276,0.00060852844,0.00010395009,0.000078607874,0.0002228176,0.000023374934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006002225,0.00045671148,0.00086144847,0.00056482543,0.00038301127,0.00080747396,0.00046084548,0.0008365577,0.0012902472],"category_scores_gemma":[0.0029445565,0.0003300218,0.00038468576,0.00040861923,0.00046098736,0.0010454042,0.00068098574,0.0007188555,0.00029577827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007708172,0.00012076666,0.0028555999,0.00032116508,0.00016746047,0.00015389858,0.0002233312,0.6765629,0.023569545,0.008897878,0.0020107422,0.2843459],"study_design_scores_gemma":[0.000013981055,0.00004532156,0.00086688943,0.000006434981,0.00001221697,0.000018232924,0.000006859206,0.9949588,0.002546851,0.0011973478,0.00031582708,0.000011252054],"about_ca_topic_score_codex":0.011381522,"about_ca_topic_score_gemma":0.00772666,"teacher_disagreement_score":0.011381522,"about_ca_system_score_codex":0.00061150343,"about_ca_system_score_gemma":0.0008397199,"threshold_uncertainty_score":0.022630513},"labels":[],"label_agreement":null},{"id":"W1983522917","doi":"10.1016/j.jprocont.2012.12.013","title":"Study of generalized delay-timers in alarm configuration","year":2013,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":79,"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; Alberta Innovates - Technology Futures","keywords":"ALARM; Timer; Computer science; Constant false alarm rate; Markov process; Process (computing); False alarm; Real-time computing; Sensitivity (control systems); Markov chain; Algorithm; Embedded system; Mathematics; Engineering; Artificial intelligence; Electronic engineering; Statistics; Machine learning","score_opus":0.007611375308445073,"score_gpt":0.2343843066143929,"score_spread":0.22677293130594783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983522917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29438737,0.001117407,0.6952711,0.00040031585,0.00017379313,0.00006850678,0.00006263424,0.00030548105,0.0082133915],"genre_scores_gemma":[0.9864042,0.00019962789,0.0119981095,0.000024869561,0.000037688216,0.000018854973,0.000021334781,0.00004237723,0.001252855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918276,0.00037586395,0.00003095199,0.00014279307,0.00014624165,0.00012132093],"domain_scores_gemma":[0.9906371,0.007206618,0.000757697,0.0003631196,0.00068789045,0.00034754453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022567497,0.0007875762,0.000838332,0.0009589481,0.00048841827,0.0012678934,0.0016574741,0.00081452617,0.0028162368],"category_scores_gemma":[0.016757127,0.0004247781,0.0006956841,0.00064573105,0.0010873905,0.0015209682,0.0005628373,0.0011056567,0.00011030004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004984025,0.0001175598,0.0028575123,0.00028086136,0.00013106817,0.0007536879,0.00032711326,0.6124114,0.009683569,0.3485108,0.00096091273,0.023467137],"study_design_scores_gemma":[0.000016812488,0.00005025873,0.00044446942,0.000014117034,0.000038062015,0.0000899299,0.000033900742,0.9833598,0.0005620335,0.015207481,0.00017206254,0.000011051038],"about_ca_topic_score_codex":0.0019265108,"about_ca_topic_score_gemma":0.0011030171,"teacher_disagreement_score":0.0028162368,"about_ca_system_score_codex":0.00094212085,"about_ca_system_score_gemma":0.000984038,"threshold_uncertainty_score":0.011934996},"labels":[],"label_agreement":null},{"id":"W1983819491","doi":"10.1016/j.jprocont.2006.11.005","title":"Real-time dynamic optimization of batch systems","year":2007,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Queen's University","funders":"","keywords":"Computer science; Control engineering; Engineering","score_opus":0.002558480358239896,"score_gpt":0.2203706602422256,"score_spread":0.2178121798839857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983819491","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11026332,0.001062613,0.87852526,0.0010702065,0.00024435684,0.00010428884,0.00013938309,0.00034407753,0.008246572],"genre_scores_gemma":[0.9648779,0.00027864307,0.029355166,0.000057867463,0.000052505235,0.00009421301,0.00010809045,0.00008449154,0.0050911815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943084,0.00023760222,0.000022117993,0.000102654245,0.0001264018,0.0000803219],"domain_scores_gemma":[0.9986533,0.0009968927,0.00011045715,0.000038408354,0.00015366111,0.000047202193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017235564,0.0011687046,0.001841388,0.00044697316,0.000486178,0.0015309637,0.00093648845,0.001380523,0.0028214173],"category_scores_gemma":[0.0044038473,0.00084569136,0.000525415,0.00059233274,0.0010328359,0.0013214861,0.0007770712,0.0011880221,0.00024116886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010400286,0.000024213543,0.000058241974,0.000031480522,0.000012293891,0.000015996806,0.000011955395,0.99247336,0.00070742646,0.002123155,0.00022757098,0.004210333],"study_design_scores_gemma":[0.000005298627,0.000012353371,0.000026342976,7.99825e-7,0.0000013756304,0.0000014054739,0.0000017314387,0.9992601,0.00013716612,0.0004913818,0.000060082577,0.0000017976457],"about_ca_topic_score_codex":0.009228107,"about_ca_topic_score_gemma":0.0041668555,"teacher_disagreement_score":0.009228107,"about_ca_system_score_codex":0.0011028743,"about_ca_system_score_gemma":0.0012414219,"threshold_uncertainty_score":0.018348813},"labels":[],"label_agreement":null},{"id":"W1984683674","doi":"10.1016/j.jprocont.2005.01.002","title":"Accounting for uncertainty in control-relevant statistics","year":2005,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Accounting; Control (management); Computer science; Econometrics; Mathematics; Business; Artificial intelligence","score_opus":0.006238738232342198,"score_gpt":0.23955975012004294,"score_spread":0.23332101188770074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984683674","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024727471,0.0007421012,0.97230804,0.00039108668,0.00013118141,0.00001550433,0.00009855289,0.00032356154,0.0012624757],"genre_scores_gemma":[0.9177853,0.0012796291,0.07791337,0.0001719174,0.00042828143,0.000041806852,0.00032947393,0.00018900313,0.0018611222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707246,0.0009952297,0.00019900913,0.00037311506,0.0010780146,0.00028213722],"domain_scores_gemma":[0.9790035,0.014871876,0.001303161,0.003213546,0.0013576193,0.00025036253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005286828,0.0011857852,0.0014322726,0.0016467442,0.0007612326,0.0031743946,0.0014036067,0.0013056968,0.0010645504],"category_scores_gemma":[0.036162123,0.00082346855,0.0010614566,0.0019927875,0.001407461,0.0057711834,0.0017610984,0.0020879654,0.00023589616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013124633,0.00006506459,0.0032151917,0.000113650094,0.00017948469,0.00015430464,0.00007247266,0.796041,0.0020799057,0.13638547,0.0012607514,0.060301382],"study_design_scores_gemma":[0.0000029047721,0.000012329298,0.00042830218,0.000006416193,0.000017859,0.000019778123,0.0000057499574,0.95517224,0.00095520884,0.04307817,0.00028994694,0.000011168216],"about_ca_topic_score_codex":0.0045156023,"about_ca_topic_score_gemma":0.0040148115,"teacher_disagreement_score":0.005286828,"about_ca_system_score_codex":0.0012584283,"about_ca_system_score_gemma":0.0024824135,"threshold_uncertainty_score":0.027959764},"labels":[],"label_agreement":null},{"id":"W1984869422","doi":"10.1016/s0959-1524(01)00047-6","title":"Real-time optimization under parametric uncertainty: a probability constrained approach","year":2002,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Process Optimization and Integration","field":"Engineering","cited_by":139,"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":"Robust optimization; Mathematical optimization; Stochastic programming; Parametric statistics; Sensitivity (control systems); Computer science; Nonlinear system; Uncertainty analysis; Nonlinear programming; Optimization problem; Stochastic optimization; Control theory (sociology); Mathematics; Engineering; Control (management); Artificial intelligence; Simulation","score_opus":0.013604960433057828,"score_gpt":0.21714711559422598,"score_spread":0.20354215516116814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984869422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039373133,0.00052466406,0.992494,0.00035453125,0.00004535101,0.000020627382,0.000030903087,0.000050252307,0.0025423234],"genre_scores_gemma":[0.73214376,0.0034142805,0.25222108,0.00047565522,0.000590844,0.00043335362,0.00024200162,0.00044767617,0.010031343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978091,0.001078019,0.00010421128,0.0003328657,0.0004970258,0.0001787882],"domain_scores_gemma":[0.992706,0.0058835596,0.0005512619,0.00024598686,0.0004520832,0.00016114894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047659287,0.0019777976,0.0034309065,0.0015872591,0.00070178905,0.0032060454,0.0025925017,0.0026700294,0.0033624296],"category_scores_gemma":[0.014408415,0.0022024382,0.0023343384,0.0021746194,0.0025189382,0.004834239,0.0027167175,0.002372984,0.00033756642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000238731,0.000024305238,0.000068595014,0.000061788916,0.000058958434,0.000049476355,0.00002421637,0.95952237,0.00021144658,0.034931436,0.0002459996,0.0047774147],"study_design_scores_gemma":[0.0000031113134,0.0000063488765,0.000022574353,0.0000038988455,0.000006916931,0.0000048094935,0.0000025253942,0.9905805,0.000048897462,0.009207455,0.000108224696,0.000004702013],"about_ca_topic_score_codex":0.0050982605,"about_ca_topic_score_gemma":0.00267039,"teacher_disagreement_score":0.0050982605,"about_ca_system_score_codex":0.0014429573,"about_ca_system_score_gemma":0.001682531,"threshold_uncertainty_score":0.025204957},"labels":[],"label_agreement":null},{"id":"W1986143653","doi":"10.1016/j.jprocont.2011.06.013","title":"Unscented Kalman filter based nonlinear model predictive control of a LDPE autoclave reactor","year":2011,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Toronto Metropolitan University","funders":"","keywords":"Autoclave; Kalman filter; Extended Kalman filter; Nonlinear system; Control theory (sociology); Model predictive control; Low-density polyethylene; Nonlinear model; Unscented transform; Materials science; Engineering; Computer science; Invariant extended Kalman filter; Control (management); Composite material; Polyethylene; Metallurgy; Physics; Artificial intelligence","score_opus":0.01182575938018524,"score_gpt":0.22050363360228412,"score_spread":0.20867787422209888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986143653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3951516,0.00055795745,0.5909874,0.00065283896,0.0002255184,0.0000622442,0.00023162836,0.0014035278,0.010727311],"genre_scores_gemma":[0.99117637,0.00006760756,0.0063686706,0.000023933588,0.000007874056,0.000020978347,0.000035455443,0.000014448889,0.0022846097],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986434,0.000028430542,0.000007045057,0.000040654173,0.000046582776,0.000012916734],"domain_scores_gemma":[0.99977964,0.00009776251,0.00003065401,0.000018266173,0.000064753214,0.000008955331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003893308,0.00034107125,0.00051176106,0.00019722356,0.00056369574,0.0006926971,0.0005823419,0.0006555745,0.0013730878],"category_scores_gemma":[0.00059112237,0.0003003482,0.0002837965,0.00015086518,0.00043952963,0.0005053817,0.00037797377,0.00056861766,0.00023479978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042728314,0.0000892985,0.000887699,0.0001181998,0.00004098123,0.0001688862,0.00011181534,0.923095,0.03970923,0.0018413392,0.00076470897,0.032745566],"study_design_scores_gemma":[0.0000071551453,0.000029852312,0.0002280769,0.0000019704423,0.000004351739,0.0000045633365,0.000003990232,0.9963108,0.0030813823,0.00014996355,0.0001735278,0.000004490854],"about_ca_topic_score_codex":0.016192636,"about_ca_topic_score_gemma":0.012239448,"teacher_disagreement_score":0.016192636,"about_ca_system_score_codex":0.0005748229,"about_ca_system_score_gemma":0.0008131438,"threshold_uncertainty_score":0.03219676},"labels":[],"label_agreement":null},{"id":"W1992852278","doi":"10.1016/j.jprocont.2005.10.002","title":"A new real-time perspective on non-linear model predictive control","year":2005,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hessian matrix; Model predictive control; Control theory (sociology); Dimension (graph theory); Stability (learning theory); Trajectory; Process (computing); Computer science; Perspective (graphical); Variable (mathematics); Work (physics); Mathematics; Linear model; Mathematical optimization; Control (management); Applied mathematics; Engineering; Artificial intelligence; Machine learning","score_opus":0.004352886331560085,"score_gpt":0.2401232671582686,"score_spread":0.2357703808267085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992852278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002094545,0.004459384,0.97764957,0.0019289799,0.0010810375,0.000011568587,0.000052024672,0.000116354786,0.0126065705],"genre_scores_gemma":[0.6890389,0.018157126,0.25334477,0.0022374443,0.009628416,0.0001669704,0.000232802,0.00037325214,0.026820311],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988593,0.00034617013,0.000076933924,0.00023120503,0.0004341764,0.000052320476],"domain_scores_gemma":[0.9981596,0.0011223223,0.00014143909,0.00023867495,0.00028038077,0.000057542493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013770787,0.0016456814,0.0013074309,0.0007881825,0.00051992794,0.0035288974,0.0025351222,0.0024057203,0.0056357966],"category_scores_gemma":[0.0038319502,0.00063586025,0.0010332892,0.0009803113,0.0022654417,0.0076095453,0.0016319705,0.004039712,0.0009514404],"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":[0.00010270309,0.0000748683,0.00017508847,0.0003998984,0.00006333717,0.000289926,0.00013813662,0.14326814,0.003328309,0.7861704,0.0038561586,0.062132936],"study_design_scores_gemma":[0.000016046,0.00010470933,0.000114268456,0.000051105282,0.000035461326,0.000116663854,0.000029458026,0.6408573,0.0011658828,0.33975855,0.017715728,0.000034742196],"about_ca_topic_score_codex":0.0006953017,"about_ca_topic_score_gemma":0.00045261715,"teacher_disagreement_score":0.0056357966,"about_ca_system_score_codex":0.0006568478,"about_ca_system_score_gemma":0.00033014957,"threshold_uncertainty_score":0.018853605},"labels":[],"label_agreement":null},{"id":"W1997686342","doi":"10.1016/j.jprocont.2011.03.005","title":"Shaping probability density functions using a switching linear controller","year":2011,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","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":"Polytechnique Montréal; Natural Sciences and Engineering Research Council of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Polytechnique Montréal","keywords":"Control theory (sociology); Probability density function; Controller (irrigation); Discontinuity (linguistics); Polynomial; Context (archaeology); Mathematics; Nonlinear system; Flexibility (engineering); Skewness; Linear system; Computer science; Applied mathematics; Mathematical optimization; Mathematical analysis; Control (management)","score_opus":0.05449882816759402,"score_gpt":0.24376359220081337,"score_spread":0.18926476403321935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997686342","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014372346,0.00005709906,0.9823033,0.00007576272,0.000042974505,0.000029090595,0.000009844963,0.00040157448,0.0027080067],"genre_scores_gemma":[0.90558004,0.00011358191,0.089334026,0.00011272326,0.0000377661,0.000086970074,0.000025629724,0.00006418339,0.0046451413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997086,0.00006946724,0.000015469712,0.000059817896,0.00011645751,0.000030146817],"domain_scores_gemma":[0.9987048,0.0008382139,0.00007750685,0.00013249007,0.0002090746,0.00003793729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007071984,0.00041467507,0.000509025,0.00038165375,0.0003815152,0.0008898243,0.00062111515,0.0007957959,0.0022766665],"category_scores_gemma":[0.0025455651,0.0002826284,0.0005055279,0.00034738553,0.000567167,0.0005406979,0.0005858051,0.0008484578,0.00037710692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036704965,0.00024061272,0.0006782675,0.00012798139,0.000084377105,0.00017923604,0.00029329085,0.69566166,0.034999155,0.06582426,0.001621767,0.19992225],"study_design_scores_gemma":[0.000011861512,0.000037476297,0.0000935202,0.000003008645,0.000009906073,0.000025687978,0.0000045206316,0.9930483,0.0024051873,0.0039947946,0.0003573797,0.000008398823],"about_ca_topic_score_codex":0.0020892604,"about_ca_topic_score_gemma":0.0013510939,"teacher_disagreement_score":0.0022766665,"about_ca_system_score_codex":0.0005731362,"about_ca_system_score_gemma":0.0005812717,"threshold_uncertainty_score":0.0076161623},"labels":[],"label_agreement":null},{"id":"W2000241355","doi":"10.1016/j.jprocont.2004.04.009","title":"Nonlinear sampled-data output feedback receding horizon control","year":2004,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Queen's University","funders":"","keywords":"Control theory (sociology); Discretization; Nonlinear system; Horizon; Observer (physics); Convergence (economics); Output feedback; Mathematics; Separation principle; Control (management); State observer; Computer science; Physics; Artificial intelligence","score_opus":0.016502669376788417,"score_gpt":0.2543709208742626,"score_spread":0.2378682514974742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000241355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039683606,0.00062455266,0.9527484,0.0002327599,0.00041993707,0.00006904315,0.00012347646,0.00070555095,0.0053927037],"genre_scores_gemma":[0.95520514,0.0002973564,0.039091755,0.000100308076,0.00007894646,0.00008847877,0.00012538677,0.000030632727,0.0049820105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996176,0.00007197372,0.000029469666,0.000098185206,0.00014458783,0.000038286646],"domain_scores_gemma":[0.9993185,0.000245914,0.00011500843,0.00007148416,0.00023017343,0.00001901894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079475617,0.0008216098,0.0010723466,0.00020125654,0.00051472225,0.001034999,0.0008913538,0.00090684433,0.0023763685],"category_scores_gemma":[0.0021152627,0.00040837543,0.00040405273,0.00030825048,0.0008588519,0.0008637638,0.0007322378,0.0010043379,0.0004415578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010252779,0.0002277949,0.00062770944,0.0006090238,0.00011029043,0.00018522984,0.00022866593,0.7960668,0.016246956,0.014170588,0.0026963046,0.1678054],"study_design_scores_gemma":[0.000035711964,0.00010840483,0.00018668099,0.000007958119,0.000012720991,0.000012848806,0.000006348264,0.99527705,0.0023712786,0.0013987968,0.0005738793,0.00000829264],"about_ca_topic_score_codex":0.004383768,"about_ca_topic_score_gemma":0.004553971,"teacher_disagreement_score":0.004383768,"about_ca_system_score_codex":0.0005230062,"about_ca_system_score_gemma":0.0008325799,"threshold_uncertainty_score":0.008716524},"labels":[],"label_agreement":null},{"id":"W2001654347","doi":"10.1016/j.jprocont.2014.10.004","title":"Analysis and control of heteroepitaxial systems","year":2014,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","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; National Science Foundation","keywords":"Representation (politics); Set (abstract data type); Process (computing); Surface (topology); Feature (linguistics); Work (physics); Control (management); Computer science; Biological system; Materials science; Control theory (sociology); Mathematics; Artificial intelligence; Mechanical engineering; Engineering; Geometry","score_opus":0.004316307178414869,"score_gpt":0.20631072755542915,"score_spread":0.2019944203770143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001654347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34008706,0.0017455323,0.60539186,0.0010373688,0.00015454002,0.000060685186,0.00015750586,0.0001676713,0.051197857],"genre_scores_gemma":[0.99219805,0.00022183193,0.003709447,0.000027658803,0.000021707181,0.000022818353,0.00002752681,0.000012529973,0.0037583183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999899,0.00002274954,0.0000038717444,0.000018088629,0.000037937985,0.000018442723],"domain_scores_gemma":[0.9997842,0.00008838828,0.00005630912,0.00001416326,0.000046391073,0.000010705794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025030915,0.00034709688,0.00044874699,0.0002757961,0.0003031332,0.00093417894,0.00044776165,0.0005254659,0.001637861],"category_scores_gemma":[0.0006048745,0.00017497843,0.00023504886,0.00020360142,0.00061621756,0.0004429737,0.0006402465,0.00039875822,0.00012905375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006684862,0.00003638991,0.00059703813,0.000050429724,0.00004125649,0.000093093426,0.00006183365,0.9181831,0.011110852,0.055386074,0.00072356866,0.013649557],"study_design_scores_gemma":[0.0000028734073,0.000009406923,0.0002027806,0.0000013657412,0.000001732697,0.0000057936245,0.0000069343123,0.9962379,0.00024324717,0.0031281742,0.00015771182,0.0000020826458],"about_ca_topic_score_codex":0.0039048698,"about_ca_topic_score_gemma":0.003169617,"teacher_disagreement_score":0.0039048698,"about_ca_system_score_codex":0.00048963353,"about_ca_system_score_gemma":0.00039155528,"threshold_uncertainty_score":0.00776428},"labels":[],"label_agreement":null},{"id":"W2003177232","doi":"10.1016/j.jprocont.2004.04.007","title":"Closed-loop subspace identification: an orthogonal projection approach","year":2004,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","cited_by":173,"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":"Singular value decomposition; Subspace topology; Kalman filter; Observability; Projection (relational algebra); Orthographic projection; Mathematics; Control theory (sociology); Toeplitz matrix; Matrix (chemical analysis); Orthogonal matrix; Algorithm; Computer science; Applied mathematics; Orthogonal basis; Artificial intelligence; Pure mathematics; Control (management); Mathematical analysis","score_opus":0.008307086814782535,"score_gpt":0.2317695176785673,"score_spread":0.22346243086378476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003177232","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026931139,0.00011145606,0.9960691,0.000029197616,0.00002130014,0.000013754358,0.000014195259,0.00015103896,0.00089682674],"genre_scores_gemma":[0.38644338,0.0011685004,0.6053271,0.00011576299,0.00013275989,0.00024322815,0.00029235586,0.00021852352,0.006058416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994717,0.00019961703,0.000029703206,0.00009729587,0.00015499795,0.000046715937],"domain_scores_gemma":[0.9995091,0.00021051578,0.000039478276,0.00007041093,0.00015205528,0.000018530185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006985369,0.0009471795,0.0010249184,0.00041330408,0.00053424324,0.0008775561,0.000672457,0.000683096,0.0022373155],"category_scores_gemma":[0.0016667506,0.0004260531,0.0006852314,0.00066678267,0.0006260927,0.0016048355,0.0011839331,0.0011018898,0.00072524656],"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":[0.00038004707,0.00021919236,0.000488349,0.0004299019,0.00019324537,0.0001834695,0.00026107204,0.35870436,0.028496616,0.084335655,0.0027632671,0.5235448],"study_design_scores_gemma":[0.000014613959,0.000076397344,0.00015587166,0.000010898738,0.000018872772,0.000050931023,0.000021890542,0.9828756,0.0030565076,0.012411966,0.001290308,0.000016157277],"about_ca_topic_score_codex":0.0014525765,"about_ca_topic_score_gemma":0.0012366392,"teacher_disagreement_score":0.0022373155,"about_ca_system_score_codex":0.00017242353,"about_ca_system_score_gemma":0.00086291647,"threshold_uncertainty_score":0.0074845552},"labels":[],"label_agreement":null},{"id":"W2005339673","doi":"10.1016/j.jprocont.2003.09.008","title":"Control of batch product quality by trajectory manipulation using latent variable models","year":2003,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":120,"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":"McMaster University","keywords":"Latent variable; Trajectory; Variable (mathematics); Control theory (sociology); Partial least squares regression; Control variable; Computer science; Product (mathematics); Inversion (geology); Process (computing); Quality (philosophy); Mathematics; Control (management); Mathematical optimization; Artificial intelligence; Machine learning","score_opus":0.02424750142479443,"score_gpt":0.2566200480651631,"score_spread":0.23237254664036866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005339673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105205745,0.00018884029,0.89269,0.00026199283,0.00004640445,0.00003922832,0.00010087891,0.0005143345,0.000952612],"genre_scores_gemma":[0.98184353,0.00008558972,0.016703902,0.000018586828,0.000013365177,0.00005264483,0.000095649026,0.000023258079,0.0011634687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992316,0.0002576448,0.000037563597,0.00024468836,0.0001309786,0.00009748562],"domain_scores_gemma":[0.9974915,0.0013333488,0.00062622025,0.0001645677,0.0003264061,0.000057968697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015731369,0.0007656008,0.00090885634,0.00042543098,0.0004256281,0.0013239861,0.0007665196,0.0005411808,0.0010685979],"category_scores_gemma":[0.003506873,0.00053331524,0.0007027348,0.00056142604,0.00095640466,0.0013672471,0.0008942509,0.0009284229,0.00022066296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062182423,0.00021890458,0.0025512013,0.000104624785,0.00013171634,0.000027470287,0.00011775671,0.9321208,0.0068520578,0.01159956,0.00052075257,0.045133404],"study_design_scores_gemma":[0.00001929,0.000055541714,0.00036061363,0.0000019213849,0.000010802294,0.0000017752021,0.000004013376,0.9970042,0.0008142022,0.0016351731,0.00008586683,0.0000066809885],"about_ca_topic_score_codex":0.010001642,"about_ca_topic_score_gemma":0.0067520565,"teacher_disagreement_score":0.010001642,"about_ca_system_score_codex":0.0011253705,"about_ca_system_score_gemma":0.0012696155,"threshold_uncertainty_score":0.019886851},"labels":[],"label_agreement":null},{"id":"W2005963889","doi":"10.1016/s0959-1524(00)00029-9","title":"Constrained multivariable control of a distillation column using a simplified model predictive control algorithm","year":2001,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Imperial Oil (Canada); Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Model predictive control; Multivariable calculus; Fractionating column; Distillation; Process control; Decomposition; Linear programming; Control theory (sociology); Computer science; Process (computing); Engineering; Mathematical optimization; Control (management); Algorithm; Control engineering; Mathematics; Chemistry; Chromatography","score_opus":0.00853494707623029,"score_gpt":0.23904409625620604,"score_spread":0.23050914917997575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005963889","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03731326,0.00023385072,0.95652354,0.00020013837,0.00008339414,0.0000735578,0.0000703487,0.00040052342,0.005101351],"genre_scores_gemma":[0.95643586,0.00014760795,0.040490963,0.00007288597,0.000042141426,0.00015147313,0.00006975705,0.000032076507,0.0025572074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997209,0.00006766861,0.000011391202,0.00006824495,0.00008971417,0.000042020965],"domain_scores_gemma":[0.9996908,0.00013637684,0.00004362496,0.000028690712,0.00008537858,0.000015227659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005149782,0.00074243353,0.0013466388,0.00030796023,0.000749839,0.0013155557,0.0008603578,0.00087775924,0.0024867367],"category_scores_gemma":[0.000952199,0.0004676509,0.0005179264,0.00042581416,0.0006941391,0.00065004756,0.00094798376,0.00077582075,0.00025444516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082845865,0.000038391932,0.000090637026,0.00006229849,0.000021330601,0.000035961028,0.000032302898,0.9749806,0.0037793426,0.0037023998,0.00037868196,0.01679526],"study_design_scores_gemma":[0.000010074165,0.000023250592,0.000051059258,0.0000011445584,0.000004319836,0.0000021519024,0.0000014454646,0.99909914,0.0003019758,0.0003767278,0.00012602577,0.0000027436115],"about_ca_topic_score_codex":0.012438894,"about_ca_topic_score_gemma":0.0099827545,"teacher_disagreement_score":0.012438894,"about_ca_system_score_codex":0.00064435834,"about_ca_system_score_gemma":0.0012898423,"threshold_uncertainty_score":0.024733007},"labels":[],"label_agreement":null},{"id":"W2006204072","doi":"10.1016/s0959-1524(02)00009-4","title":"Generalized predictive control for non-uniformly sampled systems","year":2002,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":104,"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":"Model predictive control; Constraint (computer-aided design); Control theory (sociology); Integer (computer science); Causality (physics); Sampling (signal processing); Domain (mathematical analysis); Computer science; Mathematics; State space; Control (management); Mathematical optimization; Algorithm; Statistics; Artificial intelligence","score_opus":0.00897089554383301,"score_gpt":0.22213726611125353,"score_spread":0.21316637056742052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006204072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058678545,0.0011380817,0.9356866,0.00035913245,0.00025305536,0.000035104506,0.00006995294,0.0002654528,0.0035141641],"genre_scores_gemma":[0.97848266,0.00046474722,0.017863117,0.000085492036,0.000081430015,0.00006106998,0.00007563377,0.000036980036,0.0028488801],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995449,0.00015103933,0.000021821977,0.000082610306,0.00014168117,0.00005793368],"domain_scores_gemma":[0.99921215,0.00041981044,0.00009751664,0.00010211843,0.00014541624,0.000022937576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010536069,0.0009852828,0.0011619421,0.00038805444,0.0004043427,0.0011790005,0.0010486072,0.000878941,0.0014855879],"category_scores_gemma":[0.0032734878,0.00046384215,0.00049565214,0.000601532,0.0013514085,0.0012020791,0.0012268738,0.0009808895,0.00012667518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012208658,0.00002883339,0.00021260927,0.00014258402,0.00004776672,0.00010770661,0.000087334585,0.94196385,0.002213236,0.029051598,0.00066475297,0.025357615],"study_design_scores_gemma":[0.000006613023,0.000012923085,0.000065501226,0.0000033137042,0.00000391502,0.0000039994243,0.000002934601,0.9936585,0.00016101034,0.0059703854,0.000107728585,0.0000031565537],"about_ca_topic_score_codex":0.00806811,"about_ca_topic_score_gemma":0.006883986,"teacher_disagreement_score":0.00806811,"about_ca_system_score_codex":0.00087151764,"about_ca_system_score_gemma":0.00086987833,"threshold_uncertainty_score":0.016042292},"labels":[],"label_agreement":null},{"id":"W2006577846","doi":"10.1016/j.jprocont.2013.03.009","title":"Boundary model predictive control of thin film thickness modelled by the Kuramoto–Sivashinsky equation with input and state constraints","year":2013,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fluid Dynamics and Thin Films","field":"Engineering","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 Alberta","funders":"","keywords":"Control theory (sociology); Model predictive control; Dissipative system; Boundary (topology); Quadratic equation; Mathematics; Boundary value problem; Representation (politics); Distributed parameter system; Modal; Controller (irrigation); Operator (biology); State (computer science); Optimal control; Applied mathematics; Mathematical analysis; Partial differential equation; Mathematical optimization; Computer science; Control (management); Algorithm; Physics; Geometry","score_opus":0.004693253940577221,"score_gpt":0.18583682413949168,"score_spread":0.18114357019891447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006577846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2919576,0.001027425,0.68938446,0.00079544034,0.00026038836,0.00006438012,0.00016967757,0.00043193158,0.015908686],"genre_scores_gemma":[0.99075645,0.00017949365,0.0060413736,0.000031658135,0.000015823869,0.000041051193,0.00004815261,0.000022125403,0.0028638723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998785,0.000023942535,0.000004528387,0.000034184224,0.000033510485,0.000025254181],"domain_scores_gemma":[0.99959034,0.00021926954,0.00006653919,0.000020984302,0.00008446421,0.000018478659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036074372,0.0007148972,0.0010259012,0.0002773696,0.00044632432,0.0013298943,0.00085616903,0.0010203623,0.0015312174],"category_scores_gemma":[0.0011727052,0.0005456934,0.00047878423,0.00027812057,0.00088775414,0.000958564,0.0010714526,0.0013004291,0.00018021882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006775932,0.000026456977,0.0001751167,0.00005707948,0.000012505511,0.000059667796,0.00005108517,0.98212665,0.008617211,0.004358538,0.00024352202,0.004204304],"study_design_scores_gemma":[0.0000052513947,0.000008368712,0.000047955487,0.0000021631554,0.0000022262602,0.0000016701707,0.0000032388036,0.9987924,0.0006243333,0.000442912,0.000067089764,0.0000023834386],"about_ca_topic_score_codex":0.011170477,"about_ca_topic_score_gemma":0.007932818,"teacher_disagreement_score":0.011170477,"about_ca_system_score_codex":0.0007654564,"about_ca_system_score_gemma":0.00084471045,"threshold_uncertainty_score":0.022210896},"labels":[],"label_agreement":null},{"id":"W2008359378","doi":"10.1016/s0959-1524(00)00025-1","title":"Jackknife and bootstrap methods in the identification of dynamic models","year":2001,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":50,"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":"Jackknife resampling; Statistics; Mathematics; Statistic; Regression; Confidence interval; Computer science","score_opus":0.029748335272851225,"score_gpt":0.3928778989986307,"score_spread":0.3631295637257795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008359378","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012281389,0.0007890449,0.9860801,0.00013825794,0.000042914784,0.000029438977,0.000043560092,0.00022246632,0.00037269032],"genre_scores_gemma":[0.3945831,0.0017586206,0.59942496,0.00014992603,0.00022484575,0.0005041169,0.0006398701,0.00042287816,0.002291601],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9884837,0.008909487,0.000470742,0.0007471673,0.0010985088,0.00029035917],"domain_scores_gemma":[0.92884344,0.0642803,0.0017603426,0.0021021438,0.0024003698,0.00061346294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021683779,0.0014305598,0.0033267874,0.0035000695,0.0021523547,0.0017867534,0.0027779988,0.0022551697,0.0015689258],"category_scores_gemma":[0.10100979,0.0015894882,0.0017630585,0.002750502,0.0025414724,0.00357289,0.0027059822,0.004531681,0.00074080384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054081,0.00023306733,0.0035302492,0.00033909318,0.0006312545,0.0002088704,0.0005039776,0.7109808,0.0010540568,0.074602835,0.001935727,0.20543931],"study_design_scores_gemma":[0.000026042631,0.000038123057,0.00050404016,0.000031568405,0.000029684039,0.000031486114,0.00003765355,0.9471892,0.0003657352,0.05095863,0.00075392367,0.000033936823],"about_ca_topic_score_codex":0.0075827762,"about_ca_topic_score_gemma":0.006886034,"teacher_disagreement_score":0.021683779,"about_ca_system_score_codex":0.00092813146,"about_ca_system_score_gemma":0.002105103,"threshold_uncertainty_score":0.11467618},"labels":[],"label_agreement":null},{"id":"W2008943610","doi":"10.1016/j.jprocont.2013.11.015","title":"Set-based adaptive estimation for a class of nonlinear systems with time-varying parameters","year":2014,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","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":"Queen's University; Hatch (Canada)","funders":"","keywords":"Nonlinear system; Set (abstract data type); Class (philosophy); Representation (politics); Computer science; Estimation; Polynomial; Mathematical optimization; Estimation theory; Control theory (sociology); Mathematics; Algorithm; Artificial intelligence; Engineering; Control (management)","score_opus":0.009589994354964469,"score_gpt":0.22095941708578581,"score_spread":0.21136942273082135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008943610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062186662,0.00047839878,0.93502754,0.00016584499,0.000038292324,0.00004238958,0.000047726382,0.00016144873,0.001851645],"genre_scores_gemma":[0.9505886,0.0004483375,0.045901995,0.000053879336,0.00006016082,0.00009789485,0.00013689634,0.000029262415,0.0026829804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995378,0.00009576774,0.000028069146,0.0001065048,0.00019082996,0.00004095574],"domain_scores_gemma":[0.9983398,0.0011603549,0.00018905557,0.00008920428,0.00019143667,0.000030078221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007289727,0.0006558007,0.00104681,0.00057177385,0.00038511964,0.00094383315,0.0009335157,0.0010595198,0.0009623023],"category_scores_gemma":[0.0040545384,0.00037697036,0.0006898637,0.00047448167,0.0005846936,0.00079907896,0.00085000426,0.001014672,0.00016672669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030296046,0.00012102578,0.0020198298,0.0002486098,0.00020180036,0.00027592178,0.00026662325,0.8442246,0.012231474,0.02459256,0.0010514506,0.11446322],"study_design_scores_gemma":[0.0000040318023,0.00002054115,0.00028375283,0.000004264313,0.0000067644373,0.000021725697,0.0000044085255,0.99788517,0.00038870933,0.0012311833,0.00014471103,0.0000047670983],"about_ca_topic_score_codex":0.0040208967,"about_ca_topic_score_gemma":0.0027035088,"teacher_disagreement_score":0.0040208967,"about_ca_system_score_codex":0.0004367674,"about_ca_system_score_gemma":0.00044187086,"threshold_uncertainty_score":0.007995009},"labels":[],"label_agreement":null},{"id":"W2010260361","doi":"10.1016/j.jprocont.2012.06.018","title":"Infinite-dimensional LQ optimal control of a dimethyl ether (DME) catalytic distillation column","year":2012,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":20,"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":"Linear-quadratic regulator; Mathematics; Algebraic equation; Riccati equation; Control theory (sociology); Optimal control; Algebraic Riccati equation; Diagonal; Matrix (chemical analysis); Operator (biology); Uniqueness; Diagonal matrix; Applied mathematics; Algebraic number; Mathematical analysis; Partial differential equation; Mathematical optimization; Control (management); Computer science; Chemistry; Geometry; Nonlinear system; Physics","score_opus":0.005907526363236354,"score_gpt":0.22939438916858,"score_spread":0.22348686280534363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010260361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48413113,0.00082037604,0.49234942,0.0016677104,0.000292528,0.00011223781,0.00020808174,0.00068243674,0.019736039],"genre_scores_gemma":[0.99478537,0.000059096947,0.0029278,0.000043184573,0.0000149708885,0.000027222666,0.000026091573,0.0000096228505,0.0021066985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963033,0.00010646712,0.000014577937,0.0000843771,0.00008782285,0.000076388606],"domain_scores_gemma":[0.9993561,0.000321187,0.00007070859,0.000023781284,0.00018020824,0.000047905465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008597428,0.0005900186,0.00091261975,0.00029835044,0.0008073982,0.0014957422,0.00066833216,0.0010978825,0.0017566868],"category_scores_gemma":[0.0011247798,0.00043717914,0.00042975627,0.00020230432,0.0011498953,0.00043674264,0.00092917914,0.0008555582,0.00019848414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044315917,0.00015326313,0.00069111714,0.00014544453,0.0000738173,0.00019590317,0.000084938314,0.94444484,0.037857216,0.0041758255,0.00067762466,0.011056913],"study_design_scores_gemma":[0.000016667971,0.00006799744,0.00018487059,0.0000022876548,0.000007727236,0.0000037061895,0.0000064666015,0.9974154,0.0019718513,0.00021635133,0.00009965053,0.000006971502],"about_ca_topic_score_codex":0.02300053,"about_ca_topic_score_gemma":0.010076745,"teacher_disagreement_score":0.02300053,"about_ca_system_score_codex":0.0010702437,"about_ca_system_score_gemma":0.001368149,"threshold_uncertainty_score":0.045733333},"labels":[],"label_agreement":null},{"id":"W2010923953","doi":"10.1016/j.jprocont.2007.07.004","title":"Stiction – definition, modelling, detection and quantification","year":2007,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":133,"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":"Stiction; Static friction; Computer science; Control valves; Process (computing); Reliability engineering; Engineering; Control engineering; Materials science; Microelectromechanical systems; Nanotechnology","score_opus":0.014377925664001479,"score_gpt":0.2283592973890488,"score_spread":0.21398137172504733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010923953","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011355481,0.0067775478,0.97539014,0.000647233,0.0002551534,0.000054192875,0.00011529052,0.00019288958,0.0052120313],"genre_scores_gemma":[0.79479206,0.01516365,0.17689438,0.0003650562,0.0012489947,0.00023500087,0.0006531638,0.00021320891,0.010434513],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99719894,0.000643928,0.00025522857,0.00044610183,0.0012885557,0.00016721434],"domain_scores_gemma":[0.99613297,0.001849405,0.00055985444,0.0005620059,0.0007931527,0.00010262588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001823597,0.001543234,0.0017466499,0.0029622642,0.00065451383,0.00401773,0.0017427414,0.0031063019,0.0009294974],"category_scores_gemma":[0.0053261514,0.0008082638,0.0014530157,0.0017845741,0.0049216086,0.004562829,0.0021123507,0.002807498,0.00040440186],"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":[0.00012008838,0.00011009907,0.0019239059,0.0010901254,0.00014301912,0.00071136747,0.0007815011,0.17849895,0.018104218,0.6788328,0.0034108344,0.11627306],"study_design_scores_gemma":[0.000016092219,0.00023283834,0.0012639773,0.00026567755,0.0000695041,0.0017514187,0.00015538743,0.5932084,0.011655615,0.37205133,0.019177161,0.0001526199],"about_ca_topic_score_codex":0.0010668992,"about_ca_topic_score_gemma":0.0004865505,"teacher_disagreement_score":0.00401773,"about_ca_system_score_codex":0.00086056686,"about_ca_system_score_gemma":0.0012014186,"threshold_uncertainty_score":0.00964427},"labels":[],"label_agreement":null},{"id":"W2013382991","doi":"10.1016/j.jprocont.2006.04.003","title":"Price-driven coordination method for solving plant-wide MPC problems","year":2006,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Suncor Energy (Canada); University of Alberta","funders":"","keywords":"Model predictive control; Mathematical optimization; Computer science; Scale (ratio); Reliability (semiconductor); Sensitivity (control systems); Set (abstract data type); Quadratic programming; Diagonal; Quadratic equation; Control theory (sociology); Control (management); Mathematics; Engineering","score_opus":0.005359113873211972,"score_gpt":0.23173737906138422,"score_spread":0.22637826518817225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013382991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013287619,0.00015875262,0.98091817,0.00016970158,0.000079026555,0.000044903656,0.0000323193,0.00016020019,0.0051494134],"genre_scores_gemma":[0.8475973,0.00016318429,0.147358,0.00012412226,0.00007927607,0.00023467474,0.00007975616,0.000111966714,0.0042516626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997002,0.00012392053,0.000014152754,0.000041730447,0.00008302685,0.000036934405],"domain_scores_gemma":[0.99928826,0.00043003258,0.000054382446,0.00003934636,0.00014527662,0.000042584474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011525699,0.000722408,0.0012235531,0.00039333967,0.00043543876,0.0007520682,0.0010266692,0.0010953216,0.0034532119],"category_scores_gemma":[0.002193068,0.0005165908,0.00041707992,0.0004638247,0.00054603885,0.00066347537,0.0010101661,0.0011009686,0.00032240208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005476161,0.000026266389,0.00010192777,0.000040281062,0.000020866395,0.000031954718,0.000016531189,0.9764959,0.0006049514,0.007503665,0.00072280195,0.014380056],"study_design_scores_gemma":[0.000009844793,0.000012806961,0.000014145561,0.0000010527733,0.0000016206794,0.0000027967735,0.0000014051462,0.9987662,0.00006319,0.001022393,0.00010321235,0.0000013748535],"about_ca_topic_score_codex":0.0032247915,"about_ca_topic_score_gemma":0.0027414332,"teacher_disagreement_score":0.0034532119,"about_ca_system_score_codex":0.00045433626,"about_ca_system_score_gemma":0.0009364318,"threshold_uncertainty_score":0.011552215},"labels":[],"label_agreement":null},{"id":"W2013803049","doi":"10.1016/j.jprocont.2006.01.003","title":"A hybrid formulation and design of model predictive control for systems under actuator saturation and backlash","year":2006,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":76,"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":"McMaster University","keywords":"Backlash; Control theory (sociology); Model predictive control; Actuator; Multivariable calculus; Constraint (computer-aided design); Engineering; Control engineering; Quadratic programming; Integer programming; Computer science; Mathematical optimization; Mathematics; Control (management); Artificial intelligence","score_opus":0.008159179773931153,"score_gpt":0.21747420574124388,"score_spread":0.20931502596731272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013803049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008868602,0.00029461077,0.9862742,0.00021195687,0.00006705815,0.0000445707,0.000030967494,0.00010111795,0.0041069128],"genre_scores_gemma":[0.89355284,0.00043559264,0.09861605,0.00015008595,0.0001549186,0.00043083003,0.00009966247,0.000080131314,0.0064799557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997414,0.00007636708,0.000012894567,0.000043729804,0.000094122595,0.00003150533],"domain_scores_gemma":[0.99960405,0.0002088896,0.000047035293,0.000024408337,0.0000997583,0.000015834592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009775176,0.00087428733,0.0011457152,0.00036334025,0.0004619646,0.001169151,0.0010908007,0.0014026606,0.0024483383],"category_scores_gemma":[0.0013693178,0.00064110255,0.0006988085,0.00037545344,0.00073674263,0.0009866229,0.0010577388,0.000876519,0.00027805215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043903732,0.000019980798,0.00006839982,0.000087802066,0.00002471298,0.000050735893,0.000041790045,0.9723431,0.0017559066,0.013939551,0.00043540163,0.011188781],"study_design_scores_gemma":[0.0000061260025,0.000019748179,0.000019598798,0.0000022298786,0.0000030755953,0.0000028803104,0.0000029633218,0.9980829,0.00012409725,0.00155675,0.00017756777,0.0000022306422],"about_ca_topic_score_codex":0.0044073067,"about_ca_topic_score_gemma":0.0037338445,"teacher_disagreement_score":0.0044073067,"about_ca_system_score_codex":0.000545247,"about_ca_system_score_gemma":0.00094325293,"threshold_uncertainty_score":0.008763313},"labels":[],"label_agreement":null},{"id":"W2014687689","doi":"10.1016/j.jprocont.2005.10.004","title":"An approach to fast rate fault detection for multirate sampled-data systems","year":2006,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Residual; Fault detection and isolation; Control theory (sociology); Computer science; Key (lock); Fault (geology); Generator (circuit theory); Invariant (physics); Set (abstract data type); Algorithm; Real-time computing; Power (physics); Mathematics; Artificial intelligence; Control (management)","score_opus":0.01613325951914445,"score_gpt":0.25467853858891926,"score_spread":0.2385452790697748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014687689","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000947604,0.00011982613,0.9983923,0.00004001477,0.000041113628,0.00001590209,0.000007466562,0.00008900655,0.00034681556],"genre_scores_gemma":[0.24317877,0.00096557866,0.7496281,0.00022183731,0.00033677218,0.00016377847,0.00008190981,0.00013567977,0.005287409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905616,0.00017917597,0.00005951418,0.00014187969,0.00048523245,0.00007804064],"domain_scores_gemma":[0.9986966,0.0005918229,0.0000982527,0.00021448657,0.00035916487,0.00003967852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001263611,0.0011050412,0.0012616928,0.0012687057,0.000612423,0.001575188,0.0014135494,0.0010345816,0.0026267623],"category_scores_gemma":[0.0042415205,0.0005117186,0.0010467093,0.00064887025,0.0007816044,0.0018305889,0.0016317515,0.0018968588,0.00068523467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038137022,0.00015618779,0.0006862589,0.00045544447,0.00015657478,0.0003456919,0.00036763074,0.39882833,0.033618804,0.16750677,0.0022603157,0.39523667],"study_design_scores_gemma":[0.000010873129,0.000053784825,0.000071121,0.000016300863,0.000017967019,0.00007633459,0.000013255352,0.9785549,0.0028499423,0.016610667,0.0017119169,0.000012975858],"about_ca_topic_score_codex":0.0020814866,"about_ca_topic_score_gemma":0.0020987322,"teacher_disagreement_score":0.0026267623,"about_ca_system_score_codex":0.0006831774,"about_ca_system_score_gemma":0.000757289,"threshold_uncertainty_score":0.008787453},"labels":[],"label_agreement":null},{"id":"W2019327847","doi":"10.1016/j.jprocont.2004.01.006","title":"H2 approximation of multiple input/output delay systems","year":2004,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","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 Alberta","funders":"","keywords":"Parametrization (atmospheric modeling); Control theory (sociology); Stability (learning theory); Multivariable calculus; Reduction (mathematics); Scheme (mathematics); Linear system; Mathematics; Mathematical optimization; Computer science; Applied mathematics; Control (management); Engineering; Control engineering","score_opus":0.012004718598554554,"score_gpt":0.24265278095172432,"score_spread":0.23064806235316976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019327847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046375465,0.00052782777,0.9443815,0.00033691776,0.00014356189,0.00002543014,0.00011715258,0.00018429932,0.007907894],"genre_scores_gemma":[0.96097904,0.00029577885,0.027902067,0.000061299164,0.000051628176,0.000042243104,0.00011552065,0.0000399727,0.010512373],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998293,0.000053775457,0.000006591972,0.000034864315,0.000046506655,0.000028988776],"domain_scores_gemma":[0.9996611,0.00018896992,0.000034071774,0.000025459196,0.000071377435,0.0000190128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047537984,0.0008053063,0.0007368055,0.00028716112,0.00026241265,0.0008139582,0.00049855106,0.00077959744,0.002638903],"category_scores_gemma":[0.001172589,0.00027598577,0.0003635402,0.0003097036,0.00045947192,0.0005281435,0.00058600615,0.00087767397,0.0002821422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008437568,0.000026092841,0.00026161276,0.00007399344,0.000027267588,0.000067279914,0.000048362395,0.9609688,0.002240494,0.021593152,0.0006429756,0.013965546],"study_design_scores_gemma":[0.0000029137411,0.000007539162,0.00004344123,0.0000016236261,0.000001817633,0.0000034081984,0.0000028550744,0.99752945,0.00017575586,0.0020527553,0.00017655519,0.0000019715417],"about_ca_topic_score_codex":0.008793424,"about_ca_topic_score_gemma":0.005483837,"teacher_disagreement_score":0.008793424,"about_ca_system_score_codex":0.0006905138,"about_ca_system_score_gemma":0.00056416873,"threshold_uncertainty_score":0.017484486},"labels":[],"label_agreement":null},{"id":"W2019489595","doi":"10.1016/j.jprocont.2008.09.007","title":"Economic performance assessment of advanced process control with LQG benchmarking","year":2008,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":56,"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":"Benchmarking; Benchmark (surveying); Linear-quadratic-Gaussian control; Control (management); Process (computing); Control engineering; Control system; Computer science; Engineering; Optimal control; Process control; Control theory (sociology); Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.0041242055654097005,"score_gpt":0.22613228243527425,"score_spread":0.22200807686986454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019489595","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":[0.786789,0.002821042,0.18185446,0.0018909153,0.00025149572,0.00033362667,0.0006718751,0.00028408453,0.025103625],"genre_scores_gemma":[0.99542916,0.000102918624,0.0038087342,0.000019487963,0.000027206195,0.000028290777,0.00012317253,0.000012611459,0.00044848825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9868259,0.009401435,0.00026942536,0.0005621046,0.0021602707,0.0007808772],"domain_scores_gemma":[0.97906095,0.014587907,0.0010531829,0.0015969503,0.0031886068,0.0005123155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021859478,0.0010226717,0.0019186895,0.0023121454,0.00060052326,0.0029479393,0.0013993611,0.0020547926,0.0017651443],"category_scores_gemma":[0.037140228,0.00035788608,0.00094714336,0.002211294,0.0014420664,0.0026482302,0.001869769,0.0011575999,0.00017029504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024066006,0.00025062446,0.0060996492,0.00015421394,0.00014168386,0.00010728254,0.000031981508,0.93097126,0.00079743064,0.026283074,0.00076696754,0.031989243],"study_design_scores_gemma":[0.000109632085,0.0008655199,0.005093642,0.00002598798,0.000060331735,0.00003185207,0.000047091416,0.981669,0.0010036377,0.010553552,0.0005108859,0.000028928936],"about_ca_topic_score_codex":0.0050805407,"about_ca_topic_score_gemma":0.0017951517,"teacher_disagreement_score":0.021859478,"about_ca_system_score_codex":0.0033773363,"about_ca_system_score_gemma":0.002131588,"threshold_uncertainty_score":0.115605354},"labels":[],"label_agreement":null},{"id":"W2022434399","doi":"10.1016/j.jprocont.2012.05.015","title":"A hybrid fault diagnosis strategy for chemical process startups","year":2012,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"Laurentian University","funders":"National Key Research and Development Program of China; National University of Singapore","keywords":"Process (computing); Fault (geology); Computer science; Fault detection and isolation; Principal component analysis; Reliability engineering; Fractionating column; Dynamic time warping; Quality (philosophy); Distillation; Engineering; Process engineering; Artificial intelligence","score_opus":0.012964114593383758,"score_gpt":0.26253986591617073,"score_spread":0.24957575132278698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022434399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09308281,0.000552711,0.90054303,0.00020578784,0.00013589364,0.000118151715,0.00006695816,0.0017449798,0.0035496175],"genre_scores_gemma":[0.94567895,0.00008832139,0.052146956,0.00008441988,0.000027044689,0.000041383442,0.000050333656,0.000018387062,0.0018642549],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969304,0.000043337608,0.00002047854,0.000089184236,0.0001024261,0.000051616615],"domain_scores_gemma":[0.9995858,0.00014856941,0.000059909595,0.00004246096,0.00013414615,0.000029069965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034225543,0.0009821273,0.00099141,0.0009153216,0.0005920979,0.0008126918,0.0010876511,0.0009654472,0.002092725],"category_scores_gemma":[0.00064207846,0.0002935572,0.00043103367,0.0003492531,0.00035780692,0.00072666164,0.00071822375,0.00042919826,0.00034094788],"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":[0.0013849866,0.00046714424,0.0021868963,0.0004392549,0.00018208804,0.0008492183,0.00025965876,0.25312918,0.10850986,0.0073960624,0.0025762408,0.62261945],"study_design_scores_gemma":[0.00006396997,0.0003914852,0.00071634393,0.000012575021,0.00005976873,0.00020072026,0.00003361193,0.9770597,0.018626837,0.0018872016,0.00092663127,0.000021071352],"about_ca_topic_score_codex":0.0031684192,"about_ca_topic_score_gemma":0.003929912,"teacher_disagreement_score":0.0031684192,"about_ca_system_score_codex":0.0004956999,"about_ca_system_score_gemma":0.0006297094,"threshold_uncertainty_score":0.0070008636},"labels":[],"label_agreement":null},{"id":"W2022651451","doi":"10.1016/j.jprocont.2005.06.012","title":"Control of time delay processes with uncertain delays: Time delay stability margins","year":2005,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Stability and Control of Uncertain Systems","field":"Engineering","cited_by":44,"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","keywords":"Control theory (sociology); Stability (learning theory); Perturbation (astronomy); Root locus; Mathematics; Control system; Computer science; Control (management); Engineering; Physics","score_opus":0.00699304334824461,"score_gpt":0.20542005319096454,"score_spread":0.19842700984271994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022651451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.109329276,0.0015803723,0.8774804,0.0005490202,0.00024227891,0.000041786512,0.000049637623,0.00014564369,0.01058147],"genre_scores_gemma":[0.99111325,0.0004453682,0.0063368897,0.000045861456,0.000060485763,0.000027739714,0.00001447362,0.000018832025,0.0019371208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970216,0.000062856256,0.000017281613,0.000075458294,0.000103025806,0.000039131868],"domain_scores_gemma":[0.9988986,0.0005493046,0.00021822733,0.000050545394,0.00024010478,0.00004316027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009797693,0.0008656873,0.00050488213,0.00031958878,0.0004012788,0.0014128219,0.0006405468,0.000801917,0.0020992376],"category_scores_gemma":[0.0031685564,0.00023793569,0.0003502098,0.00040374434,0.0007250296,0.0013313688,0.0012163057,0.00078403065,0.00016452694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064740377,0.000111310845,0.0003783798,0.0003735458,0.00007823082,0.00015707228,0.00039226396,0.7943021,0.024981502,0.11691656,0.0012777043,0.0603839],"study_design_scores_gemma":[0.000025520198,0.0001981762,0.00023610328,0.000020044334,0.000025490328,0.000028464841,0.000031593372,0.96933305,0.004755438,0.024130015,0.0012034344,0.0000127131825],"about_ca_topic_score_codex":0.00097208034,"about_ca_topic_score_gemma":0.0005052494,"teacher_disagreement_score":0.0020992376,"about_ca_system_score_codex":0.0005951152,"about_ca_system_score_gemma":0.00052562845,"threshold_uncertainty_score":0.007022679},"labels":[],"label_agreement":null},{"id":"W2022735044","doi":"10.1016/j.jprocont.2011.06.007","title":"Latent variable model predictive control for trajectory tracking in batch processes: Alternative modeling approaches","year":2011,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"McMaster University","funders":"","keywords":"Trajectory; Latent variable; Model predictive control; Computer science; Variable (mathematics); Identification (biology); Control theory (sociology); Tracking (education); Batch processing; Latent variable model; Principal component analysis; Artificial intelligence; Mathematics; Control (management)","score_opus":0.050313947318060916,"score_gpt":0.2399530039133955,"score_spread":0.1896390565953346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022735044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015634824,0.0005850986,0.98224026,0.00041062685,0.000059054546,0.000021781778,0.00008678429,0.00014103673,0.00082056585],"genre_scores_gemma":[0.90108424,0.0013249821,0.089994274,0.0001758643,0.000212474,0.0002501305,0.00047282636,0.00014059823,0.0063445577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986627,0.00063858496,0.000079752856,0.00025095622,0.00021377947,0.00015415016],"domain_scores_gemma":[0.9944351,0.004350571,0.0003977713,0.00024017281,0.0004977393,0.00007871385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004139372,0.001277736,0.0023426586,0.00079884595,0.00074944505,0.002516761,0.002425911,0.0016776938,0.0024234683],"category_scores_gemma":[0.008013677,0.0010080991,0.0014586903,0.0013823004,0.0017910772,0.0029100496,0.0016818182,0.0025012565,0.00043281255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000138715,0.000053322034,0.00035887817,0.00011508347,0.00009910442,0.00003187455,0.000101529135,0.95262283,0.00032346707,0.02984715,0.00046784003,0.015840203],"study_design_scores_gemma":[0.00000567798,0.000005864227,0.000037635804,0.0000027780095,0.0000063303914,0.0000011425177,0.0000028973304,0.9958503,0.000043490243,0.0039904406,0.000048965572,0.000004484547],"about_ca_topic_score_codex":0.019803807,"about_ca_topic_score_gemma":0.013974687,"teacher_disagreement_score":0.019803807,"about_ca_system_score_codex":0.0017736879,"about_ca_system_score_gemma":0.002317135,"threshold_uncertainty_score":0.039377093},"labels":[],"label_agreement":null},{"id":"W2024595124","doi":"10.1016/j.jprocont.2004.10.007","title":"Closed-loop identification with a quantizer","year":2005,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","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":"University of Alberta","funders":"University of Alberta","keywords":"Control theory (sociology); Closed loop; Loop (graph theory); Identification (biology); Scheme (mathematics); SIGNAL (programming language); Path (computing); Computer science; Identification scheme; Feedback loop; Interval (graph theory); Mathematics; Control (management); Control engineering; Engineering; Artificial intelligence; Data mining","score_opus":0.004387573076398,"score_gpt":0.2114393948992957,"score_spread":0.2070518218228977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024595124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007813998,0.0004640904,0.9879116,0.0001532698,0.00016820522,0.00007117342,0.000053186588,0.00090411026,0.0024603081],"genre_scores_gemma":[0.7310928,0.0005326099,0.25922152,0.00028324462,0.00014375415,0.0002664848,0.00012965893,0.000110565226,0.008219383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993198,0.00015698432,0.00006808142,0.00014787885,0.00027076204,0.000036576068],"domain_scores_gemma":[0.9989511,0.0005398727,0.00007747666,0.00016533509,0.00024948097,0.00001670263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090924266,0.0005039943,0.00083033106,0.00040358867,0.00074284547,0.0011331787,0.0007087205,0.00097318203,0.003819257],"category_scores_gemma":[0.002713678,0.00046567357,0.00031832774,0.00045788212,0.0006907297,0.0014171428,0.00089250674,0.0010328699,0.0009464898],"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":[0.0012268394,0.00023279397,0.0011620378,0.0014104818,0.00021923022,0.00030572418,0.0006959555,0.14974129,0.1644055,0.07010278,0.004639175,0.6058582],"study_design_scores_gemma":[0.0003798659,0.00071615103,0.001845242,0.00016101262,0.00012461851,0.00031913872,0.00006431228,0.8914719,0.0644852,0.019328853,0.021003282,0.000100373865],"about_ca_topic_score_codex":0.0018221021,"about_ca_topic_score_gemma":0.0020127103,"teacher_disagreement_score":0.003819257,"about_ca_system_score_codex":0.00045564177,"about_ca_system_score_gemma":0.0006906376,"threshold_uncertainty_score":0.012776673},"labels":[],"label_agreement":null},{"id":"W2027039283","doi":"10.1016/j.jprocont.2014.03.005","title":"Control-loop diagnosis using continuous evidence through kernel density estimation","year":2014,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"Syncrude (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Kernel density estimation; Loop (graph theory); Control theory (sociology); Kernel (algebra); Estimation; Control (management); Computer science; Mathematics; Artificial intelligence; Statistics; Engineering","score_opus":0.015777952388502943,"score_gpt":0.26479748889025906,"score_spread":0.24901953650175612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027039283","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02400491,0.0003336827,0.97467256,0.0001593287,0.000032619424,0.000021930076,0.000024900133,0.00030235449,0.00044775833],"genre_scores_gemma":[0.9231086,0.00019389423,0.0755489,0.000066741464,0.00004066834,0.000041002044,0.00007431495,0.000035239646,0.0008906856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914587,0.00021136468,0.000073335206,0.00018088981,0.0002922666,0.00009625373],"domain_scores_gemma":[0.99053276,0.0071089235,0.0005772424,0.000467485,0.0011623352,0.00015109479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017810422,0.000806366,0.0016926022,0.0010412703,0.00040443425,0.0015207655,0.0011871035,0.0018180902,0.0012975971],"category_scores_gemma":[0.013903588,0.00063814386,0.0007807423,0.00056656974,0.0010431603,0.0026611632,0.0019719556,0.0018900414,0.00030735048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016741889,0.0002822596,0.004492476,0.00053651497,0.00031255512,0.0004407414,0.00026635654,0.6513498,0.011771893,0.024856456,0.0011703074,0.30284646],"study_design_scores_gemma":[0.000016097454,0.000040959818,0.00026158604,0.000010021202,0.000014522768,0.000033880253,0.0000058902338,0.99564797,0.0011758576,0.002677332,0.00010767572,0.000008138583],"about_ca_topic_score_codex":0.0029735875,"about_ca_topic_score_gemma":0.0021393143,"teacher_disagreement_score":0.0029735875,"about_ca_system_score_codex":0.0006144831,"about_ca_system_score_gemma":0.0012295246,"threshold_uncertainty_score":0.009419203},"labels":[],"label_agreement":null},{"id":"W2027960100","doi":"10.1016/j.jprocont.2014.03.012","title":"Observer-enhanced distributed moving horizon state estimation subject to communication delays","year":2014,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":29,"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":"Control theory (sociology); Nonlinear system; Observer (physics); Estimator; State (computer science); Computer science; State observer; Bounded function; Control engineering; Engineering; Mathematics; Algorithm; Control (management); Artificial intelligence","score_opus":0.004778663000114881,"score_gpt":0.2242352501783719,"score_spread":0.21945658717825703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027960100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016056007,0.00021387049,0.98213345,0.00012182998,0.000089850306,0.000021158827,0.00004179113,0.00017747386,0.0011446107],"genre_scores_gemma":[0.9414716,0.0003280137,0.053072564,0.000069988775,0.00009187129,0.00010207098,0.00022688475,0.00005752784,0.0045794128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993592,0.00017462505,0.000042508782,0.00017825118,0.00016661042,0.00007868678],"domain_scores_gemma":[0.99724567,0.0017373447,0.00029585022,0.00017507277,0.0004952147,0.00005084152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014826512,0.0010641883,0.0018722073,0.0003442104,0.00034194233,0.0011785985,0.0010411261,0.0014835233,0.0019296161],"category_scores_gemma":[0.0050714985,0.0006129567,0.0005818606,0.000481453,0.00078848604,0.0013847821,0.0013826961,0.0016300016,0.00038907607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050474994,0.00006297613,0.00049064966,0.00025438698,0.00009353154,0.00018649975,0.000105357794,0.9416981,0.0057016783,0.011293609,0.0008452825,0.038763165],"study_design_scores_gemma":[0.000013976215,0.00003027382,0.000079288184,0.000003688771,0.000006799541,0.000009864585,0.0000029476557,0.9984282,0.0005646274,0.00074930536,0.000106967665,0.0000040393898],"about_ca_topic_score_codex":0.005758477,"about_ca_topic_score_gemma":0.004526515,"teacher_disagreement_score":0.005758477,"about_ca_system_score_codex":0.00063108286,"about_ca_system_score_gemma":0.0012602672,"threshold_uncertainty_score":0.011449873},"labels":[],"label_agreement":null},{"id":"W2029292150","doi":"10.1016/j.jprocont.2011.05.004","title":"On-line optimization of fedbatch bioreactors by adaptive extremum seeking control","year":2011,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Extremum Seeking Control Systems","field":"Engineering","cited_by":65,"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; Queen's University","funders":"Office of Science","keywords":"Control theory (sociology); Controller (irrigation); Adaptive control; Bioreactor; Engineering; Scheme (mathematics); Lyapunov function; Process (computing); Stability (learning theory); Line (geometry); Control (management); Mathematical optimization; Nonlinear system; Mathematics; Computer science; Chemistry; Artificial intelligence; Physics","score_opus":0.01742704810022887,"score_gpt":0.21778251904945398,"score_spread":0.2003554709492251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029292150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26771694,0.00074405945,0.71302754,0.000731066,0.0001974265,0.00024718686,0.00014980907,0.0005264157,0.016659606],"genre_scores_gemma":[0.9566251,0.00015473524,0.038833804,0.00009807444,0.000019563662,0.0002053098,0.000080706304,0.00007311838,0.003909603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995357,0.00019610664,0.000015990781,0.0000697002,0.00010655688,0.000076034055],"domain_scores_gemma":[0.9992931,0.00039103717,0.00008445763,0.000032809403,0.00015787386,0.00004079561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013005363,0.0013777766,0.0020077464,0.00054745714,0.00063867995,0.0016530164,0.0012551252,0.002110947,0.002532578],"category_scores_gemma":[0.0023207446,0.0009090798,0.000797979,0.00043742967,0.0007185551,0.0007424594,0.0015496224,0.0010013636,0.0003386911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016160795,0.00008870981,0.0002619636,0.000056570894,0.00003123233,0.000045961206,0.000031218242,0.9846768,0.0045184195,0.0012654444,0.00034981468,0.008512236],"study_design_scores_gemma":[0.000010615126,0.000054629974,0.000050574698,0.0000026504679,0.0000037488078,0.0000028535776,0.0000053766985,0.9986889,0.0007198768,0.00037199093,0.00008531409,0.0000035037465],"about_ca_topic_score_codex":0.00727418,"about_ca_topic_score_gemma":0.0048114005,"teacher_disagreement_score":0.00727418,"about_ca_system_score_codex":0.0010778784,"about_ca_system_score_gemma":0.0013708848,"threshold_uncertainty_score":0.014463663},"labels":[],"label_agreement":null},{"id":"W2029747079","doi":"10.1016/j.jprocont.2007.07.006","title":"A continuous stirred tank heater simulation model with applications","year":2007,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":229,"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; Center for Spirituality, Theology and Health, Duke University; Royal Academy of Engineering","keywords":"Actuator; Process (computing); Engineering; Fault (geology); Volume (thermodynamics); Fault detection and isolation; Control engineering; Identification (biology); Feature (linguistics); Computer science; Mechanical engineering; Electrical engineering","score_opus":0.005622617302820698,"score_gpt":0.23996881705939901,"score_spread":0.2343461997565783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029747079","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":[0.5828246,0.00064502115,0.3728727,0.0010421994,0.00023509585,0.00028616624,0.0020533164,0.0027008331,0.037340045],"genre_scores_gemma":[0.9728795,0.0001895059,0.018764628,0.000040455365,0.000027253827,0.00015240302,0.0005107825,0.00007505199,0.007360475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998894,0.000038341313,0.0000071136255,0.000022465936,0.000027919448,0.000014720599],"domain_scores_gemma":[0.9995134,0.0002755137,0.00003879847,0.000031815922,0.00010432975,0.000036064066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027322472,0.00066423806,0.00076460926,0.00029318186,0.0005512286,0.0007892573,0.0010021529,0.0015511799,0.0049640066],"category_scores_gemma":[0.0010222149,0.00043264386,0.00061464094,0.00043517424,0.0003215674,0.00048852956,0.00050377526,0.00076217734,0.00043461146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071671166,0.000044574015,0.00029160423,0.000023044759,0.000010006469,0.00003926642,0.000014821782,0.9947826,0.0009779454,0.001242008,0.00023264506,0.0022698338],"study_design_scores_gemma":[0.000017087572,0.0000151722825,0.00006222671,0.0000010136876,0.0000032192079,0.0000023708308,0.0000023339207,0.9992493,0.00027399993,0.00018136701,0.00018967426,0.000002261378],"about_ca_topic_score_codex":0.02502712,"about_ca_topic_score_gemma":0.010882815,"teacher_disagreement_score":0.02502712,"about_ca_system_score_codex":0.0004918353,"about_ca_system_score_gemma":0.001015166,"threshold_uncertainty_score":0.049762905},"labels":[],"label_agreement":null},{"id":"W2030902158","doi":"10.1016/j.jprocont.2011.09.004","title":"Prediction error method for identification of LPV models","year":2011,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","cited_by":98,"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; China Scholarship Council","keywords":"Identification (biology); Interpolation (computer graphics); Control theory (sociology); LTI system theory; Mean squared prediction error; System identification; Linear interpolation; Invariant (physics); Mathematics; Computer science; Linear system; Algorithm; Artificial intelligence; Data modeling; Pattern recognition (psychology)","score_opus":0.0410431984694963,"score_gpt":0.26982945167677636,"score_spread":0.22878625320728005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030902158","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038404174,0.00012878008,0.9954051,0.00002253767,0.000022259468,0.000009776235,0.000019065916,0.00016115436,0.00039091476],"genre_scores_gemma":[0.621143,0.00074018404,0.3662439,0.000082391394,0.00008583108,0.000373477,0.00046213155,0.00023484637,0.010634278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961686,0.0001320564,0.000021566006,0.00006831344,0.0001302354,0.000030906285],"domain_scores_gemma":[0.9991781,0.00049635913,0.000046740395,0.00007493695,0.0001916442,0.000012221785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089124223,0.00052256987,0.00080815406,0.00040053966,0.00037674847,0.0005265958,0.000879411,0.0008764977,0.002183934],"category_scores_gemma":[0.0029056615,0.00038038407,0.0003805308,0.00038824463,0.0003150267,0.00068605895,0.00049697846,0.0013709265,0.00078477926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017929215,0.00008353459,0.00046650786,0.0002377615,0.000073279596,0.00007423828,0.0000647624,0.7503821,0.011262381,0.017945323,0.0014747593,0.21775599],"study_design_scores_gemma":[0.0000031653583,0.000011997794,0.000086802895,0.0000046963073,0.0000039223214,0.0000074439895,0.0000021582534,0.99765104,0.0010336008,0.00090876484,0.00028265544,0.0000037870975],"about_ca_topic_score_codex":0.0050017955,"about_ca_topic_score_gemma":0.0026334529,"teacher_disagreement_score":0.0050017955,"about_ca_system_score_codex":0.00028728897,"about_ca_system_score_gemma":0.00062413974,"threshold_uncertainty_score":0.009945333},"labels":[],"label_agreement":null},{"id":"W2032136265","doi":"10.1016/j.jprocont.2012.12.008","title":"Development and industrial application of soft sensors with on-line Bayesian model updating strategy","year":2013,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Syncrude (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Syncrude","keywords":"Soft sensor; Computer science; Process (computing); Measure (data warehouse); Calibration; Data mining; Line (geometry); Bayesian probability; Particle filter; Bayesian inference; Maximization; Key (lock); Machine learning; Artificial intelligence; Kalman filter; Mathematical optimization; Statistics; Mathematics","score_opus":0.015033716095345238,"score_gpt":0.23003894942545144,"score_spread":0.2150052333301062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032136265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011245255,0.00018748858,0.9849681,0.000085243824,0.000038447328,0.000048581303,0.000018056815,0.00045094453,0.0029578023],"genre_scores_gemma":[0.6369447,0.00031882938,0.35785607,0.00011916475,0.0000452496,0.000107203145,0.00009460482,0.0000909417,0.0044231927],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929094,0.00018485363,0.00003665452,0.00012565007,0.00031865243,0.000043148077],"domain_scores_gemma":[0.9987864,0.00046396602,0.00010321434,0.00019903417,0.00040317624,0.000044189444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008247909,0.0006169913,0.00071775133,0.00052193744,0.00028744296,0.0009465877,0.0011023406,0.0009470901,0.0018557699],"category_scores_gemma":[0.0024318888,0.00043380415,0.0004665022,0.00035644916,0.000361729,0.0010057392,0.000908131,0.00085255527,0.0005056616],"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":[0.00034285238,0.00035858204,0.0021583203,0.00025992646,0.00015062511,0.00023898119,0.00020687601,0.27133372,0.06715566,0.023034228,0.0018786143,0.63288164],"study_design_scores_gemma":[0.000013490766,0.00009174744,0.0003493645,0.0000078605735,0.000023065293,0.000059101905,0.000009668172,0.9869949,0.009327299,0.001854433,0.001256403,0.00001257552],"about_ca_topic_score_codex":0.0023848405,"about_ca_topic_score_gemma":0.0025994652,"teacher_disagreement_score":0.0023848405,"about_ca_system_score_codex":0.0003914379,"about_ca_system_score_gemma":0.00088806683,"threshold_uncertainty_score":0.006208122},"labels":[],"label_agreement":null},{"id":"W2032837898","doi":"10.1016/j.jprocont.2013.08.002","title":"A unified framework for fault detection and isolation of sensor and actuator biases in linear time invariant systems using marginalized likelihood ratio test with uniform priors","year":2013,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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 Alberta","funders":"","keywords":"Prior probability; Fault detection and isolation; Benchmark (surveying); Fault (geology); Realization (probability); Likelihood-ratio test; Invariant (physics); Statistical hypothesis testing; Mathematics; Actuator; Control theory (sociology); Computer science; Algorithm; Statistics; Artificial intelligence; Bayesian probability","score_opus":0.010190868286400532,"score_gpt":0.2314263555335034,"score_spread":0.22123548724710287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032837898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00085807074,0.00007392371,0.99871814,0.00003405363,0.000008478018,0.0000152291,0.000020763333,0.00012615541,0.00014532395],"genre_scores_gemma":[0.20032604,0.0005189548,0.7949056,0.00024645805,0.0003090507,0.00046894237,0.00061516726,0.0003761742,0.0022335844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9932666,0.003188658,0.00042289836,0.0010886932,0.0015490238,0.00048408215],"domain_scores_gemma":[0.9812164,0.014413064,0.0008396377,0.0013629367,0.0018376297,0.00033041064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011494184,0.0024416477,0.0037709216,0.0023259968,0.0007181184,0.0028433478,0.004851541,0.002688291,0.0044633853],"category_scores_gemma":[0.035026185,0.0018013143,0.002837624,0.0018849478,0.003384581,0.0045825457,0.004631408,0.0033453798,0.001086688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055085047,0.00016522953,0.0014711672,0.0003998654,0.0004499524,0.00049850007,0.00022306977,0.63277835,0.0063985237,0.23731342,0.002127606,0.117623456],"study_design_scores_gemma":[0.00004506068,0.00008707137,0.00025370804,0.000017516642,0.00005059124,0.00009053972,0.0000140196225,0.95212245,0.001412061,0.045300238,0.0005712987,0.000035525893],"about_ca_topic_score_codex":0.00320145,"about_ca_topic_score_gemma":0.0029265955,"teacher_disagreement_score":0.011494184,"about_ca_system_score_codex":0.0014161537,"about_ca_system_score_gemma":0.0035123397,"threshold_uncertainty_score":0.060787797},"labels":[],"label_agreement":null},{"id":"W2042826680","doi":"10.1016/j.jprocont.2015.01.009","title":"Minimal required excitation for closed-loop identification: Some implications for data-driven, system identification","year":2015,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"Alexander von Humboldt-Stiftung","keywords":"Identifiability; SIGNAL (programming language); Sampling (signal processing); Computer science; Identification (biology); Control theory (sociology); Process (computing); Loop (graph theory); Function (biology); Signal transfer function; Algorithm; Mathematics; Transmission (telecommunications); Filter (signal processing); Analog signal; Artificial intelligence; Telecommunications","score_opus":0.05384312573305671,"score_gpt":0.3164400578446049,"score_spread":0.26259693211154816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042826680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011933662,0.00011321591,0.9823987,0.00051278534,0.000047319398,0.00003368349,0.000090443,0.00019919738,0.0046710037],"genre_scores_gemma":[0.7852907,0.00023061677,0.21010226,0.0004645344,0.000097434306,0.00021212736,0.00020056921,0.00020388924,0.0031978083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985436,0.00047646067,0.00012746485,0.00027704643,0.00047437503,0.00010102484],"domain_scores_gemma":[0.9837372,0.013511635,0.0003380411,0.0010850631,0.0011612771,0.00016671202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029621401,0.0009845434,0.0011898936,0.0005698321,0.00075970276,0.0015459756,0.0014013428,0.0015417321,0.0056154546],"category_scores_gemma":[0.021109639,0.0004037736,0.00057583593,0.0004201903,0.0014261331,0.0019864899,0.0013377698,0.001543063,0.0006719634],"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":[0.0014017542,0.00035637192,0.0012688086,0.0008058803,0.00007729559,0.0005776199,0.000409293,0.36416572,0.033540703,0.49558905,0.0032267864,0.098580614],"study_design_scores_gemma":[0.000055944263,0.0001714883,0.00035186543,0.00007356077,0.00002284455,0.00014224622,0.000055465665,0.82595,0.008636689,0.16336924,0.0011399591,0.00003062652],"about_ca_topic_score_codex":0.0007370067,"about_ca_topic_score_gemma":0.0010065717,"teacher_disagreement_score":0.0056154546,"about_ca_system_score_codex":0.0007000613,"about_ca_system_score_gemma":0.00097435573,"threshold_uncertainty_score":0.018785536},"labels":[],"label_agreement":null},{"id":"W2043300714","doi":"10.1016/j.jprocont.2003.09.002","title":"Performance assessment using a model predictive control benchmark","year":2003,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":69,"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":"International Council for Canadian Studies","keywords":"Model predictive control; Benchmark (surveying); Identification (biology); Computer science; Control (management); Engineering; Control engineering; Reliability engineering; Artificial intelligence","score_opus":0.007080006964876396,"score_gpt":0.24333664096620902,"score_spread":0.23625663400133262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043300714","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":[0.9231606,0.0009830239,0.054805808,0.0005026534,0.0001895534,0.00014298243,0.00071922725,0.0007605002,0.018735649],"genre_scores_gemma":[0.9934716,0.000068243506,0.005316245,0.000019090354,0.000009132631,0.000030830535,0.00031079914,0.000027724273,0.0007463545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989207,0.00044999892,0.000060522332,0.00010045703,0.00030751666,0.00016089942],"domain_scores_gemma":[0.9957268,0.0024927056,0.0002455387,0.00036206096,0.0010824974,0.000090252775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023558033,0.0010086981,0.0009930085,0.0013834388,0.00086710934,0.0012685637,0.0007261054,0.0016140913,0.0016438295],"category_scores_gemma":[0.008378769,0.00025599278,0.00047609594,0.0009693007,0.0005358326,0.00092216703,0.00065103895,0.0007657278,0.00024288615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009324935,0.00043899065,0.0010410433,0.00019310624,0.0000617114,0.000073348354,0.000029035426,0.9724024,0.0037285178,0.0026665353,0.0009770663,0.017455908],"study_design_scores_gemma":[0.000061974264,0.0007485193,0.001394977,0.000010035408,0.00002634122,0.000015656715,0.000024718986,0.990672,0.00596824,0.00082340266,0.0002347266,0.000019530453],"about_ca_topic_score_codex":0.011072406,"about_ca_topic_score_gemma":0.005597269,"teacher_disagreement_score":0.011072406,"about_ca_system_score_codex":0.0007567659,"about_ca_system_score_gemma":0.0010059567,"threshold_uncertainty_score":0.02201587},"labels":[],"label_agreement":null},{"id":"W2044077463","doi":"10.1016/j.jprocont.2013.02.003","title":"Information transfer methods in causality analysis of process variables with an industrial application","year":2013,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"Suncor Energy (Canada); University of Alberta","funders":"","keywords":"Transfer entropy; Dependency (UML); Entropy (arrow of time); Mutual information; Causality (physics); Process (computing); Computer science; Data mining; Root cause; Information flow; Information transfer; Identification (biology); Econometrics; Mathematics; Artificial intelligence; Engineering; Principle of maximum entropy; Reliability engineering","score_opus":0.01088317742782024,"score_gpt":0.2830143246270518,"score_spread":0.27213114719923154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044077463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004191481,0.00028886262,0.99478734,0.000075437514,0.00002655503,0.00002101297,0.000017885252,0.00008152004,0.00050985266],"genre_scores_gemma":[0.5278794,0.0020463448,0.46374786,0.00017587387,0.0003790657,0.00034738894,0.00020871582,0.00018777089,0.0050274977],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979513,0.0011646692,0.000112224276,0.00027666643,0.00037480693,0.000120327095],"domain_scores_gemma":[0.98467225,0.013504546,0.00046671665,0.0005134481,0.0007199547,0.00012315856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004703836,0.0009894567,0.001177506,0.0032671,0.00091929,0.0014647327,0.0011333358,0.0010469566,0.0029988121],"category_scores_gemma":[0.01691367,0.00050159864,0.0017296779,0.00286553,0.0017333019,0.0027462621,0.0014054719,0.001907476,0.0003709453],"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":[0.00043845264,0.00031024413,0.002197593,0.00048473265,0.0003944219,0.0003430232,0.0004887355,0.28975877,0.0055097127,0.33465827,0.0015296834,0.36388636],"study_design_scores_gemma":[0.000028392753,0.00010111596,0.00056389725,0.000029987381,0.00008438135,0.000060082588,0.00004258982,0.8544396,0.0018571672,0.14181474,0.0009478862,0.000030066933],"about_ca_topic_score_codex":0.001917948,"about_ca_topic_score_gemma":0.001059528,"teacher_disagreement_score":0.004703836,"about_ca_system_score_codex":0.0007237938,"about_ca_system_score_gemma":0.0013756754,"threshold_uncertainty_score":0.024876535},"labels":[],"label_agreement":null},{"id":"W2044087736","doi":"10.1016/j.jprocont.2004.04.008","title":"Identification of channelling and recirculation parameters of agitated pulp stock chests","year":2004,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","cited_by":20,"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":"Channelling; Research Object; Computer science; Identification (biology); Engineering; Simulation; Chemistry","score_opus":0.010013574878703283,"score_gpt":0.22408674706971357,"score_spread":0.2140731721910103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044087736","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":[0.9481104,0.000086747634,0.05040215,0.000023373097,0.0000060549132,0.000021851021,0.00009942138,0.00030700903,0.00094297715],"genre_scores_gemma":[0.9981634,0.000015388034,0.0015209137,0.0000019389613,6.782355e-7,0.0000036840804,0.000026186068,0.0000064695437,0.00026133066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999198,0.000010649485,0.0000045636302,0.000025880507,0.000024588682,0.000014548681],"domain_scores_gemma":[0.9997212,0.00012911134,0.00004475888,0.000024952134,0.0000684253,0.000011523734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016914474,0.00031662523,0.00034588724,0.000440663,0.00021693815,0.0005461747,0.0003914469,0.00041940875,0.00078654906],"category_scores_gemma":[0.000584187,0.00019423796,0.00017987342,0.00024365321,0.00023305784,0.00047221477,0.00017286652,0.00022788876,0.0001670182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015019058,0.00015548238,0.029929832,0.00018510553,0.00006139441,0.00055159075,0.0003853323,0.43266878,0.4158888,0.0012232319,0.00052774826,0.11692084],"study_design_scores_gemma":[0.000020169036,0.00010995912,0.02313107,0.00001043446,0.000023109122,0.00006446771,0.0000739901,0.9010502,0.074918985,0.00029016047,0.0002823204,0.00002511583],"about_ca_topic_score_codex":0.0043529337,"about_ca_topic_score_gemma":0.0032561307,"teacher_disagreement_score":0.0043529337,"about_ca_system_score_codex":0.00040170364,"about_ca_system_score_gemma":0.0003504132,"threshold_uncertainty_score":0.0086551905},"labels":[],"label_agreement":null},{"id":"W2044799367","doi":"10.1016/j.jprocont.2012.02.012","title":"A particle filter driven dynamic Gaussian mixture model approach for complex process monitoring and fault diagnosis","year":2012,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":104,"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":"Particle filter; Fault detection and isolation; Mixture model; Gaussian process; Bayesian inference; Fault (geology); Gaussian; Inference; Computer science; Principal component analysis; Bayesian probability; Gaussian filter; Algorithm; Dynamic Bayesian network; Pattern recognition (psychology); Artificial intelligence; Kalman filter","score_opus":0.01762840553749966,"score_gpt":0.26817524964820494,"score_spread":0.25054684411070527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044799367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00176243,0.00014558322,0.9974892,0.00004916802,0.00003476212,0.000010860708,0.00001695213,0.00019819471,0.00029272208],"genre_scores_gemma":[0.44397387,0.00092395814,0.54548097,0.00020824626,0.00020955993,0.00023610167,0.00041227296,0.0001972995,0.008357761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996189,0.0000868792,0.00002374615,0.00009803666,0.00013946742,0.000032994085],"domain_scores_gemma":[0.9993992,0.00031479128,0.000050027953,0.000050565606,0.00016055763,0.00002482302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007754992,0.0007670852,0.0013743732,0.000640385,0.0003906137,0.0008493711,0.0014846419,0.0015060116,0.0015133289],"category_scores_gemma":[0.002090742,0.00065955863,0.0009720742,0.000813798,0.00042742258,0.0010824843,0.0010576139,0.0013755366,0.0007299404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013113614,0.00007423684,0.00046761977,0.0001108044,0.00011568388,0.00009555126,0.00005910848,0.8264197,0.0038917083,0.011200934,0.0015740803,0.15585938],"study_design_scores_gemma":[0.0000027829492,0.000007758384,0.00005153568,0.000001254389,0.000005274416,0.000006970134,0.0000012489398,0.9986425,0.00016894097,0.0009168581,0.00019135176,0.0000035243106],"about_ca_topic_score_codex":0.010220389,"about_ca_topic_score_gemma":0.007738427,"teacher_disagreement_score":0.010220389,"about_ca_system_score_codex":0.0005311741,"about_ca_system_score_gemma":0.001011157,"threshold_uncertainty_score":0.020321786},"labels":[],"label_agreement":null},{"id":"W2048622864","doi":"10.1016/j.jprocont.2013.09.002","title":"Control loop diagnosis with ambiguous historical operating modes: Part 2, information synthesis based on proportional parametrization","year":2013,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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 Alberta","funders":"","keywords":"Parametrization (atmospheric modeling); Loop (graph theory); Control theory (sociology); Control (management); Proportional control; Computer science; Control engineering; Control system; Mathematics; Engineering; Physics; Artificial intelligence; Combinatorics","score_opus":0.005531184717161693,"score_gpt":0.18868591157642994,"score_spread":0.18315472685926826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048622864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0199284,0.00026287194,0.9778748,0.00009219958,0.000033275268,0.000066851186,0.000026151056,0.0005509756,0.0011645148],"genre_scores_gemma":[0.8599019,0.00026433947,0.13778932,0.00006651247,0.000042316864,0.0001131057,0.00007243122,0.0000708639,0.0016791634],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954647,0.0000920993,0.000040636016,0.00012720312,0.00014827622,0.00004532268],"domain_scores_gemma":[0.9990013,0.0007086773,0.0000761019,0.000116645795,0.00008125091,0.000016060736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007891617,0.00079177483,0.0009317615,0.0007008781,0.00046286115,0.0011180558,0.0006948383,0.0006932935,0.0031371105],"category_scores_gemma":[0.0039075636,0.00039584754,0.00042977592,0.00032040302,0.00073208957,0.0014067006,0.00079507503,0.0007865536,0.00028553727],"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":[0.00087142055,0.00014504477,0.0013134838,0.000692748,0.00012499122,0.00040196223,0.00045431068,0.37092778,0.050674047,0.020885032,0.0008486978,0.5526605],"study_design_scores_gemma":[0.000045863613,0.00015335929,0.0008470839,0.000038906688,0.000041142313,0.00012698711,0.000033869223,0.96916044,0.021405756,0.0072862753,0.0008387991,0.000021470787],"about_ca_topic_score_codex":0.0013820489,"about_ca_topic_score_gemma":0.0014714096,"teacher_disagreement_score":0.0031371105,"about_ca_system_score_codex":0.0003945743,"about_ca_system_score_gemma":0.0007898953,"threshold_uncertainty_score":0.010494709},"labels":[],"label_agreement":null},{"id":"W2048743770","doi":"10.1016/j.jprocont.2012.10.001","title":"Fouling control and optimization of a drinking water membrane filtration process with real-time model parameter adaptation using fluorescence and permeate flux measurements","year":2012,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":19,"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; Mitacs; Else Kröner-Fresenius-Stiftung","keywords":"Membrane fouling; Fouling; Extended Kalman filter; Ultrafiltration (renal); Membrane; Filtration (mathematics); Kalman filter; Permeation; Environmental engineering; Environmental science; Biological system; Process engineering; Engineering; Chemistry; Control theory (sociology); Chromatography; Computer science; Mathematics; Artificial intelligence; Control (management); Statistics","score_opus":0.02805906770756437,"score_gpt":0.25384584054439546,"score_spread":0.2257867728368311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048743770","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":[0.9440904,0.00010331847,0.054979738,0.000084616724,0.000012789989,0.00002902485,0.00003855629,0.00019506061,0.00046639214],"genre_scores_gemma":[0.9947189,0.000027382013,0.004995827,0.0000045480147,0.0000011805155,0.00001733402,0.000015941154,0.000007440807,0.00021142188],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999813,0.000037920723,0.000014007552,0.000048525348,0.0000527608,0.0000336421],"domain_scores_gemma":[0.99974185,0.000120132834,0.000040866238,0.000017948361,0.00006385082,0.000015249713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052741077,0.00069058064,0.0007090184,0.00027660982,0.00044598788,0.00077346375,0.0003557071,0.00067227177,0.00019692197],"category_scores_gemma":[0.0008100839,0.00028176207,0.0005771489,0.000216459,0.0003204947,0.00040731946,0.00035604398,0.00043744117,0.00006096186],"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":[0.0008803278,0.00049454207,0.003669007,0.00015473715,0.00007853567,0.00009827545,0.00015830122,0.40992454,0.5425398,0.00031150263,0.00015946872,0.04153107],"study_design_scores_gemma":[0.00003535527,0.0003630387,0.0034469883,0.0000032881826,0.00004584086,0.000019876361,0.000027636048,0.8025132,0.19315487,0.00013296524,0.00022866113,0.000028271676],"about_ca_topic_score_codex":0.00809293,"about_ca_topic_score_gemma":0.004582454,"teacher_disagreement_score":0.00809293,"about_ca_system_score_codex":0.0007467708,"about_ca_system_score_gemma":0.0005639295,"threshold_uncertainty_score":0.016091645},"labels":[],"label_agreement":null},{"id":"W2049730478","doi":"10.1016/j.jprocont.2009.10.001","title":"Adaptive peak seeking control of a proton exchange membrane fuel cell","year":2009,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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 Alberta","funders":"","keywords":"Control theory (sociology); Model predictive control; Context (archaeology); Nonlinear system; Proton exchange membrane fuel cell; Controller (irrigation); Power (physics); Operating point; Computer science; Engineering; Control (management); Electronic engineering; Fuel cells; Physics","score_opus":0.005518395385991085,"score_gpt":0.2111286391275439,"score_spread":0.20561024374155282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049730478","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":[0.59304327,0.00067047944,0.38345617,0.0011733451,0.0002743318,0.00008411826,0.0001010472,0.0004052722,0.020791963],"genre_scores_gemma":[0.99690455,0.00003429955,0.001805462,0.00002023442,0.00000789604,0.000008221042,0.0000050790964,0.0000035522792,0.0012107706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999263,0.000019139807,0.0000026861198,0.000016101048,0.000019909616,0.000015789505],"domain_scores_gemma":[0.99988306,0.000048877195,0.0000137182205,0.0000056129197,0.00003525021,0.000013391751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025373633,0.0003293379,0.0005927794,0.00015190276,0.0005954828,0.00061306596,0.000522672,0.0007351485,0.0014506557],"category_scores_gemma":[0.00037645103,0.00017059912,0.00018682993,0.00017366647,0.00043950073,0.00024516135,0.0005391735,0.00040435157,0.00013513998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006334025,0.00009427634,0.0006564543,0.00012096159,0.000060382732,0.0003432909,0.00011665958,0.88196117,0.07269899,0.007305643,0.0011326381,0.034876104],"study_design_scores_gemma":[0.000015858444,0.00010432164,0.00017276421,0.0000023156842,0.0000051957413,0.000013444429,0.000010580859,0.9962287,0.0025049197,0.0007202396,0.00021624028,0.000005543638],"about_ca_topic_score_codex":0.0035763243,"about_ca_topic_score_gemma":0.0021475377,"teacher_disagreement_score":0.0035763243,"about_ca_system_score_codex":0.0003553385,"about_ca_system_score_gemma":0.00036668795,"threshold_uncertainty_score":0.0071110725},"labels":[],"label_agreement":null},{"id":"W2052416356","doi":"10.1016/j.jprocont.2006.09.004","title":"Data-driven predictive control for solid oxide fuel cells","year":2006,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":127,"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":"Model predictive control; Benchmark (surveying); Solid oxide fuel cell; Control (management); Fuel cells; Computer science; Control system; Control theory (sociology); Engineering; Control engineering; Chemistry; Artificial intelligence","score_opus":0.0076689654732156705,"score_gpt":0.24241972489977184,"score_spread":0.23475075942655618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052416356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27408463,0.0015838769,0.7064839,0.0010021362,0.00057133875,0.00010628967,0.00043619383,0.0014224723,0.014309169],"genre_scores_gemma":[0.99479806,0.00010261804,0.0038727734,0.000029389683,0.00001779617,0.000023566452,0.00006023164,0.000014913468,0.001080595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999858,0.000024092114,0.0000075801104,0.000022462702,0.00006368564,0.000024089908],"domain_scores_gemma":[0.99965274,0.0001504637,0.000034421733,0.000029001849,0.0001225155,0.000010887504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039904093,0.0004951825,0.0005289927,0.0002442563,0.00041927365,0.0007989872,0.0008057902,0.00038664645,0.001403715],"category_scores_gemma":[0.0012171646,0.0002929505,0.00017711527,0.0002957013,0.00041767853,0.00065245514,0.00051869045,0.00068476005,0.00016986464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031639996,0.00007964553,0.00032105384,0.00011684681,0.00002160616,0.00006308687,0.00006031036,0.92825323,0.007695683,0.0061469306,0.0015245825,0.055400558],"study_design_scores_gemma":[0.000009250112,0.000024048562,0.00007836507,0.0000022688143,0.0000024072829,0.000002938131,0.000004225602,0.99603385,0.0018850912,0.0016897261,0.000264934,0.0000029786336],"about_ca_topic_score_codex":0.006973748,"about_ca_topic_score_gemma":0.0064632813,"teacher_disagreement_score":0.006973748,"about_ca_system_score_codex":0.00045966965,"about_ca_system_score_gemma":0.00058441586,"threshold_uncertainty_score":0.013866305},"labels":[],"label_agreement":null},{"id":"W2052501744","doi":"10.1016/j.jprocont.2005.03.007","title":"Analysis and control of a nonlinear boiler-turbine unit","year":2005,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":171,"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":"Boiler (water heating); Control theory (sociology); Nonlinear system; Turbine; Water turbine; Control engineering; Range (aeronautics); Controller (irrigation); Engineering; Computer science; Control (management); Mechanical engineering; Aerospace engineering","score_opus":0.004063300098409581,"score_gpt":0.22447447792218628,"score_spread":0.2204111778237767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052501744","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":[0.6154049,0.00079810177,0.35398522,0.00080915925,0.00015997558,0.00013071603,0.0001975642,0.00030448116,0.028209897],"genre_scores_gemma":[0.9941865,0.00006698411,0.0022618438,0.000016050159,0.00001304748,0.000018148732,0.000019091562,0.000014341938,0.0034038837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998578,0.00003854254,0.000005583803,0.000030626452,0.00004306654,0.000024409785],"domain_scores_gemma":[0.99970406,0.00017684948,0.000032878634,0.000009975928,0.000059245347,0.000016967862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044913602,0.0005837885,0.0008275522,0.00024232754,0.00081541913,0.00090203964,0.00063928374,0.0008955465,0.0027934466],"category_scores_gemma":[0.00079120876,0.0003606963,0.00041088872,0.00020058795,0.00074928754,0.00037945132,0.0006510274,0.0005257532,0.00019751562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013174058,0.00003656086,0.0005362932,0.00006704867,0.000034004574,0.00017813915,0.00005551622,0.9814736,0.0074240677,0.0039775344,0.00025880337,0.005826654],"study_design_scores_gemma":[0.0000054765924,0.000025794146,0.0003254058,0.0000011635511,0.0000059384274,0.000006964403,0.0000060851753,0.9989611,0.0002879092,0.00028359942,0.00008766201,0.0000028332204],"about_ca_topic_score_codex":0.025506793,"about_ca_topic_score_gemma":0.013610821,"teacher_disagreement_score":0.025506793,"about_ca_system_score_codex":0.0008995265,"about_ca_system_score_gemma":0.0008325759,"threshold_uncertainty_score":0.05071664},"labels":[],"label_agreement":null},{"id":"W2053561226","doi":"10.1016/j.jprocont.2004.10.006","title":"Practical solutions to multivariate feedback control performance assessment problem: reduced a priori knowledge of interactor matrices","year":2004,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":77,"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":"Interactor; Benchmark (surveying); Multivariable calculus; Variance (accounting); A priori and a posteriori; Multivariate statistics; Matrix (chemical analysis); Process (computing); Computer science; Control theory (sociology); Mathematical optimization; Control (management); Mathematics; Artificial intelligence; Engineering; Machine learning; Control engineering","score_opus":0.01649795670154457,"score_gpt":0.3111026617142603,"score_spread":0.29460470501271574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053561226","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071176877,0.00009175433,0.9914324,0.00014419615,0.000010172588,0.000012964824,0.000025462861,0.00008817544,0.0010772775],"genre_scores_gemma":[0.662125,0.00036966757,0.3326983,0.0001090026,0.00013885438,0.00015339188,0.00020607284,0.0001357649,0.0040639634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991844,0.000321686,0.000037594436,0.0001440257,0.00025034873,0.000061849125],"domain_scores_gemma":[0.99640524,0.0026005288,0.00024840134,0.00025278202,0.00042061665,0.00007249153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020141609,0.0013511961,0.0012192287,0.0006813081,0.0005176128,0.0013019695,0.0011001985,0.0016886098,0.004194495],"category_scores_gemma":[0.010371348,0.00067070196,0.00057716435,0.00048123326,0.001012246,0.0019238942,0.0017201254,0.0017182898,0.00043074533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001426051,0.000062667816,0.00027120832,0.00016799719,0.000038878024,0.000049739316,0.000101264995,0.89824045,0.003142667,0.025920117,0.0008752617,0.07098718],"study_design_scores_gemma":[0.00000707726,0.00002013958,0.00009490895,0.000005813517,0.000003800451,0.000009731103,0.000007918373,0.99036485,0.0004517249,0.008864848,0.00016336482,0.000005875194],"about_ca_topic_score_codex":0.0034359873,"about_ca_topic_score_gemma":0.0032912209,"teacher_disagreement_score":0.004194495,"about_ca_system_score_codex":0.0005554429,"about_ca_system_score_gemma":0.0013372259,"threshold_uncertainty_score":0.014032006},"labels":[],"label_agreement":null},{"id":"W2054386449","doi":"10.1016/j.jprocont.2009.10.002","title":"Time delay estimation for MIMO dynamical systems – With time-frequency domain analysis","year":2009,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","cited_by":53,"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":"MIMO; Time domain; Control theory (sociology); Series (stratigraphy); Frequency domain; Mathematics; Computer science; Algorithm; Statistics; Mathematical analysis; Control (management); Artificial intelligence","score_opus":0.0027114565362636486,"score_gpt":0.20722414910978879,"score_spread":0.20451269257352514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054386449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007285356,0.0004018087,0.9914323,0.00006444226,0.000041466585,0.000011824155,0.000021155965,0.00007102088,0.00067062065],"genre_scores_gemma":[0.8177655,0.0020125497,0.17232612,0.00009887349,0.0002426411,0.00010447186,0.00021104174,0.00009966549,0.0071391244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977094,0.00005940366,0.000018669974,0.000050730705,0.00007704278,0.000023229752],"domain_scores_gemma":[0.9992261,0.00046386852,0.00008973659,0.00006120974,0.00014739855,0.0000117925865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040890387,0.0006859256,0.00050966756,0.00056598976,0.00028995343,0.0007138838,0.00031285585,0.00059342105,0.0015932999],"category_scores_gemma":[0.0024539358,0.00031969117,0.00047618803,0.00043669267,0.00033729704,0.0009131913,0.00048114173,0.00063444197,0.00036433485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002381439,0.00008710505,0.00082924974,0.00042909707,0.00013938316,0.00015048221,0.00015245017,0.6889842,0.03249128,0.02359647,0.0014818897,0.25142032],"study_design_scores_gemma":[0.0000051003667,0.000050174665,0.00044293283,0.000013056677,0.00001843852,0.00003626872,0.000012726517,0.99072707,0.003121584,0.0046279663,0.00093292614,0.000011912061],"about_ca_topic_score_codex":0.0023628834,"about_ca_topic_score_gemma":0.0016246894,"teacher_disagreement_score":0.0023628834,"about_ca_system_score_codex":0.00028259685,"about_ca_system_score_gemma":0.00036411957,"threshold_uncertainty_score":0.0053300858},"labels":[],"label_agreement":null},{"id":"W2059061259","doi":"10.1016/j.jprocont.2007.10.016","title":"Value function-based approach to the scheduling of multiple controllers","year":2008,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"University of Alberta","funders":"","keywords":"Dynamic programming; Bellman equation; Curse of dimensionality; Mathematical optimization; Computer science; State space; Heuristic; Optimal control; Scheduling (production processes); Time horizon; Controller (irrigation); System dynamics; Control theory (sociology); Control (management); Mathematics; Artificial intelligence","score_opus":0.009285368860648563,"score_gpt":0.2020654892530597,"score_spread":0.19278012039241113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059061259","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030213117,0.000139666,0.99528223,0.00009468199,0.000052627147,0.000019692352,0.000009350375,0.0000535164,0.0013268598],"genre_scores_gemma":[0.6158445,0.00036870988,0.3778811,0.00015063077,0.00025643889,0.00023518335,0.00008750309,0.00016219367,0.0050137676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984701,0.0006938936,0.00005907941,0.00017285412,0.00041110738,0.0001929611],"domain_scores_gemma":[0.9974763,0.0017071364,0.00014483354,0.00011368678,0.00044346924,0.000114544346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034455098,0.0013082935,0.0020140891,0.0012171633,0.0008534274,0.0017525111,0.0026594477,0.0018595849,0.0034597456],"category_scores_gemma":[0.006170582,0.0011445213,0.0011730274,0.0012094341,0.001303217,0.001699838,0.0014238424,0.0024672095,0.00042576055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006403944,0.00004718956,0.00010243697,0.00003534574,0.00004286124,0.000041870397,0.00003945604,0.95681036,0.0007270061,0.020609088,0.00047649496,0.021003855],"study_design_scores_gemma":[0.000005359395,0.000011651631,0.000014401435,0.0000017868542,0.000003585936,0.0000033262138,0.0000023391992,0.9958851,0.00009530044,0.0038539472,0.00012064466,0.0000025956042],"about_ca_topic_score_codex":0.0064155124,"about_ca_topic_score_gemma":0.0044069155,"teacher_disagreement_score":0.0064155124,"about_ca_system_score_codex":0.002095227,"about_ca_system_score_gemma":0.002151201,"threshold_uncertainty_score":0.018221796},"labels":[],"label_agreement":null},{"id":"W2060808311","doi":"10.1016/j.jprocont.2009.12.005","title":"Process design in SISO systems with input multiplicity using bifurcation analysis and optimisation","year":2010,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":12,"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":"Engineering and Physical Sciences Research Council; Concordia University; Research Councils UK","keywords":"Multiplicity (mathematics); Control theory (sociology); Mathematics; Bifurcation; Continuous stirred-tank reactor; Applied mathematics; Engineering; Computer science; Nonlinear system; Mathematical analysis; Control (management)","score_opus":0.00750973895414481,"score_gpt":0.23748794843708976,"score_spread":0.22997820948294495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060808311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023669126,0.00029654315,0.97147816,0.00012588223,0.000033373417,0.00007173142,0.00001925245,0.00011693869,0.0041889795],"genre_scores_gemma":[0.91198593,0.00033128046,0.08466174,0.00005626152,0.000031654803,0.00019271363,0.000033420616,0.000057688627,0.0026493543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995084,0.00016888407,0.000031314805,0.00009051634,0.00013981873,0.00006115304],"domain_scores_gemma":[0.9989065,0.00071503315,0.0001593229,0.000042602565,0.00014178593,0.000034771434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001331102,0.001006366,0.0015541605,0.00058507634,0.00067138113,0.0016711768,0.0006257294,0.0011933782,0.0019037131],"category_scores_gemma":[0.0025076151,0.0009629146,0.0011024346,0.0004926684,0.00091408176,0.001135132,0.0012385106,0.0010095619,0.00030256237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013540927,0.000036621062,0.0002131118,0.00015688573,0.000049820464,0.00005050903,0.000075316166,0.9683433,0.0051430035,0.010406064,0.00011071784,0.015279303],"study_design_scores_gemma":[0.000015528596,0.00004836938,0.000056895246,0.0000078955045,0.00001163423,0.0000071199242,0.000006784594,0.9955278,0.0009771764,0.0031330269,0.00020287508,0.000004899527],"about_ca_topic_score_codex":0.001034278,"about_ca_topic_score_gemma":0.001117444,"teacher_disagreement_score":0.0019037131,"about_ca_system_score_codex":0.0006982736,"about_ca_system_score_gemma":0.0009268647,"threshold_uncertainty_score":0.007039666},"labels":[],"label_agreement":null},{"id":"W2064642572","doi":"10.1016/j.jprocont.2011.12.004","title":"Designing priors for robust Bayesian optimal experimental design","year":2012,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Optimal Experimental Design Methods","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":"University of Alberta","funders":"","keywords":"A priori and a posteriori; Prior probability; Process (computing); Computer science; Design of experiments; Bayesian probability; Engineering design process; Process modeling; Machine learning; Data mining; Mathematical optimization; Artificial intelligence; Engineering; Work in process; Mathematics; Statistics","score_opus":0.1733677444323993,"score_gpt":0.451264019980269,"score_spread":0.27789627554786966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064642572","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014510028,0.000065285996,0.9978758,0.000110020774,0.00001598112,0.00010903277,0.000022537659,0.00009965014,0.00025073887],"genre_scores_gemma":[0.13838007,0.0002360273,0.85780203,0.00030760045,0.00008087651,0.002184122,0.00017959143,0.00014843681,0.000681247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96166503,0.029736137,0.0010170237,0.0035027622,0.00320667,0.00087231735],"domain_scores_gemma":[0.87779176,0.10426276,0.0056329435,0.0074667274,0.0037256153,0.0011202308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05443351,0.002993788,0.0037897828,0.002858515,0.0008784307,0.0027472654,0.0043579377,0.004518795,0.00402325],"category_scores_gemma":[0.16691224,0.0042059286,0.0022721598,0.0013243053,0.006077173,0.0050381017,0.0055019245,0.0053887595,0.00095314067],"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":[0.0024810317,0.00045790835,0.001550732,0.00090974366,0.00060238177,0.0001183617,0.0004209907,0.42006296,0.006193426,0.4114373,0.0016822637,0.15408288],"study_design_scores_gemma":[0.00045027694,0.00031458656,0.00048547704,0.00012530143,0.000111829955,0.00003920778,0.000024827092,0.68474025,0.003018239,0.30906016,0.001567429,0.0000624745],"about_ca_topic_score_codex":0.0010639643,"about_ca_topic_score_gemma":0.0008553491,"teacher_disagreement_score":0.05443351,"about_ca_system_score_codex":0.0025778436,"about_ca_system_score_gemma":0.0039047047,"threshold_uncertainty_score":0.28787535},"labels":[],"label_agreement":null},{"id":"W2066551872","doi":"10.1016/s0959-1524(00)00008-1","title":"Fault diagnosis with multivariate statistical models part I: using steady state fault signatures","year":2001,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":353,"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":"Fault detection and isolation; Fault (geology); Stuck-at fault; Residual; Isolation (microbiology); Fault model; Engineering; Data mining; Fault indicator; Computer science; Plot (graphics); Multivariate statistics; Control theory (sociology); Algorithm; Artificial intelligence; Statistics; Mathematics; Machine learning; Control (management)","score_opus":0.01617273238877226,"score_gpt":0.257995884165947,"score_spread":0.24182315177717473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066551872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0317443,0.0002828521,0.9668249,0.00012394944,0.000021033367,0.000021903554,0.00005320161,0.00046064137,0.00046721098],"genre_scores_gemma":[0.87865305,0.0006002367,0.118547074,0.00007159821,0.00011959357,0.0000711751,0.0002583497,0.000092153714,0.001586767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992874,0.00019669786,0.00005275635,0.00013117494,0.0002594605,0.00007249911],"domain_scores_gemma":[0.99764997,0.0014262742,0.0002787907,0.0003443172,0.0002624527,0.000038186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010282613,0.000871462,0.001038723,0.0010727012,0.0003821476,0.0009221051,0.0006733038,0.0009197184,0.0009288618],"category_scores_gemma":[0.006378017,0.0005581271,0.00083186344,0.00093450793,0.00069737446,0.002026333,0.000759979,0.00084366114,0.00042708716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005113728,0.0002212677,0.008323292,0.00027159014,0.00019554181,0.00020229576,0.00014938347,0.5004098,0.033958178,0.020214925,0.0015069797,0.43403545],"study_design_scores_gemma":[0.0000077457,0.00008341544,0.0015296376,0.000009901063,0.000026755419,0.0001307229,0.000013475632,0.9764446,0.0077370624,0.013727172,0.00027147133,0.000018207005],"about_ca_topic_score_codex":0.0014745453,"about_ca_topic_score_gemma":0.0012058056,"teacher_disagreement_score":0.0014745453,"about_ca_system_score_codex":0.00040169005,"about_ca_system_score_gemma":0.00070485554,"threshold_uncertainty_score":0.00543797},"labels":[],"label_agreement":null},{"id":"W2068647536","doi":"10.1016/j.jprocont.2011.07.017","title":"Optimal control of convection–diffusion process with time-varying spatial domain: Czochralski crystal growth","year":2011,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Solidification and crystal growth phenomena","field":"Materials Science","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":"University of Alberta","funders":"","keywords":"Ordinary differential equation; Partial differential equation; Semigroup; Mathematics; Mathematical analysis; Boundary value problem; Distributed parameter system; Convection–diffusion equation; Domain (mathematical analysis); Convection; Parabolic partial differential equation; Crystal (programming language); Mechanics; Differential equation; Physics; Computer science","score_opus":0.010615871566117398,"score_gpt":0.22846164958259668,"score_spread":0.21784577801647928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068647536","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41952753,0.0010547691,0.569736,0.0009965015,0.00026445082,0.00008881365,0.000094271,0.00012185272,0.0081159],"genre_scores_gemma":[0.9900467,0.00015178924,0.008425561,0.000018771796,0.00001556192,0.000027080358,0.000016668107,0.0000097632665,0.0012881553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997873,0.000058841022,0.00001011841,0.00006176439,0.00004542751,0.00003659349],"domain_scores_gemma":[0.999509,0.00022597086,0.00010467432,0.000017583454,0.000107705244,0.000034963206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063981675,0.00071886677,0.0006972074,0.00032612224,0.0003636161,0.00113078,0.0007206189,0.00083445595,0.0006066892],"category_scores_gemma":[0.0015422322,0.0003681233,0.0003781897,0.0003366646,0.0010560371,0.0006076316,0.0008699002,0.00063902023,0.00005492551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003932553,0.00012831115,0.00076033844,0.00017926395,0.000057461733,0.0001140692,0.00009037758,0.90056163,0.059249517,0.025331229,0.0005588192,0.012575748],"study_design_scores_gemma":[0.000018660863,0.000029239494,0.00013096562,0.0000020724524,0.000004836021,0.000004496783,0.0000054693264,0.9960091,0.0028208413,0.0008203501,0.00014719482,0.0000068269237],"about_ca_topic_score_codex":0.007289429,"about_ca_topic_score_gemma":0.0043274914,"teacher_disagreement_score":0.007289429,"about_ca_system_score_codex":0.0010525364,"about_ca_system_score_gemma":0.0011989784,"threshold_uncertainty_score":0.014494002},"labels":[],"label_agreement":null},{"id":"W2069394074","doi":"10.1016/j.jprocont.2007.05.006","title":"Constrained minimum variance control using hybrid genetic algorithm – An industrial experience","year":2007,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"Toronto Metropolitan University","funders":"","keywords":"Genetic algorithm; Variance (accounting); Control (management); Minimum-variance unbiased estimator; Algorithm; Computer science; Mathematical optimization; Control theory (sociology); Engineering; Mathematics; Artificial intelligence; Machine learning; Statistics; Business","score_opus":0.0159817492702824,"score_gpt":0.26092902742069646,"score_spread":0.24494727815041406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069394074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25302297,0.0010856291,0.73346925,0.00022457665,0.000063279535,0.000069806396,0.000021844104,0.0003730345,0.011669626],"genre_scores_gemma":[0.86339015,0.00022895618,0.13337116,0.00005043511,0.000013652333,0.000041341955,0.000025130488,0.000043483215,0.002835593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996593,0.0001377325,0.000011143493,0.000039964572,0.00013210055,0.000019587154],"domain_scores_gemma":[0.9992981,0.00047395876,0.000026067099,0.000054509677,0.00013330397,0.000014066061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007205252,0.00036154749,0.00058400026,0.00024227142,0.0003273873,0.0007049469,0.00055791147,0.00093249604,0.000788952],"category_scores_gemma":[0.0012157145,0.00021647138,0.00029279082,0.00045273406,0.0003527798,0.00041679933,0.0003175121,0.00052366784,0.00009458781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033385245,0.00028990916,0.00078137947,0.00009489665,0.00008136828,0.000097673415,0.000119805794,0.7567395,0.012542264,0.0066501265,0.0007558367,0.22151339],"study_design_scores_gemma":[0.000033970806,0.00013892347,0.00036999508,0.0000041252592,0.000013203323,0.000021352464,0.000010086873,0.9955265,0.0023986967,0.00087028614,0.0006018011,0.000011124592],"about_ca_topic_score_codex":0.007471781,"about_ca_topic_score_gemma":0.00505396,"teacher_disagreement_score":0.007471781,"about_ca_system_score_codex":0.00041633434,"about_ca_system_score_gemma":0.0005425559,"threshold_uncertainty_score":0.014856577},"labels":[],"label_agreement":null},{"id":"W2075970146","doi":"10.1016/j.jprocont.2004.02.006","title":"Statistical properties of quadratic-type performance indices","year":2004,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Shell","keywords":"Univariate; Confidence interval; Quadratic equation; Multivariate statistics; Statistics; Limit (mathematics); Mathematics; Sampling (signal processing); Autocorrelation; Sample size determination; Applied mathematics; Computer science","score_opus":0.008418632153106754,"score_gpt":0.21583867944460705,"score_spread":0.2074200472915003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075970146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12285132,0.00071007904,0.8709258,0.0005541137,0.000084591105,0.000068039604,0.0004561316,0.00042329813,0.0039267433],"genre_scores_gemma":[0.9593559,0.0007374427,0.0345969,0.00015529628,0.00037819377,0.00018087726,0.0010900059,0.00032029155,0.0031851393],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99436307,0.00218216,0.00037580726,0.001141788,0.0014239822,0.00051320245],"domain_scores_gemma":[0.81660444,0.14890046,0.012427041,0.00784572,0.01274288,0.0014795115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01380319,0.0011927804,0.0016632971,0.0031841435,0.0005645835,0.0026107992,0.001864849,0.0015644819,0.0035311128],"category_scores_gemma":[0.10278514,0.0007659865,0.00084474543,0.002457688,0.0028457607,0.0055409605,0.001746516,0.0022304899,0.0006780755],"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":[0.00046449757,0.00025406823,0.013765359,0.00050565414,0.00024388106,0.00026482457,0.00044846247,0.36226457,0.007696325,0.55334044,0.003933688,0.05681813],"study_design_scores_gemma":[0.00002097823,0.00013971124,0.00491472,0.000029435845,0.000030318039,0.00012867464,0.000044085227,0.8685242,0.0011045415,0.12454999,0.00046156027,0.00005181675],"about_ca_topic_score_codex":0.000934524,"about_ca_topic_score_gemma":0.00055177923,"teacher_disagreement_score":0.01380319,"about_ca_system_score_codex":0.0012502159,"about_ca_system_score_gemma":0.0013678598,"threshold_uncertainty_score":0.07299906},"labels":[],"label_agreement":null},{"id":"W2076463606","doi":"10.1016/j.jprocont.2006.05.003","title":"Monitoring control performance via structured closed-loop response subject to output variance/covariance upper bound","year":2006,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","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":"","keywords":"Control theory (sociology); Controller (irrigation); Closed-loop transfer function; Covariance; Loop fission; Frequency response; Upper and lower bounds; Variance (accounting); Computer science; Transfer function; Mathematical optimization; Mathematics; Control (management); Engineering; Statistics; Artificial intelligence","score_opus":0.005546972882109734,"score_gpt":0.21684323414580256,"score_spread":0.21129626126369283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076463606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22957303,0.00013317939,0.76720226,0.0001570355,0.000042361527,0.00009889531,0.00013463749,0.0010248157,0.0016338042],"genre_scores_gemma":[0.97569364,0.000039133676,0.02364562,0.000054095348,0.000020138988,0.00005429133,0.00008923511,0.000051224066,0.00035267888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981918,0.00061874447,0.00014083849,0.00040825005,0.0005024862,0.00013788963],"domain_scores_gemma":[0.9945082,0.0032343625,0.0008953817,0.0004501227,0.00080562336,0.00010644917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016910069,0.001038869,0.0011552062,0.00048145602,0.000325528,0.0010979469,0.00050909247,0.0011662613,0.00079520384],"category_scores_gemma":[0.011787815,0.0003738783,0.00035276267,0.0003668054,0.0005641644,0.0010759989,0.0008358788,0.0008538309,0.00030391826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002810643,0.0006865971,0.007585213,0.0006474875,0.00031536003,0.00046913503,0.0005320858,0.65175277,0.13915431,0.008458839,0.0015093207,0.18607828],"study_design_scores_gemma":[0.000040368846,0.0003638375,0.0035611067,0.0000198346,0.000021596308,0.00005896346,0.00002181382,0.9775009,0.016701128,0.001568272,0.00011875388,0.000023416473],"about_ca_topic_score_codex":0.0010694432,"about_ca_topic_score_gemma":0.00093432545,"teacher_disagreement_score":0.0016910069,"about_ca_system_score_codex":0.00029003556,"about_ca_system_score_gemma":0.0006547743,"threshold_uncertainty_score":0.008943021},"labels":[],"label_agreement":null},{"id":"W2076484917","doi":"10.1016/j.jprocont.2005.07.003","title":"Parameter and delay estimation of continuous-time models using a linear filter","year":2005,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","cited_by":102,"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":"Filter (signal processing); Transfer function; Control theory (sociology); Identification (biology); Computer science; Interval (graph theory); Estimation theory; Mathematics; Simple (philosophy); Linear filter; Integer (computer science); Mathematical optimization; Algorithm; Engineering","score_opus":0.010662837114482803,"score_gpt":0.23353152206544586,"score_spread":0.22286868495096307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076484917","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03565682,0.00011421713,0.9633508,0.000050694955,0.000026598593,0.000012403007,0.000034936995,0.0003730549,0.000380549],"genre_scores_gemma":[0.8893756,0.00032417753,0.10752971,0.000037657122,0.000036909016,0.00007196663,0.00019214833,0.000058335227,0.002373392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998048,0.000042902135,0.000015684516,0.00006503445,0.000046777208,0.00002478379],"domain_scores_gemma":[0.9993767,0.00036658644,0.0000853595,0.00006232397,0.00009361067,0.000015506474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050515984,0.00053041906,0.0006412618,0.0003947024,0.0003863156,0.0007481752,0.00047512347,0.0009442825,0.00084050884],"category_scores_gemma":[0.0021137593,0.00046793357,0.00058408594,0.00035527704,0.0002894447,0.00085283886,0.00036916533,0.0008670263,0.0003646139],"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":[0.0004948152,0.00014730886,0.0017786414,0.000254213,0.00017221707,0.00014975868,0.00022157864,0.7697319,0.046247363,0.008967442,0.00072907464,0.17110585],"study_design_scores_gemma":[0.000015320347,0.000044795364,0.00038202218,0.0000051993843,0.000023652257,0.000021258658,0.000005892697,0.99301404,0.005106236,0.00090717495,0.00046295006,0.000011517992],"about_ca_topic_score_codex":0.00610239,"about_ca_topic_score_gemma":0.004326021,"teacher_disagreement_score":0.00610239,"about_ca_system_score_codex":0.00045995283,"about_ca_system_score_gemma":0.0006337306,"threshold_uncertainty_score":0.012133718},"labels":[],"label_agreement":null},{"id":"W2077732800","doi":"10.1016/j.jprocont.2005.01.004","title":"Latent variable MPC for trajectory tracking in batch processes","year":2005,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":135,"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":"Control theory (sociology); Model predictive control; Trajectory; Latent variable; Tracking (education); Exothermic reaction; Process (computing); Computer science; Controller (irrigation); Mathematics; Control engineering; Engineering; Chemistry; Control (management); Artificial intelligence; Physics","score_opus":0.009466636020507447,"score_gpt":0.2415166976867208,"score_spread":0.23205006166621334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077732800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015797257,0.00020839964,0.9828682,0.000221832,0.000045006098,0.000014310433,0.000062075786,0.00026127952,0.00052159786],"genre_scores_gemma":[0.9150069,0.00033218056,0.07788193,0.000077230055,0.00008965475,0.00012835949,0.00033672905,0.00012083836,0.0060261777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952006,0.00014806839,0.000024417881,0.00011659433,0.00011508046,0.000075781594],"domain_scores_gemma":[0.99796224,0.001420222,0.00018708898,0.00014956458,0.00021712556,0.000063733154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013463687,0.0008437037,0.0013959887,0.00036575488,0.000644206,0.001074118,0.0011095236,0.0009684053,0.0020166594],"category_scores_gemma":[0.0045520673,0.000730628,0.0006894617,0.0006185071,0.0013025122,0.001308435,0.0013287296,0.00231776,0.00038414614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014554869,0.000032045486,0.00035666997,0.00005981024,0.000036043664,0.000027661317,0.00004616496,0.9656482,0.001129233,0.014327332,0.0005356353,0.017655596],"study_design_scores_gemma":[0.0000034983004,0.0000042904276,0.000028533936,0.0000010628185,0.0000015572559,6.536012e-7,8.0613364e-7,0.9984621,0.00009958638,0.0013513796,0.000044936096,0.0000016507538],"about_ca_topic_score_codex":0.01712224,"about_ca_topic_score_gemma":0.01103049,"teacher_disagreement_score":0.01712224,"about_ca_system_score_codex":0.0013468682,"about_ca_system_score_gemma":0.0017174534,"threshold_uncertainty_score":0.03404516},"labels":[],"label_agreement":null},{"id":"W2084006760","doi":"10.1016/j.jprocont.2011.05.002","title":"Extremum-seeking algorithm design for fed-batch cultures of microorganisms with overflow metabolism","year":2011,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Extremum Seeking Control Systems","field":"Engineering","cited_by":56,"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":"Control theory (sociology); Function (biology); Noise (video); Substrate (aquarium); Mode (computer interface); Biological system; Mathematics; Computer science; Mathematical optimization; Algorithm; Biochemical engineering; Engineering; Biology; Ecology","score_opus":0.01510757622332067,"score_gpt":0.22005018180797825,"score_spread":0.20494260558465757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084006760","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051812682,0.00019651625,0.9462231,0.000103099366,0.00003163166,0.00007268419,0.00001888132,0.000117451906,0.0014240044],"genre_scores_gemma":[0.79213625,0.00017963056,0.20509934,0.000064309985,0.000022956405,0.00033884088,0.00006418187,0.00004579402,0.0020486126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997193,0.00009899168,0.000018165687,0.000058969705,0.000071980154,0.00003259935],"domain_scores_gemma":[0.9991762,0.00047290343,0.000090419584,0.000025694952,0.00020681601,0.00002797947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016040014,0.00078887836,0.0011847487,0.00034889835,0.00044144606,0.00086385437,0.0010636061,0.0013698661,0.00091619015],"category_scores_gemma":[0.0024263011,0.00056347443,0.00068840134,0.0003011456,0.0004889551,0.0004925158,0.000877932,0.00076390494,0.00016768072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016594703,0.000060889244,0.00044420952,0.00012588098,0.000056208664,0.000038984763,0.00007747022,0.9555092,0.010329694,0.003581888,0.0002414962,0.029368188],"study_design_scores_gemma":[0.000010284675,0.00004137608,0.00004277422,0.000002759993,0.000004173121,0.0000033809176,0.0000032320777,0.99857545,0.00093090546,0.00031530726,0.000067387184,0.0000030077906],"about_ca_topic_score_codex":0.0029106112,"about_ca_topic_score_gemma":0.0021442405,"teacher_disagreement_score":0.0029106112,"about_ca_system_score_codex":0.00063786143,"about_ca_system_score_gemma":0.0010264032,"threshold_uncertainty_score":0.008482873},"labels":[],"label_agreement":null},{"id":"W2085545871","doi":"10.1016/j.jprocont.2011.12.014","title":"An auto-tuning method for dominant-pole placement using implicit model reference adaptive control technique","year":2012,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Design","field":"Engineering","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 Guelph","funders":"","keywords":"Control theory (sociology); Reference model; Full state feedback; Auto tuning; Computer science; Scheme (mathematics); Control engineering; Control (management); Mathematics; Engineering; Artificial intelligence; PID controller","score_opus":0.03632510397042764,"score_gpt":0.331252788667256,"score_spread":0.29492768469682834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085545871","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047833775,0.00007587072,0.99381566,0.000014933889,0.00003652885,0.0000145237855,0.0000047956846,0.00018002247,0.0010742967],"genre_scores_gemma":[0.5070004,0.00020895552,0.48671666,0.0000735509,0.000052418036,0.00013602455,0.000047519603,0.00011180509,0.005652678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998381,0.00003564994,0.000009537441,0.000033759574,0.00006843787,0.0000145428185],"domain_scores_gemma":[0.9997209,0.00011012397,0.000024911134,0.00003455595,0.00009967162,0.000009876189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038776084,0.00059390394,0.0007829812,0.00032558263,0.00045625935,0.00043901938,0.0007967447,0.0007258498,0.0029116895],"category_scores_gemma":[0.00086459995,0.00040022496,0.00041787152,0.0002994273,0.000292184,0.00040770584,0.0005008295,0.0007172843,0.00060576265],"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":[0.0003184432,0.00015174662,0.0004558044,0.000484325,0.00011325232,0.00020477112,0.0002813465,0.38404435,0.08524293,0.013742368,0.0025785428,0.51238215],"study_design_scores_gemma":[0.000017541373,0.00006967832,0.0001367973,0.000009999655,0.00001554253,0.00005090346,0.0000080456375,0.99504423,0.0027471937,0.00061660906,0.0012738573,0.000009561103],"about_ca_topic_score_codex":0.0024433031,"about_ca_topic_score_gemma":0.0030447633,"teacher_disagreement_score":0.0029116895,"about_ca_system_score_codex":0.00017540707,"about_ca_system_score_gemma":0.00042252077,"threshold_uncertainty_score":0.009740591},"labels":[],"label_agreement":null},{"id":"W2087184065","doi":"10.1016/j.jprocont.2013.10.018","title":"A moving horizon approach to input design for closed loop identification","year":2014,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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 British Columbia","funders":"","keywords":"Control theory (sociology); Model predictive control; Controller (irrigation); TRACE (psycholinguistics); Identification (biology); Fidelity; Computer science; Minification; Open-loop controller; SIGNAL (programming language); Optimal control; System identification; High fidelity; Horizon; Scale (ratio); Closed loop; Control engineering; Control (management); Mathematical optimization; Engineering; Mathematics; Data modeling; Artificial intelligence","score_opus":0.011316320014478033,"score_gpt":0.23520879028034425,"score_spread":0.22389247026586623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087184065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008309236,0.00008030203,0.9981969,0.000024906321,0.000024985922,0.000011847431,0.000008566309,0.00008509627,0.0007364556],"genre_scores_gemma":[0.4620095,0.0005983831,0.5286192,0.00019600017,0.00016525944,0.00039021595,0.00013899128,0.00021947513,0.007662938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994555,0.00020728816,0.000035756846,0.000086588174,0.00016134784,0.000053527503],"domain_scores_gemma":[0.9989932,0.0006367736,0.00005637957,0.00006403398,0.00022749392,0.000022128263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012383715,0.001015703,0.0010226868,0.0004333061,0.0006838024,0.00081813044,0.0011582584,0.0013826776,0.005893823],"category_scores_gemma":[0.0033769011,0.00065833185,0.00080037524,0.00041417434,0.0005987201,0.0011294645,0.00096431514,0.001491792,0.0008113381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025815517,0.00010730038,0.00018359532,0.00043978723,0.00010116341,0.00014376285,0.00016289845,0.7413969,0.012415124,0.06253281,0.0018945171,0.180364],"study_design_scores_gemma":[0.00001273106,0.000096242395,0.00006511086,0.000017906323,0.000013519895,0.000013169667,0.000007878652,0.9897376,0.0018895208,0.006794864,0.0013406274,0.000010796038],"about_ca_topic_score_codex":0.003009383,"about_ca_topic_score_gemma":0.003492882,"teacher_disagreement_score":0.005893823,"about_ca_system_score_codex":0.0004942641,"about_ca_system_score_gemma":0.00075285794,"threshold_uncertainty_score":0.0197168},"labels":[],"label_agreement":null},{"id":"W2087261650","doi":"10.1016/j.jprocont.2014.09.009","title":"Frequency analysis and compensation of valve stiction in cascade control loops","year":2014,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":24,"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; National Natural Science Foundation of China","keywords":"Stiction; Cascade; Compensation (psychology); Control theory (sociology); Control valves; Loop (graph theory); Control (management); Computer science; Control engineering; Materials science; Engineering; Mathematics; Artificial intelligence; Optoelectronics; Psychology; Microelectromechanical systems","score_opus":0.0038329725511632665,"score_gpt":0.21438483978395725,"score_spread":0.21055186723279398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087261650","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":[0.54105264,0.00041840549,0.45280066,0.00008174006,0.00007180562,0.000033586388,0.000036512756,0.00033402923,0.005170678],"genre_scores_gemma":[0.9956071,0.00003123937,0.003948816,0.0000034748603,0.0000052479977,0.0000029677924,0.0000090094745,0.0000074697296,0.00038479926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998841,0.000017552384,0.000005310746,0.000015726295,0.00005775014,0.000019571577],"domain_scores_gemma":[0.99966013,0.00018136961,0.000047081197,0.000024652589,0.00007676722,0.0000100502875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002152934,0.0002830005,0.00026043042,0.00043587646,0.0002276119,0.00025754806,0.00025597494,0.00038242972,0.0010710316],"category_scores_gemma":[0.0010495066,0.00012571528,0.00026522373,0.0001613506,0.00019010801,0.00023383867,0.0001549042,0.00023866023,0.00006698038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011951238,0.00017479422,0.005568684,0.00042755195,0.00011165524,0.0007499249,0.0004896406,0.5162121,0.2738434,0.01311178,0.0007781233,0.18733728],"study_design_scores_gemma":[0.0000058474225,0.00010664274,0.0036936696,0.0000069384982,0.000014911667,0.00007258857,0.000015118091,0.98522955,0.010146869,0.00044461226,0.00025576822,0.00000740731],"about_ca_topic_score_codex":0.0015124241,"about_ca_topic_score_gemma":0.0015754467,"teacher_disagreement_score":0.0015124241,"about_ca_system_score_codex":0.00020881358,"about_ca_system_score_gemma":0.00016173584,"threshold_uncertainty_score":0.0035829544},"labels":[],"label_agreement":null},{"id":"W2088372827","doi":"10.1016/j.jprocont.2010.11.006","title":"Model predictive control with robust feasibility","year":2011,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"McMaster University","keywords":"Model predictive control; Control (management); Computer science; Control theory (sociology); Artificial intelligence","score_opus":0.021536144710330853,"score_gpt":0.21559970905630285,"score_spread":0.194063564345972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088372827","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047942554,0.00044570668,0.984189,0.0004637722,0.0001501161,0.000056648227,0.000060253664,0.00021659186,0.009623587],"genre_scores_gemma":[0.8184803,0.00065584807,0.1687953,0.00028672887,0.00034693733,0.00055195566,0.00031745288,0.00026625494,0.0102992505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986737,0.0005578013,0.00006009779,0.00024682074,0.00036166087,0.00009997159],"domain_scores_gemma":[0.9966299,0.0021864246,0.000254466,0.0003635051,0.0005165895,0.00004910622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030509823,0.001928517,0.0023031565,0.00093557476,0.0009486927,0.0023243146,0.0018110349,0.001956609,0.005998891],"category_scores_gemma":[0.01300521,0.0014459774,0.0016218919,0.0010595561,0.001851335,0.0029782818,0.003445061,0.0035932625,0.0010298451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047272912,0.00011671533,0.00021393191,0.00053466175,0.00014242853,0.00017173836,0.00010162244,0.67968273,0.002561943,0.2460386,0.0038147545,0.06614814],"study_design_scores_gemma":[0.000030322213,0.000057537,0.000044562097,0.000016851971,0.000014909615,0.000018622683,0.0000050426434,0.9473864,0.0006429083,0.05091785,0.00085297617,0.000012105005],"about_ca_topic_score_codex":0.0024628548,"about_ca_topic_score_gemma":0.0011235158,"teacher_disagreement_score":0.005998891,"about_ca_system_score_codex":0.00069875695,"about_ca_system_score_gemma":0.0016054751,"threshold_uncertainty_score":0.020068228},"labels":[],"label_agreement":null},{"id":"W2088935246","doi":"10.1016/j.jprocont.2011.09.007","title":"An iterative optimization approach to design of control Lyapunov function","year":2011,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Design","field":"Engineering","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 Alberta","funders":"","keywords":"Lyapunov function; Control-Lyapunov function; Mathematics; Mathematical optimization; Control theory (sociology); Piecewise; Lyapunov redesign; Optimization problem; Computer science; Nonlinear system; Control (management)","score_opus":0.019810362835452646,"score_gpt":0.2206324351419884,"score_spread":0.20082207230653576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088935246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014159638,0.00007142972,0.99666995,0.000031868938,0.000015547463,0.000023772729,0.000005466346,0.00005322567,0.0017128801],"genre_scores_gemma":[0.3667522,0.0004016673,0.6260621,0.00010375865,0.000081446844,0.000651621,0.0001068483,0.00021753031,0.0056227786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947697,0.00022310861,0.000030535273,0.000056506586,0.00016813568,0.00004480064],"domain_scores_gemma":[0.99904245,0.00052453484,0.000055724093,0.00004085058,0.00031285835,0.00002356646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015454016,0.0011769062,0.0011202092,0.0008288572,0.00062666135,0.0009837549,0.0013012604,0.0012319712,0.0026368657],"category_scores_gemma":[0.0034319158,0.0007988384,0.0010826684,0.000534089,0.0007083167,0.0007844824,0.0010681242,0.0012164204,0.0005653592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039491806,0.00003756021,0.00015523768,0.00012126966,0.000045747205,0.000054273853,0.00010422064,0.91346437,0.0033693248,0.035333093,0.0009230431,0.046352416],"study_design_scores_gemma":[0.0000041498765,0.000020200912,0.000029453071,0.000005796286,0.0000046880436,0.0000066275384,0.0000038299017,0.99667466,0.00033557243,0.0024786785,0.0004319026,0.000004380022],"about_ca_topic_score_codex":0.0048794863,"about_ca_topic_score_gemma":0.0036838728,"teacher_disagreement_score":0.0048794863,"about_ca_system_score_codex":0.0008719787,"about_ca_system_score_gemma":0.0014011887,"threshold_uncertainty_score":0.009702146},"labels":[],"label_agreement":null},{"id":"W2091755682","doi":"10.1016/j.jprocont.2008.11.009","title":"An approximate dynamic programming based approach to dual adaptive control","year":2009,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":58,"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":"Dynamic programming; Dual (grammatical number); Mathematical optimization; Computer science; Process (computing); Monte Carlo method; State space; Control (management); State (computer science); Optimal control; Control theory (sociology); Mathematics; Algorithm; Artificial intelligence","score_opus":0.005357573583237499,"score_gpt":0.23383890639237173,"score_spread":0.22848133280913424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091755682","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029006714,0.0001008756,0.9942351,0.000098848475,0.00005777174,0.0000126603045,0.000010404236,0.00004016099,0.0025435172],"genre_scores_gemma":[0.6356297,0.00038085217,0.3496757,0.00021769172,0.00024194692,0.00026328795,0.0000919208,0.00013236268,0.013366595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995946,0.00014673636,0.000016374892,0.0000532092,0.00014999251,0.000039100356],"domain_scores_gemma":[0.9993748,0.00034504704,0.00004022894,0.00005186977,0.00015599436,0.000032041327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091779826,0.00066233374,0.0012298011,0.00062965654,0.00051030045,0.0012716128,0.0013300633,0.0011222017,0.0035001491],"category_scores_gemma":[0.003061262,0.000639643,0.0006468753,0.0006299417,0.00090832484,0.001032269,0.0016679841,0.0015176379,0.00039581794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008688647,0.00006323634,0.00019941482,0.00007379277,0.000042176376,0.000050730432,0.000052756026,0.85731196,0.0015042224,0.092240274,0.0011667665,0.047207803],"study_design_scores_gemma":[0.0000025939923,0.000008410654,0.0000127736575,0.000001946865,0.0000021621697,0.000004838116,0.0000017958946,0.994816,0.000074997086,0.004834273,0.0002381787,0.0000020162308],"about_ca_topic_score_codex":0.0026319344,"about_ca_topic_score_gemma":0.00203016,"teacher_disagreement_score":0.0035001491,"about_ca_system_score_codex":0.0006367696,"about_ca_system_score_gemma":0.00072243554,"threshold_uncertainty_score":0.011709213},"labels":[],"label_agreement":null},{"id":"W2092919454","doi":"10.1016/j.jprocont.2010.02.012","title":"The DCT-based oscillation detection method for a single time series","year":2010,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":48,"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":"Discrete cosine transform; Robustness (evolution); Series (stratigraphy); Computer science; Algorithm; Trigonometric functions; Process (computing); Oscillation (cell signaling); Time series; Pattern recognition (psychology); Mathematics; Control theory (sociology); Artificial intelligence; Image (mathematics); Machine learning; Control (management)","score_opus":0.004593833906282088,"score_gpt":0.23389947665386765,"score_spread":0.22930564274758555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092919454","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018230105,0.00043160183,0.9789667,0.00010531724,0.00014727483,0.000037823484,0.00012417417,0.0003947192,0.001562366],"genre_scores_gemma":[0.33392465,0.0009255735,0.65762264,0.00013846236,0.0002807261,0.00011634509,0.0006023753,0.00013491903,0.006254338],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977535,0.000028710823,0.000015262956,0.00005516956,0.000108892804,0.00001662324],"domain_scores_gemma":[0.99957424,0.00017741248,0.00003032343,0.000043718574,0.0001552624,0.00001912123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036015734,0.00044738644,0.00041768173,0.00075726805,0.00026233812,0.00045343873,0.00037398346,0.0004878306,0.0028241728],"category_scores_gemma":[0.0017484898,0.00018183599,0.00028857452,0.0006224372,0.00023684268,0.0005085771,0.00024517003,0.00048643342,0.0008151749],"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":[0.00048070672,0.00006992617,0.0010631961,0.00024962937,0.00004852369,0.000113662885,0.000056961424,0.012377767,0.1982689,0.005404865,0.0024721853,0.7793937],"study_design_scores_gemma":[0.00005598708,0.0002739059,0.005876812,0.000052007632,0.00007796303,0.0007518034,0.000031725176,0.8937723,0.087155834,0.0020814731,0.009819584,0.00005055725],"about_ca_topic_score_codex":0.0019273307,"about_ca_topic_score_gemma":0.0027528494,"teacher_disagreement_score":0.0028241728,"about_ca_system_score_codex":0.00020379428,"about_ca_system_score_gemma":0.00053444953,"threshold_uncertainty_score":0.009447873},"labels":[],"label_agreement":null},{"id":"W2095923908","doi":"10.1016/j.jprocont.2003.09.009","title":"Closed-loop identification via output fast sampling","year":2003,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","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":"","keywords":"Bispectrum; Identifiability; Control theory (sociology); Closed loop; Sampling (signal processing); Identification (biology); System identification; SIGNAL (programming language); Loop (graph theory); Computer science; Linear system; Mathematics; Control engineering; Engineering; Artificial intelligence; Statistics; Data mining; Machine learning","score_opus":0.011478425562184225,"score_gpt":0.23648569933748637,"score_spread":0.22500727377530214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095923908","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0252183,0.00016995595,0.97178644,0.00006607543,0.000060915394,0.000038263308,0.000041773208,0.0008073558,0.00181091],"genre_scores_gemma":[0.8344982,0.00017791864,0.16211025,0.00006620475,0.000052778905,0.000087267355,0.00011930445,0.00012449932,0.0027634755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934405,0.00020038856,0.000039051727,0.00008441635,0.00027912858,0.00005297018],"domain_scores_gemma":[0.99818987,0.00109279,0.00010860758,0.00024945475,0.00033498855,0.000024294624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001052552,0.0006732704,0.00075673347,0.00037475204,0.0004812752,0.0009298398,0.0004300667,0.00054139434,0.001992739],"category_scores_gemma":[0.0041846354,0.0003255817,0.00026447073,0.00041915715,0.00041122685,0.001051447,0.0008456671,0.00085566996,0.00038013086],"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":[0.0032773823,0.000220514,0.0024555943,0.00065675826,0.00014515257,0.00037200688,0.00060068385,0.13146551,0.11891044,0.034034368,0.0016683912,0.7061932],"study_design_scores_gemma":[0.0000984193,0.00020419458,0.0013227003,0.000032911772,0.00003172111,0.00015540424,0.000023969917,0.9543341,0.036309548,0.0050131124,0.002449898,0.000024035726],"about_ca_topic_score_codex":0.0012787458,"about_ca_topic_score_gemma":0.0014793109,"teacher_disagreement_score":0.001992739,"about_ca_system_score_codex":0.00031345504,"about_ca_system_score_gemma":0.0005109074,"threshold_uncertainty_score":0.0066663623},"labels":[],"label_agreement":null},{"id":"W2104210191","doi":"10.1016/j.jprocont.2014.10.008","title":"Bias-eliminated subspace model identification under time-varying deterministic type load disturbance","year":2014,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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 Alberta","funders":"","keywords":"Disturbance (geology); Subspace topology; Identification (biology); Control theory (sociology); Type (biology); Computer science; Mathematics; Artificial intelligence; Control (management); Biology","score_opus":0.012399686746457072,"score_gpt":0.2390671591012564,"score_spread":0.2266674723547993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104210191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1144013,0.00027631185,0.8812,0.00012700536,0.00006408603,0.000039457078,0.00015140108,0.00059431174,0.0031461017],"genre_scores_gemma":[0.9746697,0.00021776467,0.022196062,0.00002717137,0.000013599367,0.000040336003,0.00024649204,0.000040259372,0.0025486294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997688,0.00005569509,0.000013658773,0.000052226074,0.000066388035,0.000043224158],"domain_scores_gemma":[0.9995295,0.00015581732,0.00007077126,0.000070000606,0.00016106137,0.000012830581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004739013,0.0006638754,0.0008440495,0.00026316405,0.0003373644,0.00062820385,0.00042726618,0.0005586945,0.00094387186],"category_scores_gemma":[0.0016126604,0.00024384889,0.00048951566,0.00040163126,0.0003584727,0.0006714938,0.0005527557,0.0005847898,0.000405949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002528306,0.000061536295,0.0011760009,0.00019105362,0.000098794466,0.00013154173,0.000110895024,0.9205647,0.011087811,0.0054360023,0.0006765985,0.060212214],"study_design_scores_gemma":[0.0000053081126,0.000022347713,0.00043447476,0.000003921235,0.0000070508227,0.00001573381,0.000008101102,0.99734133,0.0012670765,0.00072706304,0.00016238325,0.000005103788],"about_ca_topic_score_codex":0.0064504337,"about_ca_topic_score_gemma":0.0058613997,"teacher_disagreement_score":0.0064504337,"about_ca_system_score_codex":0.00022414776,"about_ca_system_score_gemma":0.0007478772,"threshold_uncertainty_score":0.012825787},"labels":[],"label_agreement":null},{"id":"W2112291606","doi":"10.1016/j.jprocont.2005.02.003","title":"Design of robust gain-scheduled PI controllers for nonlinear processes","year":2005,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Stability and Control of Uncertain Systems","field":"Engineering","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 Waterloo","funders":"","keywords":"Control theory (sociology); Gain scheduling; Nonlinear system; Continuous stirred-tank reactor; Robust control; Stability (learning theory); Mathematical optimization; Computer science; Engineering; Control engineering; Mathematics; Control (management)","score_opus":0.021129794167609572,"score_gpt":0.2414571130860193,"score_spread":0.22032731891840973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112291606","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016092312,0.0003532413,0.9791812,0.00010380374,0.000083685285,0.00010433332,0.000019733978,0.00032635193,0.003735415],"genre_scores_gemma":[0.9031801,0.00030951752,0.093590766,0.00009229957,0.0000720912,0.00030831643,0.000055659333,0.000040563675,0.0023506356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958354,0.000077123455,0.00002544722,0.000098746124,0.0001587053,0.000056371046],"domain_scores_gemma":[0.99932015,0.00029362738,0.00014659899,0.000042345757,0.0001702709,0.000027067346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011427766,0.0010609066,0.0007993215,0.00048078847,0.00050439703,0.001191369,0.0011612633,0.0010376233,0.0016083445],"category_scores_gemma":[0.0022743996,0.0006172589,0.0004905671,0.00026257633,0.00065628526,0.000492582,0.0006676647,0.00089633366,0.00036814544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027987667,0.00012853304,0.0002585202,0.00026862367,0.00006970469,0.00022014833,0.00014640014,0.88911265,0.01984718,0.013932599,0.0009400393,0.07479574],"study_design_scores_gemma":[0.000031040418,0.00012219115,0.000082610575,0.0000063408834,0.000011881607,0.000020922891,0.0000070371557,0.9954918,0.0021858343,0.0016370846,0.00039726958,0.000006039404],"about_ca_topic_score_codex":0.0017818384,"about_ca_topic_score_gemma":0.001972853,"teacher_disagreement_score":0.0017818384,"about_ca_system_score_codex":0.0005547733,"about_ca_system_score_gemma":0.0009804271,"threshold_uncertainty_score":0.006043613},"labels":[],"label_agreement":null},{"id":"W2122285908","doi":"10.1016/j.jprocont.2006.04.002","title":"Fault-tolerant control of a polyethylene reactor","year":2006,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":41,"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":"National Science Foundation","keywords":"Control theory (sociology); Actuator; Controller (irrigation); Fault tolerance; Lyapunov stability; Fault (geology); Filter (signal processing); Stability (learning theory); Lyapunov function; Control system; Nonlinear system; Computer science; Control engineering; Control (management); Engineering; Physics; Distributed computing","score_opus":0.002762539823337831,"score_gpt":0.19954572216375488,"score_spread":0.19678318234041706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122285908","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":[0.788251,0.00023326963,0.20522755,0.0003549907,0.00015830017,0.000044901015,0.00006343046,0.00063777785,0.0050288504],"genre_scores_gemma":[0.9973515,0.000019464745,0.0017770242,0.0000088211345,0.000004353917,0.000004192639,0.0000072626935,0.000004284491,0.0008231394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998869,0.000020779918,0.00000635699,0.000029428282,0.00003329634,0.000023302411],"domain_scores_gemma":[0.99975663,0.00007896635,0.000074398966,0.000021630014,0.000049482565,0.000018964056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002184946,0.00042165667,0.00037782482,0.00020003198,0.00050830963,0.00050331664,0.0005686293,0.00040064086,0.0009442703],"category_scores_gemma":[0.00054407143,0.00013972505,0.00019321832,0.00015117305,0.0003053134,0.00035822246,0.00030350767,0.00026027253,0.00009450695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016688871,0.00018106856,0.0015717391,0.00017986589,0.000048612237,0.00092847564,0.00021268986,0.75646806,0.18291086,0.0041552107,0.0006530238,0.05102154],"study_design_scores_gemma":[0.00004983086,0.0005613151,0.0007330913,0.0000042506517,0.00001743382,0.00007281237,0.000020866268,0.9690831,0.027947761,0.000836813,0.00066016987,0.000012492966],"about_ca_topic_score_codex":0.0026788982,"about_ca_topic_score_gemma":0.0015069089,"teacher_disagreement_score":0.0026788982,"about_ca_system_score_codex":0.00029871077,"about_ca_system_score_gemma":0.00030907444,"threshold_uncertainty_score":0.005326569},"labels":[],"label_agreement":null},{"id":"W2123865867","doi":"10.1016/j.jprocont.2004.06.008","title":"An optimal scheme for fast rate fault detection based on multirate sampled data","year":2004,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":48,"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":"Residual; Fault detection and isolation; Control theory (sociology); Computer science; Generator (circuit theory); Constraint (computer-aided design); Key (lock); Fault (geology); LTI system theory; Invariant (physics); Algorithm; Mathematics; Power (physics); Linear system; Control (management); Artificial intelligence","score_opus":0.018004538974989995,"score_gpt":0.27727234222108377,"score_spread":0.2592678032460938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123865867","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017529167,0.00020604621,0.9812781,0.00007517042,0.00011322264,0.000045396835,0.000017668754,0.00022943759,0.0005058832],"genre_scores_gemma":[0.6923103,0.00021370949,0.30577576,0.00011589149,0.00010524583,0.00010337144,0.000051676092,0.000030047338,0.0012940916],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991129,0.00021487374,0.00007926496,0.00018833786,0.00030846708,0.00009607896],"domain_scores_gemma":[0.99882156,0.00050363806,0.00014648394,0.00017873885,0.00028832204,0.00006136061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013882688,0.00067363237,0.001362237,0.0005449992,0.0006090762,0.0010575535,0.00094804843,0.0008909158,0.0016269308],"category_scores_gemma":[0.0029528756,0.00038748386,0.0004963445,0.00035376407,0.0007041365,0.0012783927,0.0011224417,0.0010186373,0.0002267526],"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":[0.0028962472,0.00031747366,0.0013286107,0.000496355,0.00015879428,0.0002324125,0.0005243723,0.2941246,0.10991017,0.0462584,0.0020525327,0.54170007],"study_design_scores_gemma":[0.000057541787,0.00015038377,0.00019432211,0.000014508092,0.000024941874,0.000050780785,0.000010670921,0.9889837,0.007054613,0.0027826903,0.0006549021,0.000020987747],"about_ca_topic_score_codex":0.0021100612,"about_ca_topic_score_gemma":0.0024915661,"teacher_disagreement_score":0.0021100612,"about_ca_system_score_codex":0.0006976284,"about_ca_system_score_gemma":0.0010486646,"threshold_uncertainty_score":0.0073419213},"labels":[],"label_agreement":null},{"id":"W2131041867","doi":"10.1016/j.jprocont.2008.10.002","title":"Decentralized robust control of a class of nonlinear systems and application to a boiler system","year":2008,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Stability and Control of Uncertain Systems","field":"Engineering","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control theory (sociology); Nonlinear system; Boiler (water heating); Control engineering; Decentralised system; Optimization problem; Engineering; Computer science; Mathematical optimization; Mathematics; Control (management)","score_opus":0.008805454343797883,"score_gpt":0.21416970378954922,"score_spread":0.20536424944575132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131041867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13250168,0.00085293164,0.8540306,0.0003497136,0.00009048412,0.00005742998,0.000063779604,0.00047419308,0.011579206],"genre_scores_gemma":[0.98090786,0.00029688267,0.016193416,0.000023396122,0.000048147307,0.000040990424,0.00003190487,0.000020207648,0.0024372195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998332,0.000037436075,0.0000063705043,0.000038791717,0.00006245081,0.000021743317],"domain_scores_gemma":[0.999686,0.00015006417,0.000048353264,0.000032926237,0.00007064794,0.000012025959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003629864,0.00041420752,0.0007908134,0.00024665485,0.00048204564,0.00059290597,0.0005256014,0.00071868184,0.0012672934],"category_scores_gemma":[0.00097479584,0.00018582313,0.00040274448,0.0002519923,0.00053390476,0.00034003833,0.0004548565,0.00040835774,0.00013559136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023397252,0.00007498628,0.000406694,0.00019797898,0.00005840862,0.00040069723,0.00013483122,0.850027,0.041401736,0.05606519,0.001308497,0.04969002],"study_design_scores_gemma":[0.000017440667,0.000065911474,0.0001933311,0.0000023292864,0.0000062417507,0.000040914787,0.0000065695563,0.99421775,0.0010407389,0.003897123,0.0005062974,0.000005422689],"about_ca_topic_score_codex":0.0015683986,"about_ca_topic_score_gemma":0.0015662392,"teacher_disagreement_score":0.0015683986,"about_ca_system_score_codex":0.0002547515,"about_ca_system_score_gemma":0.0002635889,"threshold_uncertainty_score":0.0042394996},"labels":[],"label_agreement":null},{"id":"W2143579868","doi":"10.1016/j.jprocont.2003.06.003","title":"The impact of compression on data-driven process analyses","year":2003,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":58,"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":"Computer science; Data compression; Process (computing); Data mining; Compression (physics); Benchmarking; Variance (accounting); Resource (disambiguation); Algorithm","score_opus":0.06775728886460919,"score_gpt":0.4151766104151633,"score_spread":0.3474193215505541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143579868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30206886,0.0042412737,0.68242675,0.0024474054,0.0005959368,0.00015255479,0.0007356255,0.0028781544,0.0044533513],"genre_scores_gemma":[0.90815806,0.001443265,0.08770004,0.00032102712,0.00040076455,0.00006443121,0.0006324695,0.00022725404,0.0010526074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99740976,0.00101,0.00017675754,0.00025964415,0.0009919896,0.00015183492],"domain_scores_gemma":[0.9182774,0.07315588,0.0010994794,0.0043078614,0.0028884474,0.00027102797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004296676,0.0011201281,0.0010222602,0.0012563894,0.0006620015,0.001569661,0.00068484066,0.0017365606,0.001821164],"category_scores_gemma":[0.056597658,0.00046750956,0.0005626222,0.0014403398,0.001110204,0.00288853,0.0012474207,0.0014882606,0.0004056718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042641517,0.0002418171,0.0072033596,0.0003738221,0.00015163775,0.0006988601,0.00021284066,0.5356495,0.016385179,0.015138588,0.0022016314,0.4174787],"study_design_scores_gemma":[0.000033496613,0.00008758266,0.0010632467,0.000021366779,0.000031562606,0.00011978519,0.0000233071,0.9809187,0.010724171,0.0065482594,0.00041567258,0.000012836039],"about_ca_topic_score_codex":0.0030637488,"about_ca_topic_score_gemma":0.0017637233,"teacher_disagreement_score":0.004296676,"about_ca_system_score_codex":0.00058906,"about_ca_system_score_gemma":0.0009290569,"threshold_uncertainty_score":0.022723258},"labels":[],"label_agreement":null},{"id":"W2251588794","doi":"10.1016/j.jprocont.2015.12.006","title":"Robust discrete-time set-based adaptive predictive control for nonlinear systems","year":2016,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Queen's University","funders":"","keywords":"Control theory (sociology); Model predictive control; Robustness (evolution); Nonlinear system; Robust control; Adaptive control; Lipschitz continuity; Mathematical optimization; Mathematics; Discrete time and continuous time; Estimation theory; Computer science; Algorithm; Control (management); Artificial intelligence","score_opus":0.009394596802482615,"score_gpt":0.2185458763845667,"score_spread":0.2091512795820841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2251588794","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01829149,0.00054632674,0.9772708,0.00019827143,0.00017146269,0.000042222156,0.000058177233,0.00044595887,0.0029752871],"genre_scores_gemma":[0.95993465,0.00030512648,0.036252573,0.00009294523,0.00008003117,0.00018130097,0.000120198085,0.00006782765,0.002965333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993436,0.00014652837,0.0000434523,0.00012081013,0.00028218288,0.00006337522],"domain_scores_gemma":[0.9987268,0.00075314293,0.000155825,0.000092657196,0.00024433323,0.00002729679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001124398,0.0009696965,0.0014332441,0.00059871445,0.00047115682,0.0016244163,0.0014210511,0.0010469038,0.002037183],"category_scores_gemma":[0.003275516,0.0006517622,0.00083090446,0.00056408404,0.0009621562,0.0008829411,0.0014022874,0.0016798523,0.00035250076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011860913,0.000043904136,0.00013781329,0.00010644716,0.00007680222,0.00004934011,0.00006603695,0.9580744,0.0018552485,0.0053458754,0.0007873684,0.03333819],"study_design_scores_gemma":[0.0000049659193,0.000015500846,0.000036778205,0.0000028549446,0.0000036699562,0.0000028797108,0.0000013852746,0.99885106,0.0002104458,0.00076189823,0.00010596401,0.000002665668],"about_ca_topic_score_codex":0.0122178085,"about_ca_topic_score_gemma":0.0061611384,"teacher_disagreement_score":0.0122178085,"about_ca_system_score_codex":0.00087796926,"about_ca_system_score_gemma":0.0008630304,"threshold_uncertainty_score":0.024293423},"labels":[],"label_agreement":null},{"id":"W2344934955","doi":"10.1016/j.jprocont.2016.04.003","title":"Robust Gaussian process modeling using EM algorithm","year":2016,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":56,"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; Alberta Innovates - Health Solutions","keywords":"Gaussian process; Robust regression; Conjugate gradient method; Algorithm; Stability (learning theory); Kriging; Regression; Computer science; Marginal likelihood; Bayesian linear regression; Convergence (economics); Mathematical optimization; Regression analysis; Mathematics; Gaussian; Bayesian probability; Bayesian inference; Machine learning; Artificial intelligence; Statistics","score_opus":0.02819780691223422,"score_gpt":0.2661950150392292,"score_spread":0.23799720812699499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344934955","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005325916,0.00006116838,0.99893385,0.00004175528,0.000013021588,0.000007428582,0.000016194443,0.00017058045,0.000223405],"genre_scores_gemma":[0.2001255,0.0005888113,0.792434,0.0002212239,0.00014680019,0.0002823176,0.00058792107,0.00051186996,0.005101497],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826413,0.0007490561,0.000110800596,0.00041201388,0.00036632837,0.00009769822],"domain_scores_gemma":[0.9952245,0.003308473,0.0003347335,0.00043703924,0.0006258603,0.00006943605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036661655,0.0014663963,0.0026656773,0.001308362,0.00073588727,0.0016738701,0.002603522,0.0027081785,0.0032870641],"category_scores_gemma":[0.014826368,0.0016083416,0.0023807113,0.0017711703,0.0012049841,0.002784769,0.0024526792,0.0034840964,0.0019246644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001393227,0.000075285716,0.00044096098,0.00014595075,0.00025318153,0.00008737604,0.000077828154,0.8213937,0.0015466734,0.06758283,0.002189515,0.1060674],"study_design_scores_gemma":[0.000009588624,0.000008150902,0.00005102414,0.000008010358,0.00001606497,0.000016862328,0.0000030979318,0.9812238,0.000392598,0.017661115,0.00059938733,0.000010274013],"about_ca_topic_score_codex":0.005557591,"about_ca_topic_score_gemma":0.0034641954,"teacher_disagreement_score":0.005557591,"about_ca_system_score_codex":0.00076439255,"about_ca_system_score_gemma":0.0020246885,"threshold_uncertainty_score":0.019388735},"labels":[],"label_agreement":null},{"id":"W2523593693","doi":"10.1016/j.jprocont.2016.09.008","title":"Characteristics-based model predictive control of selective catalytic reduction in diesel-powered vehicles","year":2016,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"University of Alberta","funders":"","keywords":"Selective catalytic reduction; NOx; Diesel engine; Diesel fuel; Model predictive control; Diesel exhaust fluid; Control theory (sociology); Ammonia; Exhaust gas recirculation; Controller (irrigation); Automotive engineering; Environmental science; Computer science; Diesel exhaust; Engineering; Exhaust gas; Chemistry; Catalysis; Waste management; Control (management)","score_opus":0.004908291310535237,"score_gpt":0.21580232905709287,"score_spread":0.21089403774655763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523593693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3094838,0.0009576286,0.6720763,0.00059293857,0.00022668265,0.00009697465,0.00011888565,0.0006023869,0.015844436],"genre_scores_gemma":[0.99694026,0.000073997675,0.002050878,0.00001731071,0.000009050494,0.000019612762,0.000022455348,0.000013997693,0.00085245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998529,0.000028961937,0.0000071901363,0.000028456421,0.00005516079,0.000027291446],"domain_scores_gemma":[0.9996152,0.00016888052,0.000060782146,0.000022607108,0.00011997782,0.000012577639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004216579,0.00057536084,0.0007668531,0.00042357374,0.00037941601,0.001030061,0.00081215764,0.0005907164,0.00081721024],"category_scores_gemma":[0.001026502,0.00045091377,0.00041219548,0.00036060394,0.00046774404,0.0005440089,0.0006154686,0.00059280463,0.00018828743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007845193,0.000026139809,0.00022262575,0.000037058493,0.000019226865,0.00002472796,0.000026117932,0.98452884,0.0024544864,0.0012700846,0.00024011395,0.01107212],"study_design_scores_gemma":[0.0000040602667,0.000014048195,0.00010407867,0.0000013513721,0.0000029124153,0.0000022044528,0.0000025695188,0.9990706,0.00046593518,0.0002453176,0.00008503553,0.000001991054],"about_ca_topic_score_codex":0.010606201,"about_ca_topic_score_gemma":0.0076188776,"teacher_disagreement_score":0.010606201,"about_ca_system_score_codex":0.0006413189,"about_ca_system_score_gemma":0.0006625854,"threshold_uncertainty_score":0.021088958},"labels":[],"label_agreement":null},{"id":"W2523622649","doi":"10.1016/j.jprocont.2016.08.005","title":"MV benchmark estimation based on high-frequency test signal","year":2016,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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 Alberta","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Benchmark (surveying); Identifiability; SIGNAL (programming language); Noise (video); Computer science; Multivariable calculus; Control theory (sociology); Variance (accounting); Algorithm; Test data; Control (management); Engineering; Artificial intelligence; Control engineering; Machine learning","score_opus":0.0035101527073686475,"score_gpt":0.20251241667870526,"score_spread":0.1990022639713366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523622649","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20190828,0.00077907316,0.7913129,0.00012246931,0.00011891807,0.000034458833,0.0002356757,0.001471739,0.004016585],"genre_scores_gemma":[0.97713727,0.00006619741,0.021794967,0.00002348952,0.000011532054,0.000015539534,0.00026501602,0.000029713336,0.0006563602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997087,0.00006425394,0.000019012494,0.00006233314,0.000104829516,0.000040752333],"domain_scores_gemma":[0.9991659,0.0003459473,0.00008448135,0.00006441214,0.0003054186,0.000033900815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038310402,0.0006741166,0.0005407731,0.00072797964,0.0001871596,0.00061146007,0.0004734791,0.00052830763,0.0013585278],"category_scores_gemma":[0.003057084,0.00010188037,0.00019888234,0.00039052978,0.00011703402,0.0004730478,0.00032286285,0.00032732394,0.00022826584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011543836,0.00014514095,0.008669251,0.00029503362,0.00009813284,0.00023729508,0.00007320899,0.58834004,0.06704706,0.0047671422,0.0023548943,0.32681847],"study_design_scores_gemma":[0.0000058280198,0.00006307367,0.0019872722,0.0000065548506,0.000006849714,0.000029967805,0.000007379522,0.99189854,0.0052893325,0.00047980464,0.0002196662,0.000005614756],"about_ca_topic_score_codex":0.004333802,"about_ca_topic_score_gemma":0.0031184219,"teacher_disagreement_score":0.004333802,"about_ca_system_score_codex":0.0003488087,"about_ca_system_score_gemma":0.00042938854,"threshold_uncertainty_score":0.008617163},"labels":[],"label_agreement":null},{"id":"W2575563393","doi":"10.1016/j.jprocont.2016.09.011","title":"Generalized Hamiltonian representation of thermo-mechanical systems based on an entropic formulation","year":2017,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control and Stability of Dynamical Systems","field":"Engineering","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":"","keywords":"Dissipative system; Dissipation; Mechanical system; Thermodynamic system; Hamiltonian (control theory); Hamiltonian system; Isentropic process; Mathematics; Statistical physics; Entropy (arrow of time); Representation (politics); Applied mathematics; Computer science; Physics; Thermodynamics; Mathematical analysis; Mathematical optimization; Artificial intelligence","score_opus":0.014725026662785645,"score_gpt":0.2712841845219283,"score_spread":0.2565591578591427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2575563393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14931452,0.0020807097,0.77594894,0.0016736783,0.0010543367,0.00009529872,0.0002663393,0.00018235398,0.06938385],"genre_scores_gemma":[0.922948,0.0010433199,0.045471508,0.0004945348,0.0006317082,0.00011709355,0.00017314218,0.00014877557,0.028971884],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99984336,0.000054253425,0.000008029634,0.00002028045,0.00005099934,0.000023067118],"domain_scores_gemma":[0.9997911,0.000044834425,0.00003270224,0.000034837656,0.000049684484,0.000046847996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036554923,0.00061158475,0.00082799746,0.0007719789,0.00048098675,0.0012378962,0.00097430183,0.0009558972,0.005148054],"category_scores_gemma":[0.00048299268,0.00023459594,0.000661893,0.0004226874,0.001378459,0.0015178684,0.0010922685,0.0010284196,0.0004777277],"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":[0.000015543976,0.00003995148,0.00010265845,0.00005003016,0.000021020891,0.00009789043,0.000088100416,0.058047216,0.003339802,0.9333345,0.0007418403,0.0041213976],"study_design_scores_gemma":[0.000012273687,0.000036005462,0.00021260564,0.000012982775,0.000010326457,0.000050695373,0.00004474039,0.6714421,0.0002574531,0.32616198,0.0017365552,0.000022318807],"about_ca_topic_score_codex":0.0011383725,"about_ca_topic_score_gemma":0.0017058298,"teacher_disagreement_score":0.005148054,"about_ca_system_score_codex":0.0005863441,"about_ca_system_score_gemma":0.00059309875,"threshold_uncertainty_score":0.017221928},"labels":[],"label_agreement":null},{"id":"W2592723677","doi":"10.1016/j.jprocont.2017.02.010","title":"Kalman filtering approach to multi-rate information fusion in the presence of irregular sampling rate and variable measurement delay","year":2017,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":103,"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":"Kalman filter; Sensor fusion; Covariance intersection; Covariance; Covariance matrix; Fusion; Computer science; Sampling (signal processing); Extended Kalman filter; Variable (mathematics); State variable; Control theory (sociology); Algorithm; Mathematics; Filter (signal processing); Statistics; Artificial intelligence; Computer vision","score_opus":0.02764201340291988,"score_gpt":0.2488443808673001,"score_spread":0.2212023674643802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592723677","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070581664,0.00037452104,0.99176437,0.00010127988,0.000049433314,0.00001008691,0.000021429023,0.00006548225,0.0005551752],"genre_scores_gemma":[0.80398035,0.0015308844,0.1898455,0.00009415828,0.0001971365,0.000088116394,0.00017909284,0.000054304437,0.004030523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990582,0.00026698748,0.000093041665,0.0002309998,0.00025264325,0.00009820899],"domain_scores_gemma":[0.99860245,0.0007568853,0.00017183671,0.00012807471,0.00031261233,0.000028235236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019583837,0.000648763,0.0014669759,0.0006870696,0.00054718606,0.0015137338,0.0011016887,0.0012043042,0.0008897663],"category_scores_gemma":[0.0045819953,0.00056436664,0.0009239122,0.0009842156,0.00082918175,0.0024271011,0.0013321616,0.0014172535,0.00022351096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021442854,0.000044828903,0.00079560006,0.00021593686,0.0001882966,0.00017691866,0.00023645181,0.8432961,0.006125477,0.05704212,0.0007041931,0.09095957],"study_design_scores_gemma":[0.0000054606307,0.000021359872,0.00014428349,0.0000059352933,0.000019117198,0.000018308614,0.000008942951,0.99385107,0.000752172,0.00487009,0.00028935028,0.000013829573],"about_ca_topic_score_codex":0.008710841,"about_ca_topic_score_gemma":0.005668274,"teacher_disagreement_score":0.008710841,"about_ca_system_score_codex":0.0008943259,"about_ca_system_score_gemma":0.0012332887,"threshold_uncertainty_score":0.017320275},"labels":[],"label_agreement":null},{"id":"W2604989875","doi":"10.1016/j.jprocont.2017.03.008","title":"Robust identification for nonlinear errors-in-variables systems using the EM algorithm","year":2017,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"University of Alberta","funders":"Ministère de l’Éducation, Gouvernement de l’Ontario; China Scholarship Council; Science and Technology Commission of Shanghai Municipality; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Outlier; Weighting; Nonlinear system; Expectation–maximization algorithm; Algorithm; Noise (video); Gaussian; System identification; Identification (biology); Errors-in-variables models; Maximization; Mathematical optimization; Mathematics; Estimation theory; Computer science; Control theory (sociology); Artificial intelligence; Maximum likelihood; Statistics; Data modeling","score_opus":0.02483477784351153,"score_gpt":0.27375322697964505,"score_spread":0.2489184491361335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604989875","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042102947,0.00009726697,0.9950622,0.000037620615,0.000013694746,0.00000896347,0.000009318187,0.0001244797,0.0004361039],"genre_scores_gemma":[0.67715245,0.00036639025,0.31833997,0.00007739158,0.00006310243,0.00015599559,0.00018092248,0.00011833263,0.003545415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995701,0.00017547682,0.00003246487,0.00009726211,0.00008858874,0.000036207475],"domain_scores_gemma":[0.9985682,0.001011667,0.00013692142,0.00009932909,0.00016686735,0.00001706131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013484432,0.0009025262,0.0011379925,0.0005472881,0.0003686608,0.0006154399,0.0007834548,0.00096163474,0.0019337346],"category_scores_gemma":[0.00485019,0.0004317204,0.00075063366,0.0005060847,0.0006344558,0.0009323419,0.0010523716,0.0010432284,0.00048908195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014007892,0.00002794491,0.00036352008,0.00010721169,0.00010604401,0.00005455004,0.00005041735,0.91119736,0.0025169498,0.011058722,0.00043047368,0.073946625],"study_design_scores_gemma":[0.000006995184,0.000017023436,0.0000918289,0.000005988236,0.000005211961,0.000012290989,0.0000034694453,0.99674404,0.00055219885,0.002334955,0.00022094883,0.0000049803125],"about_ca_topic_score_codex":0.002611886,"about_ca_topic_score_gemma":0.0018645915,"teacher_disagreement_score":0.002611886,"about_ca_system_score_codex":0.00030549918,"about_ca_system_score_gemma":0.0005968514,"threshold_uncertainty_score":0.0071312785},"labels":[],"label_agreement":null},{"id":"W2605403773","doi":"10.1016/j.jprocont.2017.03.012","title":"Robust probabilistic principal component analysis based process modeling: Dealing with simultaneous contamination of both input and output data","year":2017,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Outlier; Principal component analysis; Probabilistic logic; Robustness (evolution); Computer science; Principal component regression; Statistical model; Expectation–maximization algorithm; Maximization; Generalization; Robust regression; Data mining; Machine learning; Artificial intelligence; Mathematics; Mathematical optimization; Statistics; Maximum likelihood","score_opus":0.03127107801264777,"score_gpt":0.26370832840067854,"score_spread":0.23243725038803076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605403773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005327313,0.00009448226,0.9941606,0.000057091896,0.000014831654,0.000014029692,0.000021940596,0.00017306698,0.00013665925],"genre_scores_gemma":[0.6823237,0.0007755707,0.31381634,0.00009929738,0.00011816036,0.00023581652,0.00033917298,0.00025851087,0.0020335142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980714,0.0007375328,0.00011273639,0.00040997707,0.00054043793,0.00012789907],"domain_scores_gemma":[0.99679095,0.001983799,0.00043258333,0.00035493314,0.0003918064,0.00004604753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024557984,0.0016445316,0.0015798256,0.0006388866,0.00054820534,0.001437693,0.0017159579,0.0015557826,0.0005127432],"category_scores_gemma":[0.00852845,0.0012861964,0.0017853811,0.0011451697,0.0010601312,0.0017583518,0.0014023053,0.0019976299,0.0003339887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001194357,0.00005737992,0.0007537376,0.000113670125,0.00014266392,0.00009438028,0.00006319971,0.9465135,0.0047406526,0.008764284,0.0004043021,0.038232826],"study_design_scores_gemma":[0.000001894861,0.0000105621275,0.00010904095,0.0000016600821,0.00001008426,0.000012769734,0.0000016021501,0.9978508,0.0007725491,0.001125679,0.000098428405,0.00000498725],"about_ca_topic_score_codex":0.004008905,"about_ca_topic_score_gemma":0.0026401673,"teacher_disagreement_score":0.004008905,"about_ca_system_score_codex":0.00047996774,"about_ca_system_score_gemma":0.0016127724,"threshold_uncertainty_score":0.012987673},"labels":[],"label_agreement":null},{"id":"W2623384469","doi":"10.1016/j.jprocont.2017.01.003","title":"Online pattern matching and prediction of incoming alarm floods","year":2017,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":47,"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":"ALARM; Computer science; Matching (statistics); Sequence (biology); Similarity (geometry); Data mining; Process (computing); Flood myth; Artificial intelligence; Real-time computing; Pattern recognition (psychology); Engineering; Image (mathematics)","score_opus":0.00835544580141323,"score_gpt":0.2420353392098237,"score_spread":0.23367989340841047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2623384469","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":[0.60959744,0.00083714863,0.38431436,0.00037800055,0.0002166589,0.00008364117,0.0009953231,0.0019483939,0.001629007],"genre_scores_gemma":[0.97238195,0.00011083988,0.026013205,0.00003393523,0.000047730184,0.000018331179,0.00057490554,0.000027952108,0.0007910917],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992663,0.000099143486,0.00008901484,0.00022440175,0.00021481246,0.00010635083],"domain_scores_gemma":[0.99769443,0.0012470703,0.000370465,0.00018773538,0.00038328723,0.000116977804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007636175,0.0005415755,0.0011101448,0.0026375076,0.0002901661,0.00072173367,0.0012196434,0.00091123045,0.0010761536],"category_scores_gemma":[0.0038693028,0.00032179774,0.0004611432,0.0012366953,0.00025989712,0.0010455586,0.0007250966,0.00064245984,0.00043471027],"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":[0.0024238764,0.0008941801,0.07930846,0.00023909312,0.00029254015,0.0010751723,0.00016376536,0.23453273,0.025237318,0.0025739092,0.004491932,0.64876705],"study_design_scores_gemma":[0.000013086655,0.000075798824,0.0046259966,0.0000048040833,0.000018113207,0.000121697696,0.000021809581,0.9914328,0.002155094,0.0013205204,0.0002043937,0.0000059213253],"about_ca_topic_score_codex":0.0035016793,"about_ca_topic_score_gemma":0.003658807,"teacher_disagreement_score":0.0035016793,"about_ca_system_score_codex":0.00028024838,"about_ca_system_score_gemma":0.00062843,"threshold_uncertainty_score":0.0069625974},"labels":[],"label_agreement":null},{"id":"W2732540896","doi":"10.1016/j.jprocont.2017.06.005","title":"Robust gain switching control of constant bottomhole pressure drilling","year":2017,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Drilling and Well Engineering","field":"Engineering","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":"Memorial University of Newfoundland","funders":"","keywords":"Setpoint; Choke; Control theory (sociology); Underbalanced drilling; Well control; Drilling; Robustness (evolution); Engineering; Robust control; Pressure control; Controller (irrigation); Control engineering; Control system; Drilling fluid; Computer science; Control (management); Mechanical engineering","score_opus":0.009895469549575334,"score_gpt":0.21592167862456757,"score_spread":0.20602620907499225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732540896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19286272,0.000471497,0.7883303,0.00025499694,0.00034826004,0.00006584509,0.00006573053,0.0007273892,0.016873347],"genre_scores_gemma":[0.9952525,0.000046505018,0.003681721,0.000018430956,0.0000138878795,0.000013103077,0.000011283091,0.000010796527,0.00095172087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996276,0.00008338701,0.00001868461,0.00008157943,0.00011856737,0.000070137256],"domain_scores_gemma":[0.9996118,0.0001645648,0.00006946754,0.00003117209,0.00010339958,0.000019536787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059532956,0.000579804,0.00069031416,0.00027631538,0.00030776978,0.0010213898,0.00065699936,0.00048306034,0.0016300877],"category_scores_gemma":[0.0009210615,0.00023893664,0.00036773641,0.00027500393,0.00062177225,0.0003671684,0.0005711951,0.0006051496,0.00015174656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001280263,0.00021282905,0.00063689105,0.0004852136,0.00012915026,0.0002589108,0.00033251368,0.7259226,0.08823944,0.029965453,0.002402504,0.15013431],"study_design_scores_gemma":[0.000033234697,0.00028173995,0.00041597968,0.000005875774,0.000015389574,0.000018780005,0.000012876031,0.99277955,0.0038319083,0.0021318244,0.00046016852,0.000012698005],"about_ca_topic_score_codex":0.0035862445,"about_ca_topic_score_gemma":0.0020995722,"teacher_disagreement_score":0.0035862445,"about_ca_system_score_codex":0.00042370983,"about_ca_system_score_gemma":0.00046853456,"threshold_uncertainty_score":0.007130742},"labels":[],"label_agreement":null},{"id":"W2737259031","doi":"10.1016/j.jprocont.2017.06.019","title":"An accelerated alignment method for analyzing time sequences of industrial alarm floods","year":2017,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":44,"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; Tsinghua University; National Natural Science Foundation of China; Tsinghua Initiative Scientific Research Program; University of Alberta","keywords":"ALARM; Robustness (evolution); Computer science; Data mining; Inference; Process (computing); Similarity (geometry); Flood myth; Artificial intelligence; Real-time computing; Reliability engineering; Engineering","score_opus":0.03235268965498884,"score_gpt":0.32826036780937784,"score_spread":0.295907678154389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737259031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042438567,0.00034501997,0.952525,0.000042761214,0.00010841812,0.00006909552,0.0005111002,0.002882773,0.0010772444],"genre_scores_gemma":[0.16738372,0.00022916116,0.82814133,0.00004450269,0.000088721514,0.00012183839,0.0014654815,0.0003765646,0.0021486157],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915683,0.00012313934,0.000060750743,0.00024780308,0.0003280066,0.00008345289],"domain_scores_gemma":[0.99857795,0.0003900183,0.00021327933,0.00016871253,0.00057262374,0.000077403936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007233804,0.00076815306,0.0006245188,0.0027830205,0.00053421373,0.0006705585,0.00076827506,0.0005902897,0.0033684322],"category_scores_gemma":[0.0023434935,0.00034455696,0.0005277105,0.0027386383,0.000257053,0.00069298997,0.00056143274,0.0007715163,0.001709517],"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":[0.00067939144,0.00015688418,0.0068401443,0.00025786838,0.00018485296,0.0004009182,0.00022666248,0.021321476,0.21007855,0.0034249094,0.0043864204,0.75204194],"study_design_scores_gemma":[0.000117001895,0.00053561624,0.02622786,0.0000437754,0.00023889904,0.0017852972,0.00025592267,0.84482294,0.09382656,0.0056342175,0.026394986,0.00011689359],"about_ca_topic_score_codex":0.0025359795,"about_ca_topic_score_gemma":0.0041075763,"teacher_disagreement_score":0.0033684322,"about_ca_system_score_codex":0.00022061836,"about_ca_system_score_gemma":0.0010405227,"threshold_uncertainty_score":0.011268556},"labels":[],"label_agreement":null},{"id":"W2763690839","doi":"10.1016/j.jprocont.2017.09.002","title":"Distributed control of multi-agent systems over unknown communication networks using extremum seeking","year":2017,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Extremum Seeking Control Systems","field":"Engineering","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":"Queen's University","funders":"","keywords":"Control (management); Control theory (sociology); Computer science; Multi-agent system; Control engineering; Distributed computing; Engineering; Artificial intelligence","score_opus":0.021961461098415316,"score_gpt":0.2710989258368733,"score_spread":0.24913746473845796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2763690839","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0116842715,0.0002655863,0.9863971,0.00011547533,0.000024793675,0.000017415125,0.0000041541252,0.000077396966,0.001413782],"genre_scores_gemma":[0.91986525,0.00033117417,0.07812044,0.000059224545,0.000053581105,0.000106486434,0.0000137474135,0.00002122405,0.0014287902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955755,0.0001897134,0.000019723826,0.000085705,0.00010197891,0.00004523785],"domain_scores_gemma":[0.99941623,0.00035394917,0.00011496518,0.000034004293,0.00006011896,0.00002074937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010213617,0.00079680816,0.0009781325,0.00031285614,0.0005538872,0.0008445696,0.00090173114,0.0009234186,0.0006227707],"category_scores_gemma":[0.0015297207,0.0002785064,0.00052497274,0.00039269467,0.001160712,0.0008592174,0.0011370085,0.0008982196,0.00010103143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054847376,0.000027631962,0.000191108,0.00007457543,0.000043390617,0.000100255405,0.000105920495,0.95379263,0.003826816,0.020255376,0.00027531575,0.021252127],"study_design_scores_gemma":[0.000008467753,0.000031687476,0.000027300479,0.0000036809142,0.0000041997687,0.000012206781,0.000007841608,0.9962845,0.00036350876,0.0030366096,0.00021657084,0.000003278891],"about_ca_topic_score_codex":0.0013803324,"about_ca_topic_score_gemma":0.00087221834,"teacher_disagreement_score":0.0013803324,"about_ca_system_score_codex":0.00052213995,"about_ca_system_score_gemma":0.00052660354,"threshold_uncertainty_score":0.005401492},"labels":[],"label_agreement":null},{"id":"W2788138129","doi":"10.1016/j.jprocont.2017.11.007","title":"A new robust controller for non-linear periodic single-input/single-output systems using genetic algorithms","year":2018,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":9,"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; Université Laval","keywords":"Crossover; Process (computing); Controller (irrigation); Computer science; Control theory (sociology); Genetic algorithm; Control engineering; Algorithm; Engineering; Control (management); Artificial intelligence; Machine learning","score_opus":0.027293308338173432,"score_gpt":0.2568019261333928,"score_spread":0.2295086177952194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788138129","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010084621,0.00019404804,0.9871001,0.00005124925,0.0000744612,0.00003999461,0.000010471107,0.00049982517,0.0019452347],"genre_scores_gemma":[0.60328984,0.00028526635,0.3914525,0.00013674507,0.000077101984,0.00029392194,0.00008099406,0.000109272914,0.0042742947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997693,0.00003200593,0.000012698254,0.000071036025,0.0000918696,0.000023004688],"domain_scores_gemma":[0.99972504,0.000101036974,0.00004766517,0.000025019377,0.00008908118,0.00001222239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051151944,0.000787296,0.0008866193,0.0005396571,0.00041698333,0.0008089172,0.0011853059,0.0011025834,0.0014580612],"category_scores_gemma":[0.0008903866,0.00033070557,0.0006136462,0.00031543782,0.00050552905,0.00047358655,0.0005367039,0.00065978465,0.0003208946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103352024,0.00008825039,0.00026091505,0.00015955076,0.00014139702,0.0001907545,0.000099386416,0.8052321,0.029493978,0.0103999,0.0010777542,0.15275264],"study_design_scores_gemma":[0.000019387753,0.000043998385,0.00008058663,0.0000052590444,0.000015135816,0.00002331579,0.0000024633532,0.9971595,0.0015195333,0.0006353696,0.00048824126,0.0000073686906],"about_ca_topic_score_codex":0.0032659867,"about_ca_topic_score_gemma":0.0031421848,"teacher_disagreement_score":0.0032659867,"about_ca_system_score_codex":0.00041545497,"about_ca_system_score_gemma":0.0006215658,"threshold_uncertainty_score":0.006493926},"labels":[],"label_agreement":null},{"id":"W2897357771","doi":"10.1016/j.jprocont.2018.09.007","title":"Optimized PID controller for an industrial biological fermentation process","year":2018,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Viral Infectious Diseases and Gene Expression in Insects","field":"Biochemistry, Genetics and Molecular Biology","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":"Sanofi (Canada); University of Waterloo","funders":"National Research Council Canada","keywords":"Industrial fermentation; PID controller; Control theory (sociology); Controller (irrigation); Process (computing); Control engineering; Process control; Process engineering; Engineering; Computer science; Fermentation; Temperature control; Control (management); Chemistry; Biology; Biochemistry","score_opus":0.03972015496616682,"score_gpt":0.35098921698700414,"score_spread":0.3112690620208373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897357771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21811509,0.00095278985,0.75550264,0.0003076946,0.00044935054,0.00028945948,0.0001901827,0.0016975843,0.022495216],"genre_scores_gemma":[0.9706023,0.000103502396,0.026038667,0.000036943726,0.000014048531,0.00008934351,0.000038605565,0.000020701487,0.0030559255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983156,0.000022055105,0.00000902237,0.000045129407,0.0000689955,0.000023111523],"domain_scores_gemma":[0.99987864,0.000034785,0.000016752998,0.000007202854,0.000056490542,0.000006051336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029506287,0.00058194937,0.0005144873,0.00027565207,0.00050754583,0.00093070796,0.0005336239,0.00063284277,0.0018649439],"category_scores_gemma":[0.00040342,0.00027265016,0.00030673633,0.0002169669,0.00022065453,0.00018449812,0.0002479257,0.0004573554,0.00029801045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010243624,0.00034564696,0.0013905936,0.00052666187,0.00011190747,0.00029924154,0.00014724612,0.70437896,0.14610071,0.0029994538,0.0016979675,0.14097735],"study_design_scores_gemma":[0.00008905878,0.0002286136,0.0009861967,0.000009518393,0.000043939755,0.000029057033,0.000012879994,0.98017395,0.016762665,0.00028059306,0.0013703331,0.000013145871],"about_ca_topic_score_codex":0.007676209,"about_ca_topic_score_gemma":0.006416072,"teacher_disagreement_score":0.007676209,"about_ca_system_score_codex":0.00078547245,"about_ca_system_score_gemma":0.00095108873,"threshold_uncertainty_score":0.015263081},"labels":[],"label_agreement":null},{"id":"W2897521632","doi":"10.1016/j.jprocont.2018.09.008","title":"Model-based optimal boundary control of selective catalytic reduction in diesel-powered vehicles","year":2018,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Stability and Controllability of Differential Equations","field":"Engineering","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 Alberta","funders":"","keywords":"Control theory (sociology); Riccati equation; Model order reduction; Decoupling (probability); Ode; Reduction (mathematics); Quadratic equation; State-space representation; Applied mathematics; Mathematics; Computer science; Partial differential equation; Control engineering; Mathematical analysis; Engineering; Control (management); Algorithm","score_opus":0.010245285069926254,"score_gpt":0.24462042947372425,"score_spread":0.234375144403798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897521632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3306663,0.0012081022,0.64106244,0.00086431194,0.00028059713,0.00009962687,0.00011092388,0.00040782083,0.025299866],"genre_scores_gemma":[0.9958211,0.00008121037,0.0024821986,0.000023149498,0.000008135001,0.0000251307,0.00001886735,0.000012253871,0.00152776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998379,0.000041590927,0.000005506323,0.000033845477,0.00003749892,0.000043666554],"domain_scores_gemma":[0.9996582,0.00017821409,0.00004612558,0.00001323066,0.00008475189,0.000019511217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048316695,0.0007256085,0.0013040327,0.00039774546,0.00044462358,0.0012690665,0.0007685621,0.0010162639,0.0014982925],"category_scores_gemma":[0.0010407754,0.00046444073,0.00055991154,0.00020985589,0.0009522426,0.0005176192,0.0010364727,0.00066974066,0.00015533197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010003799,0.00004053245,0.00013051373,0.000043349697,0.00001773718,0.000026535175,0.000030187588,0.9873525,0.0032186788,0.0043496005,0.00023894188,0.0044514663],"study_design_scores_gemma":[0.0000094161705,0.000020076395,0.000050069433,0.0000022208335,0.0000031938312,0.0000019831627,0.000005141687,0.99877614,0.00041086428,0.0006268022,0.00009141286,0.0000026261666],"about_ca_topic_score_codex":0.013377353,"about_ca_topic_score_gemma":0.007186181,"teacher_disagreement_score":0.013377353,"about_ca_system_score_codex":0.00084520597,"about_ca_system_score_gemma":0.00096979673,"threshold_uncertainty_score":0.02659893},"labels":[],"label_agreement":null},{"id":"W2930444869","doi":"10.1016/j.jprocont.2019.03.010","title":"Incipient sensor fault diagnosis in multimode processes using conditionally independent Bayesian learning based recursive transformed component statistical analysis","year":2019,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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 Alberta","funders":"National Natural Science Foundation of China","keywords":"Conditional independence; Fault detection and isolation; Independence (probability theory); Computer science; Bayes' theorem; Orthogonal transformation; Conditional probability; Independent component analysis; Bayesian probability; Fault (geology); Algorithm; Multi-mode optical fiber; Naive Bayes classifier; Transformation (genetics); Artificial intelligence; Mathematics; Statistics","score_opus":0.007255505712423664,"score_gpt":0.2495879921716101,"score_spread":0.24233248645918642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2930444869","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025052616,0.00006266997,0.97416455,0.00005070853,0.000012041484,0.000013689783,0.000022820625,0.00028104067,0.00033985858],"genre_scores_gemma":[0.82412344,0.00011955092,0.17387302,0.000053539847,0.000024027882,0.000048111502,0.00013367277,0.00008583971,0.0015387909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995036,0.00013569089,0.000032937594,0.00012543876,0.00015841288,0.000043941443],"domain_scores_gemma":[0.99869615,0.0008112789,0.00012764247,0.00009445532,0.00024356639,0.00002692952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007929897,0.000660812,0.0007541346,0.000630281,0.0003975834,0.0007410732,0.0006144547,0.0006569739,0.0009629829],"category_scores_gemma":[0.00397051,0.00035048538,0.00077678653,0.00042637184,0.0004943539,0.0010314062,0.00073879317,0.00095476076,0.00021693009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041409925,0.00018405692,0.004007069,0.00015679617,0.00020815707,0.00018477484,0.00020767325,0.7158732,0.01992124,0.021633036,0.000967742,0.23624213],"study_design_scores_gemma":[0.0000027219385,0.000011892398,0.00034850172,0.000002081219,0.000008560865,0.000011181126,0.0000030502558,0.9964605,0.0015208783,0.0015566769,0.00006933709,0.000004561512],"about_ca_topic_score_codex":0.0050141346,"about_ca_topic_score_gemma":0.007212454,"teacher_disagreement_score":0.0050141346,"about_ca_system_score_codex":0.000541867,"about_ca_system_score_gemma":0.0008849246,"threshold_uncertainty_score":0.00996989},"labels":[],"label_agreement":null},{"id":"W2969265519","doi":"10.1016/j.jprocont.2019.06.004","title":"Accelerated multiple alarm flood sequence alignment for abnormality pattern mining","year":2019,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":29,"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; National Natural Science Foundation of China","keywords":"ALARM; Pairwise comparison; Sequence (biology); Computer science; Multiple sequence alignment; Data mining; Flood myth; Computational complexity theory; Algorithm; Pattern recognition (psychology); Artificial intelligence; Sequence alignment; Engineering; Geography","score_opus":0.018337555645084453,"score_gpt":0.25619875170245365,"score_spread":0.2378611960573692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969265519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20984998,0.00083501305,0.7714513,0.0003131844,0.00018632931,0.0002916701,0.0034589658,0.011107038,0.0025064792],"genre_scores_gemma":[0.533418,0.0002883462,0.45593154,0.00012072421,0.00007245715,0.00018386172,0.007252055,0.00033243818,0.0024005582],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883384,0.00017124775,0.00011859,0.00036527333,0.00039202647,0.00011906982],"domain_scores_gemma":[0.99767405,0.00079750834,0.000353345,0.00030731512,0.0007567862,0.00011096725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008719487,0.00074404705,0.00074905663,0.00285434,0.0007117602,0.0009632789,0.0008742811,0.00084464595,0.0032048023],"category_scores_gemma":[0.004058433,0.00036461925,0.0006934098,0.0023638639,0.0002718942,0.0008388438,0.00090109423,0.0011184089,0.0016899905],"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":[0.0014985647,0.00056289585,0.02618108,0.0005171326,0.00032088,0.0012550541,0.0004370694,0.03069081,0.18592802,0.0036305152,0.012138971,0.7368391],"study_design_scores_gemma":[0.00012590003,0.00073123025,0.023428071,0.000050594183,0.00019241104,0.0018584437,0.00038295696,0.8815398,0.06710823,0.007656295,0.016844204,0.00008181392],"about_ca_topic_score_codex":0.0022767272,"about_ca_topic_score_gemma":0.0039434116,"teacher_disagreement_score":0.0032048023,"about_ca_system_score_codex":0.00028017172,"about_ca_system_score_gemma":0.0013846542,"threshold_uncertainty_score":0.010721087},"labels":[],"label_agreement":null},{"id":"W2969278551","doi":"10.1016/j.jprocont.2019.07.006","title":"A fault detection and isolation technique using nonlinear support vectors dichotomizing multi-class parity space residuals","year":2019,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":19,"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":"Fault detection and isolation; Support vector machine; Residual; Nonlinear system; Mathematics; Algorithm; Probability distribution; Mathematical optimization; Computer science; Artificial intelligence; Statistics","score_opus":0.00859747771665742,"score_gpt":0.25089082715683464,"score_spread":0.24229334944017722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969278551","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054408286,0.00015214563,0.94344425,0.0001315261,0.00007472842,0.000037116588,0.000026897744,0.00063202385,0.001093028],"genre_scores_gemma":[0.78958625,0.00011420027,0.20806897,0.00009763894,0.000058504622,0.000046680976,0.000095079995,0.000038963524,0.0018937482],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999785,0.000039393708,0.0000162766,0.00004883867,0.000082761144,0.000027762677],"domain_scores_gemma":[0.9995252,0.0001883825,0.00006588977,0.00006188544,0.00013385164,0.000024902743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003609897,0.0005557066,0.0006284907,0.00045740898,0.0003268353,0.0005369884,0.00045141045,0.0006384553,0.0017233193],"category_scores_gemma":[0.0011024389,0.00017677058,0.00035941834,0.00023020385,0.00027894086,0.0006588673,0.0006208009,0.00095942506,0.00045367196],"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":[0.001515806,0.00023925725,0.0015415556,0.00024532474,0.00009766844,0.00037842657,0.00017526351,0.024939893,0.36887217,0.008238779,0.0016361855,0.59211975],"study_design_scores_gemma":[0.000073590316,0.0007011671,0.0019547613,0.000019851344,0.000054015505,0.00039594885,0.000061971026,0.9089082,0.08334743,0.003058553,0.0013923126,0.00003221945],"about_ca_topic_score_codex":0.00036224283,"about_ca_topic_score_gemma":0.0006082237,"teacher_disagreement_score":0.0017233193,"about_ca_system_score_codex":0.00015375727,"about_ca_system_score_gemma":0.00035338115,"threshold_uncertainty_score":0.0057650805},"labels":[],"label_agreement":null},{"id":"W2980876349","doi":"10.1016/j.jprocont.2019.09.008","title":"An optimal control strategy for a heat pump in an integrated solar thermal system","year":2019,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Solar Thermal and Photovoltaic Systems","field":"Energy","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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feed forward; Controller (irrigation); Control theory (sociology); Control engineering; Optimal control; Computer science; Thermal; Engineering; Control (management); Mathematics; Artificial intelligence","score_opus":0.013498293413047667,"score_gpt":0.26304737400682493,"score_spread":0.24954908059377726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980876349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08611188,0.00067660026,0.8893121,0.00072449254,0.00032787482,0.00019642607,0.000057646535,0.00032380197,0.02226913],"genre_scores_gemma":[0.9852401,0.00015373365,0.010939549,0.000075681426,0.000035438443,0.000079658814,0.000012958647,0.000012771278,0.0034500742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997124,0.00006864745,0.000016961807,0.00007596754,0.000073111216,0.000052940457],"domain_scores_gemma":[0.9998086,0.00006063173,0.00002602119,0.000008450066,0.000079444384,0.000016883147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000531578,0.000975257,0.0011003882,0.00034843566,0.00094984897,0.001662727,0.00071952405,0.001311536,0.0032041718],"category_scores_gemma":[0.0006096381,0.00053178024,0.0005155695,0.00032372616,0.0006653215,0.00069067837,0.0009131804,0.00071487203,0.00026107382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037907457,0.0001787743,0.00045288444,0.00032062066,0.000102992795,0.0003299331,0.00018266455,0.9058883,0.029332813,0.014912366,0.0016735542,0.04624595],"study_design_scores_gemma":[0.000028439636,0.0001348145,0.00015570599,0.0000073204997,0.00002085792,0.00001409698,0.000019086696,0.9971193,0.0011749865,0.00088757614,0.00042913176,0.00000871686],"about_ca_topic_score_codex":0.009771042,"about_ca_topic_score_gemma":0.00809399,"teacher_disagreement_score":0.009771042,"about_ca_system_score_codex":0.0007489046,"about_ca_system_score_gemma":0.00095589174,"threshold_uncertainty_score":0.019428372},"labels":[],"label_agreement":null},{"id":"W2998951169","doi":"10.1016/j.jprocont.2020.06.006","title":"A hybrid Gaussian process approach to robust economic model predictive control","year":2020,"lang":"en","type":"preprint","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Model predictive control; Control theory (sociology); Computer science; Forgetting; Controller (irrigation); Nonlinear system; Process (computing); Kernel (algebra); Autoregressive model; Artificial intelligence; Machine learning; Control (management); Econometrics; Mathematics","score_opus":0.012824186606707613,"score_gpt":0.23021862483125474,"score_spread":0.21739443822454713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998951169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004739446,0.0003635941,0.99148995,0.0002483683,0.00009873163,0.000014817198,0.000046388184,0.000086079875,0.002912628],"genre_scores_gemma":[0.84674954,0.0011373821,0.13844028,0.00023175734,0.0004630881,0.0001893763,0.00019171152,0.00016566915,0.012431243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994742,0.00022023843,0.000025910678,0.00008898972,0.00014529978,0.000045310153],"domain_scores_gemma":[0.9988857,0.0007314183,0.00008541761,0.00006980597,0.00018990437,0.000037747097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001371159,0.00086262426,0.0012939207,0.000777339,0.00035787703,0.0014678618,0.0016688767,0.0013660854,0.0028889724],"category_scores_gemma":[0.0039152624,0.00059019716,0.0008856273,0.0009510398,0.0011209672,0.0015269585,0.0016588854,0.0014888403,0.0003851771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040776322,0.00003157231,0.00014716448,0.00005380473,0.000077274555,0.000067028544,0.000030947474,0.84419084,0.00057793595,0.13917065,0.00082714634,0.014784928],"study_design_scores_gemma":[0.0000036114614,0.000006717826,0.000024077499,0.0000017696289,0.0000040181662,0.00000297003,0.0000015045624,0.98014474,0.000050540828,0.019530248,0.00022629711,0.0000035197365],"about_ca_topic_score_codex":0.00512652,"about_ca_topic_score_gemma":0.0030513434,"teacher_disagreement_score":0.00512652,"about_ca_system_score_codex":0.00069769094,"about_ca_system_score_gemma":0.0008792069,"threshold_uncertainty_score":0.010193348},"labels":[],"label_agreement":null},{"id":"W3020809103","doi":"10.1016/j.jprocont.2020.03.009","title":"Production scheduling in dynamic real-time optimization with closed-loop prediction","year":2020,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":37,"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":"Model predictive control; Scheduling (production processes); MIMO; Mathematical optimization; Computer science; Control theory (sociology); Control engineering; Engineering; Control (management); Mathematics","score_opus":0.0038902492972064944,"score_gpt":0.20050232037588897,"score_spread":0.19661207107868248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020809103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10945729,0.00055521086,0.8855057,0.00037120178,0.00013603769,0.000066079745,0.000076679295,0.0002933492,0.0035384533],"genre_scores_gemma":[0.9794201,0.00010694306,0.018989911,0.000035138837,0.000034851593,0.00004475251,0.000039602874,0.000037313428,0.0012913803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991485,0.00033534414,0.0000334325,0.0002017065,0.00014740761,0.00013348422],"domain_scores_gemma":[0.9977093,0.0017142518,0.00022210654,0.00007497765,0.00020051633,0.00007890873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002004635,0.0007547019,0.0015599085,0.0003732072,0.0005143765,0.0014483546,0.0010523319,0.0009369338,0.0014610627],"category_scores_gemma":[0.0047804443,0.0007514703,0.00041512775,0.00062498084,0.0009958887,0.0013831998,0.0007641554,0.0012026877,0.00018063583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012384383,0.000043351843,0.00013926288,0.000024960793,0.000011203193,0.000022057715,0.000015855636,0.99104285,0.00048266383,0.0016518079,0.00013952849,0.0063025397],"study_design_scores_gemma":[0.0000061179044,0.000013395549,0.000037664533,0.0000010087148,0.0000016192416,0.0000018677997,0.0000015171033,0.9992724,0.00010871189,0.0005260459,0.00002819246,0.0000015008621],"about_ca_topic_score_codex":0.008714737,"about_ca_topic_score_gemma":0.004474024,"teacher_disagreement_score":0.008714737,"about_ca_system_score_codex":0.0009559637,"about_ca_system_score_gemma":0.0016703375,"threshold_uncertainty_score":0.017328024},"labels":[],"label_agreement":null},{"id":"W3024541755","doi":"10.1016/j.jprocont.2020.04.001","title":"Multiscale model predictive control of battery systems for frequency regulation markets using physics-based models","year":2020,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Battery Technologies Research","field":"Engineering","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 British Columbia","funders":"Johnson Controls; U.S. Department of Energy; Pacific Northwest National Laboratory; National Science Foundation","keywords":"Model predictive control; Term (time); Computer science; Battery (electricity); Fidelity; Function (biology); Asset (computer security); Exploit; Energy (signal processing); Electricity; Control engineering; Control theory (sociology); Control (management); Engineering; Power (physics); Artificial intelligence","score_opus":0.03409760507512777,"score_gpt":0.2771710583083815,"score_spread":0.24307345323325374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024541755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1644561,0.0006363307,0.8168055,0.00089154654,0.00018388254,0.00004531675,0.00011693481,0.0002850278,0.016579295],"genre_scores_gemma":[0.9929998,0.00013906533,0.0052183955,0.00003606339,0.000027798003,0.00002952714,0.000026549258,0.000016317294,0.0015064349],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999088,0.000024550174,0.000003937644,0.00002142062,0.000026018535,0.000015292533],"domain_scores_gemma":[0.9997192,0.0001479333,0.00004503526,0.000019525101,0.000047663074,0.00002062785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027713142,0.00039335294,0.00082533696,0.00027153257,0.000369099,0.0008715309,0.00068589125,0.0007366405,0.0020593235],"category_scores_gemma":[0.001121122,0.00028542295,0.000587265,0.00021858234,0.0005794551,0.00077513355,0.00076871034,0.0007552667,0.00014315432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020826134,0.000022064056,0.00017475498,0.00002325083,0.000017610504,0.000027521975,0.00002096983,0.97909284,0.0015092065,0.0149631975,0.0003173903,0.003810281],"study_design_scores_gemma":[0.0000012698167,0.0000036118317,0.000035989706,7.6211563e-7,0.0000012999011,0.0000013086728,0.0000013519231,0.99861085,0.000044275228,0.0012537723,0.000044205404,0.0000012258347],"about_ca_topic_score_codex":0.006127928,"about_ca_topic_score_gemma":0.0048381975,"teacher_disagreement_score":0.006127928,"about_ca_system_score_codex":0.0005690999,"about_ca_system_score_gemma":0.0005808223,"threshold_uncertainty_score":0.012184501},"labels":[],"label_agreement":null},{"id":"W3035328458","doi":"10.1016/j.jprocont.2020.05.012","title":"Output-relevant Variational autoencoder for Just-in-time soft sensor modeling with missing data","year":2020,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoencoder; Missing data; Divergence (linguistics); Computer science; Pattern recognition (psychology); Data mining; Soft sensor; Process (computing); Gaussian process; Kriging; Set (abstract data type); Artificial intelligence; Sample (material); Data set; Data modeling; Gaussian; Artificial neural network; Machine learning","score_opus":0.031508556297041836,"score_gpt":0.2571972532953891,"score_spread":0.2256886969983473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035328458","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007031589,0.0002452782,0.9918099,0.00015214806,0.00004873636,0.000014790349,0.00006471234,0.0001785135,0.0004544152],"genre_scores_gemma":[0.7757719,0.00077706424,0.21326125,0.00036383938,0.00015992789,0.00020721286,0.00077681453,0.00028739523,0.008394623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995586,0.00013164006,0.00003114512,0.000113669295,0.00009447749,0.00007041604],"domain_scores_gemma":[0.99840635,0.0010788264,0.000088967914,0.00013613615,0.00023156349,0.000058035257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015081908,0.0013208644,0.0016580572,0.0003194386,0.0003602363,0.0009193627,0.0015219975,0.0018788797,0.0019984941],"category_scores_gemma":[0.0045171496,0.0010575101,0.0010116695,0.00047105175,0.0011188443,0.0015581293,0.0018130075,0.00267966,0.0004924158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010135668,0.000045986126,0.00028136623,0.00013561173,0.00008253957,0.0000759015,0.000070424794,0.94078696,0.002747796,0.01355661,0.0011076084,0.041007683],"study_design_scores_gemma":[0.0000017240766,0.000005648384,0.000025173034,0.0000028789427,0.0000031854352,0.0000042626607,0.0000014168835,0.9982028,0.00018248681,0.001500159,0.00006772759,0.0000026216146],"about_ca_topic_score_codex":0.008080966,"about_ca_topic_score_gemma":0.008934146,"teacher_disagreement_score":0.008080966,"about_ca_system_score_codex":0.00072988303,"about_ca_system_score_gemma":0.0017816289,"threshold_uncertainty_score":0.016067863},"labels":[],"label_agreement":null},{"id":"W3043820883","doi":"10.1016/j.jprocont.2020.06.013","title":"Recursive cointegration analytics for adaptive monitoring of nonstationary industrial processes with both static and dynamic variations","year":2020,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":84,"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":"Science and Technology Department of Zhejiang Province; National Natural Science Foundation of China","keywords":"Cointegration; Process (computing); Representation (politics); Computer science; Scheme (mathematics); Computation; Algorithm; Machine learning; Mathematics","score_opus":0.020051035593724502,"score_gpt":0.24645775909721368,"score_spread":0.22640672350348917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043820883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1332142,0.00040687574,0.864154,0.0001399436,0.000020819156,0.000024803228,0.00011257241,0.0011286776,0.000798097],"genre_scores_gemma":[0.9579795,0.00016894357,0.040796094,0.000023054406,0.000031311472,0.000027066517,0.00017185233,0.000054193755,0.0007479683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995716,0.00013575112,0.000027648883,0.000102604885,0.00010610692,0.00005632894],"domain_scores_gemma":[0.9982191,0.0012224026,0.00019915988,0.00014513642,0.00017457594,0.000039572504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070532993,0.000709691,0.0008826501,0.0010374166,0.00031448863,0.0008230111,0.0005528698,0.0005337969,0.00076075655],"category_scores_gemma":[0.0048453105,0.00032557952,0.00035693473,0.0009244829,0.0003615344,0.0011413008,0.00068125775,0.0008575832,0.00021256485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004084549,0.00017601921,0.011090272,0.00012421474,0.00015370233,0.00022156663,0.00024418533,0.6503285,0.02250996,0.019154958,0.0014501893,0.29413795],"study_design_scores_gemma":[0.000001505157,0.000012463473,0.00066966785,0.0000018824245,0.00000385077,0.000008569925,0.0000054917864,0.99613285,0.00087090844,0.002215868,0.00007404592,0.0000029738317],"about_ca_topic_score_codex":0.0036420673,"about_ca_topic_score_gemma":0.004525649,"teacher_disagreement_score":0.0036420673,"about_ca_system_score_codex":0.00034493735,"about_ca_system_score_gemma":0.00059274136,"threshold_uncertainty_score":0.0072417855},"labels":[],"label_agreement":null},{"id":"W3105721481","doi":"10.1016/j.jprocont.2020.11.001","title":"An intelligent decision-making strategy based on the forecast of abnormal operating mode for iron ore sintering process","year":2020,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Iron and Steelmaking Processes","field":"Engineering","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":"University of Alberta","funders":"Higher Education Discipline Innovation Project; China Scholarship Council; China University of Geosciences, Wuhan; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Mode (computer interface); Process (computing); Sintering; Fuzzy logic; Fuzzy rule; Computer science; Engineering; Process engineering; Fuzzy set; Artificial intelligence; Metallurgy; Materials science","score_opus":0.022133479896343074,"score_gpt":0.2985515993827225,"score_spread":0.27641811948637945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105721481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23781139,0.0007481876,0.75537807,0.00055918016,0.00020866739,0.00010800856,0.00008281129,0.00064495404,0.004458747],"genre_scores_gemma":[0.98881906,0.00008800191,0.010457513,0.000044101755,0.000028054497,0.00003235452,0.000035492223,0.000007557567,0.00048790616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954945,0.000072000454,0.0000413639,0.00014210604,0.000097109216,0.00009801526],"domain_scores_gemma":[0.9993174,0.00028755932,0.0001036914,0.000020493893,0.00020938349,0.00006148992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010163796,0.0008606566,0.0011830183,0.00073666574,0.0007032918,0.0013931202,0.00082455244,0.0010583708,0.0011195105],"category_scores_gemma":[0.0017119185,0.00042485466,0.0006625544,0.00042471633,0.00041487123,0.0008893713,0.00059109565,0.0006747314,0.00013469909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007891733,0.00023655855,0.0040314337,0.00015166352,0.00017401678,0.00033923023,0.00022299665,0.8870686,0.010485638,0.0034521504,0.00132011,0.09172849],"study_design_scores_gemma":[0.000015838305,0.00006379027,0.00042598654,0.0000038974044,0.000025510522,0.00001098354,0.000013965928,0.99821293,0.0006362829,0.00051038177,0.00007217928,0.000008407208],"about_ca_topic_score_codex":0.009149957,"about_ca_topic_score_gemma":0.0058600605,"teacher_disagreement_score":0.009149957,"about_ca_system_score_codex":0.0006999326,"about_ca_system_score_gemma":0.0011853979,"threshold_uncertainty_score":0.018193424},"labels":[],"label_agreement":null},{"id":"W3113795080","doi":"10.1016/j.jprocont.2020.10.002","title":"Performance assessment of multivariate process using time delay matrix","year":2020,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Multivariate statistics; Benchmark (surveying); Variance (accounting); Process (computing); Interactor; Multivariate analysis of variance; Matrix (chemical analysis); Computer science; Multivariate analysis; Markov chain; Algorithm; Mathematical optimization; Data mining; Mathematics; Machine learning","score_opus":0.01044803205204205,"score_gpt":0.28114527167657366,"score_spread":0.2706972396245316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113795080","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41030872,0.00046703313,0.5867864,0.000188103,0.00005477279,0.000037326838,0.00007874835,0.00031568902,0.0017631463],"genre_scores_gemma":[0.98818064,0.00011362633,0.011147223,0.000008105448,0.000013745686,0.000010830493,0.000046850488,0.000010147504,0.00046887586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992471,0.00026278512,0.000034573713,0.00013540016,0.00023010146,0.00009005001],"domain_scores_gemma":[0.997497,0.0015383447,0.00022771844,0.00011344264,0.0005548542,0.00006861733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017826792,0.00072944805,0.000592265,0.00051503687,0.00030860657,0.0008538101,0.0003925126,0.00068058126,0.0007400449],"category_scores_gemma":[0.004071804,0.00016240793,0.00041770682,0.0005887232,0.0003197546,0.0008325462,0.00054223975,0.0005395344,0.00010517552],"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":[0.0009127434,0.00012120154,0.0030251301,0.00014616211,0.000098242745,0.00008641858,0.000054099946,0.9271725,0.022360468,0.0045751478,0.0001985728,0.041249257],"study_design_scores_gemma":[0.000004446747,0.00012702693,0.00064678944,0.0000013230659,0.000010008914,0.000009638145,0.000005310743,0.99598545,0.0028626563,0.00030098876,0.00003941909,0.0000068826935],"about_ca_topic_score_codex":0.0040446455,"about_ca_topic_score_gemma":0.0018160233,"teacher_disagreement_score":0.0040446455,"about_ca_system_score_codex":0.0005208523,"about_ca_system_score_gemma":0.0006688636,"threshold_uncertainty_score":0.0094278455},"labels":[],"label_agreement":null},{"id":"W3135105311","doi":"10.1016/j.jprocont.2021.01.008","title":"Predictive warning system for nonlinear process plants","year":2021,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control theory (sociology); Process (computing); Limit (mathematics); Continuous stirred-tank reactor; Model predictive control; Nonlinear system; Controller (irrigation); Process safety; Sequence (biology); Process state; Warning system; Mode (computer interface); Engineering; Control engineering; Computer science; Work in process; Control (management); Mathematics; Artificial intelligence","score_opus":0.006036639868941367,"score_gpt":0.23386285833013076,"score_spread":0.2278262184611894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135105311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14998282,0.00052283786,0.8390771,0.00072525535,0.00043685862,0.00009601211,0.000121389545,0.0027669612,0.0062707225],"genre_scores_gemma":[0.98483074,0.00009789798,0.011549297,0.000069242866,0.000033295408,0.00003339118,0.000048543225,0.000014334796,0.0033232674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998405,0.000030310226,0.0000104090295,0.000031270167,0.00006489192,0.00002267219],"domain_scores_gemma":[0.9995326,0.00019471042,0.000054855165,0.000029592451,0.0001703719,0.000017929397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039548601,0.0003882574,0.0004291528,0.00024591587,0.00040218607,0.00048633374,0.00059105427,0.00066968065,0.0023588839],"category_scores_gemma":[0.0014689512,0.00016969061,0.00013021062,0.0001412262,0.00022425382,0.00036383732,0.0005090684,0.00076130475,0.00029418003],"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":[0.0020396756,0.00034284225,0.002109637,0.0006121828,0.00007112475,0.0008886576,0.0004305291,0.43178308,0.09683029,0.010525207,0.0094261365,0.44494057],"study_design_scores_gemma":[0.000049479582,0.00017363013,0.0005956984,0.000012255526,0.000022865526,0.000061491126,0.000009876258,0.9916339,0.005569884,0.0010460522,0.0008120337,0.000012863049],"about_ca_topic_score_codex":0.0026537902,"about_ca_topic_score_gemma":0.0032175486,"teacher_disagreement_score":0.0026537902,"about_ca_system_score_codex":0.00028831602,"about_ca_system_score_gemma":0.0005769064,"threshold_uncertainty_score":0.007891238},"labels":[],"label_agreement":null},{"id":"W3155950194","doi":"10.1016/j.jprocont.2021.03.008","title":"State estimation-based control of COVID-19 epidemic before and after vaccine development","year":2021,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":34,"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":"Coronavirus disease 2019 (COVID-19); Estimation; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Control (management); State (computer science); Virology; Computer science; Medicine; Econometrics; Mathematics; Artificial intelligence; Engineering; Outbreak; Algorithm; Internal medicine","score_opus":0.0645829701956035,"score_gpt":0.39058950232773754,"score_spread":0.32600653213213404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155950194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20962708,0.00059174455,0.7828323,0.0011074232,0.00016956731,0.00010367221,0.00018230612,0.00037390454,0.0050119204],"genre_scores_gemma":[0.9914136,0.000089599285,0.006760098,0.000042821954,0.000023198178,0.00003845713,0.000086289314,0.000012593365,0.0015334275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990069,0.00032793175,0.000038633152,0.0002638285,0.000117295116,0.00024547562],"domain_scores_gemma":[0.9922293,0.0057721627,0.0006778923,0.00019278552,0.00094538997,0.00018240212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035359531,0.0008527815,0.0013308896,0.0006290226,0.0005371878,0.0016992537,0.0010958423,0.0010056989,0.0021209074],"category_scores_gemma":[0.0122895,0.0004469497,0.0005876861,0.00035453183,0.0009206004,0.0012762054,0.0013891284,0.0015374187,0.0001824494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004995458,0.00013362209,0.0022752832,0.00009444777,0.00007655846,0.00004800373,0.00008535926,0.95849806,0.0016131505,0.0138618555,0.0005899795,0.022224251],"study_design_scores_gemma":[0.000014284008,0.000055724457,0.0003871729,0.000004486282,0.000013564851,0.0000033737836,0.000011760803,0.9974439,0.0003482103,0.0016426862,0.00007004747,0.0000047643043],"about_ca_topic_score_codex":0.016059214,"about_ca_topic_score_gemma":0.0071583306,"teacher_disagreement_score":0.016059214,"about_ca_system_score_codex":0.0010862941,"about_ca_system_score_gemma":0.0021036963,"threshold_uncertainty_score":0.03193146},"labels":[],"label_agreement":null},{"id":"W3176644733","doi":"10.1016/j.jprocont.2021.06.004","title":"Online reinforcement learning for a continuous space system with experimental validation","year":2021,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":54,"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":"Reinforcement learning; Computer science; State space; Artificial intelligence; Machine learning; Heuristic; Trajectory; Asynchronous communication; Mathematics","score_opus":0.012318515279714703,"score_gpt":0.2662024340434771,"score_spread":0.2538839187637624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176644733","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":[0.5252957,0.00028330993,0.46838427,0.0004658852,0.00012289667,0.00030762973,0.00022641267,0.0009724162,0.003941572],"genre_scores_gemma":[0.98866314,0.00001483326,0.010620906,0.000014776065,0.000002767387,0.00007356402,0.000046952642,0.000012858903,0.00055019226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893254,0.00044946145,0.000072549854,0.00016413507,0.00024989643,0.00013137807],"domain_scores_gemma":[0.9877286,0.0082875425,0.0008822093,0.0009422926,0.0019139482,0.00024548842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004842431,0.00080330507,0.0007245813,0.00045585592,0.00056995486,0.0008000032,0.001171312,0.0013836359,0.0029066245],"category_scores_gemma":[0.013081998,0.0003452593,0.00049011706,0.00021755605,0.0015526467,0.0008890641,0.001415336,0.0012871486,0.00031256606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001016194,0.00029568345,0.0015375308,0.00027215955,0.00006545417,0.00012628792,0.0001592234,0.9661995,0.009084319,0.0036401248,0.00033089236,0.017272662],"study_design_scores_gemma":[0.00006150654,0.00021108653,0.00045550163,0.000009689777,0.000009290536,0.000013824704,0.000009104343,0.99514353,0.0033202195,0.00064643746,0.000108442604,0.0000114016675],"about_ca_topic_score_codex":0.009307378,"about_ca_topic_score_gemma":0.0048832474,"teacher_disagreement_score":0.009307378,"about_ca_system_score_codex":0.0010324199,"about_ca_system_score_gemma":0.0014221199,"threshold_uncertainty_score":0.025609553},"labels":[],"label_agreement":null},{"id":"W3200834021","doi":"10.1016/j.jprocont.2021.08.016","title":"Observer-based control of vertical penetration rate in rotary drilling systems","year":2021,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":19,"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; Western University","keywords":"Control theory (sociology); Torque; Drilling; Angular velocity; Engineering; Rotational speed; Penetration (warfare); Simulation; Computer science; Control engineering; Control (management); Mechanical engineering; Physics; Artificial intelligence","score_opus":0.00734864508036046,"score_gpt":0.2029563505890001,"score_spread":0.19560770550863965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200834021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08768012,0.00036200037,0.90773,0.0001434015,0.00011610461,0.00003636285,0.00003864494,0.00030543667,0.0035880203],"genre_scores_gemma":[0.99194944,0.000096405514,0.0068562515,0.000013784406,0.0000120179175,0.000020731084,0.000021274991,0.0000109429075,0.0010190418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997476,0.00006801411,0.000019896263,0.000047873342,0.00007722266,0.000039385173],"domain_scores_gemma":[0.99916494,0.00040531135,0.00012752484,0.000035504214,0.00024633246,0.00002042759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006494667,0.0004354176,0.00063665793,0.00018223311,0.00019516695,0.0008990005,0.00045006047,0.00054714776,0.0010920141],"category_scores_gemma":[0.0019256547,0.00028828744,0.0002759363,0.00016285453,0.00037498842,0.00051026646,0.00048702615,0.0006533714,0.00018725511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007601476,0.00009393191,0.001083112,0.00036744523,0.000063868974,0.00013688156,0.00024765942,0.85406506,0.047728658,0.006650656,0.00091921387,0.08788327],"study_design_scores_gemma":[0.000017612878,0.00008159077,0.00028302503,0.0000058751457,0.0000071189875,0.0000084602825,0.000005699357,0.99668306,0.0023783522,0.00030504135,0.00021805751,0.000006179259],"about_ca_topic_score_codex":0.0045407224,"about_ca_topic_score_gemma":0.0036149756,"teacher_disagreement_score":0.0045407224,"about_ca_system_score_codex":0.00030090826,"about_ca_system_score_gemma":0.00039989088,"threshold_uncertainty_score":0.009028554},"labels":[],"label_agreement":null},{"id":"W3216607771","doi":"10.1016/j.jprocont.2021.11.001","title":"Adversarial smoothing tri-regression for robust semi-supervised industrial soft sensor","year":2021,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"State Key Laboratory of Industrial Control Technology; China National Funds for Distinguished Young Scientists; Zhejiang University; National Natural Science Foundation of China","keywords":"Smoothing; Soft sensor; Regularization (linguistics); Machine learning; Artificial intelligence; Regression; Computer science; Smoothness; Mathematics; Pattern recognition (psychology); Data mining; Statistics; Process (computing)","score_opus":0.02271950539412964,"score_gpt":0.2438712133421851,"score_spread":0.22115170794805544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216607771","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048259115,0.00019076784,0.99356997,0.000077810415,0.000027261196,0.000020824407,0.00006087435,0.0008316353,0.00039489038],"genre_scores_gemma":[0.6794904,0.00048080142,0.30652034,0.0003245694,0.00016723057,0.0002989723,0.0012649309,0.0006904682,0.010762165],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989662,0.00032053547,0.000056157503,0.00030815307,0.00022791687,0.00012098515],"domain_scores_gemma":[0.9970209,0.0017715086,0.00031215145,0.00040130553,0.0003951091,0.00009913537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021653045,0.0012519313,0.002015864,0.0006505279,0.00042910824,0.000807137,0.0019493568,0.0018229532,0.0026524742],"category_scores_gemma":[0.005316939,0.0009226446,0.0013119034,0.00083757145,0.0009959234,0.001225142,0.002061416,0.0028490194,0.0013180063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019640007,0.000095756004,0.00035345776,0.00014029763,0.0000997058,0.000062471525,0.00006253346,0.8591393,0.004296452,0.00496397,0.0025101395,0.12807956],"study_design_scores_gemma":[0.0000014226686,0.000010514685,0.00004261409,0.000002355474,0.0000022330464,0.0000042759075,0.0000014731692,0.9986833,0.00030965934,0.00084102567,0.00009868156,0.000002353377],"about_ca_topic_score_codex":0.0047235806,"about_ca_topic_score_gemma":0.005321937,"teacher_disagreement_score":0.0047235806,"about_ca_system_score_codex":0.0006468731,"about_ca_system_score_gemma":0.0012951826,"threshold_uncertainty_score":0.011451364},"labels":[],"label_agreement":null},{"id":"W4200550364","doi":"10.1016/j.jprocont.2021.11.003","title":"Simultaneous and sequential state and parameter estimation using receding-horizon nonlinear Kalman filter","year":2021,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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 Alberta","funders":"","keywords":"Estimator; Kalman filter; Smoothing; Computation; Control theory (sociology); Extended Kalman filter; Mathematics; Estimation theory; Nonlinear system; Maximum a posteriori estimation; Optimal estimation; Algorithm; Mathematical optimization; Computer science; Statistics; Maximum likelihood; Artificial intelligence","score_opus":0.01066234252281356,"score_gpt":0.2548774779170106,"score_spread":0.24421513539419704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200550364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028734133,0.00028099655,0.9694993,0.000058511756,0.00006383861,0.000024797702,0.00005455612,0.00040471452,0.00087922247],"genre_scores_gemma":[0.8178878,0.0003469086,0.1785622,0.00004835944,0.00006249362,0.00007436164,0.0002758518,0.000043548473,0.0026984813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932015,0.00008908978,0.000062027764,0.00020436659,0.00025233452,0.00007206692],"domain_scores_gemma":[0.99882656,0.00050269783,0.00017366397,0.00015365319,0.0003077083,0.000035717178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097283866,0.0007836385,0.001264555,0.0004642847,0.000581986,0.0007388136,0.000729071,0.0006913524,0.0009862512],"category_scores_gemma":[0.002782118,0.00074765313,0.00074439385,0.00056487956,0.00046182706,0.0014496993,0.0009079454,0.0010160988,0.00037130312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007836009,0.0001424476,0.004535597,0.00034658355,0.0003078929,0.00026505525,0.00032906444,0.59823686,0.030690618,0.0051946472,0.0012270662,0.3579405],"study_design_scores_gemma":[0.000017227107,0.000069466965,0.0009914188,0.000008441867,0.00003920249,0.00005043247,0.000015049255,0.99240935,0.0043735104,0.0014627102,0.00054464105,0.00001860674],"about_ca_topic_score_codex":0.01794192,"about_ca_topic_score_gemma":0.017230758,"teacher_disagreement_score":0.01794192,"about_ca_system_score_codex":0.0003959602,"about_ca_system_score_gemma":0.0017125306,"threshold_uncertainty_score":0.03567499},"labels":[],"label_agreement":null},{"id":"W4224284996","doi":"10.1016/j.jprocont.2022.03.009","title":"Dynamic real-time optimization for nonlinear systems with Lyapunov stabilizing MPC","year":2022,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"McMaster University","funders":"","keywords":"Control theory (sociology); Nonlinear system; Model predictive control; Controller (irrigation); Trajectory; Lyapunov function; Computer science; Set (abstract data type); Optimization problem; Control system; Control engineering; Mathematical optimization; Control (management); Mathematics; Engineering","score_opus":0.0033831411485080237,"score_gpt":0.21163602734600576,"score_spread":0.20825288619749774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224284996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027084582,0.0006368942,0.9608892,0.0003732409,0.000074983065,0.00005085287,0.000037266473,0.00024476185,0.010608301],"genre_scores_gemma":[0.9509692,0.0002722194,0.042573676,0.00007069766,0.00005203699,0.00010644032,0.000058429152,0.000112622554,0.005784627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996606,0.000142427,0.000012681772,0.000048479287,0.00009401298,0.000041822022],"domain_scores_gemma":[0.9995709,0.00024269136,0.000056263132,0.000027808317,0.00008587755,0.00001649025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007553332,0.0010814108,0.0008527203,0.00039170854,0.00036432286,0.00097063475,0.0006088597,0.0006828806,0.002266551],"category_scores_gemma":[0.0019793173,0.0004976231,0.00045555513,0.00040600414,0.00060780125,0.00071703386,0.0009811514,0.00085278763,0.00032524014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004510226,0.000021939326,0.00007961421,0.00005430983,0.000019707231,0.00003156062,0.000026820882,0.9815259,0.0011714355,0.0067370874,0.0003773403,0.009909211],"study_design_scores_gemma":[0.0000021799194,0.00000983575,0.000021462385,0.0000015501921,0.0000015484677,0.0000019429067,0.000002013594,0.9989085,0.00013028468,0.0008220076,0.00009744082,0.0000012452984],"about_ca_topic_score_codex":0.005242625,"about_ca_topic_score_gemma":0.0033949018,"teacher_disagreement_score":0.005242625,"about_ca_system_score_codex":0.00060899433,"about_ca_system_score_gemma":0.00086642796,"threshold_uncertainty_score":0.010424197},"labels":[],"label_agreement":null},{"id":"W4237298349","doi":"10.1016/j.jprocont.2008.06.012","title":"Editorial","year":2008,"lang":"ru","type":"editorial","venue":"Journal of Process Control","topic":"","field":"","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":"Computer science","score_opus":0.0059399127831547185,"score_gpt":0.27199861869411524,"score_spread":0.26605870591096054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237298349","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005876695,0.002991781,0.00013251309,0.031106053,0.96094346,0.000035383375,0.000046662957,0.00010077036,0.0045846663],"genre_scores_gemma":[0.00095201214,0.002777467,0.00016469268,0.018919779,0.94838357,0.000044738088,0.000065585926,0.00007099968,0.02862124],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99462044,0.0009029335,0.00053965725,0.0006847026,0.0027333687,0.00051891594],"domain_scores_gemma":[0.9749299,0.0051283548,0.0020691685,0.0014657198,0.013045989,0.0033607855],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005526321,0.0033626761,0.0029643464,0.004842258,0.0027911828,0.0068232664,0.00331424,0.012887559,0.040365458],"category_scores_gemma":[0.031956643,0.0010471226,0.002104956,0.0014758518,0.0019445718,0.0029818548,0.0015461271,0.012038672,0.0272334],"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":[0.000020180578,0.000009786379,0.000012513246,0.00006633782,0.0000067923056,0.00007295094,0.000003991856,0.000008394228,0.000027160622,0.00013317938,0.99547195,0.0041668043],"study_design_scores_gemma":[0.0000666798,0.000028861714,0.00021542328,0.00022526938,0.00003763635,0.00024071796,0.00002185001,0.00010768582,0.00016445153,0.000557311,0.9983209,0.000013171997],"about_ca_topic_score_codex":0.0012506227,"about_ca_topic_score_gemma":0.0022524118,"teacher_disagreement_score":0.95963454,"about_ca_system_score_codex":0.0025890023,"about_ca_system_score_gemma":0.0029808953,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4293150378","doi":"10.1016/j.jprocont.2022.02.002","title":"Real-time dynamic prediction model of carbon efficiency with working condition identification in sintering process","year":2022,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Iron and Steelmaking Processes","field":"Engineering","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 Alberta","funders":"","keywords":"Process (computing); Computer science; Cluster analysis; Process engineering; Artificial intelligence; Engineering","score_opus":0.005578366419844457,"score_gpt":0.22518089669865426,"score_spread":0.2196025302788098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293150378","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":[0.7112052,0.0004114014,0.28231934,0.00021273641,0.00013012331,0.000061277555,0.00016811994,0.0009000035,0.004591924],"genre_scores_gemma":[0.99756384,0.000049639086,0.0016197864,0.000007397024,0.000003651453,0.000017070288,0.000047032583,0.00000797473,0.00068372185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999731,0.000039208982,0.0000155839,0.00010688174,0.00006494623,0.000042349355],"domain_scores_gemma":[0.9996418,0.00015967067,0.000044203927,0.000028730541,0.00011214433,0.000013463139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055224646,0.0007356226,0.0009435882,0.00041012422,0.0005043441,0.0008649905,0.00066962326,0.0009087849,0.0010386544],"category_scores_gemma":[0.000880725,0.00039895688,0.00058968214,0.0004247939,0.00039408507,0.00082692003,0.0003560019,0.0006191051,0.00022153466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002500041,0.00011425484,0.0023013111,0.000066464956,0.000045296598,0.000103120095,0.000061091276,0.9759344,0.005608639,0.00045254975,0.00028079323,0.0147820385],"study_design_scores_gemma":[0.0000041795543,0.00001945061,0.000558135,9.523141e-7,0.000004678442,0.0000044936223,0.0000038513554,0.99861,0.00070487586,0.00005565887,0.00002980816,0.000003790984],"about_ca_topic_score_codex":0.017387928,"about_ca_topic_score_gemma":0.00889645,"teacher_disagreement_score":0.017387928,"about_ca_system_score_codex":0.00055449735,"about_ca_system_score_gemma":0.00066570647,"threshold_uncertainty_score":0.034573436},"labels":[],"label_agreement":null},{"id":"W4310065947","doi":"10.1016/j.jprocont.2022.11.006","title":"Extremum seeking controller tuning for heat pump optimization using failure-robust Bayesian optimization","year":2022,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Extremum Seeking Control Systems","field":"Engineering","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":"Queen's University","funders":"","keywords":"Control theory (sociology); Multivariable calculus; PID controller; Bayesian optimization; Benchmark (surveying); Nonlinear system; Acceleration; Controller (irrigation); Estimator; Optimization problem; Mathematical optimization; Mathematics; Computer science; Engineering; Control (management); Control engineering; Temperature control; Physics; Statistics","score_opus":0.014116232196311054,"score_gpt":0.2260306080117105,"score_spread":0.21191437581539943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310065947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009596713,0.00016414297,0.9885783,0.00012885524,0.000019628587,0.000028641092,0.00001474445,0.00016018654,0.0013086795],"genre_scores_gemma":[0.91890746,0.00015995717,0.07862609,0.00011184247,0.000035155324,0.00015463683,0.00005961599,0.00009800841,0.0018472205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991738,0.000343512,0.000034400848,0.0001499388,0.0002072499,0.0000910217],"domain_scores_gemma":[0.997413,0.0017886055,0.00025893206,0.0000872463,0.00039498584,0.000057243178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002971941,0.001224859,0.002066884,0.0007087826,0.0006410928,0.0012476916,0.0010895453,0.0015639599,0.0017132512],"category_scores_gemma":[0.007826176,0.00091952033,0.0008692693,0.00041428613,0.0012662888,0.0012202546,0.0016389138,0.0018113038,0.0003014087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000119040255,0.00003806276,0.00027778544,0.000093311195,0.000057566194,0.000024024226,0.00004420489,0.9736182,0.0018075906,0.006919334,0.00047481994,0.016526112],"study_design_scores_gemma":[0.0000056428435,0.000015470456,0.000060339706,0.0000050264266,0.0000036613746,0.0000028911008,0.0000020260566,0.99837697,0.00023770235,0.0012246228,0.000061220686,0.0000043738833],"about_ca_topic_score_codex":0.0042228643,"about_ca_topic_score_gemma":0.0031243183,"teacher_disagreement_score":0.0042228643,"about_ca_system_score_codex":0.000807629,"about_ca_system_score_gemma":0.0012698161,"threshold_uncertainty_score":0.015717328},"labels":[],"label_agreement":null},{"id":"W4311087257","doi":"10.1016/j.jprocont.2022.11.011","title":"Fault monitoring-oriented transition process identification of complex industrial processes with neighbor inconsistent pair-based attribute reduction","year":2022,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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 Manitoba","funders":"National Natural Science Foundation of China","keywords":"Redundancy (engineering); Process (computing); Fault detection and isolation; Fault (geology); Identification (biology); Benchmark (surveying); Reduction (mathematics); Engineering; Computer science; Reliability engineering; Data mining; Artificial intelligence; Mathematics","score_opus":0.02071702147933452,"score_gpt":0.24286862053663097,"score_spread":0.22215159905729645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311087257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11765936,0.00013071329,0.88102424,0.00004144264,0.000021763868,0.000038444556,0.00005494474,0.00049706065,0.0005320123],"genre_scores_gemma":[0.9002002,0.000051443127,0.09908124,0.000017596114,0.000010598681,0.00003920692,0.00015631074,0.000031211297,0.0004122153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999084,0.0001875377,0.000068059126,0.00026907635,0.00028843657,0.00010294257],"domain_scores_gemma":[0.9987943,0.0004900229,0.00016429024,0.0002039408,0.00030083783,0.000046562665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008624849,0.0004802954,0.0011533815,0.001258625,0.00069823506,0.0008561792,0.0010246275,0.0004201198,0.00050290563],"category_scores_gemma":[0.0023031565,0.0002443982,0.0008683834,0.0009919533,0.00043768558,0.0009831735,0.0008433982,0.000590216,0.000114747316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012288148,0.00041672855,0.016867721,0.00026191137,0.00026871296,0.0003679367,0.0006236823,0.5122827,0.04008088,0.014806627,0.0010945118,0.4116997],"study_design_scores_gemma":[0.0000072285975,0.00006486533,0.001167625,0.00000382211,0.000033971042,0.000047666817,0.000028485803,0.9908845,0.0046416884,0.0029223186,0.00018950002,0.00000835274],"about_ca_topic_score_codex":0.002837779,"about_ca_topic_score_gemma":0.0018391397,"teacher_disagreement_score":0.002837779,"about_ca_system_score_codex":0.00042555344,"about_ca_system_score_gemma":0.0008870457,"threshold_uncertainty_score":0.0056425333},"labels":[],"label_agreement":null},{"id":"W4317935307","doi":"10.1016/j.jprocont.2023.01.007","title":"Geometrical analysis of consecutive dynamic behaviors in process monitoring","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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 Alberta","funders":"","keywords":"Process (computing); Sensitivity (control systems); Computer science; Volume (thermodynamics); Work in process; Data mining; Engineering","score_opus":0.007703039303002327,"score_gpt":0.28730042601114775,"score_spread":0.27959738670814543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317935307","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32930797,0.0002725592,0.66364425,0.00016066233,0.000033691813,0.00005323765,0.00007860755,0.0003194783,0.0061295503],"genre_scores_gemma":[0.9828918,0.00008737568,0.016112268,0.000011976274,0.00001561646,0.000015212372,0.00005159695,0.000032919477,0.00078123127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966455,0.00009244925,0.000016564427,0.00006326426,0.00011873108,0.00004451167],"domain_scores_gemma":[0.9987633,0.0005977328,0.00025582238,0.00009710598,0.0002068181,0.000079174904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004407785,0.00044854172,0.00041171364,0.0014554184,0.0003562684,0.0006743448,0.00070776,0.0005302567,0.0014864441],"category_scores_gemma":[0.0028122705,0.0003053859,0.00047467847,0.00072638196,0.0010789499,0.0009704341,0.0005876788,0.0004526579,0.00013817594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037567172,0.00009928872,0.0059499624,0.00014303371,0.000060704107,0.0009304866,0.00040560283,0.61709607,0.04455818,0.26551655,0.0008503308,0.06401414],"study_design_scores_gemma":[0.0000027641568,0.000046379842,0.001987402,0.000004495685,0.0000060419943,0.00012491546,0.000027839491,0.98181653,0.0015558387,0.014096097,0.00032137914,0.000010416339],"about_ca_topic_score_codex":0.0014724868,"about_ca_topic_score_gemma":0.0007784364,"teacher_disagreement_score":0.0014864441,"about_ca_system_score_codex":0.0005942839,"about_ca_system_score_gemma":0.00030669977,"threshold_uncertainty_score":0.0049726963},"labels":[],"label_agreement":null},{"id":"W4320080932","doi":"10.1016/j.jprocont.2023.01.014","title":"Robust particle filter for state estimation in presence of bounded but uncertain parameters based on ellipsoidal set membership approach","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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 Alberta","funders":"","keywords":"Ellipsoid; Particle filter; Ellipsoid method; Mathematics; Bounded function; Particle (ecology); Filter (signal processing); Estimation theory; Mathematical optimization; Kalman filter; Algorithm; Computer science; Convex optimization; Mathematical analysis; Regular polygon; Statistics; Physics; Geometry; Convex combination; Computer vision","score_opus":0.0655098440063686,"score_gpt":0.29098825164713715,"score_spread":0.22547840764076854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320080932","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033108022,0.00008488053,0.9962191,0.000027114238,0.000021562813,0.0000075442017,0.000006863868,0.00007340979,0.000248631],"genre_scores_gemma":[0.6802347,0.0005692992,0.3155231,0.00011124902,0.00010069988,0.00019320947,0.00021087751,0.000060543385,0.0029963464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995437,0.00011131716,0.000027781134,0.000105873696,0.00017496137,0.000036393187],"domain_scores_gemma":[0.9992291,0.00042381513,0.00006471392,0.00005920675,0.0002039897,0.000019157382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092663727,0.00058958546,0.0012856384,0.0004693602,0.00042863586,0.00076011405,0.001074362,0.0011121061,0.0009204083],"category_scores_gemma":[0.0025862788,0.00038153608,0.00074528536,0.0005645203,0.00046996638,0.0010097441,0.0007717116,0.0013979684,0.00029121732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024976718,0.00011598505,0.0006357072,0.00018965783,0.00013245479,0.00010531313,0.0001910637,0.81274676,0.010590256,0.021863602,0.0015435499,0.15163592],"study_design_scores_gemma":[0.0000045166767,0.000017543196,0.00008038171,0.0000030173987,0.000004325259,0.0000080643495,0.0000031636366,0.998292,0.0004948052,0.0009084775,0.00017943412,0.000004291673],"about_ca_topic_score_codex":0.0065141185,"about_ca_topic_score_gemma":0.0026789885,"teacher_disagreement_score":0.0065141185,"about_ca_system_score_codex":0.00042100062,"about_ca_system_score_gemma":0.0009296661,"threshold_uncertainty_score":0.012952447},"labels":[],"label_agreement":null},{"id":"W4323665884","doi":"10.1016/j.jprocont.2023.02.013","title":"Economic model predictive control based on lattice trajectory piecewise linear model for wastewater treatment plants","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Science, Technology and Innovation Commission of Shenzhen Municipality; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Nonlinear system; Mathematical optimization; Model predictive control; Benchmark (surveying); Trajectory; Computer science; Control theory (sociology); Piecewise linear function; Optimization problem; Nonlinear programming; Mathematics; Artificial intelligence; Control (management)","score_opus":0.014579590068213164,"score_gpt":0.247154799225613,"score_spread":0.23257520915739985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323665884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15203965,0.00073396886,0.831134,0.00080008333,0.0001577639,0.000057529902,0.00030254482,0.00043048768,0.014343904],"genre_scores_gemma":[0.99135965,0.00018764193,0.0051979995,0.000021551745,0.000013876078,0.000045368142,0.00007675712,0.000018868153,0.0030782388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997985,0.00007501008,0.0000071623836,0.000034266905,0.00005078012,0.00003436115],"domain_scores_gemma":[0.99953747,0.0002489057,0.00006982414,0.000021762004,0.00010248705,0.000019568673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043262736,0.0005127227,0.0009981956,0.00034192458,0.0004180223,0.00097386795,0.000792473,0.00079637277,0.0019601786],"category_scores_gemma":[0.0011832161,0.0004018787,0.00051008165,0.0006647472,0.0006300344,0.000705612,0.000559935,0.0010311833,0.0001882569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000142286535,0.0000054274883,0.00006292404,0.000011804543,0.0000065069394,0.000012840586,0.0000065436298,0.99603003,0.00017190889,0.0021784327,0.000093182396,0.0014061852],"study_design_scores_gemma":[0.0000017606915,0.000004669318,0.000030916795,7.785367e-7,0.0000013192731,0.0000010284274,0.0000014677495,0.999258,0.00003613864,0.00061894505,0.000043643056,0.0000013422921],"about_ca_topic_score_codex":0.02325055,"about_ca_topic_score_gemma":0.010290064,"teacher_disagreement_score":0.02325055,"about_ca_system_score_codex":0.00097510323,"about_ca_system_score_gemma":0.00090365857,"threshold_uncertainty_score":0.046230435},"labels":[],"label_agreement":null},{"id":"W4367838936","doi":"10.1016/j.jprocont.2023.04.003","title":"Robust closed-loop dynamic real-time optimization","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"McMaster University","funders":"","keywords":"Control theory (sociology); Model predictive control; Scheme (mathematics); Computation; Nonlinear system; System dynamics; Mathematical optimization; Process (computing); Clipping (morphology); Computer science; Closed loop; Control engineering; Control (management); Mathematics; Engineering; Algorithm","score_opus":0.006567671686507414,"score_gpt":0.22217769288583092,"score_spread":0.21561002119932351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367838936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007554864,0.00023456622,0.9869804,0.00020090413,0.0000645603,0.00003705336,0.0000469426,0.00036572778,0.004514902],"genre_scores_gemma":[0.9216053,0.00022838482,0.07148555,0.0001223019,0.000075482436,0.00017041649,0.00017598244,0.00021567104,0.005921019],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890304,0.00031535866,0.00004715707,0.00029253407,0.00030702713,0.0001349079],"domain_scores_gemma":[0.99849916,0.00086037826,0.00017938903,0.0001000974,0.00032171656,0.000039265382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001691523,0.0016304182,0.0018290133,0.00045093286,0.0004879659,0.0018928762,0.0012615769,0.0013150946,0.0041516637],"category_scores_gemma":[0.0051021753,0.00058869773,0.00072644843,0.000530234,0.0010621597,0.0012281693,0.001462119,0.001516322,0.0008376721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015257193,0.000048365404,0.00009993552,0.00010414001,0.00004265408,0.000036884223,0.00003047129,0.96478623,0.0018120854,0.007978192,0.00088666996,0.024021873],"study_design_scores_gemma":[0.000008805035,0.000027519161,0.00003275107,0.0000025960826,0.0000036762544,0.000005263998,0.0000022219938,0.9980731,0.00035499054,0.0012963269,0.0001892334,0.0000034750988],"about_ca_topic_score_codex":0.0035139439,"about_ca_topic_score_gemma":0.0017251676,"teacher_disagreement_score":0.0041516637,"about_ca_system_score_codex":0.0007851088,"about_ca_system_score_gemma":0.0014307444,"threshold_uncertainty_score":0.013888717},"labels":[],"label_agreement":null},{"id":"W4378801662","doi":"10.1016/j.jprocont.2023.04.004","title":"Early warning of drillstring faulty conditions based on multi-model fusion in geological drilling processes","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Drilling and Well Engineering","field":"Engineering","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 British Columbia","funders":"","keywords":"Warning system; Fault (geology); Damages; Drilling; Fusion; Property (philosophy); Computer science; Engineering; Event (particle physics); Reliability engineering; Geology; Mechanical engineering","score_opus":0.015568041672165652,"score_gpt":0.2535283622125441,"score_spread":0.23796032054037844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378801662","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":[0.8504342,0.00048190245,0.1460248,0.00035127424,0.00014094137,0.000032487107,0.00021192498,0.0007528506,0.001569698],"genre_scores_gemma":[0.9969158,0.000035349065,0.0029104985,0.000010026779,0.000007823176,0.0000029356386,0.000044312892,0.0000060600696,0.00006714379],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971396,0.000047567646,0.000019751498,0.00006116352,0.00009646343,0.00006115121],"domain_scores_gemma":[0.9993018,0.00021171644,0.00018086957,0.000046665653,0.00019492014,0.00006405986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054976286,0.0005532913,0.0007842432,0.00092369574,0.0003839656,0.0007281528,0.00046448945,0.00080148265,0.0004506999],"category_scores_gemma":[0.0017006886,0.0002525744,0.00041099486,0.00046467016,0.0003535013,0.0011426662,0.0009877426,0.0006444683,0.00009373544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036797796,0.0004139294,0.06979971,0.0002917439,0.00026009558,0.0010955483,0.0003949332,0.71229035,0.08311157,0.002966304,0.0021019226,0.12359415],"study_design_scores_gemma":[0.000014525081,0.0000743756,0.010191052,0.0000071302215,0.00003078416,0.000047444842,0.000037458372,0.9848547,0.0039190035,0.0006970709,0.000108700144,0.000017707991],"about_ca_topic_score_codex":0.0031175932,"about_ca_topic_score_gemma":0.0027832547,"teacher_disagreement_score":0.0031175932,"about_ca_system_score_codex":0.0003290065,"about_ca_system_score_gemma":0.0006358566,"threshold_uncertainty_score":0.006198883},"labels":[],"label_agreement":null},{"id":"W4378895303","doi":"10.1016/j.jprocont.2023.103001","title":"Modeling and Bayesian inference for processes characterized by abrupt variations","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Bayesian inference; Gaussian process; Bayesian probability; Latent variable; Cauchy distribution; System dynamics; Gaussian; Dynamic Bayesian network; Process (computing); Computer science; Statistical physics; Mathematics; Artificial intelligence; Statistics; Physics","score_opus":0.009966464224164867,"score_gpt":0.25541990605483816,"score_spread":0.2454534418306733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378895303","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08790357,0.0007638344,0.9087908,0.00081071886,0.000043670338,0.000029931743,0.00016711328,0.00021041094,0.0012799428],"genre_scores_gemma":[0.9569753,0.0009380096,0.038422585,0.000100819576,0.00013884284,0.0000766076,0.00035431102,0.00006185694,0.002931613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984345,0.00056206,0.000104614264,0.00039366522,0.00030705993,0.00019808384],"domain_scores_gemma":[0.9797615,0.0177013,0.0013438201,0.00045228284,0.00053715555,0.00020392287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061287815,0.0010043608,0.0021002386,0.0021548078,0.00088604714,0.00265242,0.002578004,0.0030212998,0.0012936717],"category_scores_gemma":[0.024675583,0.0021005145,0.0016589653,0.0019091316,0.0022574188,0.0036350521,0.0017400648,0.003218506,0.0002055473],"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":[0.000048558355,0.000024302935,0.0011170243,0.000029505885,0.000057450732,0.000060071972,0.00004842083,0.9672598,0.0002563573,0.026373275,0.00015700381,0.004568245],"study_design_scores_gemma":[0.0000045577244,0.0000040319323,0.00023087602,0.0000028632433,0.000006056037,0.0000072244907,0.000004187823,0.9876957,0.000052292882,0.011933737,0.000052068648,0.000006384011],"about_ca_topic_score_codex":0.018563863,"about_ca_topic_score_gemma":0.0149627,"teacher_disagreement_score":0.018563863,"about_ca_system_score_codex":0.0020973745,"about_ca_system_score_gemma":0.001489004,"threshold_uncertainty_score":0.036911607},"labels":[],"label_agreement":null},{"id":"W4380356255","doi":"10.1016/j.jprocont.2023.103009","title":"Self-tuning kernel Gaussian method for predictive control systems","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Energy Load and Power Forecasting","field":"Engineering","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 British Columbia","funders":"","keywords":"Control theory (sociology); Kernel (algebra); Controller (irrigation); Model predictive control; Computer science; Gaussian; Gaussian process; Process (computing); Mathematical optimization; Control engineering; Engineering; Control (management); Artificial intelligence; Mathematics","score_opus":0.010371220751323698,"score_gpt":0.2572107231875239,"score_spread":0.24683950243620023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380356255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010073484,0.00025224724,0.9884121,0.00006051394,0.000057333455,0.000014793742,0.000018341398,0.00036649156,0.0007447553],"genre_scores_gemma":[0.8481241,0.00027924622,0.14575648,0.00009283398,0.00008252244,0.00009608322,0.00012404387,0.00018020862,0.0052644718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996407,0.00012037147,0.000019201865,0.00006586849,0.000112026195,0.00004185003],"domain_scores_gemma":[0.999111,0.00041481399,0.00005923698,0.00008892633,0.00030210326,0.00002400703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010663205,0.00039036377,0.0007966779,0.00029804566,0.0003109625,0.0005838912,0.0008669288,0.00075690873,0.0018749608],"category_scores_gemma":[0.0028526604,0.0003276655,0.0004108979,0.00034506022,0.0004148801,0.0007846211,0.0006526859,0.0010879791,0.00046842676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031853133,0.00013214326,0.00058105675,0.00018352908,0.000093724164,0.000040252406,0.00009822376,0.7569255,0.008769551,0.01666561,0.0026018047,0.21359004],"study_design_scores_gemma":[0.00000285957,0.000009145319,0.00005083435,0.0000014920072,0.0000021870285,0.0000021455808,9.394486e-7,0.9990601,0.0002896182,0.0004366139,0.00014199098,0.0000021154212],"about_ca_topic_score_codex":0.0052467943,"about_ca_topic_score_gemma":0.0039381557,"teacher_disagreement_score":0.0052467943,"about_ca_system_score_codex":0.0005229015,"about_ca_system_score_gemma":0.00091845565,"threshold_uncertainty_score":0.010432482},"labels":[],"label_agreement":null},{"id":"W4382360926","doi":"10.1016/j.jprocont.2023.103023","title":"The arc loss challenge: A novel industrial benchmark for process analytics and machine learning","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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; University of British Columbia","funders":"","keywords":"Benchmark (surveying); Benchmarking; Computer science; Machine learning; Data pre-processing; Process (computing); Workflow; Data mining; Preprocessor; Artificial intelligence; Feature selection; Transparency (behavior); Database","score_opus":0.0270295506586403,"score_gpt":0.26214994643175954,"score_spread":0.23512039577311925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382360926","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4167246,0.014113023,0.43150935,0.016527863,0.0033814474,0.0007419757,0.023264643,0.018024836,0.075712346],"genre_scores_gemma":[0.8615729,0.0017727986,0.106738165,0.0011796913,0.0006725603,0.00028217386,0.019159446,0.00088134053,0.0077409116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99485,0.0012389333,0.0002551865,0.0008280802,0.00243741,0.00039045114],"domain_scores_gemma":[0.9902546,0.004569345,0.0006922385,0.001971988,0.0018563641,0.0006553767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005640766,0.0015870226,0.0011671503,0.0026101698,0.0009813313,0.0026488297,0.0030385458,0.0024726877,0.0039296392],"category_scores_gemma":[0.022324495,0.00027337586,0.0007085878,0.002531162,0.0013675361,0.0033839056,0.0034072208,0.0030883646,0.0016561358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029535326,0.0024991522,0.017111959,0.0013630795,0.00038067726,0.0006548278,0.00015270957,0.39262334,0.008112956,0.050793897,0.17138706,0.35196683],"study_design_scores_gemma":[0.00024731463,0.0010859389,0.005643731,0.00007718389,0.000058594997,0.0005198083,0.00015055081,0.916585,0.009657442,0.042858377,0.0230601,0.00005590705],"about_ca_topic_score_codex":0.0037995528,"about_ca_topic_score_gemma":0.003707075,"teacher_disagreement_score":0.005640766,"about_ca_system_score_codex":0.0011742493,"about_ca_system_score_gemma":0.00196355,"threshold_uncertainty_score":0.029831529},"labels":[],"label_agreement":null},{"id":"W4385163486","doi":"10.1016/j.jprocont.2023.103035","title":"Sensor network design for post-combustion CO<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\" id=\"d1e3148\" altimg=\"si4.svg\"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> capture plants: Computational efficiency and robustness","year":2023,"lang":"lv","type":"article","venue":"Journal of Process Control","topic":"Carbon Dioxide Capture Technologies","field":"Engineering","cited_by":9,"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":"University of Alberta","keywords":"Observability; Robustness (evolution); Sensitivity (control systems); Computer science; Wireless sensor network; Optimization problem; Mathematical optimization; Real-time computing; Algorithm; Mathematics; Engineering; Computer network","score_opus":0.014615641047840527,"score_gpt":0.2368724903335663,"score_spread":0.22225684928572578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385163486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010740575,0.0003058557,0.9820356,0.0003332024,0.00007282099,0.00006763354,0.00007015306,0.00012571186,0.006248522],"genre_scores_gemma":[0.90242636,0.0006679435,0.083884545,0.00013772593,0.00007757175,0.0003219187,0.00017978356,0.00006401187,0.012240046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997212,0.00006839203,0.000009819411,0.000079632984,0.00008299953,0.00003796533],"domain_scores_gemma":[0.99959666,0.00016445876,0.000042386815,0.000018819619,0.00015738432,0.000020238393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007257333,0.00067575206,0.0004897906,0.00024095144,0.0003686868,0.00076602923,0.0009715201,0.000804905,0.003472015],"category_scores_gemma":[0.0012029995,0.00028227025,0.00043991674,0.00022582528,0.0004145346,0.00077524764,0.0006789309,0.0007944635,0.00041275818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071967166,0.000021376582,0.00026612583,0.00010147709,0.000021343663,0.000039455026,0.000048786667,0.96246994,0.0053896112,0.009167578,0.0010971144,0.021305302],"study_design_scores_gemma":[0.0000056720437,0.000041920524,0.00006152288,0.000004874308,0.0000045114384,0.0000064210863,0.000009880944,0.99700123,0.0007551911,0.0015189329,0.0005872598,0.0000025311563],"about_ca_topic_score_codex":0.0040225107,"about_ca_topic_score_gemma":0.0049156905,"teacher_disagreement_score":0.0040225107,"about_ca_system_score_codex":0.0008641322,"about_ca_system_score_gemma":0.0010843477,"threshold_uncertainty_score":0.011614978},"labels":[],"label_agreement":null},{"id":"W4385597180","doi":"10.1016/j.jprocont.2023.103049","title":"Deep learning-based model predictive control for real-time supply chain optimization","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":20,"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":"Model predictive control; Computer science; Control theory (sociology); Heuristic; Mathematical optimization; Deep learning; Controller (irrigation); Reduction (mathematics); Artificial neural network; Supply chain; Integer (computer science); Set (abstract data type); Optimization problem; Artificial intelligence; Mathematics; Control (management); Law","score_opus":0.004972731029516878,"score_gpt":0.2233734612986233,"score_spread":0.21840073026910642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385597180","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0346578,0.0009504803,0.95864856,0.0006448896,0.00016989475,0.00003846249,0.00014852396,0.0006347187,0.00410659],"genre_scores_gemma":[0.9718553,0.0002781711,0.023619832,0.00014637435,0.00006528809,0.00007817704,0.00017372542,0.000058530288,0.003724615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995833,0.00009665626,0.000023144334,0.000094714465,0.00010282541,0.00009935945],"domain_scores_gemma":[0.99877185,0.00074599445,0.00013213416,0.000053961965,0.00024665837,0.00004929767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012217893,0.0010101623,0.0017231758,0.00054051855,0.00047963244,0.0012771484,0.0011527747,0.0014580624,0.0025419595],"category_scores_gemma":[0.0027378688,0.0009558557,0.00067601935,0.00087939674,0.00090117456,0.0011078755,0.00124833,0.0023130653,0.0003214859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020720669,0.000016190703,0.00007290323,0.000016879538,0.000011418588,0.0000081602475,0.0000059307026,0.99193805,0.00016013227,0.00082802604,0.00026294656,0.0066586733],"study_design_scores_gemma":[0.0000011665489,0.0000026161533,0.000011173145,8.1254075e-7,7.5146374e-7,4.0511296e-7,4.0336428e-7,0.99958163,0.000025736997,0.0003529136,0.00002178294,6.1021944e-7],"about_ca_topic_score_codex":0.026792169,"about_ca_topic_score_gemma":0.018969156,"teacher_disagreement_score":0.026792169,"about_ca_system_score_codex":0.0013574383,"about_ca_system_score_gemma":0.0021006453,"threshold_uncertainty_score":0.053272426},"labels":[],"label_agreement":null},{"id":"W4385931440","doi":"10.1016/j.jprocont.2023.103055","title":"A scenario-based framework for the integration of scheduling and control under multiple uncertainties","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"McMaster University","funders":"","keywords":"Scheduling (production processes); Computer science; Agile software development; Mathematical optimization; Process (computing); Control theory (sociology); Control (management); Mathematics","score_opus":0.01768800753836876,"score_gpt":0.27368959329341025,"score_spread":0.2560015857550415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385931440","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047888956,0.00015181307,0.9915507,0.00025410842,0.000050907736,0.00005332613,0.00010757863,0.00017535545,0.0028673671],"genre_scores_gemma":[0.668858,0.0005885986,0.32685715,0.0001143038,0.00016485793,0.00031320992,0.00042977292,0.00011993568,0.0025542271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982103,0.0008789818,0.000095233154,0.00025103614,0.00037369513,0.00019080385],"domain_scores_gemma":[0.99847144,0.00070713175,0.00018454393,0.00014921339,0.0002915628,0.0001960954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028283831,0.0011925208,0.0012213495,0.0009395991,0.00069139554,0.002366679,0.0027808668,0.0018415484,0.0040651057],"category_scores_gemma":[0.0044268286,0.00078798947,0.0014362275,0.0013438393,0.00094608,0.002574632,0.002717043,0.0019523584,0.00047447512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046581357,0.000031524778,0.0002552517,0.00003553456,0.00005922288,0.00019116195,0.0000449031,0.92167366,0.0005495948,0.06970569,0.00053686113,0.006869991],"study_design_scores_gemma":[0.00000728345,0.000015289108,0.0000433981,0.000005769458,0.000008455624,0.000024693605,0.00001211649,0.982177,0.00009339269,0.017017737,0.00058788114,0.0000070159645],"about_ca_topic_score_codex":0.007180464,"about_ca_topic_score_gemma":0.005287104,"teacher_disagreement_score":0.007180464,"about_ca_system_score_codex":0.0010931846,"about_ca_system_score_gemma":0.0025155102,"threshold_uncertainty_score":0.014958143},"labels":[],"label_agreement":null},{"id":"W4386604986","doi":"10.1016/j.jprocont.2023.103071","title":"Selecting model features that lead to linear models of bi-product distillation towers","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"McMaster University","funders":"","keywords":"Reboiler; Linear model; Fractionating column; Nonlinear system; Distillation; Product (mathematics); Benchmark (surveying); Flow (mathematics); Computer science; Control theory (sociology); Process engineering; Mathematics; Engineering; Control (management); Chemistry; Machine learning; Artificial intelligence","score_opus":0.020188266406064474,"score_gpt":0.26373171784893523,"score_spread":0.24354345144287076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386604986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46099916,0.00024326601,0.5287364,0.00043290743,0.000047604262,0.00012476559,0.0008872071,0.0022539457,0.0062747123],"genre_scores_gemma":[0.9858311,0.00003276122,0.012301851,0.000024896059,0.000006028134,0.00004123389,0.0004151461,0.000053972934,0.0012930239],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981266,0.00005016319,0.0000103090815,0.00004565582,0.000030649117,0.00005061693],"domain_scores_gemma":[0.9988612,0.0007156304,0.00012577583,0.000088101435,0.00016559588,0.00004368097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004576589,0.0012096087,0.0007192656,0.0005887959,0.0004066314,0.0010326416,0.00069683406,0.0012302446,0.0029573727],"category_scores_gemma":[0.0029215727,0.0005816047,0.00092887104,0.0002600798,0.00040332327,0.0008617329,0.00049206696,0.00096590404,0.0006156414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021544365,0.000065450186,0.001469739,0.000049541028,0.00002928342,0.00008560265,0.000025495287,0.9840768,0.0025015378,0.00094072265,0.00042314074,0.0101172235],"study_design_scores_gemma":[0.000015434716,0.00003040466,0.00033180724,0.0000025215834,0.000010433746,0.0000072651765,0.000008324475,0.9979818,0.00092070556,0.0006087716,0.00007828706,0.0000041828084],"about_ca_topic_score_codex":0.014358879,"about_ca_topic_score_gemma":0.012378565,"teacher_disagreement_score":0.014358879,"about_ca_system_score_codex":0.0006209908,"about_ca_system_score_gemma":0.0009974237,"threshold_uncertainty_score":0.028550625},"labels":[],"label_agreement":null},{"id":"W4386905813","doi":"10.1016/j.jprocont.2023.103083","title":"Multi-level data-predictive control for linear multi-timescale processes with stability guarantee","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Model predictive control; Trajectory; Stability (learning theory); Control theory (sociology); Computer science; Resampling; Control (management); Control engineering; Engineering; Algorithm; Artificial intelligence; Machine learning","score_opus":0.054786932775541176,"score_gpt":0.29741392493744334,"score_spread":0.24262699216190217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386905813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077952985,0.0002172932,0.9902784,0.000118007156,0.00003884122,0.000026001519,0.00003461347,0.00016036656,0.0013312141],"genre_scores_gemma":[0.9419103,0.00024836152,0.05586829,0.00009177264,0.00004598706,0.00016464171,0.00009835407,0.00005077281,0.0015214321],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993973,0.0001071385,0.000036838486,0.00015371693,0.00023669482,0.00006835174],"domain_scores_gemma":[0.9988955,0.0005799476,0.00017411193,0.00008539149,0.00022959948,0.000035489968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00126119,0.00086555746,0.00087368855,0.00033707684,0.00044494728,0.0010774487,0.0011214911,0.0007060718,0.0016031929],"category_scores_gemma":[0.0026640364,0.00039937886,0.00062403746,0.00047281073,0.0008482739,0.0008796854,0.0013239785,0.0017615318,0.00020757354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080001126,0.00003528901,0.00032665944,0.000216421,0.000029883631,0.00006973617,0.000098897275,0.9475072,0.005635209,0.013731417,0.0005736897,0.03169556],"study_design_scores_gemma":[0.0000023930781,0.0000134585425,0.00003975654,0.000002866514,0.0000018332834,0.000002840788,0.0000020836242,0.9982717,0.00038080369,0.0011294495,0.00015059598,0.0000021757432],"about_ca_topic_score_codex":0.006325114,"about_ca_topic_score_gemma":0.004451191,"teacher_disagreement_score":0.006325114,"about_ca_system_score_codex":0.0008314284,"about_ca_system_score_gemma":0.0011982591,"threshold_uncertainty_score":0.01257664},"labels":[],"label_agreement":null},{"id":"W4387418550","doi":"10.1016/j.jprocont.2023.103091","title":"Image restoration and analysis with application to quality variable prediction in flotation process","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Minerals Flotation and Separation Techniques","field":"Environmental Science","cited_by":7,"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":"Institute for Oil Sands Innovation, University of Alberta; Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; University of Alberta","keywords":"Outlier; Artificial intelligence; Computer science; Froth flotation; Kalman filter; Noise (video); Process (computing); Computer vision; Pattern recognition (psychology); Image (mathematics); Chemistry","score_opus":0.008147572609760755,"score_gpt":0.3067596165184351,"score_spread":0.29861204390867435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387418550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08727402,0.0003533821,0.90904343,0.00022175352,0.00006278302,0.00008376831,0.000065896136,0.0015719874,0.0013229327],"genre_scores_gemma":[0.51053214,0.00047557714,0.4860664,0.000074865005,0.000056653204,0.00009076436,0.00010498921,0.00019108024,0.0024075753],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984145,0.00003208501,0.000009529598,0.000034056677,0.000058557976,0.000024344246],"domain_scores_gemma":[0.9993468,0.00028020606,0.000056868008,0.000054402903,0.00023646696,0.000025325771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006428607,0.00049819314,0.00061657536,0.00091989286,0.0004557603,0.0006830645,0.0005061658,0.0008605914,0.0018286078],"category_scores_gemma":[0.0015395317,0.00037689018,0.00059782516,0.0006239646,0.0005315527,0.00051325053,0.0003855781,0.0008050405,0.00041811593],"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":[0.00085045176,0.000413066,0.0028396272,0.00026511966,0.0000796253,0.00018090851,0.00019428004,0.09459719,0.1942134,0.0027160111,0.0014290974,0.70222133],"study_design_scores_gemma":[0.000017022643,0.000104142266,0.0028267866,0.0000079621095,0.00003209777,0.00008983414,0.000029005356,0.93072885,0.064403094,0.00065333303,0.0010886189,0.000019275152],"about_ca_topic_score_codex":0.004264969,"about_ca_topic_score_gemma":0.0035073396,"teacher_disagreement_score":0.004264969,"about_ca_system_score_codex":0.00033490188,"about_ca_system_score_gemma":0.00057661394,"threshold_uncertainty_score":0.00848031},"labels":[],"label_agreement":null},{"id":"W4387762673","doi":"10.1016/j.jprocont.2023.103108","title":"An alternative method for estimating Hurst exponent of control signals based on system dynamics","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"","keywords":"Hurst exponent; Autocorrelation; Benchmark (surveying); Variance (accounting); Moving average; Mathematics; Detrended fluctuation analysis; Autoregressive–moving-average model; Rescaled range; Autoregressive model; Index (typography); Statistics; Computer science","score_opus":0.012003384099621253,"score_gpt":0.29632655242720046,"score_spread":0.2843231683275792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387762673","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007065128,0.0003448117,0.99175745,0.000025001646,0.0000681706,0.000020516702,0.000044322413,0.0002947104,0.00037992586],"genre_scores_gemma":[0.39614022,0.0009063683,0.5983872,0.000087691194,0.00027245,0.00013119153,0.00044589955,0.00013509522,0.0034939945],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99945265,0.00009175778,0.000040324037,0.00014451724,0.00023572979,0.000035026413],"domain_scores_gemma":[0.99915195,0.00030512753,0.00007193134,0.000086568056,0.00035690822,0.000027462971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038893666,0.0008490753,0.00090046687,0.0015136373,0.00033282788,0.00070963963,0.00063644286,0.0008221388,0.001207322],"category_scores_gemma":[0.002325644,0.0003140491,0.0005834956,0.000845477,0.00027713698,0.00095949956,0.00050930335,0.00074718054,0.0004941245],"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":[0.00043326337,0.00013828035,0.0057327175,0.00061301584,0.000335761,0.00038072112,0.0003228757,0.15967454,0.12490741,0.01123864,0.00283502,0.6933878],"study_design_scores_gemma":[0.000014747983,0.00008650414,0.0022020447,0.000017023995,0.00003529796,0.00014972624,0.000019631176,0.98693943,0.007207994,0.0016961596,0.0015948442,0.000036473422],"about_ca_topic_score_codex":0.0034349603,"about_ca_topic_score_gemma":0.0029549524,"teacher_disagreement_score":0.0034349603,"about_ca_system_score_codex":0.0002473642,"about_ca_system_score_gemma":0.0004933333,"threshold_uncertainty_score":0.0068299174},"labels":[],"label_agreement":null},{"id":"W4388175025","doi":"10.1016/j.jprocont.2023.103111","title":"Steady-state real-time optimization using transient measurements and approximated Hammerstein dynamic model: A proof of concept in an experimental rig","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"McMaster University","funders":"","keywords":"Observability; Transient (computer programming); Control theory (sociology); Estimation theory; Identification (biology); Steady state (chemistry); Computer science; Discrete time and continuous time; Mathematical optimization; State (computer science); System identification; Optimization problem; Control engineering; Engineering; Algorithm; Mathematics; Control (management); Applied mathematics; Measure (data warehouse); Data mining","score_opus":0.02066677641998634,"score_gpt":0.27375318726770625,"score_spread":0.25308641084771993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388175025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07970991,0.00011777806,0.9122091,0.00023761003,0.00006492707,0.00028615957,0.00016477816,0.0023224212,0.0048874198],"genre_scores_gemma":[0.8635506,0.00006769874,0.13406257,0.000039149763,0.0000097674765,0.0002481828,0.00011460134,0.00017465472,0.0017328812],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995635,0.00007384079,0.00001998833,0.00008468537,0.00021612713,0.000041735922],"domain_scores_gemma":[0.99941075,0.000236281,0.00007530261,0.00010066728,0.00015090505,0.000026100635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011328581,0.0006113132,0.0007806077,0.00015798997,0.00042183604,0.00071460527,0.0010066773,0.0008854163,0.003121698],"category_scores_gemma":[0.0015301082,0.000336073,0.00034903773,0.00019935412,0.0006074234,0.0010456368,0.00060893194,0.00088579475,0.0006512412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001464312,0.0010440941,0.0015867656,0.0011664202,0.000093165334,0.0003490232,0.0002926975,0.455182,0.40563852,0.014366139,0.0026174444,0.1161994],"study_design_scores_gemma":[0.00010211653,0.00056054344,0.00073774543,0.000020004516,0.000015829479,0.00005993722,0.000028786813,0.9042022,0.09128421,0.001215784,0.0017351095,0.0000376533],"about_ca_topic_score_codex":0.0016506598,"about_ca_topic_score_gemma":0.0011764292,"teacher_disagreement_score":0.003121698,"about_ca_system_score_codex":0.00032563572,"about_ca_system_score_gemma":0.0011309701,"threshold_uncertainty_score":0.010443151},"labels":[],"label_agreement":null},{"id":"W4388994712","doi":"10.1016/j.jprocont.2023.103127","title":"Visual analytics for process monitoring: Leveraging time-series imaging for enhanced interpretability","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","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 Alberta; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Visual analytics; Computer science; Analytics; Process (computing); Machine learning; Artificial intelligence; Scalability; Visualization; Leverage (statistics); Interactive visual analysis; Big data; Convolutional neural network; Benchmark (surveying); Data science; Data mining","score_opus":0.010305830014265953,"score_gpt":0.2962777609987008,"score_spread":0.28597193098443485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388994712","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0674868,0.0009400227,0.92200077,0.0006494952,0.00018525119,0.000070892886,0.00042122725,0.004257719,0.003987824],"genre_scores_gemma":[0.59916264,0.0014033134,0.39502847,0.00035758165,0.00020459344,0.00007456157,0.00048618283,0.0007013205,0.002581327],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972385,0.000044248172,0.000015782754,0.000058250112,0.00012823674,0.000029684643],"domain_scores_gemma":[0.99905723,0.0004614397,0.00011298117,0.00012964106,0.00018706078,0.000051531617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005546059,0.0007627056,0.00047727436,0.0011006633,0.00019057535,0.0015164757,0.00062779896,0.0006158348,0.003131373],"category_scores_gemma":[0.0022384354,0.00031103604,0.00037799685,0.0006250458,0.00037892966,0.0014537098,0.0009175989,0.0009127368,0.00060771784],"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":[0.0005051558,0.00021026301,0.0014445598,0.00039877777,0.000065094464,0.0002444494,0.00021499034,0.028300688,0.5810387,0.00571011,0.003632858,0.3782344],"study_design_scores_gemma":[0.000022077802,0.0001463476,0.0022075404,0.000044188993,0.00003844746,0.0003373529,0.00008646984,0.7969031,0.1848388,0.009370681,0.005953337,0.000051769977],"about_ca_topic_score_codex":0.0008429359,"about_ca_topic_score_gemma":0.0010653356,"teacher_disagreement_score":0.003131373,"about_ca_system_score_codex":0.00027993807,"about_ca_system_score_gemma":0.00030537805,"threshold_uncertainty_score":0.010475457},"labels":[],"label_agreement":null},{"id":"W4389068070","doi":"10.1016/j.jprocont.2023.103130","title":"Robust multi-mode probabilistic slow feature analysis with application to fault detection","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":19,"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; Mitacs","keywords":"Fault detection and isolation; Probabilistic logic; Outlier; Computer science; Feature (linguistics); Mode (computer interface); Data mining; Process (computing); Field (mathematics); Pattern recognition (psychology); Artificial intelligence; Engineering; Algorithm; Mathematics","score_opus":0.007679976663046502,"score_gpt":0.23629730187458559,"score_spread":0.22861732521153907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389068070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004034839,0.00006493039,0.99553066,0.000023607368,0.0000092965165,0.0000073843275,0.000011734842,0.00013929523,0.0001783148],"genre_scores_gemma":[0.75690955,0.00027882116,0.2405494,0.000054446024,0.00008076961,0.000085078726,0.00010132238,0.00019203346,0.0017486146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969625,0.00007618011,0.000018988409,0.000076112876,0.00010638087,0.00002616128],"domain_scores_gemma":[0.99838996,0.0010357937,0.00019434321,0.000121660494,0.00023123463,0.000026972968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009715701,0.0010650677,0.0010970874,0.00078292936,0.00034387485,0.00068166305,0.0007926441,0.00064038957,0.0012930761],"category_scores_gemma":[0.0043572923,0.0004687072,0.00085125247,0.0006200304,0.00055145536,0.0009399233,0.0009900772,0.0008653178,0.00027279626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030234366,0.000046896836,0.0009962256,0.00016377332,0.00012708832,0.00016068165,0.00007830729,0.7977171,0.018218594,0.016814107,0.0007119602,0.16466288],"study_design_scores_gemma":[0.0000020388152,0.000017646771,0.000103841245,0.0000015807027,0.000004247384,0.000015465359,0.0000014565046,0.99730873,0.0005975677,0.0018590138,0.00008425768,0.0000041727344],"about_ca_topic_score_codex":0.0012902459,"about_ca_topic_score_gemma":0.0011434756,"teacher_disagreement_score":0.0012930761,"about_ca_system_score_codex":0.00028491372,"about_ca_system_score_gemma":0.00040301832,"threshold_uncertainty_score":0.0051382184},"labels":[],"label_agreement":null},{"id":"W4389766904","doi":"10.1016/j.jprocont.2023.103147","title":"Constrained model predictive control of an industrial high-rate thickener","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","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":"École de Technologie Supérieure","funders":"","keywords":"Arithmetic underflow; Model predictive control; Control theory (sociology); Slurry; Process (computing); Computer science; Engineering; Control (management)","score_opus":0.01730031230504749,"score_gpt":0.23765914544524602,"score_spread":0.22035883314019852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389766904","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":[0.56626844,0.0005864568,0.41780475,0.0005171079,0.00016315751,0.000110029505,0.00020139909,0.00057570136,0.013772916],"genre_scores_gemma":[0.9937139,0.0000566316,0.0036860378,0.000017699233,0.0000046089294,0.000028224364,0.000029825602,0.000009432265,0.0024536035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987173,0.000028320885,0.000006412749,0.00003374967,0.000034160985,0.000025525174],"domain_scores_gemma":[0.999772,0.00011047105,0.00003739396,0.000014364066,0.00005322321,0.000012425267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045642245,0.00070066703,0.00086669426,0.0002574118,0.0006623426,0.0011915698,0.00074807217,0.0009700171,0.0019865015],"category_scores_gemma":[0.00053639396,0.0004416504,0.000382193,0.00028902554,0.0004826261,0.00051251135,0.00072818605,0.0006952974,0.00016608882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012549011,0.000045016797,0.00019011306,0.00004327964,0.000015760257,0.000052836665,0.000020326957,0.9860399,0.0070965984,0.00055580057,0.00013646425,0.0056784484],"study_design_scores_gemma":[0.0000099362305,0.000041874282,0.0001305131,0.0000015930417,0.000004867957,0.0000020875175,0.0000032016617,0.9983169,0.0012838404,0.00009508106,0.00010688364,0.000003250518],"about_ca_topic_score_codex":0.01613602,"about_ca_topic_score_gemma":0.013754353,"teacher_disagreement_score":0.01613602,"about_ca_system_score_codex":0.0006570785,"about_ca_system_score_gemma":0.0008935772,"threshold_uncertainty_score":0.032084167},"labels":[],"label_agreement":null},{"id":"W4395660130","doi":"10.1016/j.jprocont.2024.103223","title":"The chemostat reactor: A stability analysis and model predictive control","year":2024,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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 Alberta","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Control theory (sociology); Model predictive control; Controller (irrigation); Riccati equation; Optimal control; Linearization; Mathematics; Observer (physics); Nonlinear system; Mathematical optimization; Computer science; Partial differential equation; Control (management)","score_opus":0.005029587223457849,"score_gpt":0.22716756224342718,"score_spread":0.22213797501996935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395660130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26145574,0.0060341908,0.6974244,0.0019158027,0.00040676695,0.0001401821,0.00027132488,0.00058678654,0.031764943],"genre_scores_gemma":[0.98296,0.0009974499,0.009305926,0.000047615857,0.000075134914,0.00004855199,0.00005759706,0.000043955482,0.0064638234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998429,0.000045996945,0.000006339971,0.0000380098,0.000051327304,0.000015359881],"domain_scores_gemma":[0.9997869,0.00012301715,0.000022184337,0.000011544578,0.000048529266,0.00000782765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048150026,0.0006852461,0.0007561438,0.0004747622,0.00051361445,0.0012607811,0.00047726318,0.000808637,0.0019065766],"category_scores_gemma":[0.0009956164,0.00040557378,0.00083060405,0.0003266886,0.00080774096,0.00077330123,0.0006057373,0.0008824512,0.00026832035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032121723,0.00010858471,0.00073039683,0.0001599188,0.000077534525,0.00012483026,0.000060913928,0.9001201,0.031692587,0.030914314,0.0012662006,0.034423433],"study_design_scores_gemma":[0.0000064363035,0.000039878047,0.00019561333,0.0000027725155,0.000008409041,0.000007870732,0.0000035963014,0.9955467,0.0016121594,0.0023129077,0.00025793663,0.00000571501],"about_ca_topic_score_codex":0.008381203,"about_ca_topic_score_gemma":0.0036053497,"teacher_disagreement_score":0.008381203,"about_ca_system_score_codex":0.0005991654,"about_ca_system_score_gemma":0.0010609778,"threshold_uncertainty_score":0.016664803},"labels":[],"label_agreement":null},{"id":"W4399428283","doi":"10.1016/j.jprocont.2024.103252","title":"A big data-driven predictive control approach for nonlinear processes using behaviour clusters","year":2024,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Model predictive control; Big data; Nonlinear system; Computer science; Control (management); Control theory (sociology); Data mining; Artificial intelligence; Physics","score_opus":0.0293936684984166,"score_gpt":0.2789352709813222,"score_spread":0.2495416024829056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399428283","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059272684,0.00010706138,0.992584,0.00007829297,0.000022452867,0.000027610151,0.000031770756,0.00015608135,0.0010654761],"genre_scores_gemma":[0.8488669,0.00030879714,0.14810053,0.00008862784,0.0000586134,0.00026442183,0.0002138853,0.000084703584,0.0020134952],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996506,0.000069363385,0.00001928096,0.00009162781,0.00013062714,0.00003848193],"domain_scores_gemma":[0.9993917,0.0002635392,0.00008689275,0.000073064904,0.00015183153,0.000033068194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005968556,0.0009641158,0.00084424973,0.00059926766,0.0005042391,0.0009898074,0.001309456,0.0005913332,0.0009873676],"category_scores_gemma":[0.001423092,0.00050027744,0.0007545815,0.0006701001,0.0008955938,0.0010785685,0.0013440998,0.0012754445,0.00016628548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005036683,0.000029845,0.00040539878,0.000092468516,0.000031201296,0.00008176451,0.00010742403,0.95272964,0.0032266635,0.011419732,0.0004159661,0.031409565],"study_design_scores_gemma":[0.0000014940796,0.000010475011,0.00005576061,0.000002306448,0.0000020282812,0.000004402943,0.0000050664808,0.99727184,0.00028633812,0.0021523398,0.00020478557,0.0000031105872],"about_ca_topic_score_codex":0.007932063,"about_ca_topic_score_gemma":0.0059008924,"teacher_disagreement_score":0.007932063,"about_ca_system_score_codex":0.00074216584,"about_ca_system_score_gemma":0.0010622593,"threshold_uncertainty_score":0.015771806},"labels":[],"label_agreement":null},{"id":"W4400761863","doi":"10.1016/j.jprocont.2024.103277","title":"Switching probabilistic slow feature extraction for semisupervised industrial inferential modeling","year":2024,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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 Alberta","funders":"","keywords":"Probabilistic logic; Artificial intelligence; Feature extraction; Feature (linguistics); Computer science; Inference; Extraction (chemistry); Pattern recognition (psychology); Statistical model; Chromatography; Chemistry","score_opus":0.018607816146618383,"score_gpt":0.26727266259272964,"score_spread":0.24866484644611125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400761863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013347629,0.00009529013,0.98558134,0.000064357606,0.000009617255,0.000021626778,0.00009248308,0.00043078518,0.00035690126],"genre_scores_gemma":[0.84104097,0.00018687443,0.1552399,0.00010628791,0.00005481014,0.00016353505,0.00075643737,0.00016035106,0.0022908435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939144,0.0001903079,0.00004215798,0.0001653616,0.00014980279,0.00006092795],"domain_scores_gemma":[0.9970108,0.0021395837,0.0002364142,0.00032276567,0.00023977447,0.000050720693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012084236,0.0006998162,0.0010294003,0.0007214476,0.0003966595,0.0008659006,0.0009716136,0.00068377703,0.0018743068],"category_scores_gemma":[0.0044555105,0.0005318882,0.00091690064,0.0006318342,0.0005551335,0.0013271809,0.001169483,0.0014035987,0.0005999686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047397416,0.00019934714,0.0021629697,0.00023591417,0.00015193732,0.00020534093,0.00015067731,0.6285948,0.016952178,0.020078298,0.0022133277,0.3285812],"study_design_scores_gemma":[0.0000022750498,0.000017183862,0.00017239813,0.0000022521285,0.000005016275,0.000012086473,0.000002755132,0.99464214,0.0008427473,0.00412962,0.00016842975,0.0000030768333],"about_ca_topic_score_codex":0.0019081151,"about_ca_topic_score_gemma":0.002635087,"teacher_disagreement_score":0.0019081151,"about_ca_system_score_codex":0.00047028103,"about_ca_system_score_gemma":0.0007011981,"threshold_uncertainty_score":0.00639081},"labels":[],"label_agreement":null},{"id":"W4401732379","doi":"10.1016/j.jprocont.2024.103295","title":"Improved similarity analysis of industrial alarm flood sequences by considering alarm correlations","year":2024,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"Science and Technology Department of Hubei Province; National Aerospace Science Foundation of China; National Natural Science Foundation of China; Wuhan Municipal Science and Technology Bureau; Hubei Provincial Collaborative Innovation Centre of Agricultural E-Commerce","keywords":"ALARM; Similarity (geometry); Flood myth; Computer science; Environmental science; Mathematics; Data mining; Engineering; Artificial intelligence; Geography; Electrical engineering","score_opus":0.018000119026307878,"score_gpt":0.27920069401796455,"score_spread":0.26120057499165666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401732379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48296434,0.0005335818,0.5133108,0.00010541977,0.00012546589,0.00006573413,0.00030039978,0.00075156405,0.0018426972],"genre_scores_gemma":[0.9379269,0.00019525173,0.06022051,0.000020065674,0.00007842832,0.000025548734,0.0005816157,0.000050782786,0.00090093055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940574,0.00008020942,0.000056512148,0.0001132194,0.00026252397,0.000081811384],"domain_scores_gemma":[0.9985476,0.0003910776,0.00026077114,0.00012371263,0.00058942597,0.00008733831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051427574,0.00049682974,0.0007121643,0.002974255,0.00034287447,0.0006364074,0.00055102614,0.0005304029,0.0009117788],"category_scores_gemma":[0.0025258728,0.0002068583,0.00055048306,0.0020852268,0.00019606708,0.0009356131,0.00054364995,0.00044740477,0.0003957472],"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":[0.0018696018,0.00053009164,0.033971805,0.00034858778,0.00024833076,0.0011309097,0.00032122506,0.13780348,0.14208597,0.0064063943,0.0029460283,0.6723376],"study_design_scores_gemma":[0.000014387873,0.00020724279,0.015933089,0.000011715724,0.000057630634,0.00041509376,0.00008088793,0.9672326,0.013256861,0.0016127429,0.0011528744,0.000024928127],"about_ca_topic_score_codex":0.0017067044,"about_ca_topic_score_gemma":0.0015171878,"teacher_disagreement_score":0.002974255,"about_ca_system_score_codex":0.0002239585,"about_ca_system_score_gemma":0.00056630647,"threshold_uncertainty_score":0.0033935905},"labels":[],"label_agreement":null},{"id":"W4402330851","doi":"10.1016/j.jprocont.2024.103301","title":"Multi-view graph convolutional network with comprehensive structural learning: Enhancing dynamics representation for industrial processes","year":2024,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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 Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Graph; Representation (politics); Theoretical computer science; Convolutional neural network; Artificial intelligence; Machine learning","score_opus":0.01852991399233941,"score_gpt":0.27128762634692766,"score_spread":0.25275771235458827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402330851","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050458707,0.0005822913,0.9448021,0.00027779525,0.00008282071,0.000027373695,0.00024092794,0.0018188424,0.0017091396],"genre_scores_gemma":[0.83560264,0.0005843833,0.15762956,0.00021239067,0.00005774949,0.000051659783,0.00095371297,0.00021018105,0.0046977503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999856,0.000024400757,0.000005840776,0.000049824273,0.000036883383,0.0000271138],"domain_scores_gemma":[0.99974114,0.00009172538,0.00002634523,0.00005432046,0.000061870414,0.000024586425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003386398,0.0008159171,0.0005796849,0.0006226731,0.00024233933,0.0005466468,0.0009869791,0.0008636679,0.0018533734],"category_scores_gemma":[0.000939857,0.00038587008,0.00070638,0.00060860097,0.00027141665,0.0011978409,0.00091995153,0.0011395117,0.0006404366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028660134,0.00021318927,0.0013223181,0.00011178471,0.00011349981,0.00010087674,0.000054714346,0.58904284,0.026505679,0.006883863,0.004293849,0.3710708],"study_design_scores_gemma":[0.0000014212553,0.000009952625,0.00009593163,0.0000013191732,0.000004492061,0.00000475621,0.0000017602443,0.9982889,0.00077859627,0.0006505152,0.00016037373,0.0000018757602],"about_ca_topic_score_codex":0.012337617,"about_ca_topic_score_gemma":0.017225834,"teacher_disagreement_score":0.012337617,"about_ca_system_score_codex":0.0005359527,"about_ca_system_score_gemma":0.0008533701,"threshold_uncertainty_score":0.024531603},"labels":[],"label_agreement":null},{"id":"W4402695755","doi":"10.1016/j.jprocont.2024.103314","title":"Image based Modeling and Control for Batch Processes","year":2024,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"Computer science; Control (management); Image (mathematics); Batch processing; Process engineering; Artificial intelligence; Control engineering; Control theory (sociology); Engineering; Programming language","score_opus":0.0072176070532888855,"score_gpt":0.24199932962176804,"score_spread":0.23478172256847915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402695755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00412841,0.0005602017,0.98927057,0.0001561239,0.000072784394,0.000036536567,0.00008146547,0.0002683899,0.005425448],"genre_scores_gemma":[0.824324,0.0022357123,0.15229447,0.00017629318,0.00016438443,0.00042704694,0.0004286619,0.0001456275,0.019803753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967456,0.00005258858,0.000016083031,0.00008712083,0.00014456421,0.000025138654],"domain_scores_gemma":[0.9997353,0.000100729936,0.000055807745,0.000023348324,0.000073424955,0.000011418787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053837546,0.0008061627,0.0007579983,0.0003794043,0.00031542548,0.0013267158,0.0010429651,0.0008612276,0.0026749962],"category_scores_gemma":[0.0007891141,0.0003221876,0.0008415177,0.00038213813,0.0007611811,0.00075886736,0.0009630293,0.0013158835,0.0005222106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037613463,0.000021596381,0.00020755641,0.00013583535,0.000019434452,0.00004217118,0.00006739835,0.9395197,0.0052085365,0.031289957,0.0008078734,0.022642247],"study_design_scores_gemma":[0.000002188959,0.000013740527,0.00004721773,0.0000040466944,0.0000029000623,0.0000044428607,0.0000035358794,0.9959817,0.0005916655,0.002285698,0.0010586357,0.0000042511842],"about_ca_topic_score_codex":0.0070572803,"about_ca_topic_score_gemma":0.003475293,"teacher_disagreement_score":0.0070572803,"about_ca_system_score_codex":0.0009473189,"about_ca_system_score_gemma":0.00076260883,"threshold_uncertainty_score":0.0140324235},"labels":[],"label_agreement":null},{"id":"W4403198320","doi":"10.1016/j.jprocont.2024.103316","title":"Robust MPC design for multi-model infinite-dimensional distributed parameter systems","year":2024,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Control theory (sociology); Distributed parameter system; Computer science; Distributed element model; Control engineering; Mathematics; Engineering; Control (management); Mathematical analysis; Artificial intelligence","score_opus":0.04441718849174936,"score_gpt":0.2696244798957034,"score_spread":0.225207291403954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403198320","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016599633,0.00042326414,0.9784146,0.00015368342,0.000040930627,0.000026931682,0.000038926948,0.00033357056,0.003968463],"genre_scores_gemma":[0.9681473,0.0002761945,0.02969671,0.000049281556,0.000028449169,0.00009319996,0.000074980024,0.000029489276,0.0016043345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967575,0.00007083463,0.000015973574,0.00008904799,0.000110439876,0.00003799721],"domain_scores_gemma":[0.99959666,0.0001409552,0.00011062097,0.000036159126,0.00009886973,0.000016794162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061259954,0.00088341825,0.00081229885,0.00031762625,0.0003499977,0.0009613122,0.0009493817,0.0006857413,0.00091584754],"category_scores_gemma":[0.0011636424,0.00038252358,0.0005078311,0.00036626277,0.00071070495,0.00056628196,0.00075055467,0.0010312019,0.00016895567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002156067,0.000009801028,0.000093982126,0.00005159361,0.000019085724,0.000047398677,0.000024686635,0.9847431,0.001961992,0.005290769,0.0002040174,0.0075320834],"study_design_scores_gemma":[0.000003351476,0.000014909839,0.000038103215,0.000002752882,0.0000031552256,0.000005344001,0.0000021154392,0.9985915,0.0003352288,0.0007721506,0.00022936899,0.000002002942],"about_ca_topic_score_codex":0.0052048946,"about_ca_topic_score_gemma":0.0026936159,"teacher_disagreement_score":0.0052048946,"about_ca_system_score_codex":0.0006584366,"about_ca_system_score_gemma":0.0009487583,"threshold_uncertainty_score":0.0103491545},"labels":[],"label_agreement":null},{"id":"W4403646694","doi":"10.1016/j.jprocont.2024.103325","title":"Just-in-time framework for robust soft sensing based on robust variational autoencoder","year":2024,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"Autoencoder; Computer science; Artificial intelligence; Control theory (sociology); Pattern recognition (psychology); Mathematical optimization; Mathematics; Artificial neural network","score_opus":0.012566818636764735,"score_gpt":0.24699079979016225,"score_spread":0.23442398115339752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403646694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021918064,0.00014598494,0.99679464,0.00008212272,0.000021121275,0.000011349814,0.000021443455,0.00009408622,0.00063748047],"genre_scores_gemma":[0.67296475,0.00096691074,0.31731862,0.00034599617,0.00015709408,0.00030215603,0.00041819867,0.0002629875,0.0072632614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991678,0.00021827656,0.00005604432,0.00024904087,0.00022973283,0.00007909194],"domain_scores_gemma":[0.9989679,0.0005747314,0.00010914699,0.00008762867,0.0002200702,0.000040523777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014364026,0.0012328153,0.0011442624,0.00043127825,0.00037217262,0.0012143758,0.0016775891,0.0012348231,0.0017682945],"category_scores_gemma":[0.002934469,0.0007293493,0.0013114967,0.00045930766,0.00123832,0.001719423,0.0018048005,0.0023293686,0.0003587797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003959079,0.00002049169,0.00038662108,0.00011264177,0.00006484581,0.00010679221,0.00009453725,0.93239874,0.0032920851,0.03212751,0.0006392034,0.03071688],"study_design_scores_gemma":[0.00000138322,0.000008764291,0.000028815135,0.000002868152,0.0000030327196,0.000009252515,0.0000031481986,0.9963999,0.0003034007,0.0029957315,0.00023879511,0.0000048279676],"about_ca_topic_score_codex":0.0068495492,"about_ca_topic_score_gemma":0.004962356,"teacher_disagreement_score":0.0068495492,"about_ca_system_score_codex":0.00081574405,"about_ca_system_score_gemma":0.0011640049,"threshold_uncertainty_score":0.013619363},"labels":[],"label_agreement":null},{"id":"W4407278886","doi":"10.1016/j.jprocont.2025.103386","title":"Predictive control of flow rates and concentrations in sewage transport and treatment systems","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Water Systems and Optimization","field":"Engineering","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":"Université Laval; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Model predictive control; Sewage; Environmental science; Flow (mathematics); Sewage treatment; Control (management); Volumetric flow rate; Environmental engineering; Control theory (sociology); Computer science; Mechanics; Physics; Artificial intelligence","score_opus":0.0035154603664495267,"score_gpt":0.20115579067784362,"score_spread":0.19764033031139408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407278886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10975037,0.0006294798,0.88310033,0.000506415,0.00013097333,0.00010208193,0.00008947821,0.00067067775,0.005020276],"genre_scores_gemma":[0.98407274,0.00020582744,0.014266568,0.00005637053,0.000022621174,0.00007576873,0.000038624516,0.000019187082,0.0012422428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995384,0.000121060904,0.000023205266,0.000090924834,0.00015941805,0.00006697788],"domain_scores_gemma":[0.9990451,0.00052893156,0.00019319545,0.000027333474,0.0001749797,0.000030514655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011240006,0.0010458386,0.00070512865,0.00036398685,0.0003596648,0.0010056065,0.0008506204,0.0007829664,0.0007213758],"category_scores_gemma":[0.0022374538,0.0005399385,0.00038805578,0.00041367084,0.0006937013,0.0005946032,0.00065883587,0.0009337576,0.0001321909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005181758,0.000041582192,0.00023084182,0.00004428867,0.000009491206,0.000027362696,0.000025448542,0.9862299,0.002838385,0.0011989361,0.00022706625,0.00907501],"study_design_scores_gemma":[0.00000582687,0.00002386157,0.0000621188,0.0000016361862,0.0000025729655,0.0000020875057,0.0000021289825,0.9987054,0.0007714419,0.0003230295,0.00009733529,0.0000026406922],"about_ca_topic_score_codex":0.010810654,"about_ca_topic_score_gemma":0.006838662,"teacher_disagreement_score":0.010810654,"about_ca_system_score_codex":0.0007032198,"about_ca_system_score_gemma":0.0010821017,"threshold_uncertainty_score":0.021495461},"labels":[],"label_agreement":null},{"id":"W4408706604","doi":"10.1016/j.jprocont.2025.103407","title":"Interpretable Dynamic Modelling and Prediction of Free Acid in Zinc Leaching Process","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Metal Extraction and Bioleaching","field":"Engineering","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":"Teck (Canada); University of Alberta","funders":"Teck Resources","keywords":"Leaching (pedology); Zinc; Process (computing); Process engineering; Computer science; Environmental science; Engineering; Metallurgy; Materials science; Soil science","score_opus":0.005650286436994714,"score_gpt":0.23572292432278102,"score_spread":0.2300726378857863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408706604","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37565932,0.0008101166,0.6147909,0.00031684694,0.000093779534,0.0001332261,0.0005481065,0.0010822553,0.0065654055],"genre_scores_gemma":[0.9840588,0.0003217349,0.012537012,0.000028649292,0.000011358468,0.0001133723,0.00021270916,0.00004160691,0.0026747333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986076,0.000019612226,0.000010631586,0.0000482866,0.000039169332,0.000021531763],"domain_scores_gemma":[0.99978536,0.00010929966,0.000028048347,0.000012940494,0.00005563215,0.00000870142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000327544,0.0007511857,0.0008137434,0.0004366092,0.00036613745,0.0010443277,0.00080556294,0.0012138693,0.0009801544],"category_scores_gemma":[0.0006747777,0.00046219258,0.00091258856,0.00032463542,0.0003958247,0.0007036502,0.0005421325,0.0008627184,0.00022456769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023268116,0.000021444428,0.00088198873,0.000035830144,0.000012885733,0.000046785233,0.00002861045,0.9903803,0.0034416479,0.0004592329,0.00007204357,0.0045960424],"study_design_scores_gemma":[0.0000019068175,0.0000071047007,0.00014849505,0.0000014377473,0.0000028873176,0.000003907457,0.000002807206,0.99901783,0.00055605086,0.00015737768,0.000097493314,0.0000027799122],"about_ca_topic_score_codex":0.015268024,"about_ca_topic_score_gemma":0.008012679,"teacher_disagreement_score":0.015268024,"about_ca_system_score_codex":0.00070618314,"about_ca_system_score_gemma":0.0009506168,"threshold_uncertainty_score":0.030358315},"labels":[],"label_agreement":null},{"id":"W4408749068","doi":"10.1016/j.jprocont.2025.103408","title":"Cross-domain knowledge transfer in industrial process monitoring: A survey","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"University of Alberta","funders":"State Key Laboratory of Industrial Control Technology; Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Process (computing); Domain (mathematical analysis); Knowledge transfer; Computer science; Process engineering; Engineering; Knowledge management; Mathematics","score_opus":0.02116627222461592,"score_gpt":0.30683404245591983,"score_spread":0.2856677702313039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408749068","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":[0.21447824,0.32893884,0.40739536,0.0046687527,0.0003832512,0.000349224,0.001020969,0.0016583172,0.041107032],"genre_scores_gemma":[0.781144,0.13686061,0.074269876,0.00089775363,0.00039854544,0.00015477893,0.0018771105,0.00011758486,0.004279699],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962054,0.0009023895,0.00038528655,0.00090086326,0.0013893954,0.00021681155],"domain_scores_gemma":[0.9808748,0.013482062,0.0012074276,0.0021830988,0.0020053042,0.00024733727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047336365,0.0007273198,0.001136057,0.005392856,0.00047558697,0.0031307852,0.0021743495,0.0015214405,0.0015939153],"category_scores_gemma":[0.019543123,0.00037474025,0.0006953852,0.0059365793,0.0008278157,0.007287929,0.0029810455,0.0009861679,0.0006218825],"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":[0.000114764895,0.00028296976,0.01151519,0.0014527129,0.00016390627,0.0001635172,0.00048764894,0.005590859,0.0015721382,0.0061262553,0.0015941805,0.9709358],"study_design_scores_gemma":[0.00009613257,0.0016560627,0.119085394,0.007185781,0.0014266217,0.009314415,0.0075949593,0.29234195,0.06414857,0.17185266,0.32493165,0.00036580543],"about_ca_topic_score_codex":0.0012774689,"about_ca_topic_score_gemma":0.0006169345,"teacher_disagreement_score":0.005392856,"about_ca_system_score_codex":0.000794702,"about_ca_system_score_gemma":0.0013610999,"threshold_uncertainty_score":0.02503419},"labels":[],"label_agreement":null},{"id":"W4409002392","doi":"10.1016/j.jprocont.2025.103413","title":"Improved gain conditioning for linear model predictive control","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Imperial Oil (Canada); Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Imperial Oil Limited","keywords":"Conditioning; Control theory (sociology); Model predictive control; Control (management); Computer science; Mathematics; Statistics; Artificial intelligence","score_opus":0.004514335207996252,"score_gpt":0.24621881741512686,"score_spread":0.24170448220713062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409002392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002216723,0.00007819544,0.9960127,0.000031331718,0.000018064986,0.000027141758,0.000014109281,0.00041370513,0.0011880363],"genre_scores_gemma":[0.4903291,0.00036768723,0.50283194,0.0002022846,0.0001040987,0.00032033297,0.00026796781,0.00046163306,0.0051150196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892706,0.00028311147,0.000055439705,0.00018185451,0.00046328965,0.00008915918],"domain_scores_gemma":[0.99849784,0.0007523996,0.00012215522,0.00021947779,0.00037555007,0.000032673055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015523422,0.0014771585,0.00095235824,0.0006110266,0.000436767,0.00096642546,0.0009618946,0.00070610474,0.0062997746],"category_scores_gemma":[0.0050352463,0.0005810587,0.0006452854,0.0005856312,0.00089356693,0.001217157,0.0012139705,0.0024377215,0.0011491769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017288835,0.00012642934,0.0004391773,0.00020151261,0.000044589215,0.000051218263,0.00010867316,0.786236,0.012537034,0.02930675,0.0018532325,0.16892251],"study_design_scores_gemma":[0.000008760826,0.000034732184,0.000052148036,0.000008048285,0.0000046350356,0.000006741185,0.000003251128,0.9943962,0.0020136787,0.002838256,0.00062656705,0.0000070278365],"about_ca_topic_score_codex":0.005753478,"about_ca_topic_score_gemma":0.0038222643,"teacher_disagreement_score":0.0062997746,"about_ca_system_score_codex":0.0006934643,"about_ca_system_score_gemma":0.0013422576,"threshold_uncertainty_score":0.021074831},"labels":[],"label_agreement":null},{"id":"W4409002420","doi":"10.1016/j.jprocont.2025.103423","title":"Fault detection and identification using a novel process decomposition algorithm for distributed process monitoring","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"McMaster University","funders":"Ontario Ministry of Research and Innovation","keywords":"Process (computing); Identification (biology); Fault detection and isolation; Computer science; Decomposition; Fault (geology); Algorithm; Data mining; Artificial intelligence","score_opus":0.008826973727546922,"score_gpt":0.29391244348408635,"score_spread":0.2850854697565394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409002420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003542581,0.000018072382,0.9956691,0.000022190416,0.000006866971,0.000016910872,0.000013226212,0.0004472393,0.00026379267],"genre_scores_gemma":[0.19456448,0.000054046945,0.803542,0.00003069527,0.000030838575,0.00012327316,0.00015062875,0.00009554097,0.0014085481],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910057,0.00015955212,0.000048375117,0.0002618508,0.0003613241,0.00006835205],"domain_scores_gemma":[0.9988128,0.00040841365,0.00018802182,0.00018367716,0.00036136888,0.000045747027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081258686,0.0009112044,0.000800725,0.0013540334,0.0005229927,0.00092551054,0.0007309877,0.00062215695,0.0018665165],"category_scores_gemma":[0.0033218516,0.00027686576,0.00059838063,0.001051131,0.00048543361,0.001067713,0.00093812187,0.0011188495,0.00056843826],"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":[0.00022521109,0.00016996305,0.0028191982,0.00007841699,0.00006793821,0.000095829055,0.00015742266,0.29001513,0.02784524,0.0176881,0.0020800822,0.6587574],"study_design_scores_gemma":[0.000012225534,0.00003370258,0.0003964153,0.0000037581754,0.00000686226,0.00003851768,0.00000985972,0.9903058,0.003910431,0.0044064233,0.00086796295,0.000008054563],"about_ca_topic_score_codex":0.0032447835,"about_ca_topic_score_gemma":0.0032083858,"teacher_disagreement_score":0.0032447835,"about_ca_system_score_codex":0.0006495629,"about_ca_system_score_gemma":0.0011686344,"threshold_uncertainty_score":0.0064517856},"labels":[],"label_agreement":null},{"id":"W4409305122","doi":"10.1016/j.jprocont.2025.103426","title":"Robust to outlier image inpainting for interface detection in primary separation vessel","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fluid Dynamics and Mixing","field":"Engineering","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":"Institute for Oil Sands Innovation, University of Alberta; Imperial Oil Resources; Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Alberta","keywords":"Inpainting; Artificial intelligence; Outlier; Computer vision; Image (mathematics); Pattern recognition (psychology); Separation (statistics); Anomaly detection; Computer science; Interface (matter); Machine learning","score_opus":0.005021843751683143,"score_gpt":0.24887365645938975,"score_spread":0.24385181270770662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409305122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.082151935,0.0003468559,0.91571814,0.0001492735,0.00004479397,0.000034129625,0.000052248382,0.00063996867,0.0008626515],"genre_scores_gemma":[0.79346335,0.00033977968,0.20327282,0.00015017792,0.000053349242,0.000047348774,0.00030555582,0.00010240676,0.0022652433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99962294,0.000048456062,0.00001595157,0.0000973829,0.0001717484,0.00004349248],"domain_scores_gemma":[0.9991757,0.00036431258,0.00013720871,0.00009845491,0.00018142996,0.0000428491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000596941,0.0005296291,0.0006724246,0.00035254835,0.00018682014,0.00046507473,0.0007151861,0.0006217071,0.00070041267],"category_scores_gemma":[0.00231848,0.00023403406,0.00050090585,0.0002788183,0.0005135806,0.00053756527,0.0007058302,0.001122892,0.0002082934],"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":[0.00064128765,0.00022804491,0.0032445565,0.00033290833,0.00010415069,0.00067662715,0.00038793022,0.368776,0.28184107,0.0052802707,0.0032227633,0.33526433],"study_design_scores_gemma":[0.0000066769558,0.00007578189,0.0008595264,0.000007128164,0.000008472803,0.00014203766,0.000020693527,0.9782528,0.019381996,0.00071291096,0.00052455254,0.0000074963],"about_ca_topic_score_codex":0.0015054718,"about_ca_topic_score_gemma":0.001909635,"teacher_disagreement_score":0.0015054718,"about_ca_system_score_codex":0.0003002234,"about_ca_system_score_gemma":0.00048952946,"threshold_uncertainty_score":0.00315696},"labels":[],"label_agreement":null},{"id":"W4409869210","doi":"10.1016/j.jprocont.2025.103432","title":"Robust and constrained tracking of PSV interface using convolutional neural networks and optimistic moving horizon estimation","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Infrared Target Detection Methodologies","field":"Engineering","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":"Convolutional neural network; Horizon; Computer science; Tracking (education); Interface (matter); Estimation; Artificial neural network; Artificial intelligence; Moving horizon estimation; Real-time computing; Mathematics; Kalman filter; Engineering; Psychology; Extended Kalman filter; Operating system","score_opus":0.027068991480796535,"score_gpt":0.28234115750119526,"score_spread":0.2552721660203987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409869210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017117599,0.000189297,0.9814253,0.00006541555,0.0000246275,0.0000146798575,0.000030715168,0.00029971523,0.00083265855],"genre_scores_gemma":[0.89539886,0.00021203556,0.10146166,0.000079050915,0.000038371127,0.000055005206,0.00015908046,0.000058455487,0.0025375013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996686,0.000040245373,0.00001752699,0.00011394012,0.000103326594,0.000056427696],"domain_scores_gemma":[0.9995665,0.00013501849,0.000119113865,0.00004225767,0.00011377322,0.000023263572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006044708,0.0010239307,0.00071400456,0.00046768496,0.00033450365,0.0007292111,0.0009873159,0.00065495656,0.0007317627],"category_scores_gemma":[0.001776099,0.000493505,0.0005124127,0.0003652556,0.00049273414,0.00081570173,0.0008975164,0.0010762387,0.0001649918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012289594,0.000028100714,0.0014573762,0.00005956557,0.00004987918,0.00008937861,0.00006082178,0.8840105,0.008262341,0.004035079,0.00068437296,0.101139694],"study_design_scores_gemma":[0.0000013316942,0.000007931026,0.00015630362,0.0000020734035,0.0000025592572,0.0000056684053,0.0000020082148,0.9985057,0.0007775175,0.00042409275,0.000111652116,0.0000031966406],"about_ca_topic_score_codex":0.016630147,"about_ca_topic_score_gemma":0.0118816495,"teacher_disagreement_score":0.016630147,"about_ca_system_score_codex":0.00080753054,"about_ca_system_score_gemma":0.0011007494,"threshold_uncertainty_score":0.03306669},"labels":[],"label_agreement":null},{"id":"W4411582731","doi":"10.1016/j.jprocont.2025.103473","title":"A novel hybrid neural network for modeling dynamic systems using physics-informed regularization","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","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 Alberta","funders":"Science and Engineering Research Board; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; University of Alberta","keywords":"Regularization (linguistics); Artificial neural network; Computer science; Artificial intelligence; Control engineering; Physics; Engineering","score_opus":0.018892944351983413,"score_gpt":0.2912545064233483,"score_spread":0.2723615620713649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411582731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011683998,0.00039935706,0.9842725,0.00016361113,0.0000498823,0.000025389048,0.000077795245,0.00041765856,0.0029098655],"genre_scores_gemma":[0.7208014,0.00066746905,0.2653283,0.000279094,0.000088073866,0.00035029708,0.0004693429,0.00014012812,0.011875836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980694,0.000050425417,0.0000095179885,0.000050886716,0.00006690342,0.000015428754],"domain_scores_gemma":[0.99982446,0.00008312543,0.000022093642,0.000015392488,0.000046266625,0.000008792661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050248514,0.00073941296,0.000572525,0.0002816584,0.00029494448,0.0006190065,0.0010928722,0.0009711481,0.0013028639],"category_scores_gemma":[0.00074312126,0.00039678073,0.0006116696,0.0003488516,0.0004290213,0.00092645985,0.00084759924,0.001038407,0.0003864079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002913711,0.0000202744,0.00022848432,0.000044192107,0.000032809574,0.000037861024,0.000018733112,0.96985537,0.0030764595,0.0047166105,0.00049729017,0.021442747],"study_design_scores_gemma":[0.0000010112359,0.0000048370007,0.000017812457,0.0000012319005,0.0000017581539,0.0000030245285,6.478344e-7,0.9992662,0.00014123702,0.0003898147,0.0001709796,0.0000014893776],"about_ca_topic_score_codex":0.006251969,"about_ca_topic_score_gemma":0.0066642486,"teacher_disagreement_score":0.006251969,"about_ca_system_score_codex":0.0004631772,"about_ca_system_score_gemma":0.00073108764,"threshold_uncertainty_score":0.012431145},"labels":[],"label_agreement":null},{"id":"W4411696372","doi":"10.1016/j.jprocont.2025.103474","title":"Big data-driven predictive control for nonlinear systems—A trajectory cluster-based contraction approach","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Australian Research Council","keywords":"Trajectory; Control theory (sociology); Nonlinear system; Contraction (grammar); Model predictive control; Cluster (spacecraft); Computer science; Big data; Nonlinear model; Control engineering; Control (management); Data mining; Engineering; Artificial intelligence; Physics; Medicine; Internal medicine","score_opus":0.01635018173945911,"score_gpt":0.256797004935427,"score_spread":0.24044682319596788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411696372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005781505,0.00009773635,0.99299824,0.00008430029,0.000022568502,0.000023415485,0.000022111313,0.000095281,0.00087469164],"genre_scores_gemma":[0.8701443,0.00032936846,0.12614225,0.00013844155,0.00006472297,0.00028949342,0.0002196439,0.000098915385,0.0025729167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992649,0.00019856951,0.000039080154,0.0001763088,0.00025092374,0.000070319125],"domain_scores_gemma":[0.99884975,0.0005292323,0.00012021362,0.000106250554,0.00033451128,0.000060012044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014875401,0.0010350469,0.0010822074,0.0006077761,0.00057654356,0.0009782591,0.0017170863,0.0007004621,0.001127086],"category_scores_gemma":[0.0025774634,0.00045847305,0.0007734387,0.00078688795,0.001122923,0.0013263697,0.0018319538,0.0014319739,0.00017203696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052827538,0.000023496757,0.00040281797,0.000082072846,0.00003621885,0.000060982817,0.00010141548,0.95658517,0.002063152,0.016223026,0.00037979824,0.023989024],"study_design_scores_gemma":[0.0000016208304,0.000012934956,0.00003897855,0.000002032709,0.0000019206325,0.0000036674742,0.000005017425,0.99734575,0.00021790221,0.00219247,0.00017528632,0.0000024393278],"about_ca_topic_score_codex":0.008027927,"about_ca_topic_score_gemma":0.004518113,"teacher_disagreement_score":0.008027927,"about_ca_system_score_codex":0.00096725137,"about_ca_system_score_gemma":0.0012669336,"threshold_uncertainty_score":0.015962422},"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":"W4412429902","doi":"10.1016/j.jprocont.2025.103490","title":"A Metropolis–Hastings-within-Gibbs approach for nonlinear state–space system estimation","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","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 Alberta","funders":"","keywords":"Metropolis–Hastings algorithm; State space; Gibbs sampling; Nonlinear system; Estimation; Computer science; State (computer science); Space (punctuation); Mathematics; Markov chain Monte Carlo; Statistical physics; Algorithm; Artificial intelligence; Statistics; Engineering; Bayesian probability; Physics","score_opus":0.00869141418451515,"score_gpt":0.2676854068588868,"score_spread":0.2589939926743717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412429902","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032500692,0.00019287156,0.9952058,0.00023489204,0.000069371345,0.00005409647,0.000034280212,0.00025379585,0.0007047162],"genre_scores_gemma":[0.37483698,0.0005956244,0.6080012,0.0006875879,0.0005385597,0.00092908816,0.00076774217,0.0007792116,0.012863941],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980281,0.0010203809,0.00011398521,0.0002606024,0.00038880782,0.00018812045],"domain_scores_gemma":[0.98837227,0.009652437,0.00025333735,0.0006206112,0.0008193951,0.00028203346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043896143,0.001403578,0.0034057961,0.0010965697,0.0012883879,0.0015430842,0.005413294,0.0030460663,0.0072180424],"category_scores_gemma":[0.014195049,0.002238903,0.0022863378,0.0017053896,0.0034011093,0.003009489,0.0039054204,0.005099804,0.0017207904],"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":[0.00026178363,0.00012198172,0.000858538,0.0001703085,0.0002370313,0.00015003064,0.00015922195,0.8651484,0.0012380143,0.08773022,0.001760932,0.042163502],"study_design_scores_gemma":[0.000013294955,0.000009290815,0.00003633712,0.000003580501,0.000008474709,0.0000069201947,0.000003856336,0.9911935,0.00013776863,0.008368181,0.00021085185,0.000007943749],"about_ca_topic_score_codex":0.0151166795,"about_ca_topic_score_gemma":0.019508295,"teacher_disagreement_score":0.0151166795,"about_ca_system_score_codex":0.0016913177,"about_ca_system_score_gemma":0.0030542489,"threshold_uncertainty_score":0.03005743},"labels":[],"label_agreement":null},{"id":"W4413095446","doi":"10.1016/j.jprocont.2025.103514","title":"Robust online identification for hybrid multirate systems based on recursive EM algorithm","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Control Systems and Identification","field":"Engineering","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 Alberta","funders":"Nanjing Institute of Technology; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Jiangsu Province","keywords":"Identification (biology); Computer science; Algorithm; System identification; Control theory (sociology); Control engineering; Engineering; Artificial intelligence; Data mining; Control (management)","score_opus":0.011432965035416222,"score_gpt":0.2438285908723096,"score_spread":0.23239562583689338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413095446","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009210415,0.00012574492,0.9896848,0.000037957292,0.00001747507,0.00001047745,0.000010979865,0.00016741057,0.00073480303],"genre_scores_gemma":[0.78601307,0.00026219725,0.2092691,0.00007005814,0.000040195373,0.000117668846,0.000107701744,0.00008334348,0.0040366803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997104,0.000093529474,0.000018872302,0.00007402059,0.00006894327,0.000034175093],"domain_scores_gemma":[0.9992642,0.00044849873,0.0000843404,0.000073967436,0.00011406023,0.000014868719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080718094,0.0006903964,0.0010965765,0.00036955578,0.00037623706,0.00072813197,0.0007385246,0.00089869683,0.0019968315],"category_scores_gemma":[0.0022193433,0.0003755741,0.0006679453,0.0003243901,0.00053478486,0.0008598701,0.0009094606,0.00095434196,0.00047333355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018190553,0.000046481226,0.0005772208,0.00013835488,0.00010699458,0.0000935079,0.00010949638,0.8763898,0.008575757,0.013498792,0.0005701553,0.09971157],"study_design_scores_gemma":[0.000004646629,0.000016485877,0.00009331393,0.0000050801514,0.0000051069164,0.000014203735,0.0000038374433,0.9978536,0.0006500861,0.0011674246,0.00018127232,0.000004948061],"about_ca_topic_score_codex":0.0026135708,"about_ca_topic_score_gemma":0.0025688794,"teacher_disagreement_score":0.0026135708,"about_ca_system_score_codex":0.0003042183,"about_ca_system_score_gemma":0.00048469196,"threshold_uncertainty_score":0.006680131},"labels":[],"label_agreement":null},{"id":"W4413450393","doi":"10.1016/j.jprocont.2025.103525","title":"Closed-loop control framework for optimal startup of cryogenic air separation units","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Spacecraft and Cryogenic Technologies","field":"Engineering","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":"McMaster University","funders":"Ontario Ministry of Research and Innovation","keywords":"Separation (statistics); Loop (graph theory); Air separation; Control (management); Closed loop; Control theory (sociology); Control engineering; Engineering; Computer science; Chemistry; Mathematics","score_opus":0.008828708111020024,"score_gpt":0.2781895924361705,"score_spread":0.26936088432515043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413450393","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014035129,0.00024050851,0.9773561,0.00018596387,0.0000662087,0.000086674685,0.000059385216,0.0002932382,0.0076768324],"genre_scores_gemma":[0.965994,0.0002115786,0.03020956,0.00004997812,0.000053194683,0.0002144979,0.00006302553,0.000034638775,0.003169428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955386,0.00011423603,0.000017923163,0.00010532873,0.00013633604,0.00007239669],"domain_scores_gemma":[0.9994019,0.00026940546,0.00010436081,0.000029364288,0.00016889618,0.000025990075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001113262,0.0010347058,0.0008640729,0.00033686962,0.00054300367,0.0015772945,0.0010717007,0.0010178923,0.0031635314],"category_scores_gemma":[0.0015281156,0.00037775366,0.0004533782,0.00029914282,0.0010459168,0.00061713945,0.0010428205,0.0013500295,0.00031750544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003333958,0.000022709115,0.000079079655,0.000043732365,0.000008613639,0.00004637783,0.000028519518,0.98533165,0.0009660169,0.0057248278,0.00028907464,0.007426162],"study_design_scores_gemma":[0.000007541627,0.000030824594,0.00004478951,0.0000037168993,0.0000032367718,0.0000042358383,0.000004692721,0.998382,0.00021886794,0.0010586446,0.00023871582,0.0000026781472],"about_ca_topic_score_codex":0.0103955725,"about_ca_topic_score_gemma":0.0064057815,"teacher_disagreement_score":0.0103955725,"about_ca_system_score_codex":0.0009309589,"about_ca_system_score_gemma":0.0018850988,"threshold_uncertainty_score":0.020670176},"labels":[],"label_agreement":null},{"id":"W4414801884","doi":"10.1016/j.jprocont.2025.103563","title":"Real-time identification of most critical alarms for alarm flood reduction","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"ALARM; Identification (biology); Process (computing); Construct (python library); Prioritization; Flood myth; Markov process; False alarm","score_opus":0.0059795978242270734,"score_gpt":0.30270476106012656,"score_spread":0.2967251632358995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414801884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44450173,0.00081184367,0.5459287,0.00043026736,0.0003442625,0.00013237573,0.0005492753,0.0039930805,0.0033084482],"genre_scores_gemma":[0.95998985,0.000086163775,0.038823783,0.00004078419,0.000056387216,0.000022293973,0.00022595556,0.0000376691,0.00071723724],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975425,0.000034148932,0.000016820277,0.00006120251,0.000093236486,0.00004041375],"domain_scores_gemma":[0.9988035,0.00046966123,0.00020274751,0.00006515485,0.00035180248,0.000107101696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042135254,0.00074665307,0.0006260999,0.001420897,0.00045850343,0.0007087323,0.0004259926,0.00047785454,0.0014787272],"category_scores_gemma":[0.0022741982,0.00018647345,0.00018474809,0.00040424307,0.00014683045,0.00064051384,0.00039455434,0.00075847557,0.0005675547],"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":[0.004275632,0.0006414503,0.037897862,0.00033821983,0.000115759125,0.00069211225,0.0003943642,0.05649198,0.28081334,0.0028997608,0.01026251,0.60517704],"study_design_scores_gemma":[0.000044972367,0.0005961056,0.027737308,0.000027963655,0.00007678094,0.00051232445,0.00018771325,0.9048337,0.060591675,0.0027660339,0.002581945,0.000043403663],"about_ca_topic_score_codex":0.00070965214,"about_ca_topic_score_gemma":0.0011802595,"teacher_disagreement_score":0.0014787272,"about_ca_system_score_codex":0.00021184132,"about_ca_system_score_gemma":0.0005791186,"threshold_uncertainty_score":0.004946828},"labels":[],"label_agreement":null},{"id":"W7083167857","doi":"10.1016/j.jprocont.2025.103550","title":"Real-time freeze point prediction using multirate measurements in the blending process","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Process (computing); Control theory (sociology); Point (geometry); Multi point; Process variable","score_opus":0.05387920022535448,"score_gpt":0.4072366987063104,"score_spread":0.35335749848095593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083167857","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":[0.7645974,0.0003328968,0.23290105,0.00012087704,0.00007828815,0.000038633887,0.00017384428,0.00073537574,0.0010216192],"genre_scores_gemma":[0.98965245,0.00006541102,0.0099639315,0.000006518798,0.0000064389515,0.0000084207195,0.000050462586,0.000024906858,0.00022143294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983776,0.000017479942,0.000010529711,0.000052161777,0.000063877465,0.0000181593],"domain_scores_gemma":[0.99938035,0.00029598712,0.00010445185,0.000053098753,0.00012877151,0.000037280057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056711055,0.0006145188,0.0008822704,0.0005349488,0.00031722803,0.0008676304,0.00051598327,0.00077995856,0.0005747067],"category_scores_gemma":[0.0016682,0.0002881495,0.00037322412,0.0006125762,0.00043438427,0.0010212483,0.00043416157,0.00067444885,0.00015847883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020289463,0.00038248624,0.024522979,0.00033913818,0.000077528326,0.00047634082,0.00031555342,0.70744693,0.13637471,0.0017061406,0.00054159976,0.12578769],"study_design_scores_gemma":[0.000006156137,0.000024422574,0.0011360093,0.0000022678546,0.0000046701803,0.000009762208,0.00000914342,0.990624,0.007974581,0.00015032894,0.000051337483,0.000007452145],"about_ca_topic_score_codex":0.0036269666,"about_ca_topic_score_gemma":0.0028719164,"teacher_disagreement_score":0.0036269666,"about_ca_system_score_codex":0.0003239849,"about_ca_system_score_gemma":0.00039791956,"threshold_uncertainty_score":0.007211745},"labels":[],"label_agreement":null},{"id":"W7117357404","doi":"10.1016/j.jprocont.2025.103614","title":"Robust soft sensing with causal and injectivity-preserving Graph Neural Network","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Graph Neural Networks","field":"Computer Science","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":"Canadian Centre for Clean Coal/Carbon and Mineral Processing Technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Graph; A priori and a posteriori; Benchmark (surveying); Artificial neural network; Noise (video); Graph theory; Pattern recognition (psychology)","score_opus":0.009426928147155785,"score_gpt":0.23622733935786902,"score_spread":0.22680041121071323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117357404","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023469744,0.000120324476,0.9750208,0.00015457737,0.000020148722,0.000020348856,0.00004074115,0.0003256895,0.0008276207],"genre_scores_gemma":[0.87706065,0.00015530435,0.12026331,0.00016773693,0.000030663767,0.000072934636,0.00014992556,0.000071262155,0.002028213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966943,0.000095256386,0.000014477808,0.00010149237,0.00008423961,0.0000351772],"domain_scores_gemma":[0.9990489,0.0005337778,0.0001422191,0.00010755828,0.00012889477,0.000038704402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008056463,0.00109404,0.0006127572,0.00045225612,0.00030680123,0.0006812532,0.0011267982,0.0010454586,0.0007900752],"category_scores_gemma":[0.0032422445,0.00043206214,0.0005438256,0.00037965085,0.0009967578,0.0015942021,0.0012869763,0.001398071,0.0001668237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004453806,0.000022274287,0.00035579025,0.000029582181,0.000019201674,0.000036437435,0.00003115402,0.96372575,0.0021719476,0.0061497767,0.00027031908,0.027143242],"study_design_scores_gemma":[0.000001345044,0.0000062421673,0.000024906287,0.0000012450021,0.0000012301535,0.0000029463704,0.0000015419109,0.9976636,0.00031363202,0.0019447956,0.000036967274,0.000001522519],"about_ca_topic_score_codex":0.0054600076,"about_ca_topic_score_gemma":0.006540879,"teacher_disagreement_score":0.0054600076,"about_ca_system_score_codex":0.0009062747,"about_ca_system_score_gemma":0.0008210443,"threshold_uncertainty_score":0.01085645},"labels":[],"label_agreement":null},{"id":"W800668060","doi":"10.1016/j.jprocont.2015.05.002","title":"Optimization-based assessment of design limitations to air separation plant agility in demand response scenarios","year":2015,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Process Optimization and Integration","field":"Engineering","cited_by":71,"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":"McMaster University","keywords":"Demand response; Optimal design; Control engineering; Point (geometry); Computer science; Optimization problem; Mathematical optimization; Engineering; Electricity; Mathematics","score_opus":0.038174558337604844,"score_gpt":0.30958586007385247,"score_spread":0.2714113017362476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W800668060","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":[0.95350933,0.000253577,0.03427713,0.000508433,0.000030004123,0.00012844507,0.00047704703,0.00015553745,0.010660516],"genre_scores_gemma":[0.99692494,0.000028790122,0.0025349048,0.000019463474,0.0000040140794,0.00003050754,0.00009498771,0.000015017847,0.00034739394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986431,0.00085831195,0.00003955222,0.000097116514,0.00017668337,0.00018525761],"domain_scores_gemma":[0.9906346,0.007955555,0.00041216056,0.00022720137,0.0006089201,0.00016161185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041891667,0.0009911456,0.0009925063,0.0014572158,0.00044099792,0.0013134986,0.0007487847,0.0015957609,0.0015924915],"category_scores_gemma":[0.010632067,0.0007094199,0.0009890477,0.0006454059,0.00058969087,0.0011875831,0.00066920184,0.0008879056,0.00016154611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018562144,0.00003544627,0.0009865861,0.000023141169,0.00001662397,0.000021836191,0.000008267826,0.99616927,0.0004929577,0.0003941855,0.00009476348,0.0015712503],"study_design_scores_gemma":[0.000016653445,0.00009492399,0.0011726491,0.000006104729,0.000012081855,0.0000060903076,0.00001994765,0.9977748,0.0004844368,0.00032845567,0.00007613368,0.000007655263],"about_ca_topic_score_codex":0.010620637,"about_ca_topic_score_gemma":0.0075661945,"teacher_disagreement_score":0.010620637,"about_ca_system_score_codex":0.0015339997,"about_ca_system_score_gemma":0.0011008101,"threshold_uncertainty_score":0.022154689},"labels":[],"label_agreement":null},{"id":"W882589513","doi":"10.1016/j.jprocont.2015.03.007","title":"Detection and diagnosis of incipient faults in sensors of an LTI system using a modified GLR-based approach","year":2015,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","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":"University of Alberta","funders":"","keywords":"Fault detection and isolation; Fault (geology); Control theory (sociology); Monte Carlo method; Statistical hypothesis testing; Invariant (physics); LTI system theory; Sample (material); Algorithm; Computer science; Engineering; Mathematics; Linear system; Statistics; Artificial intelligence; Control (management)","score_opus":0.02057508192076948,"score_gpt":0.24321128234943312,"score_spread":0.22263620042866364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W882589513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044501185,0.00037722685,0.9519017,0.00011775059,0.00005329966,0.00005841219,0.0000752139,0.0016447235,0.0012704439],"genre_scores_gemma":[0.79544616,0.0001662223,0.20192558,0.00024423425,0.00006939763,0.00009567954,0.00017683659,0.00008313829,0.0017927669],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992379,0.00016843725,0.0000611,0.00018185354,0.0002778714,0.00007286788],"domain_scores_gemma":[0.99907947,0.00041264368,0.00015609215,0.00012792696,0.00019812581,0.000025626661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005990789,0.00096923974,0.0012334461,0.0008007231,0.00023230421,0.0007100374,0.0010301635,0.0011766623,0.0013120114],"category_scores_gemma":[0.001444409,0.00032947483,0.0007679277,0.0002897332,0.0004556172,0.00076984405,0.0006192606,0.0007218771,0.0004758958],"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":[0.0012886112,0.00034748053,0.0022910216,0.00087722385,0.0002778809,0.0012050204,0.00038506062,0.23069128,0.42355606,0.005431478,0.0015785579,0.3320704],"study_design_scores_gemma":[0.000030160478,0.00033555678,0.001412724,0.000015157095,0.000055467106,0.00020047046,0.000024838926,0.9737168,0.022549333,0.0010985519,0.0005352667,0.000025734424],"about_ca_topic_score_codex":0.0013442432,"about_ca_topic_score_gemma":0.0023910461,"teacher_disagreement_score":0.0013442432,"about_ca_system_score_codex":0.00033800877,"about_ca_system_score_gemma":0.00034377145,"threshold_uncertainty_score":0.004389107},"labels":[],"label_agreement":null}]}