{"meta":{"query_hash":"01347bd33e20","filters":{"venue":"Advanced Engineering Informatics"},"cohort_total":98,"direct_labels_cover":0,"predictions_cover":98,"exported":98,"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/01347bd33e20","api":"https://metacan.xera.ac/api/v1/cohort?venue=Advanced+Engineering+Informatics"},"results":[{"id":"W1969578102","doi":"10.1016/j.aei.2006.09.002","title":"A proximity-based method for locating RFID tagged objects","year":2007,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":124,"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":"Radio-frequency identification; Software deployment; Identification (biology); Computer science; Field (mathematics); Radio frequency; Tracking (education); Power (physics); Real-time computing; Electronic engineering; Engineering; Embedded system; Telecommunications; Software engineering; Computer security; Mathematics","score_opus":0.0054001043504273635,"score_gpt":0.23616664191981418,"score_spread":0.2307665375693868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969578102","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049933484,0.00007371793,0.9908137,0.000008901746,0.00038470357,0.00045223424,0.000010050928,0.0024105897,0.00085275905],"genre_scores_gemma":[0.31450668,0.000006455371,0.6852269,0.000074763484,0.000034512268,0.00006479891,0.000026159287,0.000048840513,0.0000108900895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987069,0.000002308641,0.000546451,0.00008307205,0.00015502422,0.00050625735],"domain_scores_gemma":[0.99926037,0.00022976927,0.00006742996,0.00027144427,0.00009918572,0.000071792],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039189376,0.00023833147,0.00023079844,0.0002641026,0.000073260686,0.000033317105,0.0001987424,0.00015185733,0.0000028493484],"category_scores_gemma":[0.0002617846,0.00025138966,0.000073155294,0.00043127523,0.000015896914,0.0003657839,0.000021144604,0.00021270242,0.000008243283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068132026,0.0000052881637,0.00001601008,0.0006619038,0.000018383493,7.895447e-7,0.0004559505,0.9616834,0.0024365936,0.0022879236,0.00004524054,0.032381725],"study_design_scores_gemma":[0.0005431855,0.000036057532,0.000026360762,0.00007064801,0.000010946513,0.0000037242164,0.0003263075,0.8201119,0.16950114,0.00010427211,0.008985672,0.00027981587],"about_ca_topic_score_codex":7.0445265e-7,"about_ca_topic_score_gemma":0.0000026646433,"teacher_disagreement_score":0.30951333,"about_ca_system_score_codex":0.00012831223,"about_ca_system_score_gemma":0.000019787854,"threshold_uncertainty_score":0.99999386},"labels":[],"label_agreement":null},{"id":"W1971410313","doi":"10.1016/j.aei.2013.07.001","title":"Localization of RFID-equipped assets during the operation phase of facilities","year":2013,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Indoor and Outdoor Localization Technologies","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":"Concordia University","funders":"Concordia University","keywords":"Radio-frequency identification; Real-time locating system; Computer science; Context (archaeology); Matching (statistics); Plan (archaeology); Real-time computing; Facility management; Computer security","score_opus":0.0045399949821440235,"score_gpt":0.2025752299799504,"score_spread":0.19803523499780637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971410313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37506744,0.00007970521,0.62394536,0.000005167057,0.00011969151,0.00020573352,0.000015068602,0.00029116153,0.00027069173],"genre_scores_gemma":[0.99465007,0.00010424201,0.0051278234,0.0000048519028,0.000008768276,0.000035713587,0.000020857966,0.000016708178,0.00003095519],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991265,0.000003484332,0.00052471075,0.000036711943,0.00014779247,0.00016080072],"domain_scores_gemma":[0.99952936,0.000034410157,0.00007310458,0.00022570151,0.00011685846,0.000020569332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005624226,0.00012825399,0.00016702898,0.00011939505,0.000037442125,0.000018526292,0.00015164744,0.00007067823,0.0000348901],"category_scores_gemma":[0.000071572504,0.00010439718,0.000035927085,0.0002227813,0.000039599607,0.00057318126,0.000027582551,0.00009305997,0.000010325373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016333911,0.0000062069275,0.00006338544,0.0005660183,0.000016921744,6.151833e-8,0.0010875321,0.985356,0.008889827,0.0008843233,0.00003067352,0.0030974664],"study_design_scores_gemma":[0.00038571947,0.00002659073,0.00013298055,0.000045596167,0.0000051182956,0.0000016809971,0.0007531844,0.7872495,0.2102151,0.000046246987,0.001032062,0.000106224084],"about_ca_topic_score_codex":0.0000051978627,"about_ca_topic_score_gemma":7.993251e-7,"teacher_disagreement_score":0.61958265,"about_ca_system_score_codex":0.00004332095,"about_ca_system_score_gemma":0.000007947983,"threshold_uncertainty_score":0.42571935},"labels":[],"label_agreement":null},{"id":"W1986556727","doi":"10.1016/j.aei.2012.02.010","title":"Parametric feature constraint modeling and mapping in product development","year":2012,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Manufacturing Process and 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":"University of Alberta","funders":"","keywords":"Feature (linguistics); Concurrent engineering; New product development; Product data management; Product engineering; Feature model; Systems engineering; Constraint (computer-aided design); Computer science; Product (mathematics); Field (mathematics); Product design; Industrial engineering; Parametric design; Conceptual design; Product lifecycle; CAD; Parametric statistics; Data mining; Engineering drawing; Engineering; Human–computer interaction; Mechanical engineering; Mathematics; Process engineering","score_opus":0.008363791708149527,"score_gpt":0.18783167965599218,"score_spread":0.17946788794784266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986556727","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3997581,0.0010001325,0.5983726,0.000004669218,0.00017476098,0.00013103997,7.272172e-7,0.00022434877,0.00033361046],"genre_scores_gemma":[0.7470344,0.00013233851,0.2527612,0.000007665095,0.000019238943,0.000017061091,0.0000069403873,0.000015652506,0.000005525419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992843,0.0000013521469,0.00026862774,0.00004935037,0.000090291025,0.0003060552],"domain_scores_gemma":[0.99978393,0.000016106016,0.000022892726,0.000089754365,0.000015362935,0.00007193918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012104429,0.00014916221,0.00013502463,0.00023534488,0.000025733645,0.000026071084,0.00005197013,0.000049386603,0.0000018311928],"category_scores_gemma":[0.000030618477,0.00015390794,0.00001028413,0.00024206963,0.0000051864686,0.00054131646,0.000022023478,0.00018176461,0.0000030642113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.202089e-7,0.0000033442586,0.00031747913,0.00043112185,0.0000068479076,1.8194233e-7,0.002040988,0.9681262,0.00002631892,0.00007637423,0.0000025545935,0.028968204],"study_design_scores_gemma":[0.00016433638,0.0000019570734,0.0010512443,0.00009786526,0.0000021945277,0.0000083074,0.00016740875,0.99338436,0.0013308789,0.000004392037,0.0035777513,0.00020931712],"about_ca_topic_score_codex":3.7505814e-7,"about_ca_topic_score_gemma":2.4970362e-7,"teacher_disagreement_score":0.3472763,"about_ca_system_score_codex":0.00007216819,"about_ca_system_score_gemma":0.0000075882326,"threshold_uncertainty_score":0.6276184},"labels":[],"label_agreement":null},{"id":"W1994358972","doi":"10.1016/j.aei.2008.12.003","title":"Complex flow simulations in natural aquifer","year":2009,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Advanced Numerical Methods in Computational Mathematics","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":"McGill University","funders":"","keywords":"Cholesky decomposition; Discretization; Aquifer; Multigrid method; Computer science; Finite element method; Computational science; Factorization; Iterative method; Applied mathematics; Flow (mathematics); Algorithm; Mathematical optimization; Partial differential equation; Mathematics; Groundwater; Geology; Geometry; Mathematical analysis; Geotechnical engineering; Engineering; Physics","score_opus":0.014259832297495327,"score_gpt":0.2789470649092579,"score_spread":0.2646872326117626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994358972","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023096174,0.000089546185,0.9745376,0.000026339168,0.0003288374,0.00018039554,0.000008820244,0.00089028233,0.00084201526],"genre_scores_gemma":[0.4727581,0.000008587366,0.5270978,0.000071166694,0.000023063143,0.0000044262642,0.00001584335,0.00001691467,0.000004054841],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988246,0.0000055508635,0.00057730416,0.00007152956,0.00019382838,0.0003271936],"domain_scores_gemma":[0.99934405,0.00028751636,0.000042466527,0.00021293978,0.000041454266,0.000071590315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007022838,0.00022083447,0.00025494068,0.00019682173,0.0000290626,0.000023714234,0.0001679483,0.000057125097,0.00000941366],"category_scores_gemma":[0.00015925146,0.00023854901,0.000044549346,0.0004985633,0.000013213382,0.00050760654,0.000018572178,0.00030508128,0.000022049042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018555328,0.000010385398,0.000006726638,0.000064552245,0.000006165303,0.0000013728202,0.0003115182,0.9624468,0.00082954875,0.0017045062,0.00003266131,0.0345839],"study_design_scores_gemma":[0.00031014255,0.000020104568,0.0011545689,0.000059015583,0.0000035505857,0.0000075169605,0.00002712753,0.99184644,0.0002884391,0.0036656733,0.0023424202,0.0002750139],"about_ca_topic_score_codex":8.21984e-8,"about_ca_topic_score_gemma":3.0053178e-7,"teacher_disagreement_score":0.44966194,"about_ca_system_score_codex":0.00014029228,"about_ca_system_score_gemma":0.0000065657287,"threshold_uncertainty_score":0.9727746},"labels":[],"label_agreement":null},{"id":"W2012270409","doi":"10.1016/j.aei.2012.01.003","title":"Improving lifting motion planning and re-planning of cranes with consideration for safety and efficiency","year":2012,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":124,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Motion planning; Smoothness; Path (computing); Computer science; Motion (physics); Dual (grammatical number); Random tree; Industrial engineering; Real-time computing; Operations research; Mathematical optimization; Engineering; Simulation; Artificial intelligence; Mathematics; Robot","score_opus":0.010113707891316293,"score_gpt":0.22762239898295558,"score_spread":0.21750869109163928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012270409","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065734506,0.00033544502,0.9334463,0.000011546808,0.0001283173,0.00017736615,0.0000018178245,0.00011521497,0.000049485243],"genre_scores_gemma":[0.4480794,0.0000025766692,0.5518744,0.000011444925,0.000016627755,0.000005025762,0.000002768014,0.000006209635,0.0000015476461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991581,0.0000055632568,0.00034961937,0.00008691716,0.0001280508,0.00027173638],"domain_scores_gemma":[0.9992461,0.00028203608,0.00019642236,0.00014283656,0.000060256545,0.00007229991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035797764,0.00013170608,0.00017639148,0.000109096545,0.000115066854,0.000054851665,0.00008700302,0.000043815657,8.176577e-8],"category_scores_gemma":[0.00018831699,0.000121124984,0.000011724246,0.00013023896,0.000021481786,0.0013431536,0.000053338863,0.0000936544,1.53609e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075025546,0.0000052730106,0.002261964,0.00037176374,0.000009071047,5.770323e-7,0.009327552,0.96941113,0.00076053117,0.0012508777,8.956693e-7,0.01659288],"study_design_scores_gemma":[0.0004250807,0.00008580159,0.0030844638,0.00025460994,0.000009047809,0.000047333662,0.00038511335,0.99392134,0.0015696565,0.000011598976,0.00005053996,0.00015543161],"about_ca_topic_score_codex":0.0000012976874,"about_ca_topic_score_gemma":3.0302783e-8,"teacher_disagreement_score":0.3823449,"about_ca_system_score_codex":0.00001988257,"about_ca_system_score_gemma":0.000013523406,"threshold_uncertainty_score":0.49393335},"labels":[],"label_agreement":null},{"id":"W2015807678","doi":"10.1016/j.aei.2010.06.002","title":"A flexible and dynamic algorithm for assessment and optimization of utility sectors","year":2010,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Tehran","keywords":"Data envelopment analysis; Ranking (information retrieval); Mathematical optimization; Spearman's rank correlation coefficient; Mathematics; Efficient frontier; Variance (accounting); Computer science; Econometrics; Statistics; Machine learning; Economics","score_opus":0.02078095587288852,"score_gpt":0.3440791220741966,"score_spread":0.3232981662013081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015807678","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14747845,0.000019776582,0.8520918,0.0000133839985,0.00015076024,0.00012707217,0.000015587339,0.000033265427,0.00006988782],"genre_scores_gemma":[0.40801063,0.0000091474885,0.59194356,0.0000074893323,0.0000036665583,0.000003704073,0.000004249735,0.00000442766,0.0000131214965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988905,0.0000057561483,0.00054559234,0.000105521896,0.00031637138,0.000136212],"domain_scores_gemma":[0.998843,0.00046221295,0.00018788646,0.0002616088,0.0001889133,0.000056373745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009204529,0.00009741916,0.00020710377,0.00020805205,0.000058802674,0.000066058805,0.00014043877,0.00005215207,0.000007803536],"category_scores_gemma":[0.00048369603,0.00008204673,0.000034922465,0.00036860484,0.0000540876,0.00045903592,0.00005300579,0.00010832188,4.2574638e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014842603,0.000009835791,0.0002358805,0.000034381905,0.000008284391,4.6816446e-8,0.00029800186,0.8654496,0.0005228837,0.00041486844,0.0000036631602,0.13302106],"study_design_scores_gemma":[0.00020974118,0.000032342195,0.0019261827,0.000010815913,0.000011797655,0.0000027530077,0.00014752986,0.9962312,0.0004495363,0.00031222662,0.00058004784,0.00008582505],"about_ca_topic_score_codex":0.0000013388957,"about_ca_topic_score_gemma":0.0000023331281,"teacher_disagreement_score":0.2605322,"about_ca_system_score_codex":0.000013472179,"about_ca_system_score_gemma":0.000027452432,"threshold_uncertainty_score":0.33457687},"labels":[],"label_agreement":null},{"id":"W2030395122","doi":"10.1016/j.aei.2006.05.004","title":"Applications of agent-based systems in intelligent manufacturing: An updated review","year":2006,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":547,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; National Research Council Canada","funders":"","keywords":"Manufacturing execution system; Negotiation; Supply chain; Multi-agent system; Computer science; Manufacturing engineering; Process management; Computer-integrated manufacturing; Scheduling (production processes); Engineering; Systems engineering; Engineering management; Business; Operations management; Artificial intelligence","score_opus":0.006032637507384548,"score_gpt":0.21834671266059372,"score_spread":0.21231407515320916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030395122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062729893,0.0023829807,0.98995066,0.0000047837134,0.00016417463,0.00040040232,0.000013261364,0.00039648134,0.0004142499],"genre_scores_gemma":[0.5358009,0.0011896953,0.46179268,0.00006141493,0.00005836219,0.00026944024,0.00071595836,0.00008024407,0.00003130711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989509,0.000004572493,0.0006688199,0.00005899221,0.000133507,0.00018318265],"domain_scores_gemma":[0.99954283,0.000018938703,0.00007361858,0.00027046615,0.000042978576,0.00005119054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011040232,0.00015095684,0.00022242629,0.00018484822,0.000013441934,0.000015485539,0.00014507066,0.000054149477,0.000010178172],"category_scores_gemma":[0.000005348994,0.00016289024,0.000033263772,0.00030521423,0.000008755392,0.00021034104,0.000008766682,0.00012485766,0.000011796232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.410152e-7,0.000014945838,0.000008993573,0.0012561458,0.0000052541986,3.558302e-7,0.000030768715,0.9938846,0.0000521306,0.00021439225,0.000025686943,0.0045060855],"study_design_scores_gemma":[0.00018168663,0.000009942312,0.000047427817,0.00041793202,0.000009612586,0.0000020412688,0.000042689357,0.97930443,0.006205764,0.000005000191,0.013599571,0.00017392015],"about_ca_topic_score_codex":0.000007993865,"about_ca_topic_score_gemma":0.0000015065172,"teacher_disagreement_score":0.5295279,"about_ca_system_score_codex":0.00007358583,"about_ca_system_score_gemma":0.00001024306,"threshold_uncertainty_score":0.6642471},"labels":[],"label_agreement":null},{"id":"W2032312847","doi":"10.1016/j.aei.2007.08.002","title":"Using linear graph theory and the principle of orthogonality to model multibody, multi-domain systems","year":2008,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Real-time simulation and control systems","field":"Engineering","cited_by":28,"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":"Orthogonality; Robustness (evolution); Computer science; Virtual work; Multibody system; Algorithm; Control theory (sociology); Mathematics; Engineering; Artificial intelligence","score_opus":0.01825691087432017,"score_gpt":0.25591355653589926,"score_spread":0.2376566456615791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032312847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3734657,0.00016690911,0.6256406,0.0000014660206,0.000110664994,0.0003569039,0.000010692791,0.00012842778,0.000118661344],"genre_scores_gemma":[0.92505926,0.00004549632,0.074765265,0.000017607788,0.000019498264,0.000031003798,0.0000037917441,0.000030546787,0.000027546554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879944,0.000025657508,0.0006731513,0.00007065289,0.00019762195,0.0002334649],"domain_scores_gemma":[0.999316,0.00014903884,0.000089296336,0.00026877373,0.00007634229,0.00010055333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051002385,0.0001982701,0.00035519063,0.000112369235,0.00007354977,0.000016649159,0.00012617491,0.00006636572,8.434823e-7],"category_scores_gemma":[0.000077897756,0.0001548626,0.00007067251,0.00020319354,0.000050603074,0.0002883659,0.000039650356,0.00013480359,0.000003262894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029738168,0.0000050533213,0.00006718505,0.0002491507,0.00004475889,5.221419e-7,0.0016070823,0.9855921,0.0013741922,0.01090726,9.919344e-7,0.000121914374],"study_design_scores_gemma":[0.0014307657,0.000007733649,0.00018981761,0.0000770228,0.000012015412,0.000019649382,0.0001497992,0.9972445,0.00013607489,0.000033077853,0.0005233414,0.00017621342],"about_ca_topic_score_codex":0.000006274901,"about_ca_topic_score_gemma":5.1475166e-7,"teacher_disagreement_score":0.55159354,"about_ca_system_score_codex":0.000039802406,"about_ca_system_score_gemma":0.000014646302,"threshold_uncertainty_score":0.6315114},"labels":[],"label_agreement":null},{"id":"W2045053213","doi":"10.1016/j.aei.2015.01.005","title":"Joint probability for evaluating the schedule and cost of stochastic simulation models","year":2015,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"BIM and Construction Integration","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":"Concordia University","funders":"","keywords":"Schedule; Computer science; Joint probability distribution; Joint (building); Conditional probability; Operations research; Reliability engineering; Engineering; Statistics; Mathematics","score_opus":0.062428956612674606,"score_gpt":0.28021842896793586,"score_spread":0.21778947235526125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045053213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11236374,0.00007785119,0.88675493,0.0000057914767,0.00017772951,0.0004147787,0.0000070823075,0.00009839547,0.00009970435],"genre_scores_gemma":[0.8767933,0.0000017692057,0.12309149,0.000003895648,0.000014426607,0.000075986565,0.000006312244,0.0000098950995,0.000002893883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943066,0.000003139149,0.00031814966,0.00003564944,0.00010992814,0.00010250001],"domain_scores_gemma":[0.9995548,0.00008462215,0.000051391104,0.00011866159,0.00015186658,0.000038661616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023127915,0.000086851396,0.0001053564,0.00003832759,0.0000265049,0.000015659996,0.00004110521,0.000038017937,6.1014964e-7],"category_scores_gemma":[0.00015773621,0.000070242844,0.000021776157,0.000081704275,0.000020030113,0.0003822244,0.000010753243,0.00007668429,5.6160843e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005276654,0.0000016956366,0.0000018826438,0.00013073104,0.000008186045,3.1613079e-9,0.00074870663,0.9737037,0.00024453443,0.006420735,0.0000026516827,0.01873192],"study_design_scores_gemma":[0.00032543615,0.000047151603,0.000013092698,0.00003834599,0.000011493824,0.0000018582597,0.00025858998,0.99485624,0.0004929312,0.0038087068,0.0000694154,0.00007671849],"about_ca_topic_score_codex":4.70221e-7,"about_ca_topic_score_gemma":5.4506955e-7,"teacher_disagreement_score":0.76442957,"about_ca_system_score_codex":0.000050212042,"about_ca_system_score_gemma":0.000016863965,"threshold_uncertainty_score":0.286442},"labels":[],"label_agreement":null},{"id":"W2061372554","doi":"10.1016/j.aei.2007.09.004","title":"Special Issue on collaborative design and manufacturing","year":2008,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Manufacturing Process and 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":"National Research Council Canada","funders":"","keywords":"Ontology; Computer science; Semantics (computer science); Software engineering; Consistency (knowledge bases); Ontology engineering; Interpretation (philosophy); Product design; Process ontology; Product (mathematics); Programming language; Systems engineering; Artificial intelligence; Engineering","score_opus":0.005805665450812395,"score_gpt":0.18570156132080867,"score_spread":0.17989589586999627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061372554","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0785944,0.000115938885,0.90409255,0.00001589364,0.0012031953,0.00046901568,0.000009151268,0.0009657468,0.014534095],"genre_scores_gemma":[0.3599283,0.0028575095,0.6336235,0.0001529404,0.0027061799,0.00009777348,0.000032652242,0.00015361169,0.00044753772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933434,0.000002918514,0.00024463455,0.00006812611,0.0001299684,0.0002200417],"domain_scores_gemma":[0.9996917,0.00005301658,0.00003236493,0.0001248835,0.000021353282,0.000076674754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000043360487,0.00019674994,0.00016111184,0.00011572316,0.00008292298,0.000028215,0.000075005155,0.00006329988,0.000030424888],"category_scores_gemma":[0.000013641625,0.00019997686,0.000016072141,0.00009134042,0.000016683942,0.00039711274,0.000016399194,0.00015391337,0.00003357376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069966977,0.0000031043003,0.0000020262482,0.00011605614,0.000012609787,0.0000032592461,0.0014226985,0.984827,0.000015028252,0.00007312576,0.0008163992,0.012701691],"study_design_scores_gemma":[0.0004990837,0.000074387026,0.00029035262,0.000070791175,0.000007136182,0.000023504359,0.00009858437,0.7450352,0.050803788,0.000017498054,0.20268242,0.00039725355],"about_ca_topic_score_codex":1.9599914e-7,"about_ca_topic_score_gemma":9.834478e-8,"teacher_disagreement_score":0.28133392,"about_ca_system_score_codex":0.000048846174,"about_ca_system_score_gemma":0.000007877283,"threshold_uncertainty_score":0.81548196},"labels":[],"label_agreement":null},{"id":"W2068765329","doi":"10.1016/j.aei.2008.05.003","title":"IT-based approach for effective management of project changes: A change management system (CMS)","year":2008,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"BIM and Construction Integration","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":"SAIT Polytechnic","funders":"","keywords":"Control (management); Project management; Knowledge base; Selection (genetic algorithm); Knowledge management; Project management triangle; Change control; Computer science; Engineering; Process management; Project planning; Risk analysis (engineering); Engineering management; Systems engineering; Business; Artificial intelligence","score_opus":0.014111106241306666,"score_gpt":0.20906966946286737,"score_spread":0.19495856322156072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068765329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024103927,0.00010446408,0.98911846,0.000004041834,0.0003550325,0.001920957,0.000025541523,0.0004979454,0.005563139],"genre_scores_gemma":[0.5152795,0.00015806682,0.480585,0.000026687008,0.000054670894,0.003732877,0.00007798668,0.00004800726,0.000037212543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991887,0.0000033951426,0.00033287908,0.00008131617,0.00016327061,0.00023043818],"domain_scores_gemma":[0.9996142,0.000020215848,0.00006709661,0.0002117378,0.000052538115,0.00003422611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008002526,0.00020096934,0.00021778965,0.00023886867,0.000046557314,0.000009074765,0.000115066156,0.0000626744,8.468945e-7],"category_scores_gemma":[0.0000017148449,0.00020095705,0.000068080335,0.00030000968,0.000016989208,0.0002085629,0.00001653806,0.00007932483,0.0000025824477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002520745,0.000021680036,0.000008895933,0.017051125,0.00024710695,0.0000025514912,0.0016964583,0.9004906,0.000074973424,0.0076616397,0.00015802508,0.07256175],"study_design_scores_gemma":[0.00090770895,0.00007022258,0.0000804885,0.0004357748,0.00005415118,0.00001476488,0.0015034188,0.98013204,0.0023552685,0.0000018090108,0.014201604,0.00024272007],"about_ca_topic_score_codex":0.0000010132676,"about_ca_topic_score_gemma":3.4649418e-7,"teacher_disagreement_score":0.5128691,"about_ca_system_score_codex":0.00014743287,"about_ca_system_score_gemma":0.000003817195,"threshold_uncertainty_score":0.81947905},"labels":[],"label_agreement":null},{"id":"W2072631216","doi":"10.1016/j.aei.2007.03.003","title":"A geometric modelling framework for conceptual structural design from early digital architectural models","year":2007,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"BIM and Construction Integration","field":"Engineering","cited_by":58,"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 du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Process (computing); Conceptual design; Representation (politics); Architectural geometry; Computer science; Design process; Conceptual model; Systems engineering; Architectural technology; Hierarchy; Interdependence; Software engineering; Architecture; Engineering; Work in process; Software; Software development; Human–computer interaction; Programming language; Database","score_opus":0.016351385556920506,"score_gpt":0.2081436789079392,"score_spread":0.1917922933510187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072631216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23989914,0.00017302315,0.7584905,0.0000014157665,0.00057428674,0.00022405048,0.00005370522,0.0004776434,0.00010620802],"genre_scores_gemma":[0.54825634,0.0000062264457,0.45157033,0.000007409385,0.000083447936,0.000015615704,0.000030429655,0.000026569694,0.0000036398053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987794,0.0000016717681,0.00052442536,0.000093688686,0.00018706651,0.00041373528],"domain_scores_gemma":[0.9992036,0.00036630486,0.00006195183,0.00017951458,0.000073757794,0.00011484967],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007442473,0.00025292838,0.00021125775,0.00025330458,0.00006242513,0.000112236645,0.00015502119,0.00015187703,0.00000524332],"category_scores_gemma":[0.000043934837,0.0002551541,0.00008605153,0.00034779904,0.000032083306,0.0011352078,0.000015061642,0.00031434675,0.0000071616882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018459588,0.0000011527411,0.000009072304,0.000026604046,0.000035068333,3.2383605e-7,0.0014097583,0.928159,0.000059518392,0.01169025,0.0000033792628,0.058587443],"study_design_scores_gemma":[0.00032453585,0.00004081582,0.000034985525,0.000052049036,0.000012292496,0.000008707123,0.00032247446,0.9835559,0.001921678,0.013125317,0.0002859895,0.0003152631],"about_ca_topic_score_codex":0.0000025989846,"about_ca_topic_score_gemma":3.5470072e-7,"teacher_disagreement_score":0.30835718,"about_ca_system_score_codex":0.00009874504,"about_ca_system_score_gemma":0.000010869093,"threshold_uncertainty_score":0.99999005},"labels":[],"label_agreement":null},{"id":"W2074785524","doi":"10.1016/j.aei.2012.11.002","title":"Corrigendum to “Complex flow simulation in natural aquifer” [Adv. Eng. Inform. 23 (2009) 288–293]: An algorithm for parallel flow simulations in the finite element framework","year":2012,"lang":"en","type":"erratum","venue":"Advanced Engineering Informatics","topic":"Advanced Numerical Methods in Computational Mathematics","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":"McGill University","funders":"","keywords":"Aquifer; Finite element method; Flow (mathematics); Computer science; Algorithm; Computational science; Geology; Engineering; Geotechnical engineering; Mathematics; Groundwater; Structural engineering; Geometry","score_opus":0.03621002475913377,"score_gpt":0.3239808062320199,"score_spread":0.28777078147288615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074785524","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000048218444,0.00035260068,0.9848597,0.000028029017,0.011236699,0.0021465244,0.0007842315,0.00041615378,0.00012782581],"genre_scores_gemma":[0.0035478172,0.00007983421,0.9916413,0.00035987992,0.00087451615,0.00055853,0.0025148147,0.00020347345,0.00021984457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957237,0.00004647488,0.0020236124,0.00027456717,0.00077895133,0.0011526572],"domain_scores_gemma":[0.99602586,0.0023905,0.00030607943,0.00082747627,0.00021777379,0.00023231364],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00061292766,0.00089941174,0.0009208987,0.000776639,0.00012570454,0.00010645757,0.000817821,0.00055695296,0.000018260327],"category_scores_gemma":[0.0013716568,0.0008641838,0.00017422618,0.0012450113,0.00003053393,0.001242735,0.00011928787,0.001882872,0.000026545144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011007837,0.000048108326,0.0000026349921,0.0006245031,0.00004025499,0.0000015131249,0.003621718,0.89791363,0.0000019264896,0.00027721887,0.001505007,0.095952466],"study_design_scores_gemma":[0.0005104203,0.000090372174,0.0001251189,0.00045127852,0.000032071195,0.0000033812369,0.0002544613,0.8621401,0.0000033708739,0.0024639477,0.13306135,0.00086413685],"about_ca_topic_score_codex":0.0000014979923,"about_ca_topic_score_gemma":0.000011132984,"teacher_disagreement_score":0.13155635,"about_ca_system_score_codex":0.00063810917,"about_ca_system_score_gemma":0.00006643593,"threshold_uncertainty_score":0.9993809},"labels":[],"label_agreement":null},{"id":"W2204905125","doi":"10.1016/j.aei.2015.11.004","title":"Extending IFC to incorporate information of RFID tags attached to building elements","year":2015,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"RFID technology advancements","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Building information modeling; Context (archaeology); Radio-frequency identification; Computer science; Identification (biology); Linkage (software); Information sharing; Application lifecycle management; Architecture; Systems engineering; Software; Database; Software engineering; Engineering; World Wide Web; Computer security","score_opus":0.009092072466366393,"score_gpt":0.23734288976405174,"score_spread":0.22825081729768534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2204905125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39658257,0.000021166483,0.6014149,0.000019008445,0.00050964364,0.00037072814,0.000015533284,0.0006240856,0.0004423544],"genre_scores_gemma":[0.57239157,0.000008775697,0.4273772,0.00007361717,0.000015980679,0.000071227725,0.000021866963,0.000032060456,0.000007702224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802846,0.000003729136,0.0010324112,0.00008614401,0.00035510224,0.0004941379],"domain_scores_gemma":[0.99894035,0.000027047028,0.00015497989,0.00043128786,0.00016629824,0.0002800224],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002781141,0.0002949605,0.00033134248,0.0006946908,0.000031571642,0.000031212774,0.00038362632,0.00010756727,0.00000440562],"category_scores_gemma":[0.00025660102,0.00034467797,0.000037922706,0.000937787,0.000012814007,0.0019048876,0.00017259245,0.00021878496,0.00013732676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000108563845,0.0000060495913,0.0003957179,0.00018311648,0.000035587873,8.282828e-7,0.0012048851,0.9707689,0.0062578986,0.0011051502,0.0002105832,0.019820414],"study_design_scores_gemma":[0.0021755628,0.0003669818,0.0011125496,0.00052359205,0.00003607579,0.000019225168,0.00115589,0.71505785,0.1674488,0.0005402342,0.11029272,0.0012705376],"about_ca_topic_score_codex":0.0000013983833,"about_ca_topic_score_gemma":7.622445e-7,"teacher_disagreement_score":0.25571108,"about_ca_system_score_codex":0.00034193447,"about_ca_system_score_gemma":0.000021164704,"threshold_uncertainty_score":0.9999005},"labels":[],"label_agreement":null},{"id":"W2258435665","doi":"10.1016/j.aei.2005.05.002","title":"Special issue on collaborative environments for design and manufacturing","year":2005,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Manufacturing Process and Optimization","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":"National Research Council Canada","funders":"","keywords":"Flexibility (engineering); Distributed manufacturing; Concurrent engineering; Service (business); Distributed design patterns; Architecture; Service-oriented architecture; Product design; Engineering; Collaborative software; Systems engineering; Service provider; Computer science; Product (mathematics); Web service; Software engineering; Manufacturing engineering; Distributed computing; Knowledge management; World Wide Web","score_opus":0.004469157879572204,"score_gpt":0.19022369891587762,"score_spread":0.18575454103630543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2258435665","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071086152,0.00005338624,0.9899374,0.000019438996,0.00037898036,0.00046678502,0.000012068402,0.00021501236,0.0018082648],"genre_scores_gemma":[0.05512057,0.00058642274,0.9415412,0.00010819223,0.002059603,0.00015822465,0.00003227852,0.000084679996,0.00030881356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937916,0.000001811246,0.00023718711,0.00006940229,0.00009602255,0.000216396],"domain_scores_gemma":[0.9997279,0.00006191368,0.00003454038,0.00010506782,0.0000076034844,0.00006294436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005618093,0.00018136315,0.00013819052,0.00008238395,0.000053390035,0.000039991035,0.00006765951,0.000059366455,0.000020174368],"category_scores_gemma":[0.000010937423,0.00018859966,0.000017385884,0.000038413855,0.000008644298,0.0004028586,0.000012767467,0.00009266105,0.00002061563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001015482,0.000004027356,3.5110062e-7,0.00011425652,0.000013939839,1.3981038e-7,0.00064082345,0.92311,0.000039367504,0.00008572343,0.00046918765,0.07551205],"study_design_scores_gemma":[0.0003524392,0.000038605198,0.000025407342,0.000026684935,0.000005838556,9.586327e-7,0.00003618196,0.5546788,0.043927528,0.000009922458,0.4007324,0.00016523397],"about_ca_topic_score_codex":4.767788e-8,"about_ca_topic_score_gemma":1.2857625e-7,"teacher_disagreement_score":0.4002632,"about_ca_system_score_codex":0.00007460358,"about_ca_system_score_gemma":0.0000035223397,"threshold_uncertainty_score":0.7690871},"labels":[],"label_agreement":null},{"id":"W2294152595","doi":"10.1016/j.aei.2016.02.005","title":"Improving process conformance with Industry Foundation Processes (IFP)","year":2016,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Manufacturing Process and Optimization","field":"Engineering","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Process (computing); Foundation (evidence); Computer science; Engineering; Manufacturing engineering; Process management; Process engineering; Political science","score_opus":0.003546028914902984,"score_gpt":0.18472124523443043,"score_spread":0.18117521631952743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294152595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.173424,0.000043653105,0.82412905,0.000012407069,0.00014641108,0.00018852942,0.000004783062,0.0008988743,0.0011523055],"genre_scores_gemma":[0.97067815,0.000099002165,0.028931903,0.000023685787,0.000047598933,0.0000782224,0.0000132225805,0.000051912142,0.000076284305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990635,0.0000010346477,0.00034003324,0.000084545725,0.00019373444,0.00031716443],"domain_scores_gemma":[0.9994758,0.000034645487,0.000092355316,0.00017884409,0.00013673275,0.00008165252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000055286906,0.00022757554,0.00015492999,0.00011901834,0.000053924177,0.00006347612,0.00015238454,0.00012591039,0.000022300657],"category_scores_gemma":[0.000052069765,0.00016004474,0.000013865548,0.00027890858,0.000020067555,0.0019019746,0.00001549176,0.00019248735,0.00002100442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007284304,0.0000037163697,0.000114981696,0.0016324891,0.000014528086,5.13301e-7,0.00045712665,0.9478024,0.00017821362,0.00009817096,0.000005532661,0.049684998],"study_design_scores_gemma":[0.0017207488,0.0001344244,0.000530507,0.0013569931,0.000032689266,0.00003856617,0.00029644056,0.87763274,0.10551197,0.000055653967,0.011579955,0.0011093228],"about_ca_topic_score_codex":5.6558423e-7,"about_ca_topic_score_gemma":0.0000015614902,"teacher_disagreement_score":0.79725415,"about_ca_system_score_codex":0.000074456635,"about_ca_system_score_gemma":0.000045936984,"threshold_uncertainty_score":0.6526435},"labels":[],"label_agreement":null},{"id":"W2307011503","doi":"10.1016/j.aei.2016.03.001","title":"Ontology-based semantic approach for construction-oriented quantity take-off from BIM models in the light-frame building industry","year":2016,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"BIM and Construction Integration","field":"Engineering","cited_by":129,"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":"Building information modeling; Ontology; Information model; Computer science; Domain (mathematical analysis); Frame (networking); Building model; Semantics (computer science); Vocabulary; Building design; Systems engineering; Software engineering; Engineering; Architectural engineering; Simulation","score_opus":0.009765036172923854,"score_gpt":0.20954577400709343,"score_spread":0.19978073783416958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2307011503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21898644,0.00007302483,0.77953744,0.000053884676,0.00051933306,0.00027923778,0.000046148,0.00027073358,0.00023375446],"genre_scores_gemma":[0.72791773,0.000015656795,0.27178448,0.000041772924,0.000055596185,0.00013489902,0.000025298814,0.000021922988,0.0000026332887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988588,0.0000098101345,0.0005256521,0.0001132517,0.00015697062,0.00033551574],"domain_scores_gemma":[0.99935365,0.00014765367,0.000079914695,0.00030239756,0.00006506255,0.000051322106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014828738,0.00022592154,0.0002275029,0.00014222311,0.000066835084,0.000037769103,0.0001993458,0.0002895311,0.0000050731273],"category_scores_gemma":[0.000065284315,0.0001614123,0.00006128531,0.00029071615,0.000040183255,0.0006842076,0.000012624561,0.00036936338,0.0000030664062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001066843,0.00001138154,0.00031731,0.00011180839,0.000028013357,3.376973e-7,0.0005833615,0.9335318,0.0024726158,0.040117197,0.00002986361,0.022785608],"study_design_scores_gemma":[0.00089373544,0.00002407693,0.00021421746,0.00013450948,0.000016287533,0.000012471669,0.0008294305,0.98838705,0.0044868155,0.0010842147,0.0036502813,0.0002669071],"about_ca_topic_score_codex":0.000006700428,"about_ca_topic_score_gemma":0.000009074575,"teacher_disagreement_score":0.5089313,"about_ca_system_score_codex":0.00011218048,"about_ca_system_score_gemma":0.000022742415,"threshold_uncertainty_score":0.6582203},"labels":[],"label_agreement":null},{"id":"W2571641669","doi":"10.1016/j.aei.2016.12.008","title":"Leveraging existing occupancy-related data for optimal control of commercial office buildings: A review","year":2017,"lang":"en","type":"review","venue":"Advanced Engineering Informatics","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":122,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; National Research Council Canada","funders":"Natural Resources Canada; National Research Council Canada; Independent Electricity System Operator; Ontario Power Authority","keywords":"Occupancy; HVAC; Computer science; Data collection; Control (management); Sensor fusion; Ground truth; Air conditioning; Architectural engineering; Engineering; Artificial intelligence","score_opus":0.07700025179493214,"score_gpt":0.33400748449391965,"score_spread":0.2570072326989875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2571641669","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000001055978,0.57493454,0.4233037,0.0000013604036,0.00048827115,0.00059329206,0.00021660527,0.00026733105,0.00019381962],"genre_scores_gemma":[0.000033003198,0.88233596,0.116166376,0.000017730104,0.00007988878,0.00013813999,0.0010764502,0.00013263921,0.000019796622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99736893,0.000012246511,0.0017462497,0.00020663596,0.0002087957,0.0004571533],"domain_scores_gemma":[0.99722505,0.00030781055,0.00080138404,0.001469627,0.000106609434,0.00008954134],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005233133,0.0005986804,0.0020442898,0.00021049113,0.00012434462,0.00005535339,0.0013322727,0.0003065297,0.0000052207724],"category_scores_gemma":[0.00041680722,0.0006102395,0.00029829016,0.0002401723,0.000030350458,0.000748758,0.00018061326,0.0005120819,0.0000026736734],"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":[0.0000018000902,0.0000041880057,5.748696e-8,0.06916716,0.00018382471,4.915175e-7,0.00002364714,0.52438694,2.0345608e-7,0.000187276,0.000191892,0.4058525],"study_design_scores_gemma":[0.0002660666,0.000011916227,9.364016e-8,0.044638332,0.00050390145,0.000015527488,0.0000024172234,0.3791714,9.3360563e-7,0.0000012098544,0.5750224,0.00036582904],"about_ca_topic_score_codex":0.0000012032056,"about_ca_topic_score_gemma":2.5602617e-7,"teacher_disagreement_score":0.5748305,"about_ca_system_score_codex":0.00011085114,"about_ca_system_score_gemma":0.0000879778,"threshold_uncertainty_score":0.9996349},"labels":[],"label_agreement":null},{"id":"W2615757794","doi":"10.1016/j.aei.2017.05.002","title":"A machine learning approach for characterizing soil contamination in the presence of physical site discontinuities and aggregated samples","year":2017,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","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":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Kriging; Classification of discontinuities; Probabilistic logic; Gaussian process; Soil science; Gaussian; Contamination; Representation (politics); Ground-penetrating radar; Regression; Environmental science; Computer science; Mathematics; Machine learning; Statistics; Chemistry","score_opus":0.008854912202205634,"score_gpt":0.21587800166374482,"score_spread":0.2070230894615392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615757794","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8615337,0.000014516534,0.1379123,0.000013967703,0.000018406734,0.00018638682,0.00001718729,0.0000118841,0.0002916162],"genre_scores_gemma":[0.9803742,0.000029744768,0.019491933,0.000008118661,0.000008651999,0.000033468885,0.00003008248,0.0000055265,0.000018252053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995564,0.0000053142853,0.00016011376,0.0000530145,0.000096895004,0.00012824571],"domain_scores_gemma":[0.99952793,0.0001689996,0.00015434696,0.0001261051,0.0000068546847,0.00001578923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016779403,0.0000771349,0.00011314755,0.000019829724,0.00010798125,0.00005352719,0.00013234901,0.000016249429,5.8908955e-7],"category_scores_gemma":[0.00032924765,0.000060322618,0.000016131546,0.000030700223,0.00006075733,0.0004235869,0.00007718534,0.00008331508,3.8365545e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002697502,0.000050928076,0.037068427,0.00063294475,0.000017675775,8.968459e-7,0.05489541,0.75368017,0.018638114,0.004839627,0.0000045040088,0.13014434],"study_design_scores_gemma":[0.0002677671,0.000033847668,0.082390234,0.00005125762,0.0000051974334,0.0000015872497,0.00048042586,0.9153784,0.0005611118,0.000057168243,0.00069418404,0.0000788101],"about_ca_topic_score_codex":0.00006622933,"about_ca_topic_score_gemma":0.000016300553,"teacher_disagreement_score":0.16169825,"about_ca_system_score_codex":0.000013813531,"about_ca_system_score_gemma":0.0000017392574,"threshold_uncertainty_score":0.2459885},"labels":[],"label_agreement":null},{"id":"W2731290591","doi":"10.1016/j.aei.2017.06.005","title":"A method for clustering unlabeled BIM objects using entropy and TF-IDF with RDF encoding","year":2017,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"BIM and Construction Integration","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","keywords":"Building information modeling; Computer science; RDF; Cluster analysis; tf–idf; Information model; Data mining; Entropy (arrow of time); Information retrieval; Database; Artificial intelligence; Engineering; Term (time); Semantic Web","score_opus":0.009529279703391041,"score_gpt":0.24410464314865013,"score_spread":0.2345753634452591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2731290591","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08696161,0.00006965448,0.9116619,0.0000066949456,0.00037162908,0.00023661667,0.000007713058,0.00026136375,0.00042281052],"genre_scores_gemma":[0.40633807,0.00004033162,0.5934825,0.000010291714,0.000052298827,0.00003194951,0.0000040929176,0.00003067024,0.000009798306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999231,0.000002605271,0.00029892955,0.00008525931,0.000102605656,0.0002796311],"domain_scores_gemma":[0.9994458,0.00005452861,0.00010339435,0.00025862333,0.000060303963,0.00007737713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010154779,0.00021044486,0.00021612886,0.00011742414,0.0002284596,0.00018098528,0.00012542757,0.00007691099,0.0000024885164],"category_scores_gemma":[0.000041081366,0.00019869563,0.000033673896,0.00006526578,0.000023497416,0.0009338107,0.00003261676,0.00014834691,0.000001261826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000154114,0.000002197133,0.00008754475,0.00048329518,0.000059328613,8.836803e-7,0.0007916862,0.9433742,0.013363307,0.0031534308,0.000003082485,0.03866564],"study_design_scores_gemma":[0.0007532079,0.000038541446,0.000093587885,0.00019773409,0.000031654974,0.00006155333,0.00026079454,0.9857146,0.010417604,0.00007532167,0.0020893922,0.0002659783],"about_ca_topic_score_codex":0.0000042561705,"about_ca_topic_score_gemma":0.000007924399,"teacher_disagreement_score":0.31937647,"about_ca_system_score_codex":0.00007847946,"about_ca_system_score_gemma":0.000012977432,"threshold_uncertainty_score":0.81025726},"labels":[],"label_agreement":null},{"id":"W2897628261","doi":"10.1016/j.aei.2018.10.003","title":"Random generation of industrial pipelines’ data using Markov chain model","year":2018,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"BIM and Construction Integration","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":"Pipeline transport; Markov chain; Pipeline (software); Computer science; Process (computing); Markov process; Data mining; Markov model; Engineering; Statistics; Machine learning; Mathematics; Mechanical engineering","score_opus":0.048902363377067194,"score_gpt":0.2463009613770248,"score_spread":0.19739859799995763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897628261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13153145,0.00004348027,0.8668131,0.000002952501,0.0008944271,0.00011609755,0.000040282786,0.00016566181,0.00039254985],"genre_scores_gemma":[0.74545515,0.00003614633,0.25388777,0.0000122493775,0.00044447562,0.0000058869564,0.00011915356,0.000025821253,0.000013359078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912834,0.0000036328863,0.0005097358,0.00006407986,0.00013909036,0.00015510341],"domain_scores_gemma":[0.99940974,0.000017033764,0.000071874434,0.00036583998,0.000093861636,0.000041645162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014066955,0.00013895579,0.00016659962,0.00012354967,0.000036355796,0.000021202459,0.0001780722,0.00010429915,0.000010410646],"category_scores_gemma":[0.00005688945,0.0001431773,0.000025086736,0.00019812935,0.000027619188,0.0007374542,0.0000384117,0.00013529714,0.0000042999573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008314688,0.0000023032414,0.000008931856,0.000035592486,0.00001682722,5.6672953e-8,0.00020141927,0.9569757,0.012280798,0.00035565402,0.00020196725,0.02991245],"study_design_scores_gemma":[0.00073141255,0.000012241376,0.0000015048103,0.000047644593,0.00001629358,0.0000065199383,0.00004779548,0.98448336,0.013082899,0.00001759293,0.0014082895,0.00014446073],"about_ca_topic_score_codex":0.0000018431148,"about_ca_topic_score_gemma":0.0000026195792,"teacher_disagreement_score":0.61392367,"about_ca_system_score_codex":0.00004484013,"about_ca_system_score_gemma":0.000028449298,"threshold_uncertainty_score":0.5838601},"labels":[],"label_agreement":null},{"id":"W2950752628","doi":"10.1016/j.aei.2020.101087","title":"A decision-support method for information inconsistency resolution in direct modeling of CAD models","year":2020,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Manufacturing Process and Optimization","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Resolution (logic); CAD; Parametric statistics; Constraint (computer-aided design); Associative property; Parametric model","score_opus":0.013042660554837781,"score_gpt":0.22878307491700867,"score_spread":0.2157404143621709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950752628","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027928308,0.00005970753,0.99488276,0.000010293932,0.00012470256,0.00030242503,0.000028067543,0.00023334967,0.0015658713],"genre_scores_gemma":[0.42517555,0.000091607566,0.57458997,0.000034765824,0.000010407711,0.000042263837,0.000038550363,0.000016041324,8.47028e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998797,0.000002602337,0.00078237074,0.000058209873,0.00015279725,0.00020704369],"domain_scores_gemma":[0.9995603,0.00007557207,0.00008297645,0.00012405257,0.0000893174,0.00006780407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017022925,0.00015801017,0.00025469423,0.00019576671,0.000021261221,0.000020963334,0.00011982811,0.0000876798,0.0000022857284],"category_scores_gemma":[0.00012664752,0.00017141103,0.000052496387,0.00026878732,0.0000038457324,0.0015891445,0.000026192294,0.00012140885,0.0000020955415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000188287,0.0000026580954,0.000001829688,0.00087902724,0.000009185935,8.743914e-8,0.002512147,0.9758152,0.000024340523,0.0005795246,0.00001563527,0.020141542],"study_design_scores_gemma":[0.0005187335,0.000035763536,0.0000053442072,0.00010219351,0.000009480962,0.0000010020434,0.00012295731,0.996784,0.0008240465,0.00021126974,0.0012063502,0.00017887392],"about_ca_topic_score_codex":0.0000032581459,"about_ca_topic_score_gemma":0.0000015162649,"teacher_disagreement_score":0.4223827,"about_ca_system_score_codex":0.000060843635,"about_ca_system_score_gemma":0.000020877465,"threshold_uncertainty_score":0.6989939},"labels":[],"label_agreement":null},{"id":"W3024298046","doi":"10.1016/j.aei.2020.101103","title":"A field implementation of linear prediction for leak-monitoring in water distribution networks","year":2020,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Water Systems and Optimization","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leak; Leak detection; Field (mathematics); Software deployment; Data acquisition; Computer science; Real-time computing; Scale (ratio); Software; Transmission (telecommunications); Engineering","score_opus":0.006104989818183875,"score_gpt":0.21049930435399367,"score_spread":0.20439431453580978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024298046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10838848,0.000023282597,0.89085764,0.000014142014,0.000320145,0.00023415558,0.000018560162,0.00012842486,0.000015165643],"genre_scores_gemma":[0.98804945,0.000031912703,0.011533485,0.000006208595,0.00011026253,0.000046575056,0.0002050007,0.000015510883,0.0000016129967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932915,0.000001638988,0.0004114766,0.000039061142,0.000061004277,0.00015766903],"domain_scores_gemma":[0.9998318,0.00001677281,0.000029429148,0.00005844541,0.000031293814,0.000032254102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000054506723,0.00008837901,0.00011864997,0.00003509128,0.000014833035,0.000009329548,0.000045227716,0.000053271244,0.0000016744691],"category_scores_gemma":[0.000010184329,0.00008408464,0.000026323169,0.00010191384,0.0000018497971,0.00034283238,0.000010407818,0.00007171544,8.0353584e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061261967,0.0000013572923,0.00061781745,0.0003332743,0.000008272977,8.2106894e-8,0.0012847375,0.9953937,0.0006163288,0.000029231012,0.000049655664,0.0016594144],"study_design_scores_gemma":[0.00040804595,0.00007022926,0.0003259756,0.000053558862,0.0000055068413,4.1036256e-7,0.00022106039,0.9500591,0.0469991,0.0000014459551,0.001774685,0.0000808604],"about_ca_topic_score_codex":0.0000027015906,"about_ca_topic_score_gemma":0.0000031536322,"teacher_disagreement_score":0.87966096,"about_ca_system_score_codex":0.00004263852,"about_ca_system_score_gemma":0.0000025094805,"threshold_uncertainty_score":0.34288722},"labels":[],"label_agreement":null},{"id":"W3091965860","doi":"10.1016/j.aei.2020.101172","title":"A neuro-wavelet based approach for diagnosing bearing defects","year":2020,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Machine Fault Diagnosis Techniques","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 Windsor","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Wavelet; Computer science; Feature extraction; Artificial neural network; Classifier (UML); Wavelet transform; Signal processing; Digital signal processing","score_opus":0.010430075416715347,"score_gpt":0.22206577151828158,"score_spread":0.21163569610156624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091965860","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01994248,0.000086700304,0.9754181,0.000038374692,0.00013152555,0.00067937747,0.000027886703,0.0028676619,0.000807889],"genre_scores_gemma":[0.47567636,0.00002354606,0.5233421,0.00045137873,0.00006764048,0.00029563057,0.00005497526,0.00008767297,7.2231416e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987323,0.0000044425215,0.00048303866,0.00013791355,0.00017950457,0.0004627503],"domain_scores_gemma":[0.9991277,0.00031516087,0.000057117908,0.000262488,0.00004210708,0.00019542774],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009864884,0.00033082513,0.00032074275,0.00012784179,0.000053514796,0.00007101156,0.00026806662,0.00009999628,0.0000034905158],"category_scores_gemma":[0.00040778518,0.00037545842,0.000118766344,0.00031875208,0.000012035202,0.00045611168,0.00004707041,0.00030453177,0.0000064339256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004743865,0.000011040293,0.000086258835,0.0013663658,0.000017460192,0.0000012684201,0.00030767292,0.9871365,0.0021583517,0.00027247297,0.00076309603,0.0078748],"study_design_scores_gemma":[0.00048661532,0.000063015876,0.00011058396,0.00006650608,0.000019175173,0.0000025579127,0.000020881449,0.9537211,0.027323691,0.000009516126,0.017782269,0.00039408897],"about_ca_topic_score_codex":5.137564e-7,"about_ca_topic_score_gemma":1.1068185e-7,"teacher_disagreement_score":0.45573387,"about_ca_system_score_codex":0.00007334162,"about_ca_system_score_gemma":0.000012260051,"threshold_uncertainty_score":0.99986976},"labels":[],"label_agreement":null},{"id":"W3091997491","doi":"10.1016/j.aei.2020.101176","title":"Fault detection for non-condensing boilers using simulated building automation system sensor data","year":2020,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Automation; Fault detection and isolation; Computer science; Fault (geology); Engineering; Real-time computing; Embedded system; Artificial intelligence; Mechanical engineering","score_opus":0.01665803241469541,"score_gpt":0.23645817708368866,"score_spread":0.21980014466899325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091997491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.233691,0.000032578526,0.7632095,0.000005899009,0.0008645706,0.0004622447,0.000034478897,0.0016517214,0.000047976057],"genre_scores_gemma":[0.9424741,0.0000033656725,0.05718889,0.000030353407,0.000178917,0.000008032534,0.000040985997,0.00007297364,0.0000023743407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862623,0.000006330499,0.00068517175,0.00014808752,0.000186894,0.00034730512],"domain_scores_gemma":[0.999233,0.00007747488,0.00012007199,0.00035256453,0.000078438825,0.00013843011],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014522069,0.00026389968,0.00031986897,0.00013491549,0.00011075167,0.00010230776,0.00020754531,0.00013404997,8.923366e-7],"category_scores_gemma":[0.000112229536,0.00030185454,0.000064583444,0.00034946072,0.0000071030454,0.0009892719,0.000040762643,0.00019182629,0.000012537873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000104026185,0.0000011023252,0.0000013478939,0.0010895854,0.00005396881,8.424813e-7,0.00039190476,0.89216155,0.10170274,0.000011622219,0.000010459691,0.0045645037],"study_design_scores_gemma":[0.00084515824,0.000029306244,0.0000055930795,0.00019960837,0.000042486845,0.00002357772,0.0007044765,0.9764692,0.017382935,4.9157427e-7,0.0039724666,0.00032468856],"about_ca_topic_score_codex":0.000004152337,"about_ca_topic_score_gemma":7.7449334e-7,"teacher_disagreement_score":0.7087831,"about_ca_system_score_codex":0.00023803362,"about_ca_system_score_gemma":0.000012985333,"threshold_uncertainty_score":0.9999434},"labels":[],"label_agreement":null},{"id":"W3118158855","doi":"10.1016/j.aei.2020.101234","title":"Optimizing 3D Irregular Object Packing from 3D Scans Using Metaheuristics","year":2020,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University Network of Excellence in Nuclear Engineering","keywords":"Container (type theory); Packing problems; Object (grammar); Metaheuristic; Volume (thermodynamics); Computer science; Mathematical optimization; Bin packing problem; Key (lock); Algorithm; Engineering; Artificial intelligence; Mathematics; Mechanical engineering","score_opus":0.014432848633734395,"score_gpt":0.2050927582970104,"score_spread":0.190659909663276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118158855","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010009465,0.00046019888,0.9861107,0.000017250004,0.0006456747,0.00019611434,0.00005204582,0.0013515172,0.0011570764],"genre_scores_gemma":[0.22492225,0.00016796046,0.7743853,0.0001750692,0.00014325899,0.000008160756,0.000080857724,0.000111112946,0.0000060715324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984377,0.000008333203,0.0006807286,0.00013931548,0.00026830414,0.00046561056],"domain_scores_gemma":[0.9992225,0.00008256871,0.00010254687,0.0002949541,0.00006768484,0.00022970345],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000085931526,0.00037265164,0.00039273125,0.0001233855,0.000088568355,0.00013184221,0.00024563336,0.00012912058,0.00004826837],"category_scores_gemma":[0.000094527044,0.00042704906,0.000084942985,0.00048165373,0.00002030384,0.00069666485,0.00006700583,0.00037457264,0.00004085628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030163392,0.0000034709494,0.000029426055,0.00024188681,0.00008251543,0.0000032894616,0.003204039,0.9917118,0.001769787,0.0000983255,0.00003761311,0.0028148084],"study_design_scores_gemma":[0.00041382996,0.000018568147,0.000016672657,0.00015141493,0.000060240804,0.0000049193973,0.00016145526,0.98340786,0.0017841328,0.00000880794,0.0134800775,0.0004920189],"about_ca_topic_score_codex":0.0000039187,"about_ca_topic_score_gemma":7.117397e-7,"teacher_disagreement_score":0.21491279,"about_ca_system_score_codex":0.000109499706,"about_ca_system_score_gemma":0.000023820357,"threshold_uncertainty_score":0.99981815},"labels":[],"label_agreement":null},{"id":"W3123796372","doi":"10.1016/j.aei.2020.101233","title":"A case study comparing the completeness and expressiveness of two industry recognized ontologies","year":2021,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Semantic Web and Ontologies","field":"Computer Science","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Schneider Electric","keywords":"Haystack; Ontology; Building automation; Completeness (order theory); Computer science; Scalability; Brick; Automation; Data science; Engineering; Database; World Wide Web; Civil engineering; Mathematics","score_opus":0.038022988050555664,"score_gpt":0.2772276108915159,"score_spread":0.23920462284096022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123796372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7113576,0.00014265886,0.28802785,0.00002175888,0.00016594477,0.000111592024,7.380639e-7,0.00009524559,0.0000766103],"genre_scores_gemma":[0.90809983,0.000011174449,0.091827966,0.000026618594,0.000007668899,0.000017693048,6.360361e-7,0.0000044122366,0.000003981859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915195,0.000027598579,0.00038746942,0.00010283531,0.00013782216,0.00019233143],"domain_scores_gemma":[0.9988957,0.000354233,0.00013035085,0.00047178543,0.000112038084,0.000035912755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018510294,0.00013301197,0.0002896079,0.00005011304,0.00008951048,0.00008228106,0.00035784868,0.000048115016,7.417126e-7],"category_scores_gemma":[0.00015558509,0.0000997174,0.0000274954,0.0002525809,0.000040897652,0.00049525336,0.00041804742,0.00023062965,5.5289e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021564698,0.0002679562,0.068201125,0.0008542488,0.0003210877,0.002737611,0.0489482,0.7734923,0.0022693211,0.015772438,0.000021851953,0.08709233],"study_design_scores_gemma":[0.0028997506,0.00011874858,0.017569918,0.00021992292,0.000048055565,0.0056730756,0.038972124,0.92426616,0.009097503,0.00037250615,0.0002828379,0.0004794219],"about_ca_topic_score_codex":0.000032371936,"about_ca_topic_score_gemma":0.000027790147,"teacher_disagreement_score":0.19674224,"about_ca_system_score_codex":0.000013405572,"about_ca_system_score_gemma":0.000033224755,"threshold_uncertainty_score":0.40663576},"labels":[],"label_agreement":null},{"id":"W3184553779","doi":"10.1016/j.aei.2021.101361","title":"Product redesign using functional backtrack with digital twin","year":2021,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Digital Transformation in Industry","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":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Product (mathematics); Computer science; New product development; Product design; Process (computing); Function (biology); Field (mathematics); Space (punctuation); Product topology; Mathematics; Business","score_opus":0.01396958067191897,"score_gpt":0.18999106613614353,"score_spread":0.17602148546422455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184553779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.098336145,0.00015734888,0.8693434,0.000012210646,0.00059784425,0.00018142707,0.000046543988,0.00094630173,0.030378774],"genre_scores_gemma":[0.88883686,0.000027478787,0.11053152,0.000026738062,0.00012010559,0.000022048738,0.00013812535,0.000086977605,0.00021017522],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883854,0.0000019281806,0.00045066862,0.0000903921,0.00028238964,0.00033609165],"domain_scores_gemma":[0.9994162,0.000042141364,0.000038586066,0.00027307388,0.00012253634,0.00010748205],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000046444366,0.00024332738,0.00018633551,0.00009809465,0.000040104936,0.00016988673,0.00009770739,0.0000683424,0.000033513326],"category_scores_gemma":[0.00003176074,0.0002497505,0.000046116274,0.00045576817,0.000022803311,0.0027398532,0.000017859904,0.0002811065,0.000059019887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000351848,0.0000096597805,0.000039383554,0.00024949445,0.000047762318,0.000006524292,0.00017796934,0.98813844,0.0007063048,0.00044364983,0.00011613805,0.010061157],"study_design_scores_gemma":[0.00094980013,0.000036657988,0.00052740274,0.00039172455,0.00003067055,0.00056079455,0.0007550468,0.90335506,0.029842442,0.000044738266,0.06257679,0.0009288805],"about_ca_topic_score_codex":1.168796e-7,"about_ca_topic_score_gemma":1.5316077e-7,"teacher_disagreement_score":0.7905007,"about_ca_system_score_codex":0.00012460175,"about_ca_system_score_gemma":0.00005749304,"threshold_uncertainty_score":0.99999547},"labels":[],"label_agreement":null},{"id":"W39804063","doi":"10.1016/j.aei.2009.11.001","title":"Enabling technologies for collaborative design","year":2009,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Manufacturing Process and Optimization","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":"National Research Council Canada","funders":"","keywords":"Computer science; Collaborative design; Systems engineering; Engineering; Human–computer interaction; Software engineering; Systems design","score_opus":0.008001104381723028,"score_gpt":0.20342837309755915,"score_spread":0.19542726871583613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W39804063","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010417613,0.00042269085,0.995691,0.00002280794,0.00014872357,0.0003266978,0.000005623981,0.0019221377,0.0004185627],"genre_scores_gemma":[0.28650397,0.00043938396,0.71289504,0.000023527808,0.000019016388,0.000066226174,0.000013619987,0.000022424856,0.000016806087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993852,9.221056e-7,0.00025150174,0.00005184745,0.00007336037,0.00023717018],"domain_scores_gemma":[0.9996921,0.000050688075,0.000035188612,0.00013348786,0.00006213386,0.000026390464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006442976,0.0001548797,0.00014214325,0.00012496533,0.000047188983,0.000044974484,0.00011888725,0.0000789339,0.0000013020209],"category_scores_gemma":[0.000060537837,0.00015584113,0.000023728604,0.00022709275,0.0000061834717,0.00045005765,0.000007305078,0.0000946095,0.0000032226758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003770788,0.0000024055528,2.615233e-7,0.000114009075,0.000008125073,1.6336159e-7,0.00036618306,0.9405957,0.0003037855,0.0009632339,0.000069533366,0.05757286],"study_design_scores_gemma":[0.0002816186,0.000066516186,0.000007445939,0.000049507038,0.0000069424436,0.0000014926238,0.0002687275,0.91952115,0.05721165,0.00040749743,0.021951884,0.00022556364],"about_ca_topic_score_codex":3.0770664e-8,"about_ca_topic_score_gemma":3.695333e-8,"teacher_disagreement_score":0.2854622,"about_ca_system_score_codex":0.00004733986,"about_ca_system_score_gemma":0.00000890037,"threshold_uncertainty_score":0.6355017},"labels":[],"label_agreement":null},{"id":"W4281946833","doi":"10.1016/j.aei.2022.101638","title":"A systematic knowledge-based method for design of transformable product","year":2022,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Design Education and Practice","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 Manitoba","funders":"Ministry of Science and Technology of the People's Republic of China; Hebei Provincial Department of Human Resources and Social Security","keywords":"Transformation (genetics); Domain (mathematical analysis); Computer science; Process (computing); Knowledge base; Product (mathematics); Design knowledge; Domain knowledge; Product design; Data mining; Boom; Artificial intelligence; Industrial engineering; Engineering; Mathematics; Programming language; Embedded system","score_opus":0.015461707106475054,"score_gpt":0.26781697875647537,"score_spread":0.2523552716500003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281946833","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016870628,0.000556564,0.9965539,0.000014041351,0.0006122958,0.0012017263,0.00002059585,0.00030171312,0.0005704372],"genre_scores_gemma":[0.09232781,0.000018843955,0.9060213,0.000035614023,0.000021918524,0.0013732868,0.000023484718,0.000058204816,0.00011954827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889106,0.000033535736,0.00062263873,0.000059934184,0.0001552507,0.00023757842],"domain_scores_gemma":[0.99887174,0.0006354155,0.000103438346,0.00025851346,0.00007618744,0.000054686174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088184275,0.00016313531,0.00030420793,0.00020056222,0.000073734365,0.00001605216,0.00019774484,0.000023087352,0.000021673828],"category_scores_gemma":[0.00014781806,0.00017708218,0.00007131822,0.00039810396,0.0000054235015,0.0003541826,0.000010578597,0.00016627413,0.0000056018716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015045703,0.000025737736,1.862758e-7,0.020355374,0.000050681116,9.730266e-8,0.0019664883,0.9731245,0.0020310513,0.00057834474,0.00025230812,0.001600201],"study_design_scores_gemma":[0.00045932506,0.00007931859,9.654327e-7,0.0002394767,0.000057501857,0.000010445744,0.00042785518,0.9713103,0.009667952,0.000022777504,0.017527526,0.00019653988],"about_ca_topic_score_codex":4.0869938e-7,"about_ca_topic_score_gemma":7.6917935e-8,"teacher_disagreement_score":0.09215911,"about_ca_system_score_codex":0.00012154474,"about_ca_system_score_gemma":0.000072594645,"threshold_uncertainty_score":0.72212017},"labels":[],"label_agreement":null},{"id":"W4292013697","doi":"10.1016/j.aei.2022.101715","title":"Topic discovery innovations for sustainable ultra-precision machining by social network analysis and machine learning approach","year":2022,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Digital Transformation in Industry","field":"Engineering","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":"McMaster University","funders":"Hong Kong Polytechnic University","keywords":"Computer science; Data mining; Resource (disambiguation); Machine learning; Artificial intelligence; Data science","score_opus":0.006583913535859497,"score_gpt":0.20518157400012493,"score_spread":0.19859766046426544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292013697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043028392,0.00013926893,0.9513072,0.000011881629,0.000116950556,0.00028929464,0.00008886419,0.00037970266,0.0046384535],"genre_scores_gemma":[0.9725607,0.000027403597,0.02556636,0.000033118828,0.000048303486,0.000264319,0.00095390016,0.0000452512,0.00050066167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987871,0.000007609027,0.0005041454,0.0000893379,0.00020802,0.00040380025],"domain_scores_gemma":[0.9996055,0.000112466914,0.000074336494,0.00011564269,0.00004572748,0.00004633402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025394384,0.00019293732,0.0002487341,0.00026715235,0.00042873336,0.00016935346,0.00015159763,0.000060812035,0.000007322258],"category_scores_gemma":[0.00003966681,0.00022824176,0.00007559299,0.0012588821,0.000014005107,0.0014591952,0.000053275373,0.00046427525,4.475786e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051948405,0.000007729923,0.00020815813,0.00024566817,0.00014502618,2.468184e-7,0.000987215,0.9885228,0.000055113564,0.005276288,0.00032068376,0.004225844],"study_design_scores_gemma":[0.00042487195,0.00003513279,0.00010085522,0.000007248816,0.00007224248,0.0000045838233,0.0018803828,0.9413033,0.00013397877,0.00012454898,0.055611067,0.00030178874],"about_ca_topic_score_codex":0.0000018634178,"about_ca_topic_score_gemma":2.1392673e-7,"teacher_disagreement_score":0.9295323,"about_ca_system_score_codex":0.00015201795,"about_ca_system_score_gemma":0.0000107555725,"threshold_uncertainty_score":0.93074286},"labels":[],"label_agreement":null},{"id":"W4294896944","doi":"10.1016/j.aei.2022.101716","title":"Generation and evaluation of product concepts by integrating extended axiomatic design, quality function deployment and design structure matrix","year":2022,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Quality Function Deployment in Product Design","field":"Business, Management and Accounting","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Axiomatic design; Quality function deployment; Design structure matrix; House of Quality; Probabilistic design; Systems engineering; Computer science; Product design; Function (biology); Functional requirement; Process (computing); Design process; Product (mathematics); Design matrix; New product development; Iterative design; Domain (mathematical analysis); Quality (philosophy); Reliability engineering; Engineering design process; Engineering; Software engineering; Manufacturing engineering; Work in process; Mathematics; Machine learning","score_opus":0.04622115067064909,"score_gpt":0.29398334715641655,"score_spread":0.24776219648576747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294896944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3696124,0.0005411285,0.627956,0.0000737366,0.0004830976,0.001149684,0.0000075527532,0.00015687053,0.00001952867],"genre_scores_gemma":[0.95617014,0.0000130349335,0.04320122,0.00016314062,0.00012147667,0.00017289723,0.00010654973,0.000032157317,0.000019401989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979528,0.000101456564,0.00080783875,0.00020507595,0.0007302968,0.00020254072],"domain_scores_gemma":[0.9987834,0.00010768225,0.00057036965,0.00025343272,0.00026729953,0.000017803739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028323128,0.00022830753,0.00025238638,0.00021839872,0.00023510658,0.000121481404,0.00010203631,0.00003680151,0.000058658206],"category_scores_gemma":[0.0004977266,0.00023417812,0.000025358902,0.00039825353,0.000031418946,0.001574149,0.000107633074,0.00018012771,0.0000016692237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006963297,0.000039383227,0.00013343763,0.00061554194,0.00006534842,1.4910418e-7,0.00068318105,0.8860492,0.069836125,0.003140953,0.000876221,0.03849079],"study_design_scores_gemma":[0.0010163181,0.00008559033,0.00048050474,0.000033736203,0.00016248107,0.0000066544903,0.0009811443,0.98741674,0.00696962,0.0011599346,0.0013646976,0.00032255647],"about_ca_topic_score_codex":0.000013362709,"about_ca_topic_score_gemma":9.4731297e-7,"teacher_disagreement_score":0.58655775,"about_ca_system_score_codex":0.00013371372,"about_ca_system_score_gemma":0.000033657394,"threshold_uncertainty_score":0.95495063},"labels":[],"label_agreement":null},{"id":"W4313594908","doi":"10.1016/j.aei.2022.101868","title":"Towards emotionally intelligent buildings: A Convolutional neural network based approach to classify human emotional experience in virtual built environments","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Government of South Australia; Defense Advanced Research Projects Agency; U.S. Department of Defense","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Electroencephalography; Pattern recognition (psychology); Machine learning; Psychology","score_opus":0.03580571400879516,"score_gpt":0.2754423101096449,"score_spread":0.2396365961008497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313594908","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53273535,0.0000041470776,0.46608004,0.000062579435,0.00040252923,0.00022650346,0.000016842554,0.00018047894,0.00029151468],"genre_scores_gemma":[0.96057016,0.000005518576,0.03834601,0.0007141832,0.00010130021,0.000092170034,0.000044828532,0.000025954245,0.0000998662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794304,0.000023452549,0.000620755,0.00027543944,0.000559575,0.0005777491],"domain_scores_gemma":[0.9993369,0.00015010386,0.00009725409,0.00023537817,0.000018480194,0.00016188093],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019177231,0.0002663757,0.00021927277,0.00028761424,0.00013345722,0.00007528283,0.0004949624,0.000078207515,0.000017016078],"category_scores_gemma":[0.00015469447,0.00027715595,0.0000720121,0.00074694183,0.000069037385,0.00050705305,0.00022682731,0.0003013161,0.0000684483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009993293,0.000049750688,0.00015202341,0.000031798296,0.0000034716056,0.000003197209,0.0011456166,0.97952735,0.011070713,0.006160234,0.00014316733,0.0017026546],"study_design_scores_gemma":[0.0003932903,0.00010894355,0.005459888,0.000107824184,0.0000018817731,0.0000130436165,0.00017621036,0.9772853,0.008855458,0.00008926957,0.007186388,0.00032250021],"about_ca_topic_score_codex":0.0000016598742,"about_ca_topic_score_gemma":4.9964217e-7,"teacher_disagreement_score":0.4278348,"about_ca_system_score_codex":0.00018489141,"about_ca_system_score_gemma":0.000032339587,"threshold_uncertainty_score":0.99996805},"labels":[],"label_agreement":null},{"id":"W4319990764","doi":"10.1016/j.aei.2023.101878","title":"End-to-end point cloud-based segmentation of building members for automating dimensional quality control","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Australian Research Council; Monash University","keywords":"Point cloud; Quality (philosophy); Segmentation; Outlier; Minimum bounding box; Computer science; Point (geometry); Cloud computing; Set (abstract data type); Data mining; Computer vision; Artificial intelligence; Mathematics; Image (mathematics)","score_opus":0.014853480101532077,"score_gpt":0.25657091995429365,"score_spread":0.24171743985276156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319990764","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83654714,0.000015887332,0.16216451,0.00004106126,0.00042217885,0.00028045307,0.00022171333,0.00022390566,0.000083151936],"genre_scores_gemma":[0.8635429,0.0000011100877,0.13608125,0.000099729084,0.000023744426,0.000007640345,0.00022577166,0.0000046372015,0.000013215133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989317,0.00001583748,0.00047077838,0.00007513971,0.00024651233,0.00026002707],"domain_scores_gemma":[0.9989838,0.00063724583,0.00012801091,0.000103753846,0.000059306643,0.00008789494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065456826,0.00012048018,0.00018961505,0.000087072636,0.00008558415,0.000021755752,0.00009333491,0.000036921974,0.000058909343],"category_scores_gemma":[0.00024062142,0.00010656826,0.00006262284,0.00028512758,0.000012549888,0.00028668402,0.00000603408,0.0000680852,0.000015787258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020899348,0.000002243675,0.0023665037,0.00019112934,0.0000136269655,2.9931326e-7,0.00050073245,0.9783583,0.0023132109,0.00006713016,0.00003876762,0.016127147],"study_design_scores_gemma":[0.0005936186,0.000074256925,0.022923639,0.000084381965,0.000007823889,8.846984e-7,0.0003880252,0.9725748,0.00283652,0.000027826878,0.00032959753,0.00015865652],"about_ca_topic_score_codex":0.000047139103,"about_ca_topic_score_gemma":0.000026195601,"teacher_disagreement_score":0.026995761,"about_ca_system_score_codex":0.0000120362665,"about_ca_system_score_gemma":0.000020139712,"threshold_uncertainty_score":0.43457276},"labels":[],"label_agreement":null},{"id":"W4362736902","doi":"10.1016/j.aei.2023.101958","title":"Incorporation of BIM-based probabilistic non-structural damage assessment into agent-based post-earthquake evacuation simulation","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Evacuation and Crowd Dynamics","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":"University of Alberta","funders":"","keywords":"Probabilistic logic; Computer science; Process (computing); Building information modeling; Built environment; Construction engineering; Engineering; Civil engineering; Artificial intelligence","score_opus":0.006978012025827038,"score_gpt":0.2555755382410553,"score_spread":0.24859752621522824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362736902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48161745,0.0000031172601,0.51733947,0.000014689855,0.00021305153,0.0002892875,0.000018490296,0.00044536096,0.000059095288],"genre_scores_gemma":[0.9086149,0.0000032053929,0.09029979,0.000030315385,0.000020421026,0.000048854527,0.00093717716,0.00004096886,0.0000044025724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860257,0.000010332705,0.00070235063,0.00009127214,0.0003609863,0.00023248006],"domain_scores_gemma":[0.9990892,0.00015440912,0.00015597041,0.00029497882,0.00023010562,0.00007537465],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022216717,0.00022789917,0.00021600994,0.00038926266,0.000061176885,0.00004218264,0.00013031541,0.00010248419,0.000011845508],"category_scores_gemma":[0.000117098214,0.00024942003,0.00006754693,0.0007384677,0.000021355008,0.00055655977,0.000018691142,0.0001682243,0.000027263122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068719487,0.000006353237,0.00021847108,0.00074582803,0.000013913673,6.1114963e-7,0.00032547867,0.9882729,0.004612622,0.00064367556,0.0000061276373,0.005147169],"study_design_scores_gemma":[0.0006696957,0.00006769684,0.015818745,0.00008883887,0.00001902767,2.4805502e-7,0.00008839201,0.9810633,0.001670522,0.00014782116,0.00011588943,0.00024978913],"about_ca_topic_score_codex":0.0000022306635,"about_ca_topic_score_gemma":0.000008540965,"teacher_disagreement_score":0.42703965,"about_ca_system_score_codex":0.00021135077,"about_ca_system_score_gemma":0.00007493301,"threshold_uncertainty_score":0.9999958},"labels":[],"label_agreement":null},{"id":"W4363652187","doi":"10.1016/j.aei.2023.101962","title":"Context-driven ontology-based risk identification for onshore wind farm projects: A domain-specific approach","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Semantic Web and Ontologies","field":"Computer Science","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":"National Institute of General Medical Sciences; National Institutes of Health; Canada First Research Excellence Fund","keywords":"Identification (biology); Ontology; Context (archaeology); Risk analysis (engineering); Computer science; Reuse; Risk management; Project risk management; Process (computing); Risk assessment; Knowledge management; Engineering; Systems engineering; Project management; Project management triangle; Business; Geography; Computer security","score_opus":0.02083643050473127,"score_gpt":0.23712416719659254,"score_spread":0.21628773669186127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4363652187","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05404701,0.000084182386,0.9434749,0.000077881894,0.0004875806,0.00065822975,0.000021701442,0.0009686204,0.00017990863],"genre_scores_gemma":[0.309084,0.000049341,0.69045544,0.00007042618,0.00003593226,0.0001876677,0.00006892015,0.000018410648,0.000029872037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861777,0.00001312004,0.0005216316,0.00020604063,0.00020741981,0.00043400517],"domain_scores_gemma":[0.9987559,0.0002605398,0.00020965832,0.00061554095,0.00009386556,0.00006449885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033332457,0.00020614355,0.00025356296,0.00027775767,0.00014536241,0.00012975908,0.00067381246,0.00010300673,3.6698088e-7],"category_scores_gemma":[0.00009970724,0.00019506922,0.00009413273,0.00061176333,0.000039737854,0.00050413614,0.000085515014,0.00016800486,0.000025827732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018567418,0.00004184969,0.00035365345,0.00040063943,0.000048273912,0.0000029990545,0.00598347,0.83743316,0.0006216517,0.10165901,0.0004969563,0.052939743],"study_design_scores_gemma":[0.0008770556,0.00007000516,0.0026934294,0.000042508007,0.000009328748,0.0000074123413,0.0014270158,0.9500253,0.0009519165,0.00071174855,0.042888086,0.00029619472],"about_ca_topic_score_codex":0.0000021636538,"about_ca_topic_score_gemma":0.0000032545363,"teacher_disagreement_score":0.25503698,"about_ca_system_score_codex":0.00007364449,"about_ca_system_score_gemma":0.000048515318,"threshold_uncertainty_score":0.79546916},"labels":[],"label_agreement":null},{"id":"W4377102312","doi":"10.1016/j.aei.2023.102010","title":"An energy-efficient multi-objective integrated process planning and scheduling for a flexible job-shop-type remanufacturing system","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","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":"Remanufacturing; Mathematical optimization; Computer science; Job shop scheduling; Scheduling (production processes); Job shop; Industrial engineering; Engineering; Flow shop scheduling; Schedule; Manufacturing engineering; Mathematics","score_opus":0.011704945284183027,"score_gpt":0.24564435003336946,"score_spread":0.23393940474918642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377102312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38292623,0.000055298577,0.6138538,0.000015428172,0.00046378418,0.00056758005,0.0000031324348,0.00182332,0.00029141543],"genre_scores_gemma":[0.9696219,0.0000056339472,0.0296659,0.000092623195,0.00018451222,0.00017969524,0.00010679856,0.00007618989,0.00006674516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984924,0.0000031688662,0.00046748115,0.00022144412,0.00023133437,0.0005841645],"domain_scores_gemma":[0.99914885,0.00007083588,0.00019601501,0.00025866373,0.00028942374,0.000036234956],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041347367,0.00031167213,0.0002802524,0.0007721269,0.00025509004,0.00032783407,0.00024175586,0.00007883293,0.0000014865295],"category_scores_gemma":[0.000182814,0.00031184294,0.00004228924,0.0010639325,0.000018496867,0.0014291005,0.00014125787,0.00015371823,0.000011367834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003298975,0.000011854874,0.00020971912,0.003247455,0.000037852016,0.0000069520443,0.0009950186,0.9880237,0.0001339559,0.0053328625,0.00001625014,0.001951389],"study_design_scores_gemma":[0.00073425047,0.00002239844,0.0003115852,0.00044923564,0.000034851782,0.0000023914426,0.026042417,0.9684816,0.00061126193,0.00006674371,0.0028703478,0.0003729393],"about_ca_topic_score_codex":0.000020978092,"about_ca_topic_score_gemma":0.0000018334017,"teacher_disagreement_score":0.5866957,"about_ca_system_score_codex":0.00016695305,"about_ca_system_score_gemma":0.000024014584,"threshold_uncertainty_score":0.99993336},"labels":[],"label_agreement":null},{"id":"W4379355776","doi":"10.1016/j.aei.2023.102012","title":"Agile solution search strategy for solving multi-conflicts in product development","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Product Development and Customization","field":"Business, Management and Accounting","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 Manitoba","funders":"Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Agile software development; TRIZ; Conflict resolution; Computer science; New product development; Product (mathematics); Agile manufacturing; Ant colony optimization algorithms; Process (computing); Path (computing); Mathematical optimization; Representation (politics); Industrial engineering; Process management; Management science; Engineering; Mathematics; Algorithm; Artificial intelligence; Software engineering; Business","score_opus":0.034445144286058715,"score_gpt":0.24689017488887022,"score_spread":0.2124450306028115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379355776","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6746663,0.000093067945,0.31959692,0.00023088546,0.001130076,0.0017919926,0.0000028283407,0.0013367346,0.0011511786],"genre_scores_gemma":[0.9419075,0.000020465708,0.056786444,0.00010348123,0.00023190485,0.00019460692,0.00033347387,0.000044835393,0.00037727025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884266,9.233129e-7,0.0004314641,0.00012649392,0.0001736596,0.00042481555],"domain_scores_gemma":[0.99962616,0.000020105885,0.000087258595,0.00012989929,0.00012459287,0.00001196109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045608057,0.00015753446,0.0001442812,0.00047993183,0.00011052418,0.00011027635,0.00014301059,0.000041409734,0.000004795085],"category_scores_gemma":[0.0001538022,0.00016865511,0.000022814173,0.00082972547,0.000007847998,0.0016619709,0.00008716832,0.000103518825,0.00014459364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013929242,0.000024142533,0.00075096515,0.0010764889,0.000014811769,0.0000011918863,0.0014152857,0.9340843,0.0023199774,0.0014046762,0.0002855949,0.058608666],"study_design_scores_gemma":[0.0009132927,0.000003974681,0.009282923,0.000121276375,0.0000050176595,5.6750355e-7,0.00035150908,0.9312917,0.0036001161,0.000032529144,0.054065667,0.00033140287],"about_ca_topic_score_codex":0.000004062391,"about_ca_topic_score_gemma":0.000009517241,"teacher_disagreement_score":0.2672412,"about_ca_system_score_codex":0.00008582691,"about_ca_system_score_gemma":0.000040640654,"threshold_uncertainty_score":0.6877556},"labels":[],"label_agreement":null},{"id":"W4381949241","doi":"10.1016/j.aei.2023.102006","title":"Feature-based modeling for variable fractal geometry design integrated into CAD system","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Music Technology and Sound Studies","field":"Computer Science","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Fractal; CAD; Feature (linguistics); Fractal analysis; Parametric statistics; Fractal dimension; Computer Aided Design; Computer science; Fractal compression; Parametric model; Box counting; Geometric modeling; Geometry; Fractal landscape; Algorithm; Artificial intelligence; Mathematics; Engineering drawing; Image processing; Engineering; Image (mathematics)","score_opus":0.01138598924854831,"score_gpt":0.21554008092040208,"score_spread":0.20415409167185378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381949241","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00396353,0.00008389919,0.9926536,0.00008909315,0.0006205207,0.00028082618,0.0000061080204,0.0022352205,0.00006719636],"genre_scores_gemma":[0.2040469,0.0000068914574,0.7956994,0.00005976104,0.000018394694,0.000102946025,0.000016036753,0.000013527781,0.00003613032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990662,0.000005935862,0.00027820314,0.00012778614,0.00014780009,0.00037410497],"domain_scores_gemma":[0.99915814,0.00023329373,0.00007095506,0.00037272618,0.000114733215,0.00005013695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034994705,0.00018574046,0.00023415867,0.00033184793,0.0001922326,0.00007951016,0.000490288,0.0001489068,2.744185e-7],"category_scores_gemma":[0.0002032907,0.00017225376,0.000049424903,0.0010937037,0.000013852109,0.0005287738,0.0001093128,0.00023166208,0.00001678808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046204204,0.00000344148,0.000002872042,0.00028307718,0.000025971009,0.0000015491711,0.00054192974,0.9613623,0.00011013619,0.034349944,0.00037598523,0.0029381772],"study_design_scores_gemma":[0.0003212366,0.0000460414,0.0000024600054,0.000114645336,0.0000069618586,0.0000038065732,0.00022677753,0.98945105,0.00041148727,0.0005318539,0.008687109,0.0001965456],"about_ca_topic_score_codex":0.0000028076233,"about_ca_topic_score_gemma":3.1221902e-7,"teacher_disagreement_score":0.20008337,"about_ca_system_score_codex":0.00011759065,"about_ca_system_score_gemma":0.000058398462,"threshold_uncertainty_score":0.7024304},"labels":[],"label_agreement":null},{"id":"W4383032832","doi":"10.1016/j.aei.2023.102078","title":"User-centric immersive virtual reality development framework for data visualization and decision-making in infrastructure remote inspections","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","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":"Usability; Computer science; Visualization; Agile software development; Bridge (graph theory); Human–computer interaction; Virtual reality; Software engineering; Artificial intelligence","score_opus":0.019442447236641423,"score_gpt":0.3174635043247617,"score_spread":0.2980210570881203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383032832","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028287126,0.000015567728,0.97098917,0.000044288987,0.0001654367,0.0002986274,0.000020526919,0.00016171316,0.000017550103],"genre_scores_gemma":[0.2761444,0.00012997817,0.7235239,0.00007076651,0.000017072749,0.000013684943,0.00008763586,0.000010519178,0.000002072961],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989293,0.0000048091147,0.00044201632,0.00017350983,0.00018249553,0.00026784607],"domain_scores_gemma":[0.9987416,0.0004298746,0.00011452479,0.0005752535,0.000063530155,0.00007516616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027398538,0.00013120331,0.00014357847,0.0003070937,0.00013116961,0.00011210227,0.0004975809,0.000082387574,6.200897e-7],"category_scores_gemma":[0.0007596965,0.0001367101,0.000013962034,0.0014082254,0.000012575258,0.0012430359,0.000423091,0.00013834926,0.000005232672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053122767,0.000008666832,0.000041864358,0.000063037216,0.000011127038,5.634552e-7,0.0035930593,0.43197572,0.000018134991,0.13284802,0.00014281637,0.43129167],"study_design_scores_gemma":[0.0002078385,0.000022267319,0.0057343394,0.00016944075,0.0000026701948,0.0000040666605,0.0002846804,0.97389513,0.00005105062,0.0074924272,0.011979328,0.00015674572],"about_ca_topic_score_codex":0.000002821312,"about_ca_topic_score_gemma":0.000005730832,"teacher_disagreement_score":0.5419194,"about_ca_system_score_codex":0.00012746251,"about_ca_system_score_gemma":0.00007440316,"threshold_uncertainty_score":0.5574876},"labels":[],"label_agreement":null},{"id":"W4383163195","doi":"10.1016/j.aei.2023.102069","title":"An efficient 3D object detection method based on Fast Guided Anchor Stereo RCNN","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Advanced Neural Network Applications","field":"Computer 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":"McMaster University","funders":"Science and Technology Program of Suzhou; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Minimum bounding box; Robustness (evolution); Object detection; Computer vision; Bounding overwatch; Segmentation; Feature extraction; Pattern recognition (psychology); Feature (linguistics); Position (finance); Convolutional neural network; Pyramid (geometry); Image (mathematics); Mathematics","score_opus":0.012268340610133158,"score_gpt":0.27812306996464947,"score_spread":0.2658547293545163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383163195","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009860249,0.000004845664,0.98708946,0.000054022436,0.00040932928,0.00037133048,0.0000061432747,0.0018674856,0.00033713516],"genre_scores_gemma":[0.29875794,0.000006197337,0.70073485,0.00025846754,0.000044266482,0.00013391288,0.000017692739,0.000028395385,0.000018257977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984381,0.000021876405,0.00048120978,0.00024259095,0.0003445769,0.00047161538],"domain_scores_gemma":[0.9983743,0.00024148046,0.00014103124,0.0010058525,0.00008217558,0.00015520152],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027105265,0.00025128614,0.00019456485,0.00033892563,0.00015766479,0.00009523262,0.00064001593,0.00006891618,0.0000015485743],"category_scores_gemma":[0.000065056935,0.00025389658,0.000060044244,0.0015759179,0.000012619759,0.00066457957,0.00009795753,0.0002442929,0.000119926844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002958738,0.000016408609,0.000001933519,0.000029298944,0.000003577914,0.0000013618549,0.00023248054,0.8917579,0.001672811,0.00096937554,0.000016116892,0.10529576],"study_design_scores_gemma":[0.0003275363,0.00011865177,0.0003735426,0.000038694317,0.0000039491024,0.0000069465204,0.000024496034,0.9857023,0.008969001,0.00007013657,0.0040858393,0.0002789179],"about_ca_topic_score_codex":7.756437e-7,"about_ca_topic_score_gemma":7.975257e-7,"teacher_disagreement_score":0.2888977,"about_ca_system_score_codex":0.00012919086,"about_ca_system_score_gemma":0.00002487271,"threshold_uncertainty_score":0.9999913},"labels":[],"label_agreement":null},{"id":"W4384027914","doi":"10.1016/j.aei.2023.102095","title":"Block feature selection based on NSGA-II applied to fault diagnosis of gearboxes","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Gear and Bearing Dynamics Analysis","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":"Tsinghua University","keywords":"Classifier (UML); Pattern recognition (psychology); Artificial intelligence; Feature selection; Computer science; Feature extraction; Engineering","score_opus":0.0031320677184197673,"score_gpt":0.18742735213690564,"score_spread":0.1842952844184859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384027914","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.887967,0.00001385532,0.10772899,0.00007773489,0.00037666838,0.00043649605,0.000036866866,0.0016168195,0.0017455813],"genre_scores_gemma":[0.97496337,0.000028057348,0.024689816,0.000032728312,0.00003119705,0.000103749175,0.000035648594,0.00004264188,0.00007279683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920744,0.0000016239018,0.00026963314,0.00007810178,0.00016880254,0.0002743846],"domain_scores_gemma":[0.9995651,0.000063170344,0.000034322366,0.00021440879,0.00004219693,0.00008077486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000086570595,0.00018179872,0.00022363191,0.00045288104,0.000047298054,0.000018617951,0.00013159317,0.0000858512,0.000004828762],"category_scores_gemma":[0.000039911545,0.00019335566,0.00007321234,0.0011792793,0.000005102885,0.00007825959,0.000032655968,0.00018878236,0.000048723956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035989642,0.000008350469,0.00013211477,0.00022364467,0.000035234825,3.3258172e-7,0.00030573664,0.99327475,0.0018699237,0.00013149911,0.00028301487,0.0037318307],"study_design_scores_gemma":[0.00017889788,0.000056200806,0.0011681723,0.00008056872,0.000022010452,5.9172845e-7,0.000038909562,0.9840286,0.0070022065,0.000006705503,0.00721047,0.00020667184],"about_ca_topic_score_codex":0.0000014682745,"about_ca_topic_score_gemma":0.0000031205202,"teacher_disagreement_score":0.08699638,"about_ca_system_score_codex":0.00006439066,"about_ca_system_score_gemma":0.0000064190303,"threshold_uncertainty_score":0.7884815},"labels":[],"label_agreement":null},{"id":"W4385788468","doi":"10.1016/j.aei.2023.102140","title":"Enhancing construction robot learning for collaborative and long-horizon tasks using generative adversarial imitation learning","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Innovations in Concrete and Construction Materials","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 British Columbia","funders":"","keywords":"Reinforcement learning; Robot; Computer science; Artificial intelligence; Human–computer interaction; Adversarial system; Control (management); Machine learning","score_opus":0.01041735462965053,"score_gpt":0.2351327747687592,"score_spread":0.22471542013910867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385788468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37675655,0.00002726313,0.62129486,0.0000041687513,0.0010355291,0.00023408192,0.000008077658,0.00055653544,0.00008295233],"genre_scores_gemma":[0.57072324,0.00017601295,0.428427,0.000008552502,0.00028066465,0.000098472075,0.00018448135,0.0000660261,0.000035568297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998921,0.000015101854,0.00052943174,0.000108959415,0.00012838133,0.00029713594],"domain_scores_gemma":[0.99937665,0.0001462511,0.00012921458,0.00008566768,0.0002175305,0.0000446718],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023462842,0.00021459819,0.00023623384,0.00031211393,0.00027073853,0.0001067961,0.00005259436,0.00011706576,0.0000052505407],"category_scores_gemma":[0.00019481913,0.00025721814,0.00003233427,0.0006777215,0.000036634716,0.00079235213,0.000027022552,0.00022578526,0.0000069613793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009800184,4.991768e-7,0.000042138443,0.00018407125,0.000046753652,4.7421932e-7,0.0010951607,0.8976804,0.0880487,0.0010592543,0.0000071435547,0.011825638],"study_design_scores_gemma":[0.0005876811,0.00007873238,0.000049654176,0.00009297349,0.000028765207,0.000016219483,0.0028876301,0.9414012,0.052835543,0.00008329872,0.0016323129,0.00030599095],"about_ca_topic_score_codex":6.735713e-7,"about_ca_topic_score_gemma":7.648831e-7,"teacher_disagreement_score":0.19396666,"about_ca_system_score_codex":0.000117153024,"about_ca_system_score_gemma":0.00003300812,"threshold_uncertainty_score":0.999988},"labels":[],"label_agreement":null},{"id":"W4386041992","doi":"10.1016/j.aei.2023.102149","title":"Supersystem digital twin-driven framework for new product conceptual design","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Digital Transformation in Industry","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":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"TRIZ; Conceptual design; Product (mathematics); Product design; Computer science; Field (mathematics); New product development; Process (computing); Design technology; Systems engineering; Conceptual framework; Industrial engineering; Engineering; Human–computer interaction; Artificial intelligence; Mathematics","score_opus":0.026966878888617134,"score_gpt":0.23370816607661188,"score_spread":0.20674128718799475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386041992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007997982,0.000045955436,0.98515844,0.000029215444,0.0011604832,0.0007212018,0.00010033376,0.0029673364,0.0018190277],"genre_scores_gemma":[0.5956631,0.000046944046,0.40275288,0.00003692832,0.0004002142,0.00025999316,0.00023253569,0.00018299241,0.0004244645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984813,0.000002199749,0.00062237494,0.00010242648,0.0002463935,0.00054534903],"domain_scores_gemma":[0.9990909,0.00030668598,0.00004421444,0.00031071366,0.000059939182,0.0001875607],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009680675,0.00029772884,0.00026814936,0.00019824754,0.00004834386,0.00017952014,0.000276063,0.00014252162,0.00000855727],"category_scores_gemma":[0.0001972367,0.0003262671,0.000091468806,0.00059595343,0.000027445138,0.0020217237,0.000025806929,0.00027484738,0.00026924344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048007096,0.0000029695482,0.000011116446,0.00036397227,0.000049312253,8.915649e-7,0.0021887303,0.97727,0.00008219528,0.0063248617,0.0031087974,0.010592365],"study_design_scores_gemma":[0.0007352842,0.00007722619,0.000035526966,0.00037284658,0.00001729826,0.000016581347,0.002003494,0.8769209,0.0038872813,0.0005443377,0.11469617,0.00069305993],"about_ca_topic_score_codex":1.372483e-7,"about_ca_topic_score_gemma":3.779053e-8,"teacher_disagreement_score":0.5876651,"about_ca_system_score_codex":0.000112470545,"about_ca_system_score_gemma":0.000041039788,"threshold_uncertainty_score":0.99991894},"labels":[],"label_agreement":null},{"id":"W4388550734","doi":"10.1016/j.aei.2023.102245","title":"Multiobjective optimization-based decision support for building digital twin maturity measurement","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Digital Transformation in Industry","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":"Maturity (psychological); Standardization; Capability Maturity Model; Systems engineering; Engineering; Computer science; Process management; Risk analysis (engineering); Knowledge management; Business","score_opus":0.0139164565316413,"score_gpt":0.23229274453595722,"score_spread":0.2183762880043159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388550734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004275487,0.00000996236,0.9896562,0.000008242144,0.00070550694,0.0005108761,0.00016547693,0.0019285111,0.0027397468],"genre_scores_gemma":[0.653754,0.000013285589,0.34555402,0.00002793862,0.000052306248,0.00023977616,0.00024588883,0.00009285184,0.000019927968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984373,0.0000014182643,0.00062001916,0.00009342853,0.0004274815,0.00042033993],"domain_scores_gemma":[0.9992299,0.00018246155,0.000054177035,0.0002270819,0.00018957235,0.00011682418],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020996248,0.00025674424,0.0002067963,0.00031901165,0.000068840964,0.00017571046,0.00018682273,0.00012414488,0.000009438139],"category_scores_gemma":[0.00022280427,0.00028822376,0.00009756449,0.00056014676,0.000014496616,0.001694678,0.00002084916,0.00018493542,0.00005531705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009086084,0.000007838791,0.000010946703,0.00033539062,0.000026868614,5.664759e-7,0.00021332651,0.98564565,0.000065767104,0.00018782167,0.0003585778,0.01313818],"study_design_scores_gemma":[0.00080816215,0.000033758715,0.000054274937,0.00016042635,0.000008999782,0.0000024138574,0.00015247473,0.98231393,0.0027755888,0.000056623812,0.013307961,0.0003254114],"about_ca_topic_score_codex":1.3602438e-7,"about_ca_topic_score_gemma":1.4597913e-7,"teacher_disagreement_score":0.6494785,"about_ca_system_score_codex":0.00028672453,"about_ca_system_score_gemma":0.00003752494,"threshold_uncertainty_score":0.99995697},"labels":[],"label_agreement":null},{"id":"W4390043334","doi":"10.1016/j.aei.2023.102321","title":"A data-driven approach to predicting consumer preferences for product customization","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Color perception and design","field":"Psychology","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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Personalization; Mass customization; Machine learning; Data mining; Product (mathematics); Artificial intelligence; Mathematics","score_opus":0.0710036431366128,"score_gpt":0.3259726576946146,"score_spread":0.2549690145580018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390043334","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08650852,0.000020789843,0.904008,0.000036541365,0.00090649386,0.0012841625,0.00019918305,0.0010249293,0.0060113887],"genre_scores_gemma":[0.6810771,0.000024378924,0.31499365,0.00017345493,0.00020637846,0.00084950466,0.0014068921,0.000055428944,0.0012131766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910593,0.000007945503,0.00033873916,0.0001571506,0.00011714772,0.00027309725],"domain_scores_gemma":[0.99923426,0.000099130724,0.00006623622,0.00046195832,0.00006292669,0.000075500284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023311896,0.00012246531,0.00014521966,0.00018330642,0.000059279515,0.000033039476,0.0002606149,0.00004763609,0.000021908128],"category_scores_gemma":[0.00020935775,0.00012053372,0.000022166134,0.0004311556,0.000010360839,0.00033243946,0.000079293975,0.00009139734,0.00026556305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033990804,0.000025240539,0.00022463099,0.00018176174,0.00004193896,1.8307541e-7,0.0070487615,0.948924,0.00014038803,0.00064280076,0.017603299,0.025133016],"study_design_scores_gemma":[0.0004934952,0.000051104205,0.0026329686,0.000027056407,0.000018251321,0.000004296354,0.001337751,0.88681966,0.000021584348,0.000009018473,0.10839492,0.00018989672],"about_ca_topic_score_codex":0.0000015812445,"about_ca_topic_score_gemma":0.000001111594,"teacher_disagreement_score":0.5945686,"about_ca_system_score_codex":0.000025648224,"about_ca_system_score_gemma":0.000018048804,"threshold_uncertainty_score":0.49152225},"labels":[],"label_agreement":null},{"id":"W4390650896","doi":"10.1016/j.aei.2023.102341","title":"Product innovation based on the host gene and target gene recombination under the technological parasitism framework","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Product Development and Customization","field":"Business, Management and Accounting","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":"Product (mathematics); Parasitism; Product innovation; New product development; Function (biology); Host (biology); Computer science; Business; Industrial organization; Biology; Ecology; Evolutionary biology; Marketing; Mathematics","score_opus":0.007815161573181753,"score_gpt":0.1971401367374773,"score_spread":0.18932497516429553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390650896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21187915,0.00018139544,0.775051,0.009899785,0.0008333807,0.0005932298,0.0000019693682,0.0006953572,0.0008647045],"genre_scores_gemma":[0.9794035,0.000025861898,0.018862445,0.0013620765,0.00017047997,0.00006103705,0.00006302671,0.000018183575,0.000033405584],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993188,0.0000028613952,0.00025277893,0.00010305042,0.00017268212,0.00014987751],"domain_scores_gemma":[0.9995735,0.00008059789,0.00007558012,0.00018832012,0.00007832094,0.0000036324475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041898957,0.00013347802,0.00007816126,0.00020749054,0.00015155565,0.00025991106,0.00013064797,0.000057127963,0.0000129710825],"category_scores_gemma":[0.00028432568,0.0000820449,0.00001468634,0.0011010095,0.000030259433,0.0007372829,0.00004554265,0.00023537551,0.000033693632],"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":[0.00001044983,0.000021986421,0.00017236148,0.00028692128,0.0000314219,0.0000015735394,0.00016176753,0.27390254,0.0021248853,0.69083124,0.00052914623,0.031925708],"study_design_scores_gemma":[0.00016949668,0.00000993208,0.005529285,0.00018905464,0.000020458274,0.000003312264,0.00012691355,0.9339114,0.0052161813,0.014617905,0.039917417,0.00028867653],"about_ca_topic_score_codex":4.4313447e-7,"about_ca_topic_score_gemma":7.944588e-8,"teacher_disagreement_score":0.76752436,"about_ca_system_score_codex":0.000035731584,"about_ca_system_score_gemma":0.000012770749,"threshold_uncertainty_score":0.3345694},"labels":[],"label_agreement":null},{"id":"W4390660866","doi":"10.1016/j.aei.2023.102348","title":"VSL-Net: Voxel structure learning for 3D object detection","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Science and Technology Program of Suzhou; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Voxel; Object detection; Point cloud; Pattern recognition (psychology); Computer vision; Feature extraction; Segmentation; Feature (linguistics); Convolution (computer science); Benchmark (surveying); Artificial neural network; Geography","score_opus":0.004725352463849946,"score_gpt":0.22136998495744842,"score_spread":0.21664463249359847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390660866","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027096008,0.0002077575,0.994627,0.000058280326,0.00067044754,0.00031655983,0.00000484778,0.0012871531,0.00011834502],"genre_scores_gemma":[0.3470626,0.00004539083,0.6525208,0.0000745122,0.000092943264,0.00009213494,0.00001256113,0.00002729756,0.0000718278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990547,0.000003839474,0.0003156874,0.0001615822,0.00014430268,0.00031992127],"domain_scores_gemma":[0.99931395,0.00019708813,0.000061732564,0.00030727734,0.00005146079,0.000068484784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006751015,0.00018211098,0.00012942118,0.00014009925,0.00012861883,0.00015376405,0.00032840986,0.00006635314,0.0000013972664],"category_scores_gemma":[0.00005719468,0.00018121694,0.000058802696,0.000564266,0.0000106849575,0.0012974658,0.00008343925,0.00031498057,0.000018858842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.629606e-7,0.0000013185621,9.097819e-7,0.00010841869,0.000009128045,4.8148166e-7,0.00038120509,0.7729729,0.0034039258,0.0062260204,0.000022501301,0.21687225],"study_design_scores_gemma":[0.00010475266,0.000045718345,0.000030131156,0.000051539857,0.0000054765096,0.000017116068,0.000013746946,0.8833918,0.0055698273,0.0012483392,0.10933023,0.00019131912],"about_ca_topic_score_codex":3.1122556e-7,"about_ca_topic_score_gemma":0.0000010559838,"teacher_disagreement_score":0.344353,"about_ca_system_score_codex":0.000089782545,"about_ca_system_score_gemma":0.000021017908,"threshold_uncertainty_score":0.7389812},"labels":[],"label_agreement":null},{"id":"W4391873330","doi":"10.1016/j.aei.2024.102403","title":"AKGNN-PC: An assembly knowledge graph neural network model with predictive value calibration module for refrigeration compressor performance prediction with assembly error propagation and data imbalance scenarios","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Refrigeration and Air Conditioning Technologies","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 Saskatchewan","funders":"National Natural Science Foundation of China; Government of Xinjiang Uygur Autonomous Region of China","keywords":"Artificial neural network; Calibration; Refrigeration; Graph; Computer science; Gas compressor; Data mining; Machine learning; Artificial intelligence; Engineering; Mathematics; Theoretical computer science; Statistics; Mechanical engineering","score_opus":0.014717950774880837,"score_gpt":0.2405730851550609,"score_spread":0.22585513438018007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391873330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22198053,0.00019383786,0.7748657,0.00003431101,0.00021175425,0.0006859714,0.0001212038,0.0018405154,0.000066189095],"genre_scores_gemma":[0.8722408,0.00011494735,0.12603703,0.000014299072,0.000096395575,0.00027946013,0.0011333942,0.000061565675,0.000022143437],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988035,0.000009279781,0.00041850674,0.0002526973,0.00020067728,0.00031532548],"domain_scores_gemma":[0.999244,0.000051227173,0.00007466018,0.0004436385,0.0001138676,0.00007263025],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018184484,0.00029883065,0.00021699847,0.00017225402,0.00020379912,0.00033161067,0.00020606678,0.00012958703,4.4205228e-7],"category_scores_gemma":[0.000019246696,0.00025284104,0.000018912526,0.00035959628,0.000040680305,0.005520497,0.00004165122,0.00028426168,9.0099127e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003906846,0.000011662241,0.00006455815,0.000757666,0.000052833257,5.268546e-7,0.00047655572,0.99334896,0.00089955365,0.0013339259,0.00021691676,0.0027977505],"study_design_scores_gemma":[0.0004560494,0.0003818865,0.00039495208,0.00045546048,0.000050297134,0.000021008718,0.00006871071,0.9953843,0.0019564754,0.00007285866,0.00044405297,0.0003139717],"about_ca_topic_score_codex":6.344339e-7,"about_ca_topic_score_gemma":0.000006781204,"teacher_disagreement_score":0.65026027,"about_ca_system_score_codex":0.000090339534,"about_ca_system_score_gemma":0.000049376493,"threshold_uncertainty_score":0.9999924},"labels":[],"label_agreement":null},{"id":"W4398231700","doi":"10.1016/j.aei.2024.102600","title":"Morphology agnostic gesture mapping for intuitive teleoperation of construction robots","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Teleoperation and Haptic Systems","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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Teleoperation; Gesture; Morphology (biology); Robot; Artificial intelligence; Computer science; Human–computer interaction; Computer vision; Engineering; Computer graphics (images); Geology","score_opus":0.006493173801812446,"score_gpt":0.20515588729864548,"score_spread":0.19866271349683304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398231700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0723797,0.0003947646,0.92421746,0.000014290747,0.0015586405,0.00032840902,0.000024717248,0.00052628724,0.00055573706],"genre_scores_gemma":[0.8867602,0.000090712536,0.11285504,0.000014353849,0.00009397245,0.00008009249,0.00004902538,0.000029814999,0.000026838901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923754,0.0000028255915,0.00045241008,0.00006150472,0.00008292614,0.00016279402],"domain_scores_gemma":[0.99963564,0.00012115021,0.000028847744,0.00010777886,0.00006985228,0.000036711997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008087223,0.00013974508,0.00018757847,0.00018367333,0.000024336756,0.000037329315,0.00005879479,0.00009162712,0.000010143727],"category_scores_gemma":[0.00006602498,0.00014199127,0.00004736031,0.0001933676,0.000016637714,0.00037719568,0.000008462039,0.0001204315,0.000016629108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014656263,0.000001965734,0.000008480276,0.0009583245,0.00004360422,7.553257e-7,0.0017685634,0.95981795,0.011873507,0.008897821,0.00013232652,0.016495267],"study_design_scores_gemma":[0.0002251519,0.000039374387,0.00010689359,0.00029492253,0.000015381615,0.00005469855,0.00040425072,0.97873276,0.008438484,0.000042639447,0.011471457,0.00017399719],"about_ca_topic_score_codex":6.041491e-7,"about_ca_topic_score_gemma":7.2574113e-7,"teacher_disagreement_score":0.81438047,"about_ca_system_score_codex":0.0000658988,"about_ca_system_score_gemma":0.000016755945,"threshold_uncertainty_score":0.5790236},"labels":[],"label_agreement":null},{"id":"W4399598501","doi":"10.1016/j.aei.2024.102637","title":"DiffDD: A surface defect detection framework with diffusion probabilistic model","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Industrial Vision Systems and Defect Detection","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":"Okanagan University College; University of British Columbia, Okanagan Campus","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Probabilistic logic; Diffusion; Surface (topology); Computer science; Artificial intelligence; Mathematics; Physics; Geometry; Thermodynamics","score_opus":0.005806832307022,"score_gpt":0.19766245934299104,"score_spread":0.19185562703596903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399598501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34676233,0.00017888682,0.6502437,0.0000017510947,0.000717382,0.00024872783,0.000006034144,0.0014902196,0.00035094307],"genre_scores_gemma":[0.9741674,0.000040253748,0.025518343,0.0000060990537,0.000094641626,0.0000441835,0.0000054241355,0.00007786792,0.000045757897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998887,0.00000546095,0.00041260672,0.000120115794,0.00025325146,0.00032157532],"domain_scores_gemma":[0.99945605,0.00011702941,0.000030757405,0.00026060076,0.00004054319,0.00009501479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013588331,0.000275418,0.00022755757,0.00015454268,0.00006812078,0.00013603858,0.000085339154,0.00019478358,0.0000048061447],"category_scores_gemma":[0.00004831406,0.00022948676,0.00008923966,0.0005332332,0.000012019931,0.0005265654,0.000020455585,0.0005006956,0.000051923736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001106089,0.0000037822833,0.0000025041406,0.0006177985,0.00003416983,0.00000273045,0.0005623534,0.98210925,0.0036238404,0.0004379714,0.000015916716,0.012578644],"study_design_scores_gemma":[0.00020809857,0.00008874997,0.000013781019,0.0006175407,0.000028692197,0.000039621184,0.00006210119,0.99195945,0.0032790825,0.00013599549,0.003254958,0.00031190057],"about_ca_topic_score_codex":0.0000023291689,"about_ca_topic_score_gemma":0.0000023450878,"teacher_disagreement_score":0.6274051,"about_ca_system_score_codex":0.00020771075,"about_ca_system_score_gemma":0.000018258223,"threshold_uncertainty_score":0.93581986},"labels":[],"label_agreement":null},{"id":"W4400108501","doi":"10.1016/j.aei.2024.102649","title":"Optimization to identify the adapted product design and product adaptation process with initial evaluation of information quality in branches of AND-OR tree based on information entropy","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Product Development and Customization","field":"Business, Management and Accounting","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":"University of Calgary","funders":"","keywords":"Computer science; Product (mathematics); Entropy (arrow of time); Adaptation (eye); Process (computing); Tree (set theory); Data mining; Mathematics; Biology","score_opus":0.0257965863878307,"score_gpt":0.27626475584184873,"score_spread":0.250468169454018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400108501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17220782,0.00003083143,0.8256649,0.00021800472,0.000118868964,0.0015312956,0.0000030887284,0.00008172898,0.00014347877],"genre_scores_gemma":[0.9647319,0.000009811504,0.034884054,0.00008547906,0.000035264755,0.00011711911,0.00012622337,0.000009240248,9.5284753e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986013,0.00001649427,0.0006679021,0.00008436285,0.0005080661,0.00012187891],"domain_scores_gemma":[0.998994,0.000068664995,0.00029620496,0.00013626066,0.000495046,0.000009846483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013719432,0.00014950066,0.00016087803,0.0005933295,0.000044031174,0.00014963193,0.000080062695,0.000030724605,0.0000039674537],"category_scores_gemma":[0.0007424228,0.00010753669,0.00001156017,0.0010207406,0.000022074872,0.006188193,0.000021266324,0.00008575081,0.0000022488464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024911962,0.000010244602,0.000091835194,0.00134248,0.000010239376,4.1199527e-8,0.0028629443,0.91732085,0.00006470236,0.0007664981,0.0000065410372,0.07727453],"study_design_scores_gemma":[0.0007781006,0.00003915068,0.004137046,0.00042175667,0.000042592084,6.722892e-7,0.0006934482,0.9916369,0.0017970864,0.000063821935,0.00024256551,0.00014688136],"about_ca_topic_score_codex":0.000008535028,"about_ca_topic_score_gemma":0.000005201092,"teacher_disagreement_score":0.79252404,"about_ca_system_score_codex":0.000049006212,"about_ca_system_score_gemma":0.000106233485,"threshold_uncertainty_score":0.44862905},"labels":[],"label_agreement":null},{"id":"W4400956229","doi":"10.1016/j.aei.2024.102717","title":"Defect detection on multi-type rail surfaces via IoU decoupling and multi-information alignment","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Industrial Vision Systems and Defect Detection","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 Waterloo","funders":"Natural Science Basic Research Program of Shaanxi Province; Natural Science Foundation of Hunan Province; China Scholarship Council; University of Waterloo; National Natural Science Foundation of China","keywords":"Decoupling (probability); Computer science; Artificial intelligence; Engineering; Materials science; Control engineering","score_opus":0.010984771624871291,"score_gpt":0.2271057419252353,"score_spread":0.216120970300364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400956229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22520335,0.00037399208,0.77007765,0.0000035487067,0.0027374956,0.00032703436,0.000007378549,0.0011509138,0.00011865417],"genre_scores_gemma":[0.9864205,0.00016299725,0.013231956,0.00002674962,0.000054619508,0.00003151235,0.000014415456,0.00004118221,0.0000160754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988641,0.0000058450737,0.000552887,0.00009361749,0.00020603521,0.00027750226],"domain_scores_gemma":[0.99955475,0.00008759148,0.000048826827,0.00016886955,0.00004737062,0.00009259158],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023636721,0.00026212333,0.00020256425,0.00030425665,0.00007747005,0.00018077342,0.00005999327,0.00015033537,0.000004288248],"category_scores_gemma":[0.000053812437,0.00025278362,0.00007015294,0.00034685244,0.000008946195,0.0011574491,0.000021009351,0.00029684626,0.00013304297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000842017,0.000004596691,0.0000044442654,0.0003304359,0.00004376071,0.0000010205539,0.00055291934,0.8714962,0.0098229125,0.000026754722,0.000030467436,0.117678076],"study_design_scores_gemma":[0.00045852474,0.000120946184,0.0000847645,0.00022960025,0.000016128235,0.000024220988,0.00013021208,0.93110025,0.0317083,0.0000019841884,0.03584966,0.00027539342],"about_ca_topic_score_codex":0.0000060917923,"about_ca_topic_score_gemma":0.000004122663,"teacher_disagreement_score":0.7612171,"about_ca_system_score_codex":0.00021537692,"about_ca_system_score_gemma":0.0000074520035,"threshold_uncertainty_score":0.99999243},"labels":[],"label_agreement":null},{"id":"W4401390190","doi":"10.1016/j.aei.2024.102749","title":"Contrastive learning of defect prototypes under natural language supervision","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Key Research and Development Program of Zhejiang Province; Homewood Research Institute; CNPC Chuanqing Drilling Engineering Company Limited; State Key Laboratory for Manufacturing Systems Engineering; National Aerospace Science Foundation of China","keywords":"Natural (archaeology); Natural language processing; Computer science; Natural language; Artificial intelligence; Linguistics; Psychology; Geology; Philosophy","score_opus":0.0022935851480852584,"score_gpt":0.1963995511040455,"score_spread":0.19410596595596025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401390190","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21236317,0.0030038005,0.7822955,0.0000035859528,0.00041176434,0.00022954482,0.0000046570017,0.0009439878,0.0007439351],"genre_scores_gemma":[0.98428386,0.00012893521,0.015444232,0.0000043476175,0.000023635446,0.000020007094,0.000023342265,0.000030942592,0.000040679264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994422,0.0000023110815,0.00023899491,0.000049556085,0.00010829791,0.00015860185],"domain_scores_gemma":[0.9997938,0.00005837583,0.000017775348,0.00007529656,0.000025351224,0.000029359078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000577521,0.00013168745,0.00013283339,0.00011486195,0.000018766099,0.00003502596,0.00006540807,0.000046816553,0.000012489275],"category_scores_gemma":[0.000023903061,0.00011959816,0.00005022832,0.00015635898,0.0000080185255,0.00039899242,0.000015313028,0.00022768175,0.000008780268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025279437,0.0000015349458,0.0000056306953,0.0012490759,0.000032687796,9.709104e-7,0.0017451037,0.976832,0.0012365083,0.0005163206,0.0000046911364,0.01837298],"study_design_scores_gemma":[0.000120208235,0.000024785171,0.00011887787,0.0002858224,0.000012487632,0.0000041617745,0.00024358489,0.9772678,0.020120474,0.000011764904,0.0016469908,0.00014304821],"about_ca_topic_score_codex":0.000001091868,"about_ca_topic_score_gemma":3.305807e-7,"teacher_disagreement_score":0.7719207,"about_ca_system_score_codex":0.000036324942,"about_ca_system_score_gemma":0.0000075915996,"threshold_uncertainty_score":0.48770714},"labels":[],"label_agreement":null},{"id":"W4402285675","doi":"10.1016/j.aei.2024.102802","title":"A multi-phase integrated scheduling method for cloud remanufacturing systems","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Remanufacturing; Cloud computing; Computer science; Scheduling (production processes); Industrial engineering; Distributed computing; Systems engineering; Manufacturing engineering; Engineering; Operations management; Operating system","score_opus":0.011703637853973464,"score_gpt":0.2678331510824212,"score_spread":0.2561295132284477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402285675","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007669405,0.00039659056,0.9867299,0.00008086695,0.0024479285,0.0010171832,0.000008797691,0.0013144385,0.00033486346],"genre_scores_gemma":[0.14164563,0.00001924609,0.85572594,0.00029858455,0.0010787363,0.00042698384,0.00016773328,0.00014554457,0.0004916026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983909,0.0000033431982,0.0006470003,0.00019936508,0.00021466533,0.0005446854],"domain_scores_gemma":[0.99923086,0.00015271128,0.00013045134,0.00030342524,0.00015696835,0.000025574753],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00069311267,0.00032925032,0.0002998186,0.00065409596,0.0001174901,0.00081344467,0.0002769516,0.00008392466,0.000010451617],"category_scores_gemma":[0.00028226344,0.0003137757,0.00011801642,0.0006364108,0.000011024884,0.0021427157,0.00014727122,0.00024981122,0.00007126197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016269943,0.00002055526,0.000002313174,0.0073937406,0.00009337736,0.000013446114,0.00026926328,0.9494273,0.00026704173,0.023856755,0.0003512255,0.018288698],"study_design_scores_gemma":[0.0006671871,0.000009288401,0.0000017913607,0.00037423143,0.000050875922,0.0000030063377,0.0017246458,0.682424,0.0001435511,0.0000990391,0.31423694,0.0002654185],"about_ca_topic_score_codex":0.00003466268,"about_ca_topic_score_gemma":0.0000016173137,"teacher_disagreement_score":0.31388572,"about_ca_system_score_codex":0.00020773764,"about_ca_system_score_gemma":0.000024226727,"threshold_uncertainty_score":0.99993145},"labels":[],"label_agreement":null},{"id":"W4402308234","doi":"10.1016/j.aei.2024.102792","title":"An automatic unsafe states reasoning approach towards Industry 5.0’s human-centered manufacturing via Digital Twin","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Digital Transformation in Industry","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":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Industry 4.0; Artificial intelligence; Embedded system","score_opus":0.0095808198191182,"score_gpt":0.23185438255139698,"score_spread":0.22227356273227877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402308234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36205858,0.00015759753,0.6139718,0.000006481731,0.00074888254,0.0003325145,0.00011533667,0.005224318,0.017384443],"genre_scores_gemma":[0.97258013,0.000023525427,0.02656669,0.000019651532,0.00010588984,0.00006842328,0.0004189739,0.00014673333,0.00006996294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783844,0.0000050473163,0.0009355167,0.00017250964,0.00039620808,0.0006522622],"domain_scores_gemma":[0.9991738,0.00004267506,0.00005268852,0.0004337028,0.000029593239,0.00026750393],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001406255,0.000499365,0.00034032983,0.0003759317,0.00007696626,0.0007785817,0.00039754767,0.00034676155,0.00003643321],"category_scores_gemma":[0.000012148419,0.0005261372,0.00010592842,0.00036292235,0.000036682315,0.005866451,0.000044953333,0.001123416,0.000076030985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012226743,0.000019266081,0.000012342752,0.0016589661,0.00009141942,0.000007477945,0.0019729603,0.9121182,0.0001386985,0.0005169422,0.000087484295,0.08337505],"study_design_scores_gemma":[0.0002904836,0.000044163557,0.0001692557,0.00058177963,0.000021105689,0.00008319229,0.0010112387,0.98527384,0.004934302,0.00010220058,0.006853992,0.0006344621],"about_ca_topic_score_codex":0.0000015555472,"about_ca_topic_score_gemma":1.719946e-7,"teacher_disagreement_score":0.61052155,"about_ca_system_score_codex":0.0002950369,"about_ca_system_score_gemma":0.00002739538,"threshold_uncertainty_score":0.999719},"labels":[],"label_agreement":null},{"id":"W4402436572","doi":"10.1016/j.aei.2024.102804","title":"Optimal charging scheduling for Indoor Autonomous Vehicles in manufacturing operations","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Innovation and Economic Development Trois Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scheduling (production processes); Automotive engineering; Computer science; Engineering; Real-time computing; Manufacturing engineering; Operations management","score_opus":0.0070962028839813744,"score_gpt":0.2212059478007596,"score_spread":0.21410974491677823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402436572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11982883,0.0004208832,0.8774169,0.000016316551,0.000635109,0.00030250291,0.00002243264,0.0012021255,0.0001549171],"genre_scores_gemma":[0.5917065,0.000114200935,0.40786588,0.000010722034,0.00006636604,0.000105602616,0.000049911123,0.000055155055,0.000025643016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885243,0.0000023257257,0.0004997747,0.00012674989,0.000100690304,0.0004179989],"domain_scores_gemma":[0.999606,0.00011544872,0.000017306109,0.00016951247,0.000021617847,0.00007010657],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012537588,0.00025219802,0.0002093623,0.00033922552,0.000067027555,0.00014579247,0.00013204337,0.0000980673,0.000004235691],"category_scores_gemma":[0.000038415787,0.00027902113,0.000057796053,0.00016344621,0.000012908154,0.00083129253,0.000027606031,0.00030988964,0.000014476855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026968703,0.0000040326295,0.0000017798453,0.00063507445,0.000020290167,0.000003055856,0.0007817778,0.98361564,0.0004359624,0.0014973456,0.000006721401,0.012995611],"study_design_scores_gemma":[0.0002841886,0.000017313692,0.000024366034,0.0002439835,0.00001069893,0.000008340346,0.00013330461,0.9727593,0.02099548,0.00007046764,0.0051310197,0.0003215041],"about_ca_topic_score_codex":0.0000013647793,"about_ca_topic_score_gemma":0.0000020604691,"teacher_disagreement_score":0.4718777,"about_ca_system_score_codex":0.00021867939,"about_ca_system_score_gemma":0.00002130087,"threshold_uncertainty_score":0.9999662},"labels":[],"label_agreement":null},{"id":"W4402679706","doi":"10.1016/j.aei.2024.102810","title":"Autoencoder-Based fault detection using building automation system data","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoencoder; Automation; Fault detection and isolation; Fault (geology); Computer science; Data mining; Building automation; Engineering; Real-time computing; Reliability engineering; Artificial intelligence; Artificial neural network; Seismology","score_opus":0.011761187687187395,"score_gpt":0.23764200655367884,"score_spread":0.22588081886649145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402679706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055551875,0.00038126617,0.9349691,0.000003531944,0.0029149314,0.00023115055,0.00003059554,0.0056433715,0.0002741367],"genre_scores_gemma":[0.9399475,0.0000069457496,0.059767276,0.000007138597,0.00013908371,0.00002868869,0.000030572945,0.00006561451,0.000007186201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987271,0.000007918825,0.0005807401,0.00013436573,0.00024417747,0.00030573085],"domain_scores_gemma":[0.9992875,0.00006063867,0.000044123244,0.0004926933,0.00003261322,0.00008242346],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022706503,0.00024174593,0.00021261572,0.00032198487,0.00007577081,0.00020770326,0.0002395361,0.00011807958,0.0000029026385],"category_scores_gemma":[0.000030280175,0.0002560345,0.000055291854,0.00049210276,0.000007695348,0.0012518036,0.00003277623,0.00025056142,0.00004678429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015948239,0.0000014835789,7.2575835e-7,0.0017392766,0.00003828845,0.00000313499,0.00011852899,0.961279,0.015117741,0.00019496877,0.000012794856,0.021492489],"study_design_scores_gemma":[0.00021650371,0.000013723789,0.0000070879514,0.0006062749,0.00003147236,0.000056633304,0.0001543297,0.9757914,0.0049637724,0.0000020694501,0.017881334,0.00027542107],"about_ca_topic_score_codex":0.000007166475,"about_ca_topic_score_gemma":0.0000026916966,"teacher_disagreement_score":0.8843956,"about_ca_system_score_codex":0.0004409695,"about_ca_system_score_gemma":0.00002639533,"threshold_uncertainty_score":0.9999892},"labels":[],"label_agreement":null},{"id":"W4403589602","doi":"10.1016/j.aei.2024.102886","title":"Optimized machine learning methods for identifying the stiffness loss of CRTS-II slab track based on vehicle vibration signals","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","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":"University of British Columbia","funders":"Fundamental Research Funds for Central Universities of the Central South University; National Natural Science Foundation of China","keywords":"CRTS; Track (disk drive); Vibration; Slab; Stiffness; Structural engineering; Computer science; Engineering; Acoustics; Artificial intelligence; Mechanical engineering; Physics; Computer graphics (images)","score_opus":0.010582732858381036,"score_gpt":0.28319067223091254,"score_spread":0.2726079393725315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403589602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01959373,0.00040418163,0.9776591,0.00002262916,0.001275086,0.00034139675,0.000014564676,0.0005185566,0.00017076104],"genre_scores_gemma":[0.6496512,0.000047451653,0.3499782,0.00002280581,0.00010261044,0.0000806467,0.000027821927,0.00006339567,0.000025796347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988594,0.000014285343,0.0005423606,0.000096277436,0.00016913521,0.00031852955],"domain_scores_gemma":[0.99909693,0.00049971935,0.00006777562,0.0002174566,0.000073334726,0.00004477244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004487865,0.00024202891,0.00026704563,0.00017696456,0.00012441719,0.00008778388,0.00018235076,0.00008388255,0.000008321484],"category_scores_gemma":[0.00015504188,0.0001923556,0.00012108161,0.00031211006,0.000020136778,0.0005083895,0.000028192477,0.00037017596,0.0000023229525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002036072,0.0000032102475,0.0000015130727,0.00097417424,0.000042034015,7.7336466e-7,0.0015750307,0.9294342,0.02705192,0.00041645978,0.00001197696,0.040468376],"study_design_scores_gemma":[0.00043535995,0.000052221585,0.000031042393,0.00041268006,0.0000325562,0.0000026973842,0.000119568096,0.91401434,0.077215835,0.000071817114,0.0074120862,0.00019977576],"about_ca_topic_score_codex":0.0000010104551,"about_ca_topic_score_gemma":1.3746994e-7,"teacher_disagreement_score":0.6300575,"about_ca_system_score_codex":0.00008782498,"about_ca_system_score_gemma":0.000019190466,"threshold_uncertainty_score":0.7844034},"labels":[],"label_agreement":null},{"id":"W4404315085","doi":"10.1016/j.aei.2024.102933","title":"A Unet-inspired spatial-attention transformer model for segmenting gear tooth surface defects","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Industrial Vision Systems and Defect Detection","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":"Okanagan University College; University of British Columbia, Okanagan Campus","funders":"","keywords":"Transformer; Tooth surface; Computer science; Artificial intelligence; Engineering; Electrical engineering; Mechanical engineering","score_opus":0.011205981938493658,"score_gpt":0.2203848774359911,"score_spread":0.20917889549749746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404315085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1935796,0.0002495154,0.8027343,0.0000039922375,0.0013210681,0.0005019236,0.000031662505,0.001184965,0.00039292593],"genre_scores_gemma":[0.97025937,0.00004263429,0.029199926,0.000010085644,0.00012865651,0.00007364686,0.00003725751,0.00008593475,0.00016250409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870855,0.000003622087,0.00058105827,0.00011294795,0.00019918603,0.00039466476],"domain_scores_gemma":[0.9996018,0.00006806897,0.0000362776,0.00016721488,0.000045976605,0.00008070101],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023666867,0.0002541818,0.00024160428,0.00015947728,0.00007677817,0.00013509818,0.00008083885,0.00016030388,0.000004022357],"category_scores_gemma":[0.000022759328,0.00025852388,0.00015552269,0.00027787976,0.0000065412014,0.0007168795,0.000007670368,0.00022447862,0.000032017706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000063292873,0.0000029368869,0.0000031733039,0.0010271826,0.000043912827,7.140048e-7,0.0006214183,0.9571022,0.019671947,0.00022540009,0.00012929393,0.021165524],"study_design_scores_gemma":[0.0004663309,0.0000449089,0.000009815926,0.00033447682,0.00003320495,0.000007006637,0.00007713871,0.97571737,0.009517666,0.00002542485,0.01346639,0.00030025002],"about_ca_topic_score_codex":0.0000043977866,"about_ca_topic_score_gemma":0.0000036419344,"teacher_disagreement_score":0.77667975,"about_ca_system_score_codex":0.00015162062,"about_ca_system_score_gemma":0.000022231388,"threshold_uncertainty_score":0.9999867},"labels":[],"label_agreement":null},{"id":"W4404708849","doi":"10.1016/j.aei.2024.102957","title":"A stacked graph neural network with self-exciting process for robotic cognitive strategy reasoning in proactive human-robot collaborative assembly","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Robot Manipulation and Learning","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Cognition; Computer science; Artificial neural network; Artificial intelligence; Process (computing); Graph; Robot; Human–robot interaction; Human–computer interaction; Engineering; Psychology; Theoretical computer science; Neuroscience","score_opus":0.012700899913703474,"score_gpt":0.26674425966322995,"score_spread":0.25404335974952647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404708849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22104152,0.00035611907,0.7748726,0.0000064278356,0.00021290884,0.0011266016,0.0000036047434,0.0014503465,0.0009298954],"genre_scores_gemma":[0.9714447,0.000018643224,0.027892282,0.000013182345,0.00009140025,0.00033781206,0.00008029979,0.000106794185,0.000014834727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984665,0.0000142030185,0.00054349727,0.0001792303,0.00020579986,0.00059079216],"domain_scores_gemma":[0.9993173,0.00023212995,0.00008437834,0.00011231032,0.00016096277,0.000092950504],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018195211,0.00036604973,0.00035261686,0.00027704219,0.00011855324,0.00019733612,0.00010974887,0.00010223662,0.0000030090202],"category_scores_gemma":[0.000053065996,0.00036483185,0.000050121325,0.0012245933,0.000018580395,0.001319732,0.000015448595,0.00052875816,0.000003655888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022785074,0.000009661983,0.0001019084,0.0010432008,0.00014450446,0.0000098932105,0.005459227,0.99003166,0.00009967141,0.0013781338,0.0000067443602,0.0016925811],"study_design_scores_gemma":[0.0007616113,0.00016268533,0.0008858918,0.0011332484,0.000051762963,0.000016692567,0.0031129937,0.9930125,0.00029061182,0.000052577896,0.000048463226,0.00047098848],"about_ca_topic_score_codex":0.0000014218082,"about_ca_topic_score_gemma":0.000011797772,"teacher_disagreement_score":0.7504032,"about_ca_system_score_codex":0.0001622871,"about_ca_system_score_gemma":0.000059859318,"threshold_uncertainty_score":0.9998804},"labels":[],"label_agreement":null},{"id":"W4404869857","doi":"10.1016/j.aei.2024.102958","title":"Spatio-temporal attention-based hidden physics-informed neural network for remaining useful life prediction","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Artificial intelligence; Computer science; Machine learning","score_opus":0.01154729663356071,"score_gpt":0.2373368047712571,"score_spread":0.22578950813769638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404869857","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007065924,0.000045138237,0.9897092,0.00015758086,0.0005723168,0.00041989336,0.000018993243,0.0018868591,0.00012407464],"genre_scores_gemma":[0.46158668,0.00000761763,0.5376715,0.00012241412,0.00020659396,0.00025857502,0.00008951043,0.000018302542,0.000038804985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989371,0.0000033606598,0.0004759454,0.00013436738,0.00016487778,0.0002843579],"domain_scores_gemma":[0.99926895,0.00013335512,0.000104522485,0.00032327225,0.0000770474,0.0000928304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013809708,0.00016303083,0.00014295717,0.00009343293,0.00014528338,0.00021367935,0.0002737023,0.000064957196,0.0000019976155],"category_scores_gemma":[0.000035656492,0.00016777779,0.00011064296,0.00052654074,0.000013046917,0.0012584211,0.000048567377,0.00015446146,0.000012817941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004103674,0.000007220386,0.0002345257,0.000275007,0.000021062171,3.7519558e-7,0.00024243478,0.93478316,0.000031515083,0.025066148,0.0012114217,0.038123045],"study_design_scores_gemma":[0.0001600908,0.00007086191,0.00033321933,0.000108452754,0.000009480427,0.000003031892,0.000016417032,0.9562789,0.00024964428,0.000564956,0.042039,0.00016592746],"about_ca_topic_score_codex":0.0000014385386,"about_ca_topic_score_gemma":9.838135e-7,"teacher_disagreement_score":0.45452073,"about_ca_system_score_codex":0.00009482812,"about_ca_system_score_gemma":0.00009198694,"threshold_uncertainty_score":0.68417794},"labels":[],"label_agreement":null},{"id":"W4405143047","doi":"10.1016/j.aei.2024.102976","title":"Sequence–spectrogram fusion network for wind turbine diagnosis through few-shot time-series classification","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Time Series Analysis and Forecasting","field":"Computer Science","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":"University of British Columbia","funders":"","keywords":"Spectrogram; Series (stratigraphy); Sequence (biology); Fusion; Shot (pellet); One shot; Turbine; Artificial intelligence; Computer science; Pattern recognition (psychology); Speech recognition; Algorithm; Engineering; Geology; Materials science; Aerospace engineering; Chemistry","score_opus":0.02061956705807505,"score_gpt":0.24863404271209713,"score_spread":0.22801447565402208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405143047","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019849404,0.000635087,0.99459445,0.00027593167,0.00055356324,0.0003346823,0.000013459425,0.00072529365,0.0008825856],"genre_scores_gemma":[0.05587117,0.00039503007,0.94283617,0.00008112042,0.0002673361,0.00017974938,0.00010264379,0.000032351185,0.00023445235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866474,0.0000052286146,0.0005071352,0.0001887068,0.00020303343,0.00043118297],"domain_scores_gemma":[0.99921215,0.00015158408,0.00010416657,0.00038720385,0.0000787899,0.00006610864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019841934,0.00022166599,0.00023663495,0.00008189572,0.00016070381,0.0003851344,0.00038820406,0.000075695265,0.000014868553],"category_scores_gemma":[0.000054368513,0.00020179935,0.0001376718,0.00078376743,0.000024384039,0.0024439779,0.000108188884,0.00016126808,0.000042968866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000067762153,0.000013847569,0.000022734042,0.00032272513,0.00007903094,0.0000031756344,0.0014612424,0.7212282,0.0007858187,0.124184355,0.0010911141,0.15080099],"study_design_scores_gemma":[0.00008450359,0.00010240987,0.000029822862,0.00015105764,0.000020990845,0.000011812808,0.000057756824,0.80812734,0.0005759055,0.0010663115,0.18955356,0.00021849324],"about_ca_topic_score_codex":0.000001893125,"about_ca_topic_score_gemma":0.0000012342439,"teacher_disagreement_score":0.18846245,"about_ca_system_score_codex":0.000094315554,"about_ca_system_score_gemma":0.000031371037,"threshold_uncertainty_score":0.8229139},"labels":[],"label_agreement":null},{"id":"W4405401847","doi":"10.1016/j.aei.2024.103038","title":"Self-supervised learning for remaining useful life prediction using simple triplet networks","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","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":"University of Ottawa","funders":"National Science and Technology Council","keywords":"Simple (philosophy); Artificial intelligence; Machine learning; Computer science; Philosophy","score_opus":0.010678914079890765,"score_gpt":0.23281488586724863,"score_spread":0.22213597178735786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405401847","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005735238,0.0001150764,0.99062604,0.000021658247,0.00027981278,0.0003223112,0.0000032820121,0.0027951605,0.000101437916],"genre_scores_gemma":[0.32938948,0.000052011397,0.67025864,0.000048371465,0.0001058929,0.00009514898,0.000014035098,0.000021345859,0.000015085375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908376,0.0000050735566,0.0004003685,0.00013352616,0.00011407085,0.00026322244],"domain_scores_gemma":[0.9994402,0.000112915906,0.00006477567,0.00023840829,0.000057122743,0.000086583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020771734,0.00014070736,0.00013291831,0.00014723302,0.00017329874,0.00020800983,0.00023008318,0.00007909927,0.0000015551061],"category_scores_gemma":[0.00004683567,0.00014929769,0.00007536919,0.0005247186,0.0000060765624,0.001051109,0.00007354734,0.00022826149,0.000003444109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011101179,0.000003773544,0.00001324037,0.00014395674,0.000017832159,2.8203803e-7,0.0004953421,0.9756074,0.00016435698,0.008227858,0.00004523125,0.015279624],"study_design_scores_gemma":[0.00012351997,0.000053547006,0.000017630231,0.000055790657,0.000011222506,0.000011061148,0.00005343526,0.9345988,0.0002992507,0.00013410472,0.064495265,0.00014634866],"about_ca_topic_score_codex":5.519431e-7,"about_ca_topic_score_gemma":7.0122354e-8,"teacher_disagreement_score":0.32365423,"about_ca_system_score_codex":0.00009102953,"about_ca_system_score_gemma":0.00004022328,"threshold_uncertainty_score":0.60881835},"labels":[],"label_agreement":null},{"id":"W4405401876","doi":"10.1016/j.aei.2024.103035","title":"Scalable probabilistic deterioration model based on visual inspections and structural attributes from large networks of bridges","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Infrastructure Maintenance and Monitoring","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":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Probabilistic logic; Scalability; Computer science; Reliability engineering; Data mining; Artificial intelligence; Engineering; Database","score_opus":0.0038634676558226585,"score_gpt":0.20905756930016686,"score_spread":0.2051941016443442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405401876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36820602,0.00013725701,0.6307082,0.0000030176209,0.00046592375,0.00009209637,0.000046371653,0.0003025071,0.00003861222],"genre_scores_gemma":[0.97281295,0.000033189866,0.026948802,0.000009518799,0.00008921768,0.000017184924,0.000055868193,0.000030135536,0.000003139836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992689,0.0000021071678,0.00030783468,0.0000781202,0.00010931148,0.0002337596],"domain_scores_gemma":[0.999701,0.00006777864,0.000025544266,0.00012150951,0.000036627465,0.00004754463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004789579,0.0001756803,0.0001686759,0.000112895286,0.000043693624,0.00006095756,0.00005620872,0.0000729445,0.0000023810496],"category_scores_gemma":[0.000027832788,0.00016640672,0.000033825272,0.00016190593,0.000016141486,0.00040762973,0.000018717696,0.00020351332,0.0000012784582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000039343345,0.0000018772133,0.00009108087,0.000271056,0.00001542189,7.0179084e-7,0.00021713154,0.99433583,0.0013134453,0.000856426,0.000011223903,0.0028818697],"study_design_scores_gemma":[0.0001705757,0.000036721365,0.0015094321,0.00029383335,0.000017517035,0.0000018092769,0.000040937735,0.9955488,0.0019118773,0.00015025755,0.00014832696,0.00016994218],"about_ca_topic_score_codex":0.000002178065,"about_ca_topic_score_gemma":0.000001930834,"teacher_disagreement_score":0.6046069,"about_ca_system_score_codex":0.0000941286,"about_ca_system_score_gemma":0.000013533878,"threshold_uncertainty_score":0.67858696},"labels":[],"label_agreement":null},{"id":"W4405866055","doi":"10.1016/j.aei.2024.103083","title":"A novel remaining useful life prediction method under multiple operating conditions based on attention mechanism and deep learning","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mechanism (biology); Artificial intelligence; Computer science; Deep learning; Machine learning","score_opus":0.00796665457105392,"score_gpt":0.26122704408539416,"score_spread":0.2532603895143402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405866055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047715183,0.0000869754,0.9481426,0.000022975128,0.00031820507,0.00027123783,0.000027800636,0.0031104586,0.000304545],"genre_scores_gemma":[0.64858675,0.000048658017,0.35100412,0.000057783247,0.000041352905,0.00011721306,0.00007857822,0.000060447306,0.000005116749],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989105,0.000012146539,0.0004634647,0.00014044916,0.00019729961,0.00027614378],"domain_scores_gemma":[0.99922645,0.000419588,0.000038644775,0.00016826762,0.00003653278,0.00011050242],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030679977,0.00026197464,0.00020444853,0.00033676316,0.000114956725,0.00015152918,0.000078967976,0.00011948594,0.000009031455],"category_scores_gemma":[0.000267277,0.00028429256,0.000056485158,0.00027220973,0.000009446589,0.000670779,0.000030263182,0.0005190052,0.0000082264805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010260529,0.000007651633,0.000068387126,0.00043877488,0.00003449506,0.0000011469583,0.0003843431,0.9738476,0.01962841,0.0031387422,0.000011992114,0.0024374141],"study_design_scores_gemma":[0.00033203856,0.00006764401,0.0010023302,0.00062918034,0.000029550503,0.000014958624,0.00018246488,0.9932512,0.003286493,0.00008940094,0.0008457435,0.00026898872],"about_ca_topic_score_codex":0.0000019246506,"about_ca_topic_score_gemma":0.0000014238575,"teacher_disagreement_score":0.60087156,"about_ca_system_score_codex":0.00013428336,"about_ca_system_score_gemma":0.000012246248,"threshold_uncertainty_score":0.9999609},"labels":[],"label_agreement":null},{"id":"W4407847668","doi":"10.1016/j.aei.2025.103224","title":"Deep learning-based rebar detection and instance segmentation in images","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Hunan University; Precast/Prestressed Concrete Institute; McGill University","keywords":"Rebar; Artificial intelligence; Segmentation; Deep learning; Computer science; Computer vision; Pattern recognition (psychology); Image segmentation; Engineering; Structural engineering","score_opus":0.002989233913601114,"score_gpt":0.21524582333234563,"score_spread":0.2122565894187445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407847668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012591204,0.000140393,0.9864297,0.00007471717,0.00009226905,0.00018418177,2.5317314e-7,0.00024522643,0.00024208105],"genre_scores_gemma":[0.6350189,0.00008112949,0.3646852,0.00012129203,0.000003910587,0.000065130545,0.0000022887152,0.000005259398,0.000016906668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993673,0.0000071351237,0.000268335,0.00010523376,0.000082746534,0.00016922336],"domain_scores_gemma":[0.9995726,0.000099270204,0.0000706293,0.00019539995,0.000032210985,0.000029903304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007143365,0.000106616135,0.00009418935,0.00018192893,0.000066083325,0.000044789318,0.0001636254,0.000034600813,2.2945166e-7],"category_scores_gemma":[0.000048981034,0.000120323435,0.0000135688115,0.00065724284,0.0000143128,0.0008642051,0.000055851848,0.00018294479,0.0000027133922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017386886,0.000004020554,0.00009695607,0.000045648714,0.0000015350522,3.285198e-7,0.00012913404,0.8682869,0.0017932539,0.0018716005,9.83488e-7,0.12776789],"study_design_scores_gemma":[0.00033814213,0.000018843568,0.002594868,0.00005265919,0.0000015538069,0.0000014629296,0.000035695637,0.98383576,0.010757257,0.0003592967,0.0018935339,0.0001109071],"about_ca_topic_score_codex":7.685398e-7,"about_ca_topic_score_gemma":0.000004967059,"teacher_disagreement_score":0.6224277,"about_ca_system_score_codex":0.00008610429,"about_ca_system_score_gemma":0.000010892199,"threshold_uncertainty_score":0.49066472},"labels":[],"label_agreement":null},{"id":"W4408390904","doi":"10.1016/j.aei.2025.103264","title":"Industrial applications of digital twins: A systematic investigation based on bibliometric analysis","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Basic Research Program of Shaanxi Province; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Guangdong Province","keywords":"Computer science; Data science; Engineering","score_opus":0.013222464281717224,"score_gpt":0.22401247073628602,"score_spread":0.2107900064545688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408390904","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024688749,0.000055759097,0.95960134,0.000009536008,0.00019948934,0.0007461176,0.00009648518,0.0005471581,0.01405536],"genre_scores_gemma":[0.99467534,0.000008594004,0.004872246,0.000023446697,0.000013498415,0.00024337167,0.00011267651,0.000018734232,0.000032072243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998399,0.0000044530566,0.0010059306,0.0000703504,0.00031764238,0.00020260266],"domain_scores_gemma":[0.999021,0.00028368123,0.0001225725,0.0003907916,0.000105433566,0.00007654997],"candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.00014269227,0.00020662896,0.00039167848,0.029808573,0.000027935828,0.00011469502,0.00023496061,0.00014479746,0.0000045704555],"category_scores_gemma":[0.00016139683,0.00021551846,0.0001294121,0.075526424,0.000025451123,0.0009506742,0.000014354376,0.00022792189,0.00001725958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019559616,0.000012571326,0.0004663052,0.0059934594,0.00020519583,8.7154916e-8,0.00007796404,0.9889127,0.00001472561,0.001313269,0.000038518738,0.002963272],"study_design_scores_gemma":[0.0005177213,0.000026323423,0.00039541032,0.0013342919,0.00023195292,4.5907467e-7,0.00016521955,0.9946496,0.0015808791,0.00006507819,0.0008031204,0.00022994094],"about_ca_topic_score_codex":3.5906362e-7,"about_ca_topic_score_gemma":1.2010479e-7,"teacher_disagreement_score":0.9699866,"about_ca_system_score_codex":0.00013061229,"about_ca_system_score_gemma":0.000036779762,"threshold_uncertainty_score":0.9811877},"labels":[],"label_agreement":null},{"id":"W4408538117","doi":"10.1016/j.aei.2025.103263","title":"LLM-MANUF: An integrated framework of Fine-Tuning large language models for intelligent Decision-Making in manufacturing","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; Chongqing Science and Technology Commission; Fundamental Research Funds for the Central Universities; Ministry of Science and Technology of the People's Republic of China","keywords":"Computer science; Decision-making models; Group decision-making; Manufacturing engineering; Artificial intelligence; Industrial engineering; Engineering","score_opus":0.005217509720654744,"score_gpt":0.24775136506448578,"score_spread":0.24253385534383104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408538117","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06905269,0.0003212318,0.92936754,0.0000029569314,0.0003234535,0.00028928384,0.000022795828,0.00029768862,0.00032238188],"genre_scores_gemma":[0.57787246,0.000070423404,0.42192683,0.000023772773,0.00001090907,0.000034898534,0.00002810927,0.000026844487,0.000005801124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986549,0.000004341849,0.00072386,0.00011380368,0.00013867149,0.0003643927],"domain_scores_gemma":[0.99918324,0.00032496997,0.00007880524,0.00030022842,0.000065699125,0.000047084195],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021519426,0.00024924634,0.00031604792,0.0004674013,0.00003993636,0.00004264606,0.0002567601,0.00014927388,0.000007680082],"category_scores_gemma":[0.00019132314,0.00026060853,0.00005906327,0.0002963876,0.000008524083,0.0007664108,0.000054751228,0.0002898544,9.57481e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020640522,0.000020522535,0.000005627237,0.0007596933,0.000022423072,8.830767e-7,0.0031737753,0.90116787,0.00002180811,0.0023735524,0.000005747322,0.09242743],"study_design_scores_gemma":[0.0003320483,0.000028115566,0.000030469466,0.0015394982,0.000012998346,9.758736e-7,0.0011989622,0.98334146,0.01059472,0.0013136095,0.0013653728,0.00024179656],"about_ca_topic_score_codex":0.0000025717998,"about_ca_topic_score_gemma":0.000009804422,"teacher_disagreement_score":0.50881976,"about_ca_system_score_codex":0.00012642186,"about_ca_system_score_gemma":0.000017767341,"threshold_uncertainty_score":0.9999846},"labels":[],"label_agreement":null},{"id":"W4408925380","doi":"10.1016/j.aei.2025.103260","title":"SLDAE: An interpretable stacked Denoising Auto-Encoders for fan fault diagnosis on steelmaking workshops","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","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":"Western University","funders":"National Natural Science Foundation of China","keywords":"Noise reduction; Steelmaking; Fault (geology); Artificial intelligence; Pattern recognition (psychology); Encoder; Computer science; Engineering; Geology; Metallurgy; Seismology; Materials science","score_opus":0.005853314295098214,"score_gpt":0.23685318470042985,"score_spread":0.23099987040533163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408925380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07542045,0.00018811689,0.918249,0.000019217705,0.0018066382,0.00060449616,0.000019476915,0.0013940624,0.0022985425],"genre_scores_gemma":[0.98291683,0.000047305253,0.016093144,0.00015281104,0.00005969048,0.0004230942,0.000020420688,0.00006346525,0.00022321644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855536,0.000008614897,0.0006388699,0.00013833931,0.0001734735,0.00048531397],"domain_scores_gemma":[0.999158,0.00021795293,0.00006976435,0.000383487,0.00006388968,0.00010690386],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017211771,0.00032080023,0.00035395796,0.00032782392,0.00011965129,0.00015561703,0.0002450719,0.0001483355,0.0000055241585],"category_scores_gemma":[0.00012430405,0.0003463375,0.00010856119,0.00039387777,0.000012764951,0.00078478345,0.000025359423,0.0002944169,0.000011927727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020010046,0.000011066584,0.00002775661,0.00043340854,0.000077852084,6.1089145e-7,0.0010031183,0.9364322,0.0010770763,0.0016626247,0.00013373011,0.059120536],"study_design_scores_gemma":[0.00076770195,0.000070921,0.000058650738,0.0005857759,0.000026012636,0.0000021630951,0.0011346721,0.9316371,0.003935146,0.000026586902,0.061415024,0.0003402604],"about_ca_topic_score_codex":0.0000034449724,"about_ca_topic_score_gemma":0.000008946765,"teacher_disagreement_score":0.9074964,"about_ca_system_score_codex":0.0002753202,"about_ca_system_score_gemma":0.000018819748,"threshold_uncertainty_score":0.99989885},"labels":[],"label_agreement":null},{"id":"W4409737878","doi":"10.1016/j.aei.2025.103361","title":"Prognostics of complex machinery with sparse multilabel multimodal run-to-failure data: A graph neural network approach","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Machine Fault Diagnosis Techniques","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":"Hydro-Québec","funders":"Région Occitanie Pyrénées-Méditerranée; Ecole Nationale d'Ingénieurs de Tunis","keywords":"Prognostics; Artificial neural network; Computer science; Artificial intelligence; Graph; Machine learning; Data mining; Pattern recognition (psychology); Theoretical computer science","score_opus":0.011179789926884292,"score_gpt":0.2509937024499,"score_spread":0.2398139125230157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409737878","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06461406,0.00015780295,0.93069637,0.00002690808,0.00023423054,0.0011556504,0.00018552731,0.001674955,0.0012544806],"genre_scores_gemma":[0.43288928,0.000030693773,0.5665634,0.000066225504,0.000033139513,0.00010320405,0.0002554322,0.0000521398,0.000006431799],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812526,0.0000101609585,0.00079875,0.00020259806,0.00028679834,0.0005764554],"domain_scores_gemma":[0.99840313,0.00017979238,0.000104565566,0.0010672989,0.000102569255,0.00014263479],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019622785,0.00046586376,0.00055858726,0.0003394013,0.000056727706,0.00004745787,0.00081513193,0.00012530616,0.0000028968802],"category_scores_gemma":[0.00012366284,0.00043742656,0.00005840661,0.0010176154,0.000046042787,0.00057019433,0.00030217247,0.00047071138,0.0000030827957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018185416,0.000051086376,0.0011536531,0.0011285599,0.00011695906,0.0000021427977,0.00022475034,0.98700887,0.00044725157,0.0006402521,0.0026415533,0.006566751],"study_design_scores_gemma":[0.00065234635,0.000065723965,0.0024334309,0.0003357552,0.00006035946,0.0000098033115,0.00004794561,0.9869289,0.00077645684,0.000017515928,0.008235606,0.0004361482],"about_ca_topic_score_codex":0.000008746061,"about_ca_topic_score_gemma":0.000013262289,"teacher_disagreement_score":0.36827523,"about_ca_system_score_codex":0.000059432397,"about_ca_system_score_gemma":0.000022778246,"threshold_uncertainty_score":0.9998078},"labels":[],"label_agreement":null},{"id":"W4410019015","doi":"10.1016/j.aei.2025.103413","title":"A Graph-Based approach for individual fall risk assessment through a wearable inertial measurement unit sensor","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","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":"Liberty Mutual Insurance","keywords":"Wearable computer; Units of measurement; Inertial measurement unit; Unit (ring theory); Computer science; Inertial frame of reference; Engineering; Artificial intelligence; Mathematics; Embedded system; Physics","score_opus":0.032135961657637165,"score_gpt":0.2646916356414444,"score_spread":0.23255567398380722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410019015","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031807548,0.0000733982,0.99351484,0.00007495686,0.00045406295,0.00090990844,0.000036940586,0.00045630024,0.001298814],"genre_scores_gemma":[0.3552905,0.00000921535,0.64409536,0.00018293486,0.00002212961,0.00034477407,0.000018380606,0.000012683491,0.000024007668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813527,0.000034286906,0.0006330303,0.00021146666,0.0005639841,0.00042196835],"domain_scores_gemma":[0.99855167,0.00021193441,0.00023161204,0.00055604713,0.00037356786,0.000075158096],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007134818,0.00027244448,0.00033321235,0.00027679902,0.00015917531,0.00024963496,0.00059245736,0.00009624135,9.628113e-7],"category_scores_gemma":[0.00018130886,0.00027603045,0.0001352836,0.0007098851,0.000019782161,0.0013061877,0.0001356792,0.00028619592,0.0000036041552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018151486,0.00014485352,0.0003584646,0.00068957306,0.00024339197,7.561376e-7,0.001149682,0.95058805,0.00048946164,0.0034492791,0.00023022767,0.042638093],"study_design_scores_gemma":[0.0019006472,0.00009999663,0.00038726573,0.00022061568,0.000045254426,0.0000034733323,0.00020618297,0.97782046,0.0023762826,0.0002174873,0.016365891,0.00035644474],"about_ca_topic_score_codex":0.000020498448,"about_ca_topic_score_gemma":0.00000575213,"teacher_disagreement_score":0.35210976,"about_ca_system_score_codex":0.00017660357,"about_ca_system_score_gemma":0.00022269966,"threshold_uncertainty_score":0.9999692},"labels":[],"label_agreement":null},{"id":"W4410106010","doi":"10.1016/j.aei.2025.103367","title":"Integrated fuzzy system dynamics–fuzzy agent-based modeling of construction crew motivation and productivity","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"BIM and Construction Integration","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; Canada Research Chairs; Canada First Research Excellence Fund; University of Alberta","keywords":"Crew; Productivity; Fuzzy logic; Computer science; Engineering; Artificial intelligence; Aeronautics; Economics","score_opus":0.004842735871657765,"score_gpt":0.18137791623529667,"score_spread":0.1765351803636389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410106010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28476322,0.00007702616,0.7119626,0.000008328685,0.00060753373,0.00016963905,0.000014555496,0.00040763954,0.0019894561],"genre_scores_gemma":[0.94050545,0.000019233665,0.059359528,0.0000042218153,0.000014194781,0.000026168653,0.0000471684,0.000015241867,0.000008823032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911994,0.000006867277,0.00052291114,0.000085698244,0.0001123755,0.00015218699],"domain_scores_gemma":[0.99951786,0.00003425918,0.00007483991,0.00018247038,0.0001552823,0.00003528206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009867367,0.00018184738,0.00021907451,0.00030163582,0.00004745716,0.000029176383,0.00006446952,0.00010196189,9.083641e-7],"category_scores_gemma":[0.000056919675,0.00019121634,0.000037253732,0.00043029594,0.000032691984,0.00046407193,0.000012607605,0.00018979654,0.0000010136029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000071334157,0.0000038355142,0.00020652394,0.0010961416,0.00003148699,9.702666e-8,0.00009180463,0.92740184,0.0010121451,0.032019857,0.0000035935739,0.03812553],"study_design_scores_gemma":[0.00031239676,0.000012906083,0.0001413772,0.00041676342,0.000024216439,0.0000072901553,0.0008306899,0.9940238,0.003822431,0.0001558479,0.00010341765,0.00014887146],"about_ca_topic_score_codex":0.000006884736,"about_ca_topic_score_gemma":0.000004364718,"teacher_disagreement_score":0.65574217,"about_ca_system_score_codex":0.00024280192,"about_ca_system_score_gemma":0.00003270656,"threshold_uncertainty_score":0.7797576},"labels":[],"label_agreement":null},{"id":"W4410202973","doi":"10.1016/j.aei.2025.103429","title":"A novel explainable stacking ensemble model for estimating design floods: A data-driven approach for ungauged regions","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Hydrology and Watershed Management Studies","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":"Institut National de la Recherche Scientifique","funders":"","keywords":"Stacking; Computer science; Data mining; Artificial intelligence; Physics","score_opus":0.03901509068451805,"score_gpt":0.2595417244425357,"score_spread":0.22052663375801765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410202973","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045121092,0.000013449506,0.99742377,0.000064454165,0.00010137483,0.0010504304,0.000027516096,0.00014627032,0.0007215147],"genre_scores_gemma":[0.04353828,0.000008328761,0.95528495,0.00016505246,0.000011625744,0.00050994457,0.00009821598,0.000016611859,0.00036700288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897563,0.0000038935855,0.000336827,0.00018231633,0.00009107405,0.00041022775],"domain_scores_gemma":[0.9992862,0.00017398082,0.00008724957,0.0004016494,0.00001393753,0.000037010264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003466348,0.00017833896,0.0002080144,0.000069587106,0.0003122836,0.000030134244,0.00038194086,0.000057255358,9.493047e-7],"category_scores_gemma":[0.00020447938,0.00017679267,0.000038831353,0.00016869126,0.00003711325,0.0007571095,0.00037287697,0.00009326311,0.0000024352673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019461462,0.000026347643,0.00000879431,0.00030366625,0.00005440447,9.8078715e-8,0.0010575216,0.99350584,0.00035478635,0.0017633454,0.0012686307,0.0016371311],"study_design_scores_gemma":[0.0007513588,0.000029150704,0.00000391895,0.000044119683,0.00005818031,8.444059e-7,0.00024063162,0.9958137,0.00017387567,0.0007155531,0.0019853546,0.00018329751],"about_ca_topic_score_codex":0.0000028285303,"about_ca_topic_score_gemma":0.0000018300713,"teacher_disagreement_score":0.04308707,"about_ca_system_score_codex":0.000097302356,"about_ca_system_score_gemma":0.000011173517,"threshold_uncertainty_score":0.7209396},"labels":[],"label_agreement":null},{"id":"W4410251548","doi":"10.1016/j.aei.2025.103455","title":"A polynomial speed normalized health indicator for both incipient fault detection and prognosis of variable-speed wind turbine bearings","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Machine Fault Diagnosis Techniques","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 Manitoba","funders":"National Natural Science Foundation of China","keywords":"Wind speed; Turbine; Fault (geology); Polynomial; Variable (mathematics); Fault detection and isolation; Engineering; Marine engineering; Computer science; Mathematics; Artificial intelligence; Meteorology; Mechanical engineering; Geology; Physics; Seismology; Mathematical analysis","score_opus":0.004439226670297672,"score_gpt":0.24525178235515913,"score_spread":0.24081255568486146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410251548","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59811246,0.00036924842,0.3979124,0.000043333763,0.00040921054,0.0016792067,0.00008651147,0.0010615406,0.00032606837],"genre_scores_gemma":[0.84131694,0.00019084812,0.15817122,0.000095251824,0.000033284767,0.000092095295,0.000035925874,0.000049565857,0.000014872807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984148,0.0000067992805,0.000874264,0.00012830914,0.00016841305,0.0004074131],"domain_scores_gemma":[0.9992675,0.00013390605,0.000176668,0.0002443534,0.000060671577,0.00011688219],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030331168,0.00029676454,0.0004873224,0.00058161723,0.000067011046,0.000036901725,0.0001790629,0.00013871252,0.00000291316],"category_scores_gemma":[0.00014353315,0.00032196575,0.00006756899,0.00051833683,0.00002872161,0.0003987902,0.00007855831,0.0002498756,7.418406e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001201467,0.00009901288,0.0013950439,0.00743386,0.00029402456,4.6111563e-7,0.0020768812,0.90851146,0.04132619,0.0006883408,0.0010542435,0.037000358],"study_design_scores_gemma":[0.0019329949,0.00023667492,0.0024945263,0.0005081172,0.000050742732,0.000005499194,0.00007355624,0.8611949,0.10568331,0.000024378498,0.02739235,0.00040296372],"about_ca_topic_score_codex":0.000025329342,"about_ca_topic_score_gemma":0.0000037008751,"teacher_disagreement_score":0.24320446,"about_ca_system_score_codex":0.00020969192,"about_ca_system_score_gemma":0.000045832316,"threshold_uncertainty_score":0.9999232},"labels":[],"label_agreement":null},{"id":"W4411115380","doi":"10.1016/j.aei.2025.103526","title":"Latent subdomain assignment based on pseudo domain labels for fault diagnosis of unseen data","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Machine Fault Diagnosis Techniques","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 Ottawa","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Domain (mathematical analysis); Fault (geology); Computer science; Artificial intelligence; Algorithm; Data mining; Pattern recognition (psychology); Mathematics; Biology","score_opus":0.010094228384322853,"score_gpt":0.2715642504425188,"score_spread":0.26147002205819597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411115380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03518567,0.000111252404,0.9609338,0.00009837725,0.00033682314,0.0010148552,0.00049972296,0.00088129763,0.00093822007],"genre_scores_gemma":[0.52070343,0.0002053136,0.4776471,0.0002015159,0.000025901294,0.0007753726,0.00035261633,0.000069815345,0.000018928878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840856,0.000009398447,0.000776378,0.00015743048,0.0002588238,0.00038941222],"domain_scores_gemma":[0.998116,0.00060922955,0.000097154225,0.001041357,0.000058020978,0.00007821834],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036359177,0.0003293097,0.00040049708,0.00032928213,0.00003998441,0.000029810282,0.0006557804,0.0001290336,0.000010694889],"category_scores_gemma":[0.00020738089,0.00033878785,0.000083970655,0.00034785757,0.000021322141,0.00033844096,0.00011017377,0.00021141802,0.0000048505894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013769592,0.00006044315,0.0005774016,0.0012030911,0.000072124836,8.3079306e-7,0.00009283297,0.979577,0.0006837644,0.0027214524,0.0035959065,0.011401391],"study_design_scores_gemma":[0.00092106796,0.00010152263,0.00041821646,0.00058930815,0.00003818161,4.3900525e-7,0.000021628755,0.922381,0.038605053,0.0002496593,0.03634942,0.00032450058],"about_ca_topic_score_codex":0.0000028051616,"about_ca_topic_score_gemma":0.0000022286683,"teacher_disagreement_score":0.48551777,"about_ca_system_score_codex":0.00018317677,"about_ca_system_score_gemma":0.00002616087,"threshold_uncertainty_score":0.9999064},"labels":[],"label_agreement":null},{"id":"W4411728729","doi":"10.1016/j.aei.2025.103577","title":"Knowledge vortex network for continuous bearing remaining useful life prediction","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Machine Fault Diagnosis Techniques","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 Manitoba","funders":"National Natural Science Foundation of China","keywords":"Bearing (navigation); Vortex; Computer science; Engineering; Artificial intelligence; Physics; Mechanics","score_opus":0.005847454153244541,"score_gpt":0.24809006326994734,"score_spread":0.2422426091167028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411728729","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035567086,0.000723575,0.9500405,0.000014706725,0.0013303199,0.0006868551,0.000027432161,0.00280249,0.008807023],"genre_scores_gemma":[0.6396611,0.00027849787,0.35890254,0.00011519055,0.00029191803,0.0003720824,0.00007505638,0.000106933105,0.00019671285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985555,0.0000048376737,0.00071700034,0.000114072616,0.00010489817,0.0005036978],"domain_scores_gemma":[0.9991358,0.000270073,0.000064840315,0.00033964898,0.00008681874,0.00010281845],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027559855,0.00029292688,0.00036467236,0.00022240904,0.00007632365,0.000062936095,0.00023002984,0.00015257957,0.0000044873973],"category_scores_gemma":[0.00028570255,0.00033908442,0.00009764831,0.00041515782,0.000013867418,0.0004624583,0.000064346554,0.0002986475,0.000013293288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054938587,0.000009951177,0.0009828908,0.0008351083,0.00008450019,3.1761772e-7,0.00038791358,0.9791546,0.0002365997,0.003269195,0.0061132656,0.008920173],"study_design_scores_gemma":[0.00044652523,0.00003614034,0.0016722804,0.00051254116,0.000036123598,0.0000019031916,0.000048607242,0.8481927,0.0016461061,0.00020047801,0.14693284,0.00027377353],"about_ca_topic_score_codex":9.58094e-7,"about_ca_topic_score_gemma":0.0000025927366,"teacher_disagreement_score":0.60409397,"about_ca_system_score_codex":0.00015273831,"about_ca_system_score_gemma":0.000027303277,"threshold_uncertainty_score":0.9999061},"labels":[],"label_agreement":null},{"id":"W4411980080","doi":"10.1016/j.aei.2025.103591","title":"Contexts Matter: Robot-Aware 3D human motion prediction for Agentic AI-empowered Human-Robot collaboration","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto; University of Texas at San Antonio; National Science Foundation","keywords":"Robot; Human–robot interaction; Motion (physics); Human motion; Artificial intelligence; Computer science; Human–computer interaction","score_opus":0.003909205649999582,"score_gpt":0.23819303324240723,"score_spread":0.23428382759240765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411980080","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07244857,0.00011501177,0.92389935,0.0000599389,0.00068849436,0.00065847934,0.00005511549,0.0016082744,0.00046677777],"genre_scores_gemma":[0.9860293,0.000017550752,0.013089247,0.000100462144,0.000035102454,0.00020039684,0.0003037547,0.000041936262,0.00018219971],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876684,0.0000051657544,0.000673784,0.000120718054,0.00009958955,0.00033389425],"domain_scores_gemma":[0.9993968,0.000033241435,0.000082541046,0.0003303129,0.0001118091,0.0000453083],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011656039,0.0002589778,0.0002757996,0.00027988473,0.00021751388,0.000050223498,0.00017658704,0.00026145965,0.000024793218],"category_scores_gemma":[0.00002007146,0.00031125054,0.000064345,0.00033265183,0.000030235527,0.00068783306,0.00003363347,0.00027896801,0.000025284702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006189016,0.000015519434,0.00026611527,0.00061354734,0.00007778602,5.7274275e-7,0.0003341469,0.98340523,0.00674056,0.0031276816,0.00055346556,0.0048591797],"study_design_scores_gemma":[0.0012947991,0.00007311721,0.0064518945,0.00021212484,0.00006291392,0.000004333115,0.00013877227,0.9752537,0.010300856,0.00042904195,0.005415893,0.0003625562],"about_ca_topic_score_codex":9.3173304e-7,"about_ca_topic_score_gemma":0.0000043250056,"teacher_disagreement_score":0.9135808,"about_ca_system_score_codex":0.00025632567,"about_ca_system_score_gemma":0.000016978649,"threshold_uncertainty_score":0.99993396},"labels":[],"label_agreement":null},{"id":"W4412470227","doi":"10.1016/j.aei.2025.103655","title":"Large language Models-empowered automatic knowledge graph development based on multi-modal data for building health resilience","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Hong Kong Polytechnic University","keywords":"Modal; Computer science; Resilience (materials science); Knowledge graph; Knowledge management; Graph; Data science; Artificial intelligence; Theoretical computer science","score_opus":0.017481202907824848,"score_gpt":0.3045036570114266,"score_spread":0.2870224541036018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412470227","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018920435,0.00045864604,0.9953983,0.00008896498,0.0005636562,0.00070874207,0.000036320907,0.00076536636,0.00008800382],"genre_scores_gemma":[0.16746755,0.000014995487,0.83178365,0.0005031263,0.000011590779,0.000090702466,0.000071696246,0.00002113514,0.000035577512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783987,0.000017867293,0.000774302,0.00036290073,0.00024958584,0.00075544947],"domain_scores_gemma":[0.9976756,0.00035511577,0.00020366354,0.0015381268,0.000076799806,0.00015070461],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000504935,0.0003326601,0.00035621322,0.0004320269,0.00026061814,0.000093756455,0.0018112909,0.00007664252,4.808549e-7],"category_scores_gemma":[0.00015821162,0.00033431145,0.00006204757,0.0009924169,0.000020004432,0.0015148763,0.000529432,0.00027767383,0.000003775582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057925567,0.00006281472,0.0000042273064,0.00046497202,0.000016665725,0.0000010247247,0.0013534966,0.93504685,0.000046532514,0.015030702,0.00015830492,0.047808636],"study_design_scores_gemma":[0.0010941969,0.000053253676,0.00006716108,0.0005443915,0.0000043301693,0.000001353819,0.000087367516,0.9922313,0.0004404629,0.0002621831,0.0048959544,0.00031806366],"about_ca_topic_score_codex":8.4615846e-7,"about_ca_topic_score_gemma":0.00000842381,"teacher_disagreement_score":0.1655755,"about_ca_system_score_codex":0.0001868023,"about_ca_system_score_gemma":0.00024188991,"threshold_uncertainty_score":0.9999109},"labels":[],"label_agreement":null},{"id":"W4412690338","doi":"10.1016/j.aei.2025.103684","title":"Process mechanisms fusion enhanced spatially scalable convolution network for multi-indicator prediction in process industries","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","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":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Process (computing); Convolution (computer science); Scalability; Fusion; Computer science; Artificial intelligence; Data mining; Process engineering; Machine learning; Artificial neural network; Engineering; Database","score_opus":0.0053210977164223185,"score_gpt":0.22333638165627376,"score_spread":0.21801528393985145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412690338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.103484996,0.000108003354,0.8932047,0.00000839597,0.0012127425,0.0009880333,0.000021898344,0.00072428264,0.00024696635],"genre_scores_gemma":[0.98348475,0.000024975348,0.015456602,0.000028370156,0.00006020389,0.0007732793,0.000049061324,0.000033345106,0.00008941961],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865097,0.0000056644394,0.0006672435,0.00011723542,0.00015708295,0.0004018124],"domain_scores_gemma":[0.9995564,0.000040652452,0.00008996434,0.00015997337,0.000091096925,0.00006191747],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019107926,0.00023234835,0.00028586888,0.0002406749,0.00008590522,0.00003965949,0.00014174725,0.00021826771,0.000004212525],"category_scores_gemma":[0.00008920883,0.00025406605,0.00004017808,0.00062531774,0.0000116702095,0.0005345789,0.000013370008,0.00025320856,0.000007635018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027707427,0.000012723035,0.000070901064,0.0009589896,0.000026060721,1.4713872e-7,0.00045648296,0.9882296,0.003363384,0.0003470785,0.000044229902,0.006462737],"study_design_scores_gemma":[0.0015872869,0.000047660946,0.0002982858,0.0004526839,0.000014775545,0.0000012737102,0.0003751449,0.9637414,0.031682782,0.00013002245,0.001450631,0.00021802537],"about_ca_topic_score_codex":0.0000030962283,"about_ca_topic_score_gemma":0.000019937785,"teacher_disagreement_score":0.87999976,"about_ca_system_score_codex":0.00019704731,"about_ca_system_score_gemma":0.000057934016,"threshold_uncertainty_score":0.9999912},"labels":[],"label_agreement":null},{"id":"W4413343877","doi":"10.1016/j.aei.2025.103784","title":"Enhancing construction safety compliance through a blockchain-enabled worker certification management system","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Blockchain; Certification; Compliance (psychology); Business; Risk analysis (engineering); Engineering; Computer security; Engineering management; Computer science; Management","score_opus":0.0322754304746753,"score_gpt":0.3724650489888055,"score_spread":0.3401896185141302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413343877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010751558,0.00013590872,0.9405117,0.00029680892,0.0017743744,0.0018045488,0.000018524817,0.00044342148,0.04426315],"genre_scores_gemma":[0.58688974,0.00027653444,0.40902236,0.00044641978,0.00014573608,0.0009786866,0.000112955066,0.00003628942,0.0020912532],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780816,0.00005038496,0.0011266909,0.00013813905,0.00028878287,0.0005878404],"domain_scores_gemma":[0.99864364,0.00041624517,0.00020802129,0.0003873815,0.000250617,0.00009409309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005181363,0.00017264506,0.00026076465,0.00017447409,0.0006789444,0.000012684615,0.00017480111,0.00013213963,0.00002509122],"category_scores_gemma":[0.000099205085,0.00017222473,0.000042905678,0.00067948794,0.00002600013,0.00023795474,0.000093872724,0.0005056888,0.00019931475],"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":[0.00055098755,0.0000479228,0.0032483884,0.03387489,0.00018242515,0.0000048021507,0.0039650984,0.39843,0.00030452982,0.45148936,0.00049292453,0.10740869],"study_design_scores_gemma":[0.004314932,0.00006897667,0.03262405,0.013875864,0.0000705447,0.000011185054,0.018450635,0.67619777,0.0006876226,0.0007395894,0.25223356,0.0007252599],"about_ca_topic_score_codex":0.000011321783,"about_ca_topic_score_gemma":0.0000046988916,"teacher_disagreement_score":0.5761382,"about_ca_system_score_codex":0.00079032296,"about_ca_system_score_gemma":0.00014414563,"threshold_uncertainty_score":0.70231205},"labels":[],"label_agreement":null},{"id":"W4413366889","doi":"10.1016/j.aei.2025.103778","title":"Leveraging linked data for space constraints checking of mobile cranes in modular construction assembly lookahead planning","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"BIM and Construction Integration","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Worldwide Universities Network; University of Auckland","keywords":"Modular design; Computer science; Modular construction; Space (punctuation); Systems engineering; Distributed computing; Engineering drawing; Theoretical computer science; Engineering; Programming language; Operating system","score_opus":0.013130944557142955,"score_gpt":0.25290981802778956,"score_spread":0.2397788734706466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413366889","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27639803,0.0002833459,0.72147715,0.0000060393136,0.00068308145,0.0002860755,0.0000263434,0.00020027242,0.00063962414],"genre_scores_gemma":[0.86463463,0.000048390564,0.13512614,0.000007498711,0.000023349232,0.000054297834,0.00007908385,0.000017535614,0.00000905767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989301,0.000004006269,0.0006275831,0.00010502871,0.000102369835,0.0002308987],"domain_scores_gemma":[0.99937814,0.0001017676,0.0000847427,0.00033273775,0.00007228937,0.000030314955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017002769,0.00017743107,0.00027174482,0.00031946553,0.000036091515,0.000030221257,0.00019847677,0.00011029903,0.0000030896779],"category_scores_gemma":[0.00007741218,0.00020701996,0.000037296748,0.0003172539,0.000045362074,0.0006477407,0.000037197387,0.00020550913,8.616033e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006356469,0.0000044093294,0.00037092846,0.0007138385,0.000036411755,3.0688926e-7,0.00055046426,0.93786836,0.0075961133,0.003101943,0.000017034348,0.049733814],"study_design_scores_gemma":[0.0006821596,0.000014244182,0.0005154669,0.0006538004,0.000018323977,0.000011018588,0.0015816719,0.98435855,0.008593091,0.00017269875,0.0032043394,0.00019460927],"about_ca_topic_score_codex":0.0000027603146,"about_ca_topic_score_gemma":0.0000017779795,"teacher_disagreement_score":0.58823663,"about_ca_system_score_codex":0.00008482796,"about_ca_system_score_gemma":0.000037480313,"threshold_uncertainty_score":0.8442029},"labels":[],"label_agreement":null},{"id":"W4414090142","doi":"10.1016/j.aei.2025.103803","title":"Physics-informed deep learning method for surface roughness prediction in milling process","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Advanced machining processes and optimization","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 British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Surface roughness; Mean squared error; Surface finish; Root mean square; Leverage (statistics); Process (computing); Artificial neural network; Mean absolute percentage error","score_opus":0.004591595288886932,"score_gpt":0.26101115784361106,"score_spread":0.25641956255472415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414090142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010274887,0.00024265464,0.9870061,0.0000059092263,0.0004256377,0.00038852464,0.000007341731,0.0007378275,0.00091110816],"genre_scores_gemma":[0.26641393,0.00024985612,0.73287773,0.000026448055,0.00003962372,0.00013855852,0.00012236505,0.000056870216,0.00007462591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988557,0.000003620162,0.00054944353,0.00010653718,0.00011419009,0.00037047634],"domain_scores_gemma":[0.99945223,0.00019680196,0.000073997675,0.00013457483,0.00010005764,0.000042352116],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014730211,0.00023982285,0.00026505094,0.00013798955,0.0000809412,0.000042873387,0.00013897906,0.00010671369,0.0000014884067],"category_scores_gemma":[0.00013741177,0.00027257303,0.000047206886,0.00063636457,0.000007801269,0.0009840742,0.000020208574,0.0003044484,0.0000018726298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012097397,0.0000060413904,0.00008993341,0.0018507134,0.000020749447,1.465742e-7,0.0015037261,0.9612294,0.00019142276,0.00065394153,0.0000043351697,0.034437504],"study_design_scores_gemma":[0.0007575189,0.000027161068,0.00004735595,0.00032380281,0.000016234173,9.632145e-7,0.0005223003,0.99069804,0.0033439829,0.0003446081,0.0036732152,0.00024479543],"about_ca_topic_score_codex":9.577424e-7,"about_ca_topic_score_gemma":0.0000025426766,"teacher_disagreement_score":0.25613904,"about_ca_system_score_codex":0.00017144915,"about_ca_system_score_gemma":0.00002762231,"threshold_uncertainty_score":0.99997264},"labels":[],"label_agreement":null},{"id":"W4414115232","doi":"10.1016/j.aei.2025.103821","title":"Integrated machine learning framework for performance-based seismic assessment of post-tensioned steel-timber hybrid frames with energy-dissipating braces","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Seismic Performance and Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; University of British Columbia; National Natural Science Foundation of China","keywords":"Downtime; Latin hypercube sampling; Residual; Feature selection; Bayesian probability; Feature (linguistics); Percentile; Incremental Dynamic Analysis; Fragility","score_opus":0.0037745292680306667,"score_gpt":0.21633981491902007,"score_spread":0.2125652856509894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414115232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42415515,0.00012320255,0.575023,0.000015129883,0.0001150308,0.00011190865,0.000025319765,0.00028614837,0.00014512806],"genre_scores_gemma":[0.7377561,0.00012676427,0.26165196,0.00013484141,0.000016450736,0.00006731191,0.00015766651,0.000041820407,0.00004712723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848545,0.0000074161303,0.0007182248,0.00013208152,0.00023626642,0.00042057622],"domain_scores_gemma":[0.99897236,0.000257025,0.00016091559,0.0003070282,0.0002319981,0.000070702285],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012793294,0.00036862923,0.00051542296,0.0004350004,0.00012381798,0.000046763886,0.00021399156,0.00010712176,0.000013555593],"category_scores_gemma":[0.000077223216,0.00031903558,0.00011724073,0.0006492242,0.000031810778,0.0005161783,0.000032263262,0.00044897737,0.000001276444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026879758,0.000015980379,0.0013975966,0.0010188447,0.00017458599,6.86646e-7,0.0001813822,0.96796024,0.0009388058,0.00026703355,0.000011700283,0.028006267],"study_design_scores_gemma":[0.00069418666,0.00015491758,0.0005660055,0.0011626105,0.00008284324,0.000002767952,0.0002960213,0.9748153,0.018062586,0.000021190755,0.0037875113,0.0003540535],"about_ca_topic_score_codex":0.0000085753545,"about_ca_topic_score_gemma":0.0000019478227,"teacher_disagreement_score":0.31360093,"about_ca_system_score_codex":0.00011943105,"about_ca_system_score_gemma":0.00008542371,"threshold_uncertainty_score":0.99992615},"labels":[],"label_agreement":null},{"id":"W4414572452","doi":"10.1016/j.aei.2025.103908","title":"PIFL: Physics-informed federated learning for progressive degradation estimation in energy storage devices","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Advanced Battery Technologies Research","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":"University of Toronto","funders":"","keywords":"Adaptability; Federated learning; Component (thermodynamics); Generalization; Feature (linguistics); Deep learning; Battery (electricity); Convergence (economics); Data modeling","score_opus":0.007881419276365782,"score_gpt":0.26611088390424137,"score_spread":0.2582294646278756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414572452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043426804,0.00020966918,0.95419246,0.000020785694,0.00017513681,0.00040341375,0.0000043414634,0.0011581085,0.00040929654],"genre_scores_gemma":[0.8312485,0.00011856642,0.16778187,0.000029191828,0.000013579147,0.00051156816,0.00017355826,0.000043595486,0.00007957092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877965,0.000004925773,0.00050646503,0.00010249229,0.00015615756,0.00045031993],"domain_scores_gemma":[0.999387,0.00022344603,0.000078833546,0.00017800831,0.000096353055,0.000036344656],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008492016,0.0002414576,0.00024487008,0.00037441278,0.000087339744,0.000081531885,0.00019540395,0.00012906612,0.000001970969],"category_scores_gemma":[0.00029015396,0.00026635404,0.000039463,0.00076920306,0.000022955019,0.0011603354,0.000055234166,0.00031703134,0.0000036962322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010904068,0.0000065349354,0.00004796011,0.0005224271,0.000023041379,8.28809e-7,0.00011343126,0.83287644,0.00049910194,0.0012073109,0.000034671273,0.16465732],"study_design_scores_gemma":[0.00060487865,0.000046404224,0.00017005143,0.00027545283,0.000005863345,0.0000013572488,0.00024320658,0.97766745,0.014724985,0.00028360306,0.0057325903,0.0002441502],"about_ca_topic_score_codex":0.0000013663024,"about_ca_topic_score_gemma":0.000008657981,"teacher_disagreement_score":0.7878217,"about_ca_system_score_codex":0.0004357825,"about_ca_system_score_gemma":0.000045220397,"threshold_uncertainty_score":0.99997884},"labels":[],"label_agreement":null},{"id":"W4415296682","doi":"10.1016/j.aei.2025.103987","title":"Cross-domain fault diagnosis of RV reducer rolling bearings based on relative shaft synchronous resampling and adaptive gradient freezing transfer learning","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Machine Fault Diagnosis Techniques","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":"MD Precision (Canada)","funders":"","keywords":"Reducer; Fault (geology); Control theory (sociology); Resampling; Transfer (computing)","score_opus":0.0053604816770352285,"score_gpt":0.24804482287759208,"score_spread":0.24268434120055685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415296682","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44927722,0.00025408433,0.548176,0.00001295751,0.00012492506,0.00034385623,0.000016353952,0.0006195177,0.0011750949],"genre_scores_gemma":[0.87036335,0.00024353422,0.12902972,0.000035398196,0.000018417382,0.00020947553,0.000022669985,0.00006613434,0.000011277206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981871,0.00001754521,0.0008515481,0.00020767456,0.00026426173,0.00047182903],"domain_scores_gemma":[0.9986952,0.0007040749,0.00008282556,0.00031778944,0.00009710002,0.00010303088],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003461168,0.00042513193,0.0005025536,0.00054039,0.00013150394,0.00006520915,0.0002206851,0.00018635973,0.000008046151],"category_scores_gemma":[0.0002775177,0.0004689087,0.00011855832,0.00048774827,0.00006461136,0.00059349777,0.00005850996,0.0007879739,0.0000020662783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025754089,0.000023226363,0.0025960836,0.00064154126,0.000081314094,0.0000022370107,0.0017194169,0.98513144,0.0013458363,0.002090362,0.000007712658,0.0063350736],"study_design_scores_gemma":[0.0007720067,0.00017661609,0.0017579723,0.0018731214,0.000043160897,0.0000018874949,0.000184052,0.95387375,0.03885165,0.00017652403,0.0018427537,0.0004464852],"about_ca_topic_score_codex":0.000014521524,"about_ca_topic_score_gemma":0.000002925736,"teacher_disagreement_score":0.42108613,"about_ca_system_score_codex":0.0002686389,"about_ca_system_score_gemma":0.000023259821,"threshold_uncertainty_score":0.99977624},"labels":[],"label_agreement":null},{"id":"W4415515973","doi":"10.1016/j.aei.2025.103989","title":"Artificial intelligence for eco-design: a systematic review","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Applications of artificial intelligence; Artificial Intelligence System; Artificial intelligence, situated approach; Artificial neural network","score_opus":0.012031873095293293,"score_gpt":0.23674439929625304,"score_spread":0.22471252620095974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415515973","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000035599587,0.0012770225,0.99303573,0.0004204055,0.00041948786,0.0030292806,0.0000011509196,0.0003189804,0.0014623395],"genre_scores_gemma":[0.1961187,0.0026810865,0.74109787,0.04221745,0.0012975257,0.012756316,0.00028936053,0.0003671096,0.0031745953],"study_design_codex":"systematic_review","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984328,0.0000033903116,0.0009152058,0.00011283876,0.00017178991,0.00036396942],"domain_scores_gemma":[0.9989819,0.00019020837,0.00023283322,0.00038696764,0.0001974797,0.000010641313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007255948,0.00022675304,0.00042932061,0.00037358602,0.00009555331,0.00017594722,0.00036780126,0.000042071388,0.000013685133],"category_scores_gemma":[0.0011025714,0.00021750103,0.000100828984,0.00079029606,0.000010780219,0.0010429542,0.00013817164,0.00009686141,0.000086436325],"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":[0.000006634102,0.000013726235,5.259891e-7,0.5625923,0.000051489813,0.0000011494254,0.000036322,0.2784893,0.000004687786,0.15413994,0.00094870414,0.003715214],"study_design_scores_gemma":[0.00009902615,0.000009593077,0.0000016443881,0.039480668,0.00033773025,7.1401166e-7,0.00069116824,0.9268462,0.00011233571,0.0099553205,0.022098592,0.00036700442],"about_ca_topic_score_codex":0.0000022120794,"about_ca_topic_score_gemma":7.995523e-7,"teacher_disagreement_score":0.6483569,"about_ca_system_score_codex":0.000110913716,"about_ca_system_score_gemma":0.000020557201,"threshold_uncertainty_score":0.88694346},"labels":[],"label_agreement":null},{"id":"W4415533747","doi":"10.1016/j.aei.2025.104016","title":"A new open set fault diagnosis method based on adversarial discrimination and deep evidential fusion under limited labeled samples","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Machine Fault Diagnosis Techniques","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":"Novelis (Canada)","funders":"National Science and Technology Major Project; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Adversarial system; Classifier (UML); Deep learning; Class (philosophy); Deep neural networks; Fault (geology); Pattern recognition (psychology)","score_opus":0.013564493204871715,"score_gpt":0.31191389248875784,"score_spread":0.2983493992838861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415533747","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017913138,0.00011142995,0.9791069,0.00015448549,0.0004115383,0.00075478584,0.00002933301,0.00085298077,0.0006654076],"genre_scores_gemma":[0.3238305,0.00036986807,0.67481565,0.00036990712,0.000051395422,0.0003093392,0.00016732689,0.00005903624,0.000026965316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987299,0.00002317284,0.0005405164,0.00016322882,0.00023192631,0.0003112714],"domain_scores_gemma":[0.9987708,0.00061551336,0.00007442463,0.00036592036,0.000056024546,0.00011735484],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024295186,0.00033837487,0.00035497002,0.00041442132,0.00008170902,0.000197612,0.00037740238,0.00015597095,0.000028517934],"category_scores_gemma":[0.00034243584,0.0003505383,0.000060398434,0.00045883757,0.000010929527,0.00082628825,0.00018356522,0.00028390842,0.000004712263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027540853,0.000019660589,0.00016694645,0.00037120792,0.00004627363,8.67356e-7,0.00037303925,0.92731863,0.000625779,0.0022711826,0.0011121458,0.06766672],"study_design_scores_gemma":[0.0013594195,0.000075363925,0.0014563178,0.0005532472,0.000074012125,0.0000012283743,0.00008764934,0.97019607,0.013809785,0.00029725474,0.011716456,0.00037320293],"about_ca_topic_score_codex":0.000047316655,"about_ca_topic_score_gemma":0.00004269259,"teacher_disagreement_score":0.30591735,"about_ca_system_score_codex":0.00019007165,"about_ca_system_score_gemma":0.00003226279,"threshold_uncertainty_score":0.9998947},"labels":[],"label_agreement":null},{"id":"W4416529140","doi":"10.1016/j.aei.2025.104065","title":"3D reconstruction by looking: Instantaneous blind spot detection for indoor SLAM through mixed reality","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Robotics and Sensor-Based Localization","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 Waterloo","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Pipeline (software); Mixed reality; Augmented reality; Iterative closest point; Workflow; Detector; Precision and recall; Simultaneous localization and mapping; Ground truth; Point cloud","score_opus":0.0063436324157446254,"score_gpt":0.21584379433113818,"score_spread":0.20950016191539356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416529140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067500435,0.00009678687,0.9286697,0.000013085598,0.0013900405,0.00040976744,0.000033513632,0.00054602313,0.001340645],"genre_scores_gemma":[0.90545046,0.00024380494,0.09387893,0.000050333038,0.000058436708,0.00006886374,0.00013542952,0.000051468534,0.00006227928],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885666,0.000005348034,0.0006044507,0.00010738241,0.00012355746,0.00030257332],"domain_scores_gemma":[0.9994476,0.00008913142,0.000078308934,0.00023834358,0.000100115605,0.000046486515],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010305143,0.00023266781,0.00023865189,0.00014475758,0.00009573482,0.000057631252,0.00010219796,0.000170898,0.000002454457],"category_scores_gemma":[0.00009032497,0.00026676533,0.000061191284,0.00037655313,0.000019754212,0.00046956097,0.000015071517,0.00019676692,0.000003533044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026738022,0.0000074917916,0.0000068850013,0.0004773508,0.000043119708,4.1670765e-7,0.00018390444,0.9372932,0.0058340896,0.000768042,0.0001302844,0.055228528],"study_design_scores_gemma":[0.0009047895,0.00004072374,0.000014968317,0.000121164565,0.000030033714,0.000014915837,0.0001145676,0.9150582,0.05719816,0.00019332209,0.026044874,0.0002642844],"about_ca_topic_score_codex":0.000003738828,"about_ca_topic_score_gemma":0.0000076086794,"teacher_disagreement_score":0.83795005,"about_ca_system_score_codex":0.0002556782,"about_ca_system_score_gemma":0.00001800609,"threshold_uncertainty_score":0.9999785},"labels":[],"label_agreement":null},{"id":"W4416544980","doi":"10.1016/j.aei.2025.104095","title":"Recursive deep learning with multi-scale attention for energy demand dynamics","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Mean squared error; Residual; Robustness (evolution); Mean absolute percentage error; Perceptron; Deep learning; Artificial neural network; Noise reduction","score_opus":0.0025721393540580092,"score_gpt":0.1858386883290272,"score_spread":0.1832665489749692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416544980","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025219174,0.00022982924,0.9718047,0.000005654608,0.00041215887,0.000112497866,0.000006511946,0.0005154787,0.0016940264],"genre_scores_gemma":[0.5290301,0.00021932027,0.46966535,0.000036467805,0.000046583034,0.00012850386,0.00019976115,0.00007546888,0.0005984785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991708,0.000003118037,0.00032789615,0.00008099313,0.000084199804,0.0003329885],"domain_scores_gemma":[0.99960124,0.00008364543,0.00005157531,0.00014004209,0.000067040375,0.000056454504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000068182606,0.00021464915,0.00019895278,0.00016090876,0.00009093263,0.000037879672,0.000110120505,0.00009043354,0.0000012379447],"category_scores_gemma":[0.00002742544,0.00021584358,0.000055633263,0.00026215642,0.000014006099,0.0003490423,0.00002125052,0.00017776617,0.0000015323358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008562922,0.000005817544,0.00018818988,0.0003302129,0.000054284,4.858521e-7,0.00026713035,0.9680997,0.00019810043,0.0032228585,0.000011019558,0.027613651],"study_design_scores_gemma":[0.00070036703,0.00004638939,0.00014494204,0.00029992504,0.000029066026,0.0000051814313,0.0002518093,0.9839831,0.001352116,0.00003699709,0.012895608,0.0002544853],"about_ca_topic_score_codex":0.0000016034068,"about_ca_topic_score_gemma":0.000037449736,"teacher_disagreement_score":0.5038109,"about_ca_system_score_codex":0.00013053209,"about_ca_system_score_gemma":0.000008879395,"threshold_uncertainty_score":0.8801846},"labels":[],"label_agreement":null},{"id":"W4417037193","doi":"10.1016/j.aei.2025.104191","title":"Single image-based Gaussian splatting for 3D reconstruction of movable articulated objects","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Robot Manipulation and Learning","field":"Engineering","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":"Information Technology Research Centre; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea; National Research Foundation Singapore; Ministry of Culture, Sports and Tourism; Institute for Information and Communications Technology Promotion; Korea University; Korea Creative Content Agency; National Science Foundation","keywords":"Gaussian; Segmentation; 3D reconstruction; Motion capture; Rendering (computer graphics); RGB color model; Leverage (statistics); Ground truth","score_opus":0.006086223579357979,"score_gpt":0.20738837828481885,"score_spread":0.20130215470546087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417037193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054791827,0.000043679353,0.9406813,0.000008599076,0.00046726607,0.00022849084,0.0000018445885,0.0004587629,0.0033182695],"genre_scores_gemma":[0.5757032,0.0000032452328,0.42417705,0.000014938611,0.000012733303,0.000017495047,0.000020328109,0.000020105499,0.00003091991],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991289,0.0000031985833,0.0004982969,0.000059227852,0.00007871469,0.00023170203],"domain_scores_gemma":[0.9995599,0.00008804868,0.000079312405,0.0001598009,0.00007582367,0.000037155594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009408557,0.00014672056,0.00019679811,0.00023670032,0.000046612033,0.00002936346,0.00006732516,0.00007287757,0.000009142334],"category_scores_gemma":[0.00011152412,0.00016714311,0.000056927147,0.00034966588,0.000013322695,0.00037690354,0.0000096057065,0.00012725346,0.000003990496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000056894264,0.0000053880367,0.00003140966,0.0007321403,0.000025246283,1.654223e-7,0.00018109482,0.9540867,0.032363463,0.00026742512,0.000015539632,0.012285693],"study_design_scores_gemma":[0.0005327275,0.000020007634,0.00016927377,0.00031826372,0.000017668286,0.000002318419,0.00011766548,0.9508766,0.04627629,0.00002318736,0.0014956585,0.00015032616],"about_ca_topic_score_codex":7.9803743e-7,"about_ca_topic_score_gemma":6.7468824e-7,"teacher_disagreement_score":0.52091134,"about_ca_system_score_codex":0.000084264466,"about_ca_system_score_gemma":0.000016821086,"threshold_uncertainty_score":0.68158984},"labels":[],"label_agreement":null},{"id":"W4417299138","doi":"10.1016/j.aei.2025.104233","title":"Dynamic Kolmogorov–Arnold networks for time-varying degradation modeling in solid oxide fuel cells","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Advancements in Solid Oxide Fuel Cells","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; University of Alberta; Cummins Incorporated","keywords":"Degradation (telecommunications); Oxide; Fuel cells; Solid oxide fuel cell; Science, technology and society","score_opus":0.00717882808157716,"score_gpt":0.24399436732880472,"score_spread":0.23681553924722756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417299138","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12070069,0.00024272288,0.8769991,0.000012003388,0.0007814479,0.0005940388,0.000014966969,0.00023127171,0.0004238114],"genre_scores_gemma":[0.40622857,0.0001953799,0.5926697,0.00017063838,0.000022849566,0.00016952325,0.00006400042,0.00005067253,0.00042864648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979264,0.0000102475915,0.0009908653,0.00021461988,0.00020150095,0.0006563398],"domain_scores_gemma":[0.99902725,0.0002024714,0.00018602161,0.0004157546,0.00009999136,0.00006850176],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003781567,0.00030673228,0.00037456083,0.00027169226,0.00008693106,0.00008363686,0.00038098975,0.00013162379,0.00001312336],"category_scores_gemma":[0.000118565265,0.00034422436,0.00006877863,0.00039379374,0.000023028051,0.0011264447,0.00013659676,0.0002231065,0.00003789272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021609923,0.000016323454,0.00000343891,0.00036727134,0.000007944962,7.097182e-7,0.00017016366,0.9059077,0.091412134,0.00024733314,0.000017328372,0.0018280556],"study_design_scores_gemma":[0.0007540997,0.000020844078,0.0000037384182,0.00031847553,0.000016222548,0.0000013054411,0.00009256274,0.97003955,0.027103731,0.0003796448,0.00095162424,0.00031822448],"about_ca_topic_score_codex":0.0000030965762,"about_ca_topic_score_gemma":0.0000031861198,"teacher_disagreement_score":0.28552788,"about_ca_system_score_codex":0.00035564537,"about_ca_system_score_gemma":0.000047154033,"threshold_uncertainty_score":0.999901},"labels":[],"label_agreement":null},{"id":"W7108206087","doi":"10.1016/j.aei.2025.104126","title":"I-FCSAM: An integrated framework of few-shot learning and segment anything model for vision-based indoor built environment management","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Robotics and Sensor-Based Localization","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Built environment; Development environment; Facility management; Key (lock)","score_opus":0.006050439110151629,"score_gpt":0.23291256894058307,"score_spread":0.22686212983043144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7108206087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03709851,0.00010210486,0.9619747,0.000011171611,0.000120074816,0.00037857625,0.000010462419,0.00019865441,0.00010579467],"genre_scores_gemma":[0.5222507,0.00013820762,0.47741604,0.000036013953,0.000005479867,0.000038301077,0.000060809554,0.00002766573,0.00002675795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899745,0.0000055264495,0.00050345686,0.000104849205,0.0001472116,0.00024150679],"domain_scores_gemma":[0.9995327,0.00009550292,0.000064736036,0.00020404627,0.00003484611,0.00006813957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001421836,0.0002181811,0.00023346835,0.00022315393,0.000064151056,0.000039109113,0.00010379545,0.00010462728,0.000002383566],"category_scores_gemma":[0.000032775562,0.00023133974,0.000042196625,0.00017464739,0.000017889692,0.00021904336,0.0000321351,0.00020649652,7.2724265e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000132125415,0.000018480592,0.000044551467,0.0009976736,0.00004537938,3.372125e-7,0.00043457723,0.9800748,0.00071502867,0.0017289319,0.000009258514,0.015917733],"study_design_scores_gemma":[0.0005354088,0.00006548355,0.000055603407,0.00041936943,0.000035140103,2.6815005e-7,0.00025711945,0.9938656,0.002658119,0.00014128431,0.0017517107,0.00021486111],"about_ca_topic_score_codex":9.4872445e-7,"about_ca_topic_score_gemma":3.5547524e-7,"teacher_disagreement_score":0.4851522,"about_ca_system_score_codex":0.00011450882,"about_ca_system_score_gemma":0.00001289314,"threshold_uncertainty_score":0.94337606},"labels":[],"label_agreement":null},{"id":"W7114802657","doi":"10.1016/j.aei.2025.104109","title":"WAM-ONTO: A semantic framework for water infrastructure asset management","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Water Systems and 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":"Asset management; Ontology; Workflow; SPARQL; Asset (computer security); IT asset management; Semantic Web; Consistency (knowledge bases); XBRL; Domain (mathematical analysis)","score_opus":0.0020068170314834293,"score_gpt":0.19368361228659228,"score_spread":0.19167679525510886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114802657","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008564807,0.00006915127,0.98698336,0.000018430708,0.0012647788,0.00044767457,0.000010178896,0.00051666447,0.00212494],"genre_scores_gemma":[0.4725677,0.00007042543,0.5265062,0.00006760464,0.00005759352,0.0001721414,0.000083085826,0.000044423068,0.00043081606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991014,0.0000016777914,0.00040141065,0.00007050929,0.00009393518,0.0003310684],"domain_scores_gemma":[0.9996161,0.000027084816,0.000022266659,0.00025682506,0.000036332123,0.000041382493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000065700806,0.00020155593,0.0001969878,0.00015835986,0.000046689926,0.000066729415,0.00015051036,0.00011159931,0.000005682379],"category_scores_gemma":[0.000010704015,0.00017001257,0.0000542528,0.00015870077,0.0000054015813,0.00031234534,0.000039806706,0.00013411418,0.000011657826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027063766,0.0000023196592,0.000019932733,0.0017432689,0.000079119476,4.9064846e-7,0.00044864477,0.98431283,0.0000715774,0.009900069,0.0012552765,0.0021637653],"study_design_scores_gemma":[0.0003873771,0.0000123869995,0.000172254,0.00035359955,0.00003193353,0.0000023961982,0.00011488733,0.90142477,0.0036741132,0.001295043,0.092282705,0.00024853868],"about_ca_topic_score_codex":3.3803548e-7,"about_ca_topic_score_gemma":9.3875764e-7,"teacher_disagreement_score":0.4640029,"about_ca_system_score_codex":0.00008720981,"about_ca_system_score_gemma":0.0000032465473,"threshold_uncertainty_score":0.6932911},"labels":[],"label_agreement":null},{"id":"W7116334614","doi":"10.1016/j.aei.2025.104238","title":"Linking microstructure informatics with characterization knowledge in additively manufactured composites through customized and hybrid vision-language representations for automated qualification","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Machine Learning in Materials Science","field":"Materials 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":"McGill University; National Research Council Canada","funders":"Fonds de recherche du Québec – Nature et technologies; National Research Council Canada","keywords":"Interpretability; Visualization; Encoder; Bottleneck; Collocation (remote sensing); Characterization (materials science); Segmentation; Normalization (sociology); Similarity (geometry)","score_opus":0.0035672905409102587,"score_gpt":0.2762246485175267,"score_spread":0.27265735797661644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116334614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5973023,0.00002223697,0.40113866,0.000031468004,0.0002104069,0.0006402452,0.00011750867,0.00043701666,0.00010015558],"genre_scores_gemma":[0.5395421,0.000019536168,0.45946184,0.00009409423,0.000018650157,0.00015104779,0.00065533305,0.00001892959,0.00003849409],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858594,0.0000332325,0.00077214447,0.00015535991,0.00015396338,0.00029933092],"domain_scores_gemma":[0.9989113,0.00029724147,0.0003489137,0.00027230746,0.00012811508,0.000042088897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033760563,0.00023712809,0.00031711668,0.00023536591,0.00017181254,0.00025678726,0.00023845905,0.00007434318,0.000011105855],"category_scores_gemma":[0.0001731378,0.00020940718,0.000024422367,0.00035092584,0.000072283205,0.0013288187,0.000087067194,0.00014711877,0.000007304321],"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":[0.00010243211,0.000029642653,0.00010980274,0.0009422296,0.000010270566,7.009419e-7,0.0110884495,0.38573268,0.5984435,0.0007099766,0.000040387782,0.002789937],"study_design_scores_gemma":[0.001483295,0.000059219958,0.00672011,0.00050785544,0.00001847573,0.000011437317,0.000719111,0.85718465,0.13104537,0.000042710635,0.0019382037,0.00026959163],"about_ca_topic_score_codex":0.0000059024533,"about_ca_topic_score_gemma":0.0000040782256,"teacher_disagreement_score":0.47145194,"about_ca_system_score_codex":0.00008682646,"about_ca_system_score_gemma":0.000054049484,"threshold_uncertainty_score":0.85393775},"labels":[],"label_agreement":null},{"id":"W7116869709","doi":"10.1016/j.aei.2025.104263","title":"Co-MixPL: An optimized semi-supervised learning method for tunnel water leakage detection","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Water Systems and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National University's Basic Research Foundation of China; National Natural Science Foundation of China","keywords":"Discriminative model; Leakage (economics); Reliability (semiconductor); Regression; Labeled data; Training set","score_opus":0.005129122955505167,"score_gpt":0.22762180764984802,"score_spread":0.22249268469434286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116869709","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014407153,0.000040279137,0.9822478,0.0000066137786,0.00070408924,0.00044866998,0.0000064719666,0.0011024721,0.001036404],"genre_scores_gemma":[0.4348344,0.000040434013,0.5638633,0.000039819006,0.00008416384,0.00024590734,0.00022400937,0.00008920756,0.0005788304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988434,0.0000132028335,0.0005421811,0.00010805003,0.00010601696,0.00038719477],"domain_scores_gemma":[0.99951875,0.00006392635,0.00003543879,0.00023070279,0.000077529185,0.00007367619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028020845,0.00025301302,0.00029624597,0.00024471045,0.00011390845,0.00009189353,0.00014120729,0.0001420953,0.0000061149804],"category_scores_gemma":[0.000035541656,0.00023067696,0.000078632416,0.00017342847,0.000006179079,0.000838847,0.000016659913,0.00021953402,0.000010228927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014324435,0.000004795923,0.0000034084953,0.0005815005,0.000045319728,2.7497452e-7,0.001312437,0.972852,0.017830415,0.0000688674,0.00004480612,0.0072418824],"study_design_scores_gemma":[0.00097430305,0.00004182772,0.000012056111,0.00007408925,0.000023277067,0.0000034055008,0.00024326655,0.87826324,0.101226225,0.000014786596,0.018867781,0.0002557306],"about_ca_topic_score_codex":0.0000033340007,"about_ca_topic_score_gemma":0.0000041321086,"teacher_disagreement_score":0.42042723,"about_ca_system_score_codex":0.00011035836,"about_ca_system_score_gemma":0.000006501479,"threshold_uncertainty_score":0.94067335},"labels":[],"label_agreement":null},{"id":"W7117482822","doi":"10.1016/j.aei.2025.104284","title":"Iteratively modified variational mode extraction (IMVME): A noise-robust transient feature nonlinear extraction approach for aero-engine fault diagnosis","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Machine Fault Diagnosis Techniques","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":"Wuhan University of Science and Technology; Natural Science Foundation of Hubei Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Transient (computer programming); Feature extraction; Control theory (sociology); Robustness (evolution); Feature (linguistics); Noise (video); Filter (signal processing); Adaptive filter; Fault (geology); Pattern recognition (psychology)","score_opus":0.009170146203006907,"score_gpt":0.2729387942657313,"score_spread":0.2637686480627244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117482822","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008436241,0.00018896488,0.9857156,0.000087950815,0.00057534524,0.0013031192,0.00024795643,0.0016583409,0.0017864536],"genre_scores_gemma":[0.21592121,0.0003151871,0.7805376,0.00008346946,0.00012884963,0.0021365562,0.0007102389,0.00008267654,0.00008419466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982118,0.000011326906,0.00076061767,0.00023050206,0.00030175413,0.0004840028],"domain_scores_gemma":[0.9989026,0.0003126877,0.00011931718,0.00035964552,0.00019348678,0.00011222196],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019918829,0.0005038392,0.00043709468,0.00047881212,0.00013127418,0.00011970428,0.00025404996,0.00031522,0.0000065289846],"category_scores_gemma":[0.00015291735,0.0005488545,0.00018093678,0.0005516087,0.000017511788,0.0012786223,0.000025630607,0.0006057495,0.0000031105697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028968348,0.00010354871,0.000018448622,0.00097014493,0.00011738071,7.589938e-7,0.00046498992,0.9819131,0.00452001,0.0013066006,0.0015486132,0.00900747],"study_design_scores_gemma":[0.0009115766,0.00004784799,0.00030257102,0.00017930513,0.000072406074,0.000010952013,0.000058868278,0.96248466,0.015877204,0.000048433216,0.019517915,0.00048828823],"about_ca_topic_score_codex":0.000004204802,"about_ca_topic_score_gemma":0.0000020490586,"teacher_disagreement_score":0.20748498,"about_ca_system_score_codex":0.00037578162,"about_ca_system_score_gemma":0.00003377653,"threshold_uncertainty_score":0.9996963},"labels":[],"label_agreement":null},{"id":"W7117568884","doi":"10.1016/j.aei.2025.104288","title":"Ball tree structure-informed phase space warping: a robust algorithm for dynamic degradation tracking under variable speed conditions","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Machine Fault Diagnosis Techniques","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 Manitoba; University of Alberta","funders":"Wuhan University of Science and Technology; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Control theory (sociology); Curvature; Trajectory; Nonlinear system; Tracking (education); Ball (mathematics); Phase space; Vibration; Dynamic time warping","score_opus":0.007625715965975797,"score_gpt":0.2902595311865722,"score_spread":0.28263381522059644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117568884","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013028243,0.000116491734,0.98242563,0.00003198441,0.0005385344,0.00087361893,0.0002672208,0.0017479234,0.0009703294],"genre_scores_gemma":[0.16651967,0.00011666159,0.8319853,0.00009391923,0.00003496528,0.00017075961,0.00089906604,0.00007891955,0.00010074552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848294,0.000004411894,0.0007042004,0.0001352126,0.00017606575,0.0004971867],"domain_scores_gemma":[0.99902755,0.00028406785,0.000102130994,0.00036935534,0.00012105796,0.00009583938],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001123498,0.00038242206,0.00036511512,0.0004433376,0.00011218201,0.000114264956,0.0002639745,0.00017727588,0.00001845572],"category_scores_gemma":[0.00015711221,0.0004306116,0.000093685354,0.0005552316,0.000020915066,0.001065557,0.000037507107,0.00030971534,0.0000032901578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004967718,0.000023099059,0.0000029730575,0.00057378184,0.00009469584,4.4334345e-7,0.00014084387,0.94600457,0.0067249453,0.0049578296,0.000500729,0.0409711],"study_design_scores_gemma":[0.001562879,0.000058142352,0.00005725497,0.00029697182,0.000066485474,0.000006881184,0.00010925978,0.96873474,0.018024001,0.00077674445,0.009890447,0.00041616973],"about_ca_topic_score_codex":0.0000034433563,"about_ca_topic_score_gemma":0.00001344858,"teacher_disagreement_score":0.15349142,"about_ca_system_score_codex":0.0004727843,"about_ca_system_score_gemma":0.000064113774,"threshold_uncertainty_score":0.99981457},"labels":[],"label_agreement":null}]}